Study to quantify and analyse the VAT gap in the EU-25 Member States : report 2009.
Date: 2021-10-07
Institutions: Directorate-General for Taxation and Customs Union
Subjects: EU Member State, VAT, commitment of expenditure, econometrics, national accounts, quantitative analysis, report, revenue

Study to quantify and analyse  
the VAT Gap  
in the EU-25 Member States 

Report 2009 

Reckon LLP Data analsis – Economic regulation – Competition law 

September 2009 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
VAT Gap in the EU-25 Member States – Report 2009 

EUROPEAN COMMISSION 

Directorate-General for Taxation and Customs Union  
Directorate C — Indirect Taxation and Tax administration 
Unit C.4 — Tax administration and fight against tax fraud  

E-mail:  TAXUD-UNIT-C4@ec.europa.eu@ec.europa.eu  

European Commission 
B-1049 Brussels 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
VAT Gap in the EU-25 Member States – Report 2009 

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Section 1: Introduction and summary 

CONTENTS  

SECTION 1: INTRODUCTION AND SUMMARY ...........................................................................5 

SECTION 2: TOP-DOWN ESTIMATES OF THE VAT GAP ..........................................................12 
Results................................................................................................................................................................. 12 
Sensitivity analysis............................................................................................................................................. 16 
Full set of results ................................................................................................................................................ 22 

SECTION 3: ECONOMETRIC ANALYSIS OF THE VAT GAP .....................................................47 
Previous econometric studies of the determinants of the VAT gap............................................................... 47 
Empirical modelling of top–down VAT gap estimates..................................................................................... 48 
Conclusions from the econometric analysis of the VAT gap ......................................................................... 58 

SECTION 4: AN OUTLINE OF THE TOP-DOWN APPROACH.....................................................59 

SECTION 5: DATA SOURCES ...................................................................................................63 
Final and intermediate expenditure................................................................................................................... 63 
Gross fixed capital formation ............................................................................................................................ 68 
VAT rates ............................................................................................................................................................. 70 
VAT receipts........................................................................................................................................................ 72 

SECTION 6: ASSUMPTIONS .....................................................................................................74 
Completeness of national accounts.................................................................................................................. 74 
Proportion of intermediate consumption on which VAT is not recoverable ................................................. 74 
Interpreting the data on final consumption ...................................................................................................... 77 
Gross capital formation...................................................................................................................................... 78 

SECTION 7: ADJUSTMENTS ....................................................................................................84 
Business entertainment expenditure ................................................................................................................ 84 
Company cars ..................................................................................................................................................... 84 
“Tank tourism”.................................................................................................................................................... 84 
Exemption granted to small businesses .......................................................................................................... 85 
Supplies in domestic territories with different VAT regimes .......................................................................... 87 
Adjustments that have not been carried out .................................................................................................... 88 

SECTION 8: DEFINITIONS OF THE VAT GAP...........................................................................90 
Possible definitions of the VAT gap.................................................................................................................. 90 
Worked examples of the differences between the definitions for the VAT gap ............................................ 91 
Choice of definition for the study...................................................................................................................... 95 

SECTION 9: REVIEW OF PUBLISHED ESTIMATES OF VAT FRAUD AND THE VAT GAP ........96 
Published top-down estimates of the VAT gap ................................................................................................ 96 
Bottom-up quantification of the components of VAT fraud ............................................................................ 99 
Published estimates of MTIC fraud ................................................................................................................. 102 

APPENDIX ..............................................................................................................................107 

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Section 1: Introduction and summary 

TABLES 
Table 1  Aggregate estimates of the VAT gap, 2000-2006 (EUR billion) .........................................................8 
Table 2  Aggregate estimates of the VAT gap as a share of theoretical liability, 2000-2006.............................9 
Table 3  Estimates of the VAT gap, 2006 (EUR million) ..................................................................................9 
Sensitivity to assumption on propex of the financial services sector..................................................18 
Table 4 
Sensitivity to assumption on propex of the education sector..............................................................19 
Table 5 
Table 6 
Sensitivity to assumption on the split of GFCF on dwellings ............................................................20 
Table 7   Sensitivity to assumption on the VAT treatment of company cars.....................................................21 
Table 8  Austria: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) ...............................23 
Table 9  Belgium: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) .............................24 
Table 10  Czech Republic: VAT receipts, theoretical liability and gap, 2000–2006 (CZK million)..................25 
Table 11  Germany: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) ............................26 
Table 12  Denmark: VAT receipts, theoretical liability and gap, 2000–2006 (DKK million) ...........................27 
Table 13  Estonia: VAT receipts, theoretical liability and gap, 2000–2006 (EEK million) ...............................28 
Table 14  Spain: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million)..................................29 
Table 15  Finland: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million)...............................30 
Table 16  France: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) ................................31 
Table 17  Greece: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million)................................32 
Table 18  Hungary: VAT receipts, theoretical liability and gap, 2000–2006 (HUF million) .............................33 
Ireland: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million)................................34 
Table 19 
Table 20 
Italy: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million)....................................35 
Table 21  Lithuania: VAT receipts, theoretical liability and gap, 2000–2006 (LTL million) ............................36 
Table 22  Luxembourg: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) ......................37 
Table 23  Latvia: VAT receipts, theoretical liability and gap, 2000–2006 (LVL million) .................................38 
Table 24  Malta: VAT receipts, theoretical liability and gap, 2000–2006 (MTL million) .................................39 
Table 25  Netherlands: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million)........................40 
Table 26  Poland: VAT receipts, theoretical liability and gap, 2000–2006 (PLN million) ................................41 
Table 27  Portugal: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) .............................42 
Table 28  Sweden: VAT receipts, theoretical liability and gap, 2000–2006 (SEK million)...............................43 
Table 29  Slovenia: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) .............................44 
Table 30  Slovakia: VAT receipts, theoretical liability and gap, 2000–2006 (SKK million) .............................45 
Table 31  United Kingdom: VAT receipts, theoretical liability and gap, 2000–2006 (GBP million) ................46 
Table 32   Candidate explanatory variables.........................................................................................................49 
Table 33  Random effects model results.............................................................................................................52 
Table 34   Panel corrected standard error modelling results................................................................................53 
Table 35  Robust regression modelling results...................................................................................................55 
Table 36   Instrumental variable robust regression modelling results .................................................................56 
Table 37   Robust regression without VAT burden, with gross capital formation variable.................................57 
Table 38   Eurostat use table availability as of 30 June 2008 ..............................................................................65 
Table 39    Breakdown of “CPA 15 – Manufacture of food and beverages”.......................................................71 
Table 40   Thresholds for application of special scheme for small businesses, May 2008..................................86 
Table 41   Estimated VAT revenue foregone due to small business exemption..................................................86 
Table 42   Calculation of different definitions of the VAT gap in worked examples..........................................94 
Table 43   Other published estimates of the VAT gap.........................................................................................97 
Table 44   HMCE bottom-up and top-down estimates of VAT losses ..............................................................100 
Table 45  Swedish VAT tax gap reported in Skatteverket (2008) ....................................................................102 
Table 46  HMRC estimates of attempted MTIC fraud, 2000/2001-2005/2006 ................................................104 
Table 47   EU-25 Member States ......................................................................................................................107 
Table 48   List of 2-digit CPA products ............................................................................................................108 

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Section 1: Introduction and summary 

SECTION 1: INTRODUCTION AND SUMMARY 

1. 

This  report  is  concerned  with  quantifying  and  analysing  the  VAT  gap  in  each  EU 
Member State over the period 2000–2006.  It has been prepared by Reckon LLP in the 
context of a study carried out for the European Commission. 

We provide estimates of the VAT gap 

2. 

We provide estimates of the VAT gap based on a comparison of accrued VAT receipts 
with  a  theoretical  net  VAT  liability  for  the  economy  as  a  whole.    We  estimate  the 
theoretical  net  liability  by  identifying  the  categories  of  expenditure  that  give  rise  to 
irrecoverable VAT and combining these with appropriate VAT rates.   

3. 

The VAT gap is not a measure of VAT fraud.  For example: 

(a)  The VAT gap might include VAT not paid as a result of legitimate tax avoidance 

measures. 

(b)  The VAT gap is estimated primarily on the basis of national accounts data, and 
therefore depends on the accuracy and the completeness of such data.  Moreover, 
it does not take account of taxable activities that are outside the scope of national 
accounts. 

(c)  Due to lack of data, we do not adjust our estimates of the VAT gap to remove the 
VAT  that  is  not  collected  due  to  insolvencies  arising  as  a  result  of  regular 
business activity,  yet this portion of VAT that is not remitted is not due to VAT 
fraud. 

4. 

The estimates presented in this report are of the VAT gap, as defined above, and not of 
VAT fraud.  

We focus on a top-down approach to estimating the VAT gap 

5. 

6. 

7. 

The  main  focus  of  our  research  is  to  derive  top-down  estimates  of  the  VAT  gap, 
obtained  by  comparing  total  accrued  tax  receipts  with  a  theoretical  tax  liability 
calculated  from  general  economic  data.    They  can  be  contrasted  with  bottom-up 
estimates, which are derived by extrapolating data relating to individual companies or 
discovered frauds. 

Our  top-down  approach  relies  on  published  national  accounts.   We  are  only  able  to 
develop  these  estimates  for  those  Member  States  where  relevant  national  accounts 
data are publicly available. 

We are unable to produce similar estimates on the basis of a bottom-up approach that 
compiles information from surveys or other studies on estimates of particular types of 
VAT fraud, as: 

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Section 1: Introduction and summary 

(a)  Published  data  on  the  size  of  different  types  of  VAT  fraud  are  insufficient  — 
indeed they are scant — to allow us to piece together an estimate of VAT fraud in 
the economy as a whole.   

(b)  There may be a selection bias.  Presenting the value of the different types of VAT 
fraud detected by tax agencies, as reported by some in annual reports, would risk 
giving  a  distorted  account  of  the  relative  importance  of  different  types  of VAT 
fraud as well as of overall level of VAT fraud.   

(c)  The raw data underlying the estimates of particular types of VAT fraud that we 
aware  of  are  based,  almost  invariably,  on  operational  data  held  by  the  tax 
agencies.    Generally,  these  are  confidential,  as  are  the  methods  used  to  derive 
them.   

The top-down approach allows us to estimate the VAT gap in the economy as a whole 
but it does not allow us to characterise it in terms of identifying what sectors, or trade 
in what goods, or what types of business are more susceptible to VAT fraud. 

Our  top-down  approach  is  comparable  to  those  that  we  have  found  published  by 
national tax agencies.  It is also related to the approach followed in the compilation of 
VAT  own  resources  accounts  submitted  annually  by  each  Member  State  to  the 
European Commission. 

8. 

9. 

We draw on national accounts to compute net VAT liability 

10. 

11. 

12. 

13. 

A  top-down  estimate  of  the  VAT  gap  is  based  on  comparing  accrued  VAT  receipts 
with  a  theoretical  net  VAT  liability  for  the  economy  as  a  whole.    We  estimate  the 
theoretical  net  liability  by  identifying  and  measuring  the  categories  of  expenditure 
that give rise to irrecoverable VAT.   

The  main  categories  of  relevant  expenditure  that  give  rise  to  irrecoverable  VAT  are 
final  consumption  expenditure  by  households,  non-profit 
institutions  serving 
households  (NPISH)  and  government,  intermediate  consumption  expenditure  on 
goods and services used in making exempt supplies of goods and services; and gross 
fixed capital formation on assets and changes in the stock of valuables which can be 
allocated to exempt supplies of goods and services. 

National accounts data provide the primary information for our estimates of the value 
of  these  transactions  that  give  rise  to  irrecoverable  VAT.    Within  this,  we  draw  in 
particular  on  published  use  tables  which  report  the  use  of  goods  and  services  by 
product  category  and  by  type  of  use.    Because  of  this  structure,  use  tables  lend 
themselves to the task of identifying transactions that give rise to irrecoverable VAT. 

Our  analysis  is  broadly  based  on  Member  States’  use  tables  downloaded  from 
Eurostat on 30 June 2008.  Use tables were available for all Member States for some 
of  the  years  in  the  period  2000–2006,  with  the  exception  of  Greece  and  Latvia  for 
which  use  tables  were  only  found  for  some  years  prior  to  2000  and  of  Cyprus,  for 
which  we  were  told  by  the  National Accounts  Division  of  the  Cypriot  Ministry  of 
Finance  that  no  use  tables  were  publicly  available.    Because  these  tables  are  at  the 
heart of our top-down approach we do not include Cyprus in our analysis. 

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Section 1: Introduction and summary 

14. 

To  estimate  the  VAT  liability  associated  with  transactions  on  which  VAT  is  not 
recovered,  it  is  necessary  to  apply  the  appropriate  VAT  rate.    Information  on  the 
applicable VAT rates is drawn from national VAT legislation and secondary sources. 

We adjust our estimate to account for items affecting net VAT liability 

15.  We  make  a  series  of  adjustments  to  the  estimates  we  obtain  through  the  approach 
outlined above to take account of a number of features common to the VAT systems in 
most Member States.  In particular, we adjust our estimates to reflect the fact that in 
most  Member  States  an  exemption  from  registration  is  granted  to  businesses  with  a 
turnover  below  a  certain  threshold.    We  also  make  adjustments  to  reflect  the 
limitations  relating  to  the  recoverability  by  corporations  of  expenditure  on 
entertainment and on the purchase of company cars.   

16. 

In the case of Luxembourg, our estimation makes a further adjustment to capture the 
contribution  to  the  VAT  liability  associated  with  “tank  tourism”,  the  practice  of 
haulage  and  transport  companies  from  other  Member  States  filling  up  their  trucks 
with  diesel  and  petrol  in  Luxembourg,  whether  for  logistical  reasons  or  to  benefit 
from differences in fuel prices.  

Limitations of estimating theoretical VAT liability from national accounts 

17. 

18. 

19. 

There  are  three  main  types  of  limitations  in  using  national  accounts  data  for  the 
purpose of estimating VAT gap.  The first relates to the fineness of the data available, 
the second relates to the coverage of national accounts insofar as they measure taxable 
activity, and the third to the question of whether the national accounts data accurately 
measure what they are supposed to.  We consider the implication of each in turn. 

The minutiae of the VAT system in most Member States is such that it would require 
unfeasibly  detailed  information  about  the  pattern  of  transactions  in  an  economy  to 
ensure  that  the  correct VAT  treatment  is  applied  to  a  given  volume  of  trade.   As  an 
example,  it  would  be  necessary  to  know  how  expenditure  by  Danish  consumers  on 
football  matches  is  divided  between  games  where  both  teams  fielded  professionals 
and games where that is not the case; the former attract a standard VAT rate, the latter 
are exempt.  We are certain that pursuing data that would allow us make this split, and 
that  would  allow  us  to  accommodate  all  of  the  details  of  the  VAT  legislation  more 
generally, is not a feasible exercise. 

The  top-down  approach  requires  instead  an  exercise  of  judgement  to  identify  those 
trades that are thought to have a material impact on the measure of net VAT liability at 
the economy-wide level.  For example, in many Member States, the rates applied to 
food products are lower than those applied to beverages.  Further, expenditure on food 
products  and  beverages  account  for  a  significant  share  of  household  consumption.  
Based on this, we take a view that it is appropriate to consider the contribution to net 
VAT liability of the expenditure in these two classes of products separately.  Indeed, 
we  take  the  view  that  it  is  appropriate  to  examine  expenditure  in  these  classes  of 
products  at  an  even  greater  level  of  detail,  for  example  distinguishing  between 
alcoholic and non-alcoholic drinks.  There are data which allow us to explore this.  On 
some  other  points,  which  would  also  have  a  material  impact  on  our  estimates,  the 
national accounts data available to us do not allow for detailed analysis. 

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Section 1: Introduction and summary 

20. 

21. 

Our top-down approach relies on the extent of the overlap between taxable activities 
and  those  that  contribute  to  national  accounts.    Some  activities  fall  outside  this 
overlap,  as  is  the  case  of  own  house  building,  or  of  the  exemptions  to  small 
businesses.    To  address  this,  it  is  necessary  to  draw  on  data  outside  of  national 
accounts, where such data are available.  

Our  top-down  estimation  of  the  VAT  gap  relies  on  the  premise  that  the  national 
accounts  data,  and  the  use  tables  in  particular,  have  been  compiled  in  line  with  the 
European  System  of Accounts  95  (ESA  95).   An  important  corollary  of  this  is  that 
national accounts data are deemed to include the contribution of the shadow economy, 
as ESA 95 requires.  An inadequate or inconsistent estimation by statistics offices of 
this  contribution  will  have  a  direct  impact  on  our  estimates  of  the  theoretical  VAT 
liability. 

We have contrasted our approach with that of other institutions 

22. 

Our work has benefitted from contact with national tax authorities, national statistics 
offices and other relevant government departments in Member States.   

23.  We shared details of our approach and interim results with relevant institutions from 
Member  States  and  have  used  the  information  and  feedback  received  from  them  to 
improve our estimates.   

24. 

At a late stage of the study, for almost all Member States, we were granted access to 
the  VAT  own  resources  accounts  that  are  submitted  annually  to  the  European 
Commission.    There  is  considerable  overlap  in  the  methods  used  in  our  top-down 
approach to estimating VAT gap and the approach adopted by Member States in those 
submissions for the purpose of estimating the weighted average VAT rate.  We have 
not  sought  to  reconcile  our  estimates  with  those  reported  in  the  own  resources 
accounts,  nor  have  we  used  data  contained  within  them.    Rather,  we  have  used  the 
workings submitted within the own resources accounts to help us identify errors and 
unjustified assumptions that we might have made in our analysis.  Where these were 
found, we sought to correct them by drawing on alternative, published information or 
on information obtained directly from national institutions. 

Our top-down estimates of VAT gap 

25.  We have estimated The VAT gap for each of the Member States, other than Cyprus, 
for each of the years in the period 2000-2006.   Table 1 presents our estimates of the 
VAT gap for the EU-25, the EU-10 and for the EU-15 in value terms.  Table 2 reports 
the gap as a share of the net theoretical liability. 

Table 1 

Aggregate estimates of the VAT gap, 2000-2006 (EUR billion) 

EU-10 

EU-15 

EU-25 

2000 

6.5 

84.4 

90.9 

2001 

8.3 

96.2 

104.5 

2002 

8.3 

98.9 

107.1 

2003 

7.6 

101.1 

108.7 

2004 

8.6 

103.6 

112.3 

2005 

8.1 

105.2 

113.3 

2006 

7.9 

98.8 

106.7 

Note: EU-10 and EU-25 exclude Cyprus.  Non-Euro currencies converted to EUR using the average exchange rate in each 
year.. 

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Section 1: Introduction and summary 

Table 2 

Aggregate estimates of the VAT gap as a share of theoretical liability, 2000-2006  

EU-10 

EU-15 

EU-25 

2000 

20% 

12% 

13% 

2001 

22% 

13% 

14% 

2002 

20% 

13% 

14% 

2003 

19% 

14% 

14% 

2004 

19% 

13% 

14% 

2005 

16% 

13% 

13% 

2006 

14% 

12% 

12% 

Note: EU-10 and EU-25 exclude Cyprus. 

26.  We estimate the overall VAT gap in the EU-25 has, in value terms, shown an increase 
over  the  period  from  2000  to  2005,  but  a  drop  from  2005  to  2006.   As  a  share  of 
theoretical liability, we estimate that the VAT gap for the EU-25 remained fairly stable 
from 2000 to 2004 and that it then fell by two percentage points between 2004 and 
2006.  

Table 3 

Estimates of the VAT gap, 2006 (EUR million) 

Member State 

Theoretical VAT 
liability 

VAT receipts 

VAT gap 

VAT gap as a share of 
theoretical liability 

AT 

BE 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

EU-25 

22,844 

25,360 

9,216 

164,115 

23,611 

1,325 

63,013 

15,176 

140,817 

21,746 

8,882 

14,043 

119,197 

2,335 

1,961 

1,751 

463 

41,269 

23,784 

14,371 

29,294 

2,764 

4,632 

155,697 

907,667 

19,735 

22,569 

7,541 

147,150 

22,560 

1,215 

61,595 

14,418 

131,017 

15,183 

6,813 

13,802 

92,860 

1,826 

1,941 

1,374 

410 

39,888 

22,127 

13,757 

28,487 

2,647 

3,320 

128,721 

800,955 

3,108 

2,791 

1,675 

16,965 

1,051 

111 

1,418 

758 

9,800 

6,563 

2,070 

241 

26,337 

510 

20 

378 

53 

1,381 

1,657 

614 

807 

116 

1,312 

26,976 

106,712 

Note: EU-25 excludes Cyprus.  Non-Euro currencies converted to EUR using the average exchange rate in 2006.  

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14% 

11% 

18% 

10% 

4% 

8% 

2% 

5% 

7% 

30% 

23% 

2% 

22% 

22% 

1% 

22% 

11% 

3% 

7% 

4% 

3% 

4% 

28% 

17% 

12% 

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Section 1: Introduction and summary 

We examine the sensitivity of our results to the set of most material assumptions 

27.  We identified four assumptions in our top-down approach which we expect to have a 
material impact on the estimated VAT gap and for which we have a limited empirical 
basis.    These  concern  the  assumptions  on  the  proportion  of  consumption  by  the 
education and by the financial sectors on which VAT is not recoverable, on how gross 
fixed  capital  formation  on  dwellings  is  split  between  investments  in  new  dwellings 
and expenditure on major improvements to existing dwellings and on the size of the 
adjustment to be done to account for the VAT treatment of company cars. 

28.  We examined the sensitivity of our results to these assumptions and find that they do 
have  some  impact  on  our  results.    The  impact  is  greatest  with  regards  to  the 
assumption on the proportion of consumption by the financial sector on which VAT is 
not recoverable.  

Comparison with other published estimates of VAT gaps 

29.  We  have  found  published  top-down  estimates  of  the VAT  gap  relating  to  the  period 
from  2000  to  2006  for  only  a  handful  of  Member  States,  namely  for  Denmark, 
Germany,  Italy,  Sweden  and  the  UK.    Other  than  for  Germany,  these  estimates  are 
computed by the relevant national tax agency or  statistics office.  The  estimates are 
typically  accompanied  by  only  a  brief  description  of  the  precise  data  sources  and 
assumptions  that  underpin  the  estimation.    This  limits  our  ability  to  interpret  the 
differences between our  estimates and those  that  we have found published.  But  we 
are  aware  of  two  general  points  that  are  likely  to  explain  much  of  the  differences.  
First,  we  expect  national  tax  agencies  and  statistics  offices  to  have  access  to  more 
detailed or recent national accounts data than that which are published, and therefore 
available to us in the course of the study.  We note too that national accounts data are 
often revised over time and differences between our set of estimates of the VAT and 
those  published  by  national  tax  agencies  will  also  differ  where  we  have  drawn  on 
different  revisions  of  the  data.    Second,  we  have  extracted  figures  on  accrued  VAT 
receipts from Eurostat, and, with the exception of Denmark, these do not match the 
figures used in the estimates of the Member States mentioned earlier. 

30. 

Not withstanding these differences, we, we find that our estimates of the VAT gap for 
Germany, Italy and the UK follow a similar trend to the published estimates for these 
countries that we have come across. 

31.  We  have  found  even  fewer  —  two  —  published  bottom-up  estimates  of  VAT  gaps; 
one  by  the  UK’s  HM  Customs  and  Excise  in  2002  and  the  second  by  the  Swedish 
Skatteverket  in  2008.    These  studies  draw  on  a  range  of  data  sources,  including 
surveys and operational data, to identify the relative importance of different types of 
VAT losses.  Both agencies qualify their findings by noting the significant degree of 
uncertainty  around  their  estimates.    We  do  not  think  it  reasonable  to  draw  on  the 
findings of these two isolated studies to make inferences about the characterisation of 
the VAT gap across the EU.  

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Section 1: Introduction and summary 

Econometric analysis of the VAT gap 

32.  We  conducted  econometric  analysis  to  assist  in  the  understanding  of  the  nature  and 
causes  of  the  VAT  gap,  and  to  identify  country-specific  characteristics  that  appear 
related to different levels of the VAT gap. 

33. 

34. 

The variable found to have the strongest relationship with the size of the VAT gap was 
that connected with the perceived level of corruption in the country.  The relationship 
implies that lower perceived corruption is associated with a lower VAT gap.   

The  main  difference  between  our  analysis  and  the  results  obtained  by  other  studies 
surrounds  the  relationship  between  the  VAT  gap  and  the  VAT  burden.    If  the  VAT 
burden,  characterised  by  the  ratio  between  the  theoretical VAT  liability  and  GDP,  is 
treated  as  a  candidate  explanatory  variable,  then  we  find  that  it  has  a  significant 
positive relationship with the VAT gap.  This is in line with the limited literature on 
this topic, and with the theory that a higher tax burden should lead to higher levels of 
evasion.  We have identified a risk that this estimated relationship may be biased by 
measurement  errors  in  the  estimation  of  the  theoretical  liability.    Once  this  risk  has 
been  taken  into  account  by  using  an  instrumental  variable  regression,  we  find  no 
statistically significant relationship between the VAT gap and the VAT burden.   

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11 

 
Section 2: Top-down estimates of the VAT gap 

SECTION 2: TOP-DOWN ESTIMATES OF THE VAT GAP 

35. 

36. 

This section sets out, in more detail, the results of our top-down estimation of the VAT 
gap across Member States in the period 2000-2006.  Our analysis draws on an array 
of  assumptions  and  we  present  in  this  section  the  relative  impact  of  some  of  these 
assumptions  on  the  estimated  VAT  gap.    This  sensitivity  analysis  focuses  on  those 
assumptions  that  are  of  greater  materiality  and  for  which  we  have  less  evidence 
underpinning  them.    Charts  and  tables  reporting  the  full  set  of  results  for  each 
Member State are presented at the end of this section. 

In this section we refer at times to the EU-25 to mean the set of Member States in our 
analysis.  We do so for convenience, as strictly speaking this is not correct: Cyprus is 
not  included  in  our  analysis  because  no  use  tables  are  available  for  it,  and  we  have 
therefore  only  estimated  the VAT  gap  for  24  Member  States.    Similarly,  we  use  the 
term EU-10 to refer to those Member States that joined the EU in 2004 although again 
Cyprus is not included. 

Results 

37. 

The estimates of the VAT gap for each Member State over the period 2000–2006 are 
set out in a chart and in an accompanying table in the pages at the end of this section.  
Ahead of presenting those charts, we set out a brief overview and discussion of our 
estimates. 

There is no common trend in VAT gap across Member States 

38. 

39. 

40. 

41. 

42. 

There  is  no  common  trend  in  the  estimated  VAT  gap  over  the  period  2000–2006 
across the 24 Member States.   

For  most,  the  estimated  VAT  gap  exhibits  a  slight  downward  trend,  the  decreases 
tending  to  be  sharper  over  the  period  2003  to  2006.    Over  the  entire  period  of 
observations the decreases have been most pronounced in Luxembourg (from 12 per 
cent in 2000 to 1 percent in 2006), in Poland (from 22 per cent in 2000 to 7 per cent in 
2006) and in Slovenia (from 16 per cent in 2000 to 4 per cent in 2006).  

The  estimated  gap  for  Belgium,  Denmark,  Spain,  Ireland,  the  Netherlands,  Poland, 
Sweden  and  Slovenia  have  shown  a  steady  year-on-year  fall  for  most  years, 
particularly in the latter half of the period analysed. 

For Austria, Germany, France, Finland and the UK, the VAT gap has been estimated 
to be relatively stable, fluctuating within a relatively narrow band. 

For Greece, Hungary and Lithuania, we estimate that the VAT gap has increased from 
2000 or 2001 to the later years of our sample period.  We have estimated the Greek 
gap to have increased from 20 per cent in 2001 to 30 per cent in 2006.  For Hungary, 
we  estimated  the  gap  to  have  increased  in  the  earlier  years  and  to  have  remained 
relatively stable since; the gap increased from 15 per cent in 2000 to 25 per cent in 
2002  and  has  remained  at  that  level,  or  slightly  below,  until  2006.    The  gap  for 
Lithuania is estimated to have risen from 15 per cent in 2000 to 28 per cent in 2004, 
from which it subsequently fell; for 2006 it was estimated to be 22 per cent.  

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12 

 
Section 2: Top-down estimates of the VAT gap 

43. 

The  observation  that  for  many  of  the  Member  States  the  estimated  VAT  gap  have 
fallen  over  the  period,  particularly  in  the  second  half  of  the  sample  period,  applies 
more  widely  than  to  the  Member  States  joining  the  EU  in  2004  though  it  is  most 
noticeable amongst the newer members.  In this regard, we note that the VAT gap for 
Estonia dropped from 21 per cent in 2004 to 8 per cent in 2006 and for Latvia they 
fell from 31 to 22 per cent over that same period.  We have already noted above that 
for Lithuania we estimated the VAT gap falling between 2004 (28 per cent) and 2006 
(22 per cent).  The reforms to the VAT legislation and/or the greater effort in gaining 
fiscal efficiency that may be associated with joining the EU are potential explanations 
of the remarkable fall in the VAT gap of these Member States.  However, the estimates 
for  the  Czech  Republic  and  Slovakia  are  contrary  to  this  interpretation.    In  those 
Member States, the estimated gaps have risen between 2004 and 2006: from 24 to 28 
per cent in the case of Slovakia and from 13 to 18 per cent in the case of the Czech 
Republic  (although  until  2003  the  estimated  gap  for  the  Czech  Republic  fluctuates 
around 16 per cent).  

44. 

Figure  1  provides  a  summary  of  the  discussion  set  out  above.   The  figure  plots  the 
estimated VAT gap expressed as a share of theoretical net liability in 2006 against the 
estimate we obtained for 2000.  The dashed diagonal line in the figure is a 45 degree 
line.    For  those  Member  States  lying  above  this  diagonal  line,  the  VAT  gap  is 
estimated  to  have  increased  between  2000  and  2006.  For  those  below  the  line,  the 
VAT  gap  is  estimated  to  have  fallen.   As  can  be  read  from  Figure  1,  most  Member 
States are in the latter group. 

Figure 1  Comparison of estimated VAT gap in 2000 and 2006 

30%

20%

10%

y
t
i
l
i
b
a
i
l

f
o

e
r
a
h
s

a

s
a

p
a
g
T
A
V
6
0
0
2

0%

0%

GR

SK

IT

LV

HU

LT

CZ

UK

FR

FI

PT

NL

IE

SE

AT

DE

EE

LU

BE

DK

ES

10%

MT

SI

PL

20%

30%

2000 VAT gap as a share of liability

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13 

 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

A closer look at the trend of the VAT gap for some Member States 

45.  We  turn  now  to  an  analysis  of  the  evolution  of  the  VAT  gap  for  specific  Member 
States.    For  many  Member  States,  we  find  little  to  comment  on  with  regard  to  the 
evolution of the VAT gap between 2000 and 2006 that is not already apparent from 
the relevant figures and tables presented at the end of this section.  In many cases, we 
have no particular insights to offer on the movement of accrued receipts relative to the 
estimated  VAT  liability.    Clearly,  it  could  be  of  interest  to  understand  why  receipts 
may have grown faster or slower than estimated liability — did the tax authority put 
more resources in the fight against VAT fraud or did it change its mode of operations 
— but this is beyond the scope of the study. 

46. 

In the light of this, the discussion below focuses on a set of Member States for which 
we observe movements in the estimated VAT gap which might merit a supplementary 
comment.  

The Czech Republic 

47. 

As a share of the theoretical net VAT liability, the estimated VAT gap for the Czech 
Republic remained at around the 16 per cent level from 2000 to 2003 but in 2004 fall 
to 13 per cent, dropped marginally to 12 per cent in 2005 but then increased to 18 per 
cent  in  2006.    Behind  this  increase  between  2005  and  2006  lies  the  fact  that  the 
estimated  net  VAT  liability  rose  considerably,  from  CZK  245  to  CZK  261  million, 
whilst  accrued  receipts  fell  slightly,  from  CZK  215  billion  to  CZK  214  billion  in 
2006.    In  turn,  the  increased  liability  was  driven  almost  entirely  by  a  significant 
growth in household final consumption.  There were no changes in the standard rates 
over this period.  We note, lastly, that our estimates for the Czech Republic for both 
2005  and  2006  are  based  on  extrapolated  use  tables,  as  described  in  Section  5.  
However, we take comfort from the fact that the Czech total household consumption 
did register such high levels of growth in this period and so we do not believe that the 
estimated jump in the VAT gap are driven by the method we have used to extrapolate 
use tables.  

Estonia 

48. 

Our  estimates  of  the  Estonian  VAT  gap  show  a  sharp  fall  between  2004  and  2005, 
from  21  per  cent  to  9  per  cent.    There  was  no  change  to  the  standard  rate  in  these 
years  nor  does  it  appear  to  us  that  that  the  VAT  legislation  with  regards  to  the 
applicability of rates was changed in any other way that would impact significantly on 
the trend of the net VAT liability.  Rather, it has been the trend in accrued receipts 
which has changed since 2004; whilst this had grown at an average rate of around 9 
per  cent  a  year  between  2000  and  2004,  between  2004  and  2006  receipts  grew  by 
around 28 per cent a year. 

Hungary 

49. 

The  trend  of  the  VAT  gap  estimated  for  Hungary  is  an  interesting  one:  it  increases 
steadily from 15 per cent in 2000 to 25 per cent in 2002 and, following a slight fall in 
2003,  it  remains  relatively  stable  at  around  24  per  cent.    We  have  no  insight  to 
account for the increase in the VAT gap in the earlier years.  We do note, however, 

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14 

 
Section 2: Top-down estimates of the VAT gap 

that the VAT rate system was simplified in 2004 and the standard rate  was reduced 
from 25 to 20 per cent in January 2006.  Neither appears to have had an appreciable 
impact on VAT gap, which remained relatively flat. 

Luxembourg 

50. 

51. 

52. 

Our estimates for Luxembourg show a considerable drop in the VAT gap from 12 per 
cent in 2000 to 2 per  cent in 2004 before increasing to 5 per cent in 2005 and then 
falling back to 1 per cent in 2006.  There is a great margin of uncertainty surrounding 
our estimate of Luxembourg VAT gap. 

One source of this uncertainty relates to the sensitivity of the results to the assumption 
on the value of propex of the financial sector.  We reported in Table 6 that changing 
the value of that parameter from 50 per cent to 25 per cent would cause our estimate 
of  the  VAT  gap  in  2006  to  be  10.3  percentage  points  lower,  and  if,  instead,  the 
parameter had been set at 100 per cent, the gap would have been 8.8 percentage points 
higher.   

A  second  cause  of  uncertainty  relates  to  the  estimation  of  the  net  VAT  liability 
associated  with  “tank  tourism”,  the  practice  of  foreign  haulage  and  transport 
businesses  filling  up  their  trucks  in  Luxembourg  to  take  advantage  of  the  price 
differences.  This is a significant activity as may be inferred from the observation in a 
recent  Commission  document  that  “whereas  the  consumption  of  diesel  per  capita  is 
less  than  750  litres  in  other  Member  States,  it  amounts  to  more  than  4,200  litres  in 
Luxembourg”.  1  The significance of this activity is also explicitly recognized in the 
submissions  by  Luxembourg  to  the  United  Nations  Framework  Convention  on 
Climate  Change  on  its  national  inventory  of  greenhouse  gases;  its  2008  submission 
reports that, in 2006, 18 per cent of diesel sold in Luxembourg was consumed in the 
country whereas the remaining 82 per cent was “exported”.2 

53.  We  understand  that  trade  relating  to  “tank  tourism”  should  be  recorded  in  the 
Luxembourg  use  tables  as  exports  of  diesel  or  petrol.    Our  top–down  approach 
assumes  that,  in  general,  no  VAT  liability  arises  from  exports.    In  this  instance, 
however, we understand that foreign trucks will pay the VAT due on the diesel or fuel 
and that those entitled to a VAT refund will not necessarily always apply for a refund 
from  the  Luxembourg  tax  agency.    It  is  necessary,  therefore  to  estimate  the 
contribution to the  Luxembourg net theoretical  VAT liability that is associated with 
these “exports”.  

1   European Commission (2007) “Accompanying document to the Proposal for a Council Directive amending Directive 

2003/96/EC as regards the adjustment of special tax arrangements for gas oil used as motor fuel for commercial purposes 
and the coordination of taxation of unleaded petrol and gas oil used as motor fuel”, Staff Working Paper {COM(2007) 52 
final}, p. 8.  HTML version available from http://eur-
lex.europa.eu/LexUriServ/LexUriServ.do?uri=CELEX:52007SC0170:EN:HTML , accessed on 3 August 2009. 

2   Ministère de l’Environnement, Luxembourg (2008) “Luxembourg’s National Inventory Report 1990-2006 – Submission 
under the United Nations Convention on Climate Change and voluntary submission under the Kyoto Protocol”, Table 
3.44.  Available from 
http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/4303.php , accessed 
on 3 August 2009. 

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15 

 
                                                 
Section 2: Top-down estimates of the VAT gap 

54.  We have not come across published data on the amount of “tank tourism” on which 
the  VAT  is  not  recovered.    For  the  purpose  of  our  estimation  we  have  assumed,  as 
noted  in  Section  6  that  all  of  the  exports  relating  to  the  product  category  “Coke, 
refined petroleum products and nuclear fuels” refer to sales of diesel or petrol to “tank 
tourists”  and  have  further  assumed  that  none  of  the  VAT  associated  with  this  is 
recovered.   

Portugal 

55.  We estimate that there was a fall in the VAT gap as a share of liabilities in Portugal 
over the sample period as a whole and a sharp drop in particular between 2004 (8 per 
cent)  and  2005  (3  per  cent).    Use  tables  are  available  for  the  period  2000–2005 
implying that the estimated drop is not linked to our forecasting technique.  Annual 
reports  from  the  Portuguese  Ministry  of  Finance  and  Public  Administration  on  the 
fight against fiscal fraud and evasion for the more recent years give emphasis to the 
greater operational effort in targeting VAT fraud, notably MTIC but we are unable to 
identify  specific  initiatives  as  the  cause  of  the  observed  fall  in  the  VAT  gap  from 
2004 to 2005.  We note, however, that the standard rate of VAT did increase in this 
period, from 19 to 21 per cent on the 1 May 2005. 

UK 

56. 

57. 

Our estimates for the UK show a relatively stable trend in the VAT liability over the 
period 2000–2006.  The VAT gap as a share of liability has also remained fairly stable 
at around 17 per cent from 2000 to 2002, then falling to 15 per cent in 2003 and 2004 
before increasing to 18 per cent in 2005.  We note that the increase between 2004 and 
2005 is led by a slowdown in the growth of receipts rather than due an extraordinary 
increase in estimated liability. 

This  observed  trend  in  the VAT  gap  share  is  in  keeping  with  the  trends  in  HMRC’s 
own estimates of VAT gap.3  HMRC’s estimates of VAT gap also show a decline until 
2004 before increasing in 2005.  Significantly, HMRC also estimates that there was a 
sharp  rise  in  MTIC  fraud  in  2005,  which  possibly  contributed  to  the  rise  in  the 
estimated gap.    

Sensitivity analysis 

58. 

Of the assumption underpinning our top-down estimation of the VAT gap we identify 
four which we expect to have a material impact on our estimate of the VAT gap and 
for  which  we  have  limited  empirical  backing.    Because  of  this,  we  think  it  is  of 
interest  to  examine  the  impact  on  our  estimates  of  considering  variations  to  these 
assumptions.  We carry out such a sensitivity analysis on the following:  

(a)  Across  all  Member  States,  we  assume  that  60  per  cent  of  the  value  of 
intermediate consumption of the financial sector attracts irrecoverable VAT.  

3   HMRC (2007) Measuring indirect tax losses, 2007.  Available from http://www.hmrc.gov.uk/pbr2007/mitl.pdf, accessed 

on 3 August 2009. 

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16 

 
                                                 
Section 2: Top-down estimates of the VAT gap 

(b)  Across  all  Member  States,  we  assume  that  80  per  cent  of  the  value  of 
intermediate consumption of the education sector attracts irrecoverable VAT. 

(c)  For  four  Member  States  (Belgium,  Italy,  Portugal  and  the  UK)  we  assume  that 
half  of  the  gross  fixed  capital  formation  on  dwellings  is  accounted  for  by 
investments in new dwellings and half by expenditure on major improvements to 
existing  dwellings.    This  assumption  is  necessary  as  national  VAT  legislation 
accords  different  VAT  treatment  to  the  two  categories  of  expenditure  and  we 
have not found data that allow us to make this split for these countries. 

(d)  Across  all  Member  States,  we  assume  that  half  of  the  gross  fixed  capital 
formation  on  products  within  the  CPA  category  “Motor  vehicles,  trailers  and 
semi-trailers” attract VAT that is not recoverable. 

The  sensitivity  analysis  is  done  by  considering  other  values  for  the  parameter 
associated with each of the assumptions and, for each, calculating the associated VAT 
gap.  The first two assumptions refer to the share of intermediate consumptions by a 
given  sector  on  which  VAT  is  not  recoverable;  we  have  denoted  this  parameter  as 
propex.    As  an  example,  and  with  regard  to  the  first  assumption,  we  examine  the 
sensitivity of our results by computing the change in the estimated VAT gap had we 
assumed that the propex associated with the financial sector was 100 per cent, and at 
the other extreme, had we assumed it to be 25 per cent. 

For each Member State we carry out the sensitivity analysis relating to the first two 
assumptions and to the fourth listed above using the most recent  year for which use 
tables have been published by Eurostat.  This is to ensure that the sensitivity analysis 
is not vitiated by any possible effects that might arise due to the extrapolation of the 
use tables we carried out.  The exceptions to this are Greece and Latvia as the most 
recent published use tables refer to  years prior to 2000 and we have carried out the 
sensitivity on the basis of the data for 2000.  The sensitivity of our results to the third 
assumption  listed  —  on  the  split  of  GFCF  on  dwellings  —  is  done  on  the  basis  of 
2006  estimates  as  the  parameter  relating  to  that  assumption  is  applied  to  data  on 
GFCF obtained from Eurostat and hence has no interaction with the extrapolation of 
the use tables.  

59. 

60. 

Sensitivity to the assumption on the propex of the financial sector 

61. 

62. 

Our estimates assume that, across all Member States, 60 per cent of the value of the 
intermediate consumption of the financial sector attracts irrecoverable VAT.  That is to 
say, we assume that the value of propex for the financial sector, as defined by NACE 
category  “J.  Financial  intermediaries”,  is  60  per  cent.    The  grounds  for  this 
assumption are set out in Section 6. 

Table 4 presents the impact on the estimation of the VAT gap for each Member State 
of changing this assumption.  In particular, it reports how the VAT gap would change 
if rather than assuming a propex of 60 per cent we assumed it to be (a) 25 per cent, 
and (b) 100 per cent.  

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17 

 
Section 2: Top-down estimates of the VAT gap 

Table 4 

Sensitivity to assumption on propex of the financial services sector 

Member State 

AT 

BE 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

Year 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2005 

2004 

2000 

2004 

2002 

2004 

2004 

2006 

2000 

2001 

2004 

2003 

2005 

2005 

2004 

2004 

2003 

VAT gap as a share 
of theoretical 
liability (%) 

Percentage points change to VAT gap if 
assume finance propex of 

25 per cent 

100 per cent 

12.5% 

12.4% 

12.5% 

13.8% 

7.1% 

21.3% 

7.9% 

3.8% 

6.7% 

23.8% 

23.7% 

2.6% 

27.5% 

27.6% 

1.0% 

30.6% 

15.5% 

5.6% 

19.9% 

3.0% 

1.9% 

7.8% 

23.7% 

14.3% 

–1.0 

–1.4 

–1.0 

–1.2 

–0.7 

–0.3 

–0.7 

–0.9 

–1.5 

–0.5 

–0.7 

–2.0 

–0.7 

–0.3 

–10.3 

–0.6 

–0.5 

–1.4 

–0.9 

–1.4 

–0.8 

–0.7 

–0.5 

–2.7 

1.0 

1.4 

1.0 

1.2 

0.7 

0.3 

0.7 

0.9 

1.4 

0.5 

0.7 

1.9 

0.6 

0.3 

8.8 

0.6 

0.5 

1.4 

0.9 

1.3 

0.7 

0.7 

0.5 

2.6 

63. 

As would be expected, changing the assumption on the propex of the financial sector 
has greatest impact for those Member State where this sector is particularly important: 
hence  the  considerable  impact  on  the  estimated VAT  gap  for  Luxembourg  and,  to  a 
much less but still significant extent, for the UK and Ireland.  For most other Member 
States,  the  rise  or  fall  in  the  estimated  VAT  gap  of  assuming  one  of  the  alternate 
values for the parameter would be within 1.5 percentage points of our estimate.  

Sensitivity to the assumption on the propex of the educational sector 

64. 

Our estimates assume that the propex attributable to the education sector, NACE code 
“N Education” is 80 per cent; a discussion of this assumption is set out in Section 6.  
We examine the impact  on our estimates of the VAT  gap if this parameter takes the 
value of 50 per cent, and, alternatively, if we assume it to be 100 per cent.  The results 
are presented in Table 5. 

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Section 2: Top-down estimates of the VAT gap 

Table 5 

Sensitivity to assumption on propex of the education sector 

Member State 

AT 

BE 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

Year 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2005 

2004 

2000 

2004 

2002 

2004 

2004 

2006 

2000 

2001 

2004 

2003 

2005 

2005 

2004 

2004 

2003 

VAT gap as a share 
of theoretical 
liability (%) 

Percentage points change to VAT gap if 
assume finance propex of 

50 per cent 

100 per cent 

12.5% 

12.4% 

12.5% 

13.8% 

7.1% 

21.3% 

7.9% 

3.8% 

6.7% 

23.8% 

23.7% 

2.6% 

27.5% 

27.6% 

1.0% 

30.6% 

15.5% 

5.6% 

19.9% 

3.0% 

1.9% 

7.8% 

23.7% 

14.3% 

–0.3 

–0.2 

–0.5 

–0.3 

–0.6 

–0.5 

–0.3 

–0.5 

–0.4 

–0.1 

–0.4 

–0.3 

–0.2 

–0.2 

–0.2 

–0.5 

–0.2 

–0.3 

–0.3 

–0.3 

–0.6 

–0.5 

–0.2 

–0.6 

0.2 

0.1 

0.3 

0.2 

0.4 

0.3 

0.2 

0.3 

0.2 

0.0 

0.2 

0.2 

0.1 

0.1 

0.1 

0.3 

0.1 

0.2 

0.2 

0.2 

0.3 

0.3 

0.1 

0.4 

65. 

The results in Table 5 report that had we assumed the propex of the education sector 
to have been 50 or 100 per cent rather than 80 per cent, our estimate of the share of 
the  VAT  gap  would  have  changed  by  at  most  0.6  percentage  points,  and  for  most 
Member States it would have changed considerably less.    

Sensitivity to the assumption on the split of GFCF on dwellings 

66. 

GFCF  associated  with  dwellings  is  made  up  of  investment  in  new  dwellings  and  of 
expenditure  in  major  improvements  to  existing  dwellings.    In  some  Member  States, 
different  VAT  rates  apply  to  the  supply  of  goods  or  services  relating  to  these  two 
activities and, for these Member States, it is necessary to identify the contribution of 
each of the activities.  Whilst we have found disaggregated data that allow us to split 
GFCF between the two types of capital formation for some of the relevant Member 
States, for four of the Member States we have made an  assumption about that split.  
This assumption is relevant to Belgium, Italy, Portugal and the UK.  In particular, and 

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19 

 
 
Section 2: Top-down estimates of the VAT gap 

as discussed in Section 6 we assume that each of the two forms of capital formation 
relating to dwellings account for half of the GFCF on dwellings.   

67. 

For these Member States, Table 6 reports the impact on the estimated VAT gap under 
two scenarios: first, that the GFCF associated with new dwellings account for 25 per 
cent of total GFCF on dwellings and, second, that the percentage is 75 percent. 

Table 6 

Sensitivity to assumption on the split of GFCF on dwellings 

Member State 

Year 

VAT gap as a share 
of theoretical 
liability (%) 

BE 

IT 

PT 

UK 

2006 

2006 

2006 

2006 

11.0% 

22.1% 

4.3% 

17.3% 

Percentage points change to VAT gap if 
assume ratio of GFCF on new dwellings 
to improvements to existing dwellings 

25:75 

–2.2 

0.7 

0.5 

1.7 

75:25 

2.2 

–0.8 

–0.5 

–1.7 

68. 

The impact on the estimated VAT gap of changing the assumption about the split of 
GFCF on dwellings is greatest for Belgium and the UK.  For Belgium, our estimate of 
the  VAT  gap  would  be  around  2.2  percentage  points  higher  or  lower  depending  on 
which of the alternative values is assumed.  In contrast, the impact of considering the 
more extreme values of the parameter is relatively small for Italy and Portugal. 

Sensitivity to the assumption on the VAT treatment of company cars 

69.  We examine the sensitivity of our estimates of the VAT gap to the assumption on the 
share  of  gross  fixed  capital  formation  in  the  economy  on  products  of  the  sector 
“DM34 Motor vehicles, trailers and semi–trailers” on which VAT is recoverable.  The 
estimates  reported  earlier  in Table  3  assume  that  that  share  is  50  per  cent.   Table  7  
below reports the changes to those estimates if that share were assumed to be 25 or 75 
per cent. 

www.reckon.co.uk  

20 

 
 
Section 2: Top-down estimates of the VAT gap 

Table 7  

Sensitivity to assumption on the VAT treatment of company cars 

Member State 

Year 

VAT gap as a share 
of theoretical 
liability (%) 

Percentage points change to VAT gap if 
assume share of GFCF to be not 
recoverable of  

25 per cent 

75 per cent 

AT 

BE 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

2004 

2004 

2004 

2004 

2004 

2004 

2004 

2005 

2004 

2000 

2004 

2002 

2004 

2004 

2006 

2000 

2001 

2004 

2003 

2005 

2005 

2004 

2004 

2003 

12.5% 

12.4% 

12.5% 

13.8% 

7.1% 

21.3% 

7.9% 

3.8% 

6.7% 

23.8% 

23.7% 

2.6% 

27.5% 

27.6% 

1.0% 

30.6% 

15.5% 

5.6% 

19.9% 

3.0% 

1.9% 

7.8% 

23.7% 

14.3% 

–0.7 

–0.9 

–1.4 

–0.7 

–0.6 

–1.3 

–0.9 

–0.4 

–0.6 

–0.6 

–0.7 

–1.3 

–0.6 

–0.5 

–0.8 

–1.2 

–0.2 

–0.8 

–1.1 

–0.6 

–0.8 

–1.0 

–0.6 

–0.3 

0.7 

0.9 

1.4 

0.6 

0.6 

1.2 

0.9 

0.4 

0.6 

0.6 

0.7 

1.3 

0.6 

0.5 

0.7 

1.2 

0.2 

0.7 

1.1 

0.6 

0.8 

1.0 

0.6 

0.3 

70. 

As reported in Table 7, our estimates of the VAT gap for the Czech Republic, Estonia, 
Ireland,  Latvia,  Poland  and  Slovenia  are  relatively  sensitive  to  this  assumption:  in 
each  of  these  Member  States  the  estimated  VAT  gap  would  change  by  at  most  1.4 
percentage points, and in most cases less than 1 percentage point, if the more extreme 
parameter values were chosen.  

Conclusions on the sensitivity analyses 

71.  We  have  carried  out  sensitivity  analyses  for  those  assumptions  which  we  expect  to 
have  a  more  material  impact  on  our  estimates  and  for  which  we  have  relatively 
limited empirical support.  Not surprisingly, the results of the analyses do show that 
each  of  these  assumptions  have  a  material  impact  on  the  estimated  VAT  gap.  
However, the impact is relatively limited with respect to the assumption on company 

www.reckon.co.uk  

21 

 
 
Section 2: Top-down estimates of the VAT gap 

cars  and  to  the  assumption  on  the  propex  associated  with  the  education  sector; 
typically the estimates are affected by less than 1.5 percentage points.  

72.  We also find that the sensitivity of our results to the assumption on the propex of the 
financial  sector  is  limited  for  most  of  the  Member  States.    The  two  significant 
exceptions in this regard are the UK and Luxembourg. 

73. 

As a final comment on the sensitivity analysis, we note that whilst altering the values 
of  the  parameters  relating  to  the  above  assumptions  will  affect  the  level  of  the 
estimated VAT gap as discussed above, we would not expect the trend in the gap to be 
similarly affected.  Rather, the parameter values assumed for a given assumption will 
only affect the trend to the extent that the structure of consumption changes over time. 

Full set of results 

74. 

Taking  each  Member  State  in  turn,  we  set  out  over  the  following  pages  a  chart  and 
accompanying table showing the estimates of the estimated VAT liability, receipts and 
VAT gap over the period 2000–2006. 

75. 

The tables break down the total  estimated VAT  liability into the liability associated 
with: 

(a)  household final consumption; 

(b)  gross fixed capital formation; 

(c)  other  consumption,  which  covers  government 

intermediate  and 

final 

consumption and intermediate consumption of other sectors; and 

(d)  the set of adjustments relating to small business exemptions, company cars and 
business  entertainment  and,  for  Luxembourg,  "tank  tourism",  and  changes  in 
valuables. 

76. 

The  data  sources  we  have  used  to  compile  our  estimate  do  not  adopt  a  common 
approach  to  classifying  government  expenditure.    For  example,  gross  fixed  capital 
formation on private dwellings could be classified as being carried out by households 
in  some  cases  and  government  or  non–financial  corporations  on  others.  
Consequently,  we  choose  not  to  separately  identify  the  liability  arising  from 
government expenditure. 

www.reckon.co.uk  

22 

 
Section 2: Top-down estimates of the VAT gap 

Austria

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

26

24

22

20

18

16

14

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

16

14

12

10

8

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 8 

Austria: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

AT 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

19,295 

19,726 

19,978 

20,585 

21,250 

22,020 

22,844 

13,038 

13,437 

13,676 

14,067 

14,539 

15,056 

15,654 

2,570 

3,288 

398 

2,556 

3,317 

415 

2,464 

3,456 

381 

2,545 

3,536 

436 

2,562 

3,702 

445 

2,630 

3,855 

479 

2,772 

3,966 

451 

Actual VAT receipts  

VAT gap  

16,840 

2,455 

17,251 

2,475 

17,972 

2,006 

17,893 

2,692 

18,590 

19,414 

19,735 

2,660 

2,606 

3,108 

VAT gap as a share of 
theoretical liability 

13% 
† Estimates compiled using forecasted use table data 

13% 

10% 

13% 

13% 

12% 

14% 

www.reckon.co.uk  

23 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Belgium

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

28

26

24

22

20

18

16

14

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

18

16

14

12

10

8

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 9 

Belgium: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

BE 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

20,224 

20,764 

21,275 

21,829 

22,959 

24,047 

25,360 

12,414 

13,017 

13,245 

13,669 

14,074 

14,769 

15,531 

2,554 

4,341 

915 

2,568 

4,301 

878 

2,629 

4,529 

872 

2,465 

4,823 

872 

2,828 

5,080 

976 

2,980 

5,274 

1,025 

3,202 

5,530 

1,096 

Actual VAT receipts  

VAT gap  

18,130 

2,094 

17,817 

2,946 

18,591 

2,684 

18,730 

3,098 

20,122 

21,362 

22,569 

2,837 

2,685 

2,791 

VAT gap as a share of 
theoretical liability 

14% 
† Estimates compiled using forecasted use table data 

10% 

13% 

14% 

12% 

11% 

11% 

www.reckon.co.uk  

24 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Czech Republic

)
n
o

i
l
l
i

b
(

K
Z
C

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

275

250

225

200

175

150

125

100

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

20

16

12

8

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 10  Czech Republic: VAT receipts, theoretical liability and gap, 2000–2006 (CZK million) 

CZ 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

166,392 

175,418 

185,630 

196,790 

233,855 

244,671  261,196 

112,156 

118,010 

121,072 

127,426 

141,673 

140,266  151,190 

19,530 

28,683 

6,023 

20,507 

29,709 

7,191 

24,030 

31,603 

8,925 

23,273 

36,291 

9,799 

32,480 

49,057 

10,646 

37,219 

39,817 

55,354 

56,400 

11,832 

13,789 

Actual VAT receipts  

141,341 

149,271 

155,136 

164,250 

204,618 

215,118  213,728 

VAT gap  

25,051 

26,147 

30,494 

32,540 

29,237 

29,553 

47,468 

VAT gap as a share of 
theoretical liability 

15% 
† Estimates compiled using forecasted use table data 

15% 

16% 

17% 

13% 

12% 

18% 

www.reckon.co.uk  

25 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Germany

170

160

150

140

130

120

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

16

14

12

10

8

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 11  Germany: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

DE 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

158,538 

160,460 

157,694 

158,376 

159,352 

160,664  164,115 

97,402 

101,232 

100,174 

100,350 

102,069 

102,464  103,947 

30,540 

27,670 

2,926 

28,309 

28,261 

2,657 

26,863 

28,314 

2,343 

26,437 

29,108 

2,481 

26,060 

28,506 

2,717 

25,819 

27,301 

29,750 

30,042 

2,630 

2,825 

Actual VAT receipts  

140,020 

139,090 

136,810 

137,190 

137,430 

139,810  147,150 

VAT gap  

18,518 

21,370 

20,884 

21,186 

21,922 

20,854 

16,965 

VAT gap as a share of 
theoretical liability 

13% 
† Estimates compiled using forecasted use table data 

12% 

13% 

13% 

14% 

13% 

10% 

www.reckon.co.uk  

26 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Denmark

180

170

160

150

140

130

120

)
n
o

i
l
l
i

b
(

K
K
D

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

10

8

6

4

2

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 12  Denmark: VAT receipts, theoretical liability and gap, 2000–2006 (DKK million) 

DK 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005 

2006† 

135,645 

140,878 

143,523 

145,160 

154,301 

163,228  176,119 

81,768 

83,562 

86,080 

86,885 

92,828 

96,522  102,746 

19,398 

29,929 

4,551 

20,309 

32,368 

4,639 

19,089 

33,867 

4,487 

19,094 

34,829 

4,352 

19,943 

37,047 

4,483 

22,139 

25,311 

39,637 

42,930 

4,930 

5,132 

Actual VAT receipts  

123,777 

128,550 

132,394 

135,088 

143,277 

155,463  168,276 

VAT gap  

11,868 

12,328 

11,129 

10,072 

11,024 

7,765 

7,843 

VAT gap as a share of 
theoretical liability 

9% 

9% 

8% 

7% 

7% 

5% 

4% 

†  Estimates  compiled  using  forecasted  use  table  data.  Revised  estimates  for  Denmark  calculated  with  use  tables 
downloaded from Eurostat in July 2009. 

www.reckon.co.uk  

27 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Estonia

)
n
o

i
l
l
i

b
(

K
E
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

22

20

18

16

14

12

10

8

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

24

20

16

12

8

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 13  Estonia: VAT receipts, theoretical liability and gap, 2000–2006 (EEK million) 

EE 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

Actual VAT receipts  

VAT gap  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

9,267 

10,652 

12,033 

13,301 

14,790 

16,692 

20,739 

6,571 

7,436 

8,272 

9,155 

10,239 

11,364 

13,589 

1,071 

1,482 

143 

8,142 

1,125 

1,347 

1,663 

206 

8,892 

1,760 

1,714 

1,797 

251 

1,888 

2,001 

257 

2,216 

2,050 

285 

2,850 

2,341 

137 

4,004 

2,748 

399 

10,178 

1,855 

11,141 

2,160 

11,638 

15,176 

19,009 

3,152 

1,516 

1,731 

15% 

16% 

21% 

9% 

8% 

VAT gap as a share of 
theoretical liability 

17% 
† Estimates compiled using forecasted use table data 

12% 

www.reckon.co.uk  

28 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Spain

70

60

50

40

30

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

16

12

8

4

0

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 14  Spain: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

ES 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002† 

2003† 

2004 

2005† 

2006† 

41,565 

44,525 

47,265 

50,397 

54,363 

58,599 

63,013 

28,804 

30,614 

32,028 

33,698 

36,067 

38,553 

41,028 

6,178 

5,608 

975 

6,878 

6,042 

991 

7,431 

6,746 

1,060 

8,254 

7,316 

1,130 

8,989 

8,094 

1,211 

9,885 

8,857 

1,305 

10,766 

9,816 

1,403 

Actual VAT receipts  

VAT gap  

37,640 

3,925 

39,117 

5,408 

41,475 

5,790 

45,915 

4,482 

50,084 

56,197 

61,595 

4,278 

2,402 

1,418 

VAT gap as a share of 
theoretical liability 

12% 
† Estimates compiled using forecasted use table data 

9% 

12% 

9% 

8% 

4% 

2% 

www.reckon.co.uk  

29 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Finland

16

14

12

10

8

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

8

6

4

2

0

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 15  Finland: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

FI 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005 

2006† 

11,099 

11,599 

12,210 

13,048 

13,589 

14,195 

15,176 

6,518 

6,833 

7,128 

7,587 

7,853 

8,149 

8,661 

1,653 

2,816 

111 

1,663 

2,966 

137 

1,731 

3,193 

158 

1,874 

3,387 

199 

1,990 

3,538 

209 

2,112 

3,683 

252 

2,248 

3,967 

300 

Actual VAT receipts  

10,869 

11,118 

11,680 

12,455 

12,949 

13,658 

14,418 

VAT gap  

230 

481 

VAT gap as a share of 
theoretical liability 

4% 
† Estimates compiled using forecasted use table data 

2% 

530 

4% 

593 

5% 

640 

537 

758 

5% 

4% 

5% 

www.reckon.co.uk  

30 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

France

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

150

140

130

120

110

100

90

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

12

10

8

6

4

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 16  France: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

FR 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

111,119 

114,078 

117,990 

122,180 

127,879 

133,793  140,817 

71,008 

73,075 

75,250 

77,614 

80,661 

83,530 

86,440 

17,780 

19,751 

2,579 

17,942 

20,451 

2,610 

18,284 

21,854 

2,603 

19,416 

22,542 

2,608 

20,694 

23,727 

2,797 

22,184 

23,896 

25,149 

27,326 

2,930 

3,154 

Actual VAT receipts  

105,887 

107,465 

109,353 

112,460 

119,294 

125,768  131,017 

VAT gap  

5,231 

6,613 

8,637 

9,720 

8,585 

8,025 

9,800 

VAT gap as a share of 
theoretical liability 

6% 
† Estimates compiled using forecasted use table data 

5% 

7% 

8% 

7% 

6% 

7% 

www.reckon.co.uk  

31 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Greece

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

22

20

18

16

14

12

10

8

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

34

30

26

22

18

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 17  Greece: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

GR 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

Actual VAT receipts  

VAT gap  

2000† 

2001† 

2002† 

2003† 

2004† 

2005† 

2006† 

12,900 

13,737 

14,942 

16,410 

17,666 

19,456 

21,746 

8,199 

8,792 

9,381 

10,100 

10,828 

12,229 

13,385 

2,973 

1,501 

226 

9,824 

3,076 

3,169 

1,530 

246 

3,536 

1,744 

281 

4,169 

1,809 

332 

4,569 

1,900 

369 

4,795 

2,025 

407 

5,707 

2,165 

490 

10,960 

2,777 

11,969 

2,973 

12,043 

4,367 

12,573 

13,398 

15,183 

5,093 

6,058 

6,563 

VAT gap as a share of 
theoretical liability 

20% 
† Estimates compiled using forecasted use table data 

24% 

20% 

27% 

29% 

31% 

30% 

www.reckon.co.uk  

32 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Hungary

2500

2300

2100

1900

1700

1500

1300

1100

)
n
o

i
l
l
i

b
(
F
U
H

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

26

22

18

14

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 18  Hungary: VAT receipts, theoretical liability and gap, 2000–2006 (HUF million) 

HU 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

1,366,496 

1,584,177 

1,780,756 

1,941,165 

2,399,106  2,483,996  2,347,243 

916,738 

1,036,046 

1,118,808 

1,258,368 

1,559,005  1,584,940  1,467,214 

232,003 

293,011 

363,193 

351,705 

411,850 

414,325 

382,406 

Other consumption  

185,652 

219,086 

256,641 

287,871 

385,306 

441,378 

452,785 

Net adjustments  

32,103 

36,035 

42,115 

43,222 

42,945 

43,353 

44,837 

Actual VAT receipts  

1,159,959 

1,230,216 

1,340,914 

1,539,868 

1,831,647  1,856,547  1,800,345 

VAT gap  

206,537 

353,961 

439,842 

401,297 

567,459 

627,449 

546,898 

VAT gap as a share of 
theoretical liability 

15% 

22% 

25% 

21% 

24% 

25% 

23% 

† Estimates compiled using forecasted use table data 

www.reckon.co.uk  

33 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Ireland

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

16

14

12

10

8

6

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

10

8

6

4

2

0

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 19 

Ireland: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

IE 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

Actual VAT receipts  

VAT gap  

2000 

2001 

2002 

2003† 

2004† 

2005† 

2006† 

8,096 

8,521 

9,415 

10,509 

11,385 

12,750 

14,043 

4,526 

4,668 

5,121 

5,716 

6,033 

6,574 

7,091 

2,060 

1,139 

372 

7,657 

440 

2,094 

1,413 

348 

7,999 

523 

2,324 

1,569 

400 

9,168 

247 

2,656 

1,673 

464 

9,814 

694 

3,074 

1,783 

494 

3,637 

1,939 

600 

4,192 

2,098 

662 

10,947 

12,364 

13,802 

437 

386 

241 

VAT gap as a share of 
theoretical liability 

6% 
† Estimates compiled using forecasted use table data 

5% 

3% 

7% 

4% 

3% 

2% 

www.reckon.co.uk  

34 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Italy

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

120

110

100

90

80

70

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

30

28

26

24

22

20

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 20 

Italy: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

IT 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

99,869 

103,362 

106,055 

109,046 

112,358 

115,646  119,197 

73,284 

75,101 

76,650 

78,534 

80,889 

82,549 

85,070 

9,552 

13,121 

3,913 

10,103 

14,283 

3,876 

10,460 

15,086 

3,859 

11,058 

15,783 

3,671 

11,384 

16,184 

3,902 

11,925 

12,309 

17,327 

17,809 

3,845 

4,009 

Actual VAT receipts  

VAT gap  

77,473 

22,396 

78,056 

25,306 

80,382 

25,673 

79,099 

29,947 

81,515 

30,843 

85,317 

92,860 

30,329 

26,337 

VAT gap as a share of 
theoretical liability 

24% 
† Estimates compiled using forecasted use table data 

22% 

24% 

27% 

27% 

26% 

22% 

www.reckon.co.uk  

35 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Lithuania

9

8

7

6

5

4

3

)
n
o

i
l
l
i

b
(
L
T
L

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

30

26

22

18

14

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 21  Lithuania: VAT receipts, theoretical liability and gap, 2000–2006 (LTL million) 

LT 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

4,105 

4,440 

4,684 

4,989 

5,608 

6,583 

8,063 

3,091 

3,365 

3,494 

3,789 

4,267 

5,057 

6,006 

441 

541 

32 

468 

551 

56 

541 

602 

47 

617 

541 

42 

763 

513 

64 

828 

623 

74 

1,215 

745 

98 

Actual VAT receipts  

VAT gap  

3,471 

635 

3,544 

895 

3,843 

841 

3,836 

1,154 

4,059 

1,549 

5,138 

1,445 

6,303 

1,760 

VAT gap as a share of 
theoretical liability 

20% 
† Estimates compiled using forecasted use table data 

15% 

18% 

23% 

28% 

22% 

22% 

www.reckon.co.uk  

36 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Luxembourg

2

1.8

1.6

1.4

1.2

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

1

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

16

12

8

4

0

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 22  Luxembourg: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

LU 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments*  

Actual VAT receipts  

VAT gap  

VAT gap as a share of 
theoretical liability 

1,397 

1,446 

1,454 

1,526 

1,690 

1,927 

1,961 

730 

214 

324 

129 

747 

224 

356 

118 

806 

219 

356 

73 

811 

216 

370 

129 

881 

215 

446 

147 

951 

962 

215 

497 

264 

221 

566 

211 

1,234 

163 

1,314 

133 

1,383 

71 

1,467 

59 

12% 

9% 

5% 

4% 

1,654 

1,838 

1,941 

36 

2% 

90 

20 

5% 

1% 

*Includes estimated VAT liability from “tank tourism” by business vehicles 

www.reckon.co.uk  

37 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Latvia

)
n
o

i
l
l
i

m

(

i

L
V
L
,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

1400

1200

1000

800

600

400

200

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

34

30

26

22

18

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 23  Latvia: VAT receipts, theoretical liability and gap, 2000–2006 (LVL million) 

LV 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

Actual VAT receipts  

VAT gap  

2000† 

2001† 

2002† 

2003† 

2004† 

2005† 

2006† 

482 

518 

562 

647 

753 

949 

1,219 

324 

358 

395 

475 

548 

667 

869 

48 

94 

17 

335 

148 

43 

96 

22 

351 

167 

50 

91 

26 

384 

178 

51 

89 

32 

461 

186 

65 

94 

46 

519 

235 

122 

91 

69 

704 

245 

150 

102 

98 

956 

263 

VAT gap as a share of 
theoretical liability 

32% 
† Estimates compiled using forecasted use table data 

31% 

32% 

29% 

31% 

26% 

22% 

www.reckon.co.uk  

38 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Malta

)
n
o

i
l
l
i

m

(

L
T
M

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

220

200

180

160

140

120

100

80

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

18

14

10

6

2

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 24  Malta: VAT receipts, theoretical liability and gap, 2000–2006 (MTL million) 

MT 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

Actual VAT receipts*  

VAT gap  

2000 

2001 

2002† 

2003† 

2004† 

2005† 

2006† 

124 

132 

133 

145 

166 

182 

199 

81 

18 

22 

3 

103 

22 

87 

21 

20 

3 

111 

20 

86 

22 

23 

2 

128 

5 

4% 

90 

27 

25 

2 

117 

28 

109 

114 

117 

26 

28 

3 

143 

23 

33 

33 

3 

171 

12 

37 

41 

4 

176 

23 

19% 

14% 

6% 

11% 

*Eurostat data in EUR converted to MTL using a fixed exchange 

VAT gap as a share of 
theoretical liability 

16% 
† Estimates compiled using forecasted use table data 
rate of 1 EUR = 0.4293 MTL. 

17% 

www.reckon.co.uk  

39 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Netherlands

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

42

38

34

30

26

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

12

8

4

0

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 25  Netherlands: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

NL 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

30,873 

35,457 

36,752 

37,163 

37,951 

39,571 

41,269 

16,419 

18,928 

19,679 

19,514 

19,945 

20,537 

20,780 

7,565 

6,194 

695 

7,722 

7,964 

843 

7,856 

8,451 

766 

8,272 

8,640 

738 

8,179 

9,013 

815 

8,674 

9,534 

826 

9,380 

10,222 

887 

Actual VAT receipts  

VAT gap  

28,849 

2,024 

32,509 

2,948 

33,493 

3,259 

34,754 

2,409 

35,811 

36,950 

39,888 

2,140 

2,621 

1,381 

VAT gap as a share of 
theoretical liability 

8% 
† Estimates compiled using forecasted use table data 

7% 

9% 

6% 

6% 

7% 

3% 

www.reckon.co.uk  

40 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Poland

)
n
o

i
l
l
i

b
(

N
L
P

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

100

90

80

70

60

50

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

24
20

16
12

8

4

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 26  Poland: VAT receipts, theoretical liability and gap, 2000–2006 (PLN million) 

PL 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001† 

2002 

2003 

2004† 

2005† 

2006† 

65,811 

69,506 

72,820 

75,136 

81,530 

85,643 

92,660 

45,498 

47,614 

50,844 

51,281 

55,894 

58,221 

61,789 

6,850 

11,254 

2,209 

6,794 

12,983 

2,116 

6,492 

13,697 

1,787 

6,610 

14,923 

2,323 

7,877 

8,393 

9,400 

15,526 

16,642 

18,687 

2,233 

2,387 

2,783 

Actual VAT receipts  

VAT gap  

51,615 

14,196 

52,810 

16,696 

58,115 

14,705 

60,212 

14,924 

66,242 

15,288 

75,783 

86,203 

9,860 

6,457 

VAT gap as a share of 
theoretical liability 

24% 
† Estimates compiled using forecasted use table data 

22% 

20% 

20% 

19% 

12% 

7% 

www.reckon.co.uk  

41 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Portugal

16

14

12

10

8

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

10

8

6

4

2

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 27  Portugal: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

PT 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

Actual VAT receipts  

VAT gap  

2000 

2001 

2002 

2003 

2004 

2005 

2006† 

10,241 

10,737 

11,549 

11,861 

12,571 

13,403 

14,371 

6,759 

7,085 

7,663 

7,927 

8,318 

8,931 

9,540 

1,249 

1,880 

354 

9,734 

508 

1,313 

2,005 

334 

1,401 

2,163 

323 

1,330 

2,298 

305 

1,461 

2,464 

328 

1,382 

2,740 

350 

1,571 

2,923 

338 

10,021 

10,690 

11,092 

11,574 

13,006 

13,757 

716 

859 

7% 

769 

6% 

997 

397 

614 

8% 

3% 

4% 

VAT gap as a share of 
theoretical liability 

7% 
† Estimates compiled using forecasted use table data 

5% 

www.reckon.co.uk  

42 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Sweden

280

260

240

220

200

)
n
o

i
l
l
i

b
(

K
E
S

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

180

T
A
V

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

10

8

6

4

2

0

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 28  Sweden: VAT receipts, theoretical liability and gap, 2000–2006 (SEK million) 

SE 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005 

2006† 

208,030 

216,982 

225,179 

234,229 

243,455 

256,213  271,100 

121,518 

126,116 

130,748 

136,713 

141,294 

147,213  153,978 

19,575 

59,985 

6,952 

20,365 

63,342 

7,159 

22,098 

65,356 

6,977 

22,880 

67,220 

7,415 

24,772 

69,520 

7,869 

28,507 

31,458 

72,357 

76,852 

8,136 

8,812 

Actual VAT receipts  

194,860 

204,629 

215,697 

225,145 

233,966 

251,309  263,632 

VAT gap  

13,170 

12,353 

9,482 

9,084 

9,489 

4,904 

7,468 

VAT gap as a share of 
theoretical liability 

6% 
† Estimates compiled using forecasted use table data 

6% 

4% 

4% 

4% 

2% 

3% 

www.reckon.co.uk  

43 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Slovenia

)
n
o

i
l
l
i

b
(

R
U
E

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

3

2.8

2.6

2.4

2.2

2

1.8

1.6

1.4

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

18

14

10

6

2

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 29  Slovenia: VAT receipts, theoretical liability and gap, 2000–2006 (EUR million) 

SI 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005 

2006† 

1,902 

2,055 

2,268 

2,336 

2,508 

2,699 

2,764 

1,242 

1,316 

1,435 

1,513 

1,636 

1,747 

1,763 

296 

298 

66 

358 

309 

72 

405 

353 

75 

387 

357 

79 

430 

361 

81 

478 

390 

84 

523 

390 

87 

Actual VAT receipts  

VAT gap  

1,599 

303 

1,718 

337 

1,982 

287 

2,140 

195 

2,311 

197 

2,472 

2,647 

227 

116 

VAT gap as a share of 
theoretical liability 

16% 
† Estimates compiled using forecasted use table data 

16% 

13% 

8% 

8% 

8% 

4% 

www.reckon.co.uk  

44 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

Slovakia

)
n
o

i
l
l
i

b
(

K
K
S

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

180

160

140

120

100

80

60

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

32

28

24

20

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 30  Slovakia: VAT receipts, theoretical liability and gap, 2000–2006 (SKK million) 

SK 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004 

2005† 

2006† 

89,365 

98,705 

106,506 

116,139 

138,493 

154,476  172,477 

59,698 

66,723 

71,816 

79,127 

97,219 

103,957  113,591 

11,354 

15,872 

2,441 

11,600 

17,297 

3,086 

12,934 

18,654 

3,102 

12,694 

22,062 

2,256 

14,421 

24,396 

2,457 

17,721 

21,445 

30,194 

34,739 

2,604 

2,702 

Actual VAT receipts*  

VAT gap  

65,301 

24,064 

73,938 

24,767 

77,779 

28,727 

91,324 

24,815 

105,649 

116,880  123,628 

32,844 

37,596 

48,848 

VAT gap as a share of 
theoretical liability 

25% 
† Estimates compiled using forecasted use table data 
rate of 1 EUR = 30.1260 SKK. 

27% 

27% 

21% 

24% 

24% 

28% 

*Eurostat data in EUR converted to SKK using a fixed exchange 

www.reckon.co.uk  

45 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 2: Top-down estimates of the VAT gap 

United Kingdom

)
n
o

i
l
l
i

b
(

P
B
G

i

,
s
t
p
e
c
e
r
d
n
a

y
t
i
l
i

b
a

i
l

T
A
V

110

100

90

80

70

60

50

y
t
i
l
i

b
a

i
l

f
o

e
g
a
t
n
e
c
r
e
P

20

18

16

14

12

2000

2001

2002

2003

2004

2005

2006

Estimated VAT liability

VAT gap, percentage of liability

VAT receipts

Table 31  United Kingdom: VAT receipts, theoretical liability and gap, 2000–2006 (GBP million) 

UK 

Total theoretical VAT 
liability  

Of which 

Household 
consumption  

Gross fixed capital 
formation  

Other consumption  

Net adjustments  

2000 

2001 

2002 

2003 

2004† 

2005† 

2006† 

76,500 

80,449 

85,348 

90,287 

95,559 

101,646  106,143 

51,836 

54,200 

57,201 

59,909 

62,915 

65,374 

68,018 

4,748 

18,370 

1,546 

5,150 

19,722 

1,378 

5,800 

21,100 

1,246 

6,485 

22,583 

1,310 

7,277 

8,098 

9,421 

23,891 

27,301 

27,780 

1,476 

872 

924 

Actual VAT receipts  

VAT gap  

64,202 

12,298 

67,100 

13,349 

71,066 

14,282 

77,343 

12,944 

81,550 

14,009 

83,415 

87,753 

18,231 

18,390 

VAT gap as a share of 
theoretical liability 
† Estimates compiled using forecasted use table data 

16% 

17% 

17% 

14% 

15% 

18% 

17% 

www.reckon.co.uk  

46 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 3: Econometric analysis of the VAT gap 

SECTION 3: ECONOMETRIC ANALYSIS OF THE VAT GAP 

77. 

This section presents an econometric analysis of the VAT gap. 

78. 

The aim of the analysis is to contribute to the understanding of the nature and causes 
of the VAT gap, and to identify country characteristics that appear related to different 
levels of the VAT gap. 

79. 

This section is structured as follows: 

(a)  First, we review published econometric studies of the causes of the VAT gap. 

(b)  Second, we provide an empirical analysis of the VAT gap figures obtained by the 

top–down method reported elsewhere in this report. 

Previous econometric studies of the determinants of the VAT gap 

80. 

Before  presenting  our  own  empirical  analysis,  we  review  the  findings  from  other 
studies  on  the  topic.    This  review  informs  the  choice  of  candidate  explanatory 
variables used in our own models. 

81. 

There have been few investigations of the determinants of VAT losses. 

82. 

83. 

Christie and Holzner (2006) did not include a review of other similar papers “there is 
typically  no  econometric  modelling  involved  at  all”  within  the  studies  that  measure 
the  size  of  tax  evasion.4    Keen  and  Smith  (2007)  suggest  that  “the  difficulty  of 
measuring  VAT  noncompliance  […]  has  impeded  serious  empirical  work”  on  the 
determinants of the VAT gap.5   

Keen  and  Smith  (2007)  comment  on  only  one  study  that  provided  an  econometric 
analysis of the determinants of VAT compliance.  This paper is Agha and Houghton 
(1996) which constructs and analyses a cross–section of VAT compliance rates for 17 
OECD member countries in 1987.6 

84. 

Agha and Houghton (1996) found that: 

(a)  a higher VAT rate is associated with lower VAT compliance; 

(b)  the number of VAT rates negatively affect the level of VAT compliance; 

(c)  VAT compliance increases the longer VAT has been in operation; and 

(d)  smaller  countries  (in  terms  of  population)  tend  to  have  higher  levels  of 

compliance.   

4   Christie, E and M. Holzner (2006) “What explains tax evasion? An empirical assessment based on European data”, WIIW 

Working Paper 40.  Available from http://www.wiiw.ac.at/pdf/wp40.pdf, accessed on 3 August 2009. 

5   Keen, M and S. Smith (2007) “VAT fraud and evasion: What do we know, and what can be done?”, IMF Working Paper.  

Available from http://papers.ssrn.com/sol3/papers.cfm?abstract_id=964339, accessed on 3 August 2009. 

6   Agha, A and J. Haughton (1996) “Designing VAT systems: Some efficiency considerations”, The Review of Economics 

and Statistics 78, No.2, pp. 303-308 

www.reckon.co.uk  

47 

 
                                                 
Section 3: Econometric analysis of the VAT gap 

85. 

Other factors, including the VAT base as a proportion of GDP, the severity of penalties 
for late payment, and the proportion of the population registered to pay VAT, had no 
statistically significant impact on compliance.   

86.  We have found two other studies which provide econometric analysis of the VAT gap. 

87. 

Otranto, Pisano and Polidoro (2003) examines the determinants of VAT fraud in Italy 
in the period 1982–2001.7  The authors find that VAT evasion is positively affected by 
GDP,  by  the  share  of  the  fiscal  burden  and  by  the  ratio  of  gross  profits  and  value 
added over the economy, and that it is negatively affected by one period lagged values 
of  the  number  of  taxpayers  checked  by  the  authorities.    The  authors  also  find  that 
initiatives  by  government  to  grant  amnesty  to  tax  evaders  in  a  given  period  have  a 
negative and only transitory effect on the level of the VAT gap.  

88. 

Christie  and  Holzner  (2006)  analyses  data  for  29  European  countries  from  2000  to 
2003.8  The effects on VAT compliance identified by this paper are as follows: 

(a)  a higher weighted average VAT rate reduces VAT compliance; 

(b)  greater judicial and legal effectiveness increases VAT compliance; 

(c)  countries  where  citizens  want  more  power  for  local  authorities  (which  is, 
according  to  the  authors,  a  proxy  for  tax  morale)  tend  to  have  lower  levels  of 
VAT compliance; and  

(d)  countries  with  a  large  proportion  of  GDP  from  travel  revenues  tend  to  have 

higher levels of VAT compliance.   

89. 

Christie  and  Holzner  (2006)  found  that  other  factors,  such  as  the  confidence  in  the 
health care system, measures of income inequality, a measure of poverty, complexity 
of the VAT system, a corruption index, and GDP per capita, did not have a statistically 
significant relationship to the estimated rate of VAT compliance. 

Empirical modelling of top–down VAT gap estimates 

90.  We draw on some of the ideas found in the literature to develop our own econometric 

analysis of the determinants of the estimated VAT gap. 

Data used in the modelling 

91. 

The dependent variable for our econometric analysis is the VAT gap share, defined as 
the VAT gap divided by the theoretical VAT liability.  Data for this variable come from 
our top–down analysis reported in Section 2. 

7   Otranto, E., S. Pisani and F. Polidoro (2003) “Un modello statistico per comprendere le determinanti dell’evasione” in R. 
Convenevole and S. Pisani “Le basi imponibili IVA Un’analisi del periodo 1982-2001”, Working paper 2003/1 of Agenzia 
Entrate, Ministerio dell’Economia e della Finanze, Italy.  Available from 
http://www1.agenziaentrate.it/ufficiostudi/pdf/2003/basi%20imponibili%20IVA%2082-01.pdf , accessed on 3 August 
2009. 

8   Christie, E and M. Holzner (2006) “What explains tax evasion? An empirical assessment based on European data”, WIIW 

Working Paper 40.  Available from http://www.wiiw.ac.at/pdf/wp40.pdf, accessed on 3 August 2009. 

www.reckon.co.uk  

48 

 
                                                 
Section 3: Econometric analysis of the VAT gap 

92. 

This variable measures the proportion of theoretical liability that is not remitted. 

Table 32   Candidate explanatory variables 

Variable 

Judicial/legal effectiveness 
index  

Proportion of population who 
think that it is unjustifiable to 
cheat on taxes if you have a 
chance 

Transparency International 
Corruption Perceptions Index  

Theoretical VAT liability 
divided by GDP 

Underlying factor(s) 
captured by the variable 

Proxy for the punishment rate 
and the audit rate and may be 
an indicator of the shadow 
economy 

Moral standards 

Source 

Kaufman (2004), “Corruption, Governance 
and Security: Challenges for the Rich 
Countries and the World”,  Chapter in the 
World Bank’s Global Competitiveness 
Report 2004/2005 
1999 wave of the World Values Survey9.  
(Proportion of answers 1–5 for the question 
F116) 

Level of corruption 

Transparency International’s annual 
Corruption Perceptions Index report 

VAT burden 

Reckon’s top–down analysis and Eurostat 

Standard VAT rate 

VAT burden 

National VAT legislation 

GINI coefficient (measure of 
income inequality) 

Income inequality 

Eurostat (income and living conditions 
indicators) 
UK data taken from its national statistics 
office 

Eurostat (income and living conditions 
indicators) 
UK data taken from the Institute of Fiscal 
Studies 

Poverty 

At risk of poverty rate (cut–off 
point: 60% of median 
equivalised income after social 
transfers) 

GDP per capita (EUR in 1995 
prices) 

Wealth/level of development 

Eurostat (national accounts) 

Unemployment rate 

Income inequality / poverty 

Eurostat (EU Labour Force Survey) 

Government final consumption 
expenditure divided by GDP 

Overall tax burden / level of 
government controls 
(including tax inspections) 

Eurostat (national accounts) 

GDP (Euros in 1995 prices) 

Size of the economy 

Eurostat (national accounts) 

Population 

Country size 

Eurostat 

EU membership (dummy 
variable) 

EU membership might make 
certain types of fraud easier 
(e.g. MTIC).  Conversely, EU 
membership might coincide 
with a crack down on VAT 
fraud.   

Takes the value 1 if the country is the EU, 
and zero otherwise.  10 countries joined on 
1 May 2004; variable pro–rated for the year 
2004 

9   European Values Study Group and World Values Survey Association.  European and World Values Surveys Four Wave 
Integrated Data File, 1981-2004, v.20060423, 2006. Aggregate File Producers: Análisis Sociológicos Económicos y 
Políticos (ASEP) and JD Systems (JDS), Madrid, Spain/Tilburg University, Tilburg, The Netherlands. Data Files 
Suppliers: Analisis Sociologicos Economicos y Politicos (ASEP) and JD Systems (JDS), Madrid, Spain/Tillburg 
University, Tilburg, The Netherlands/ Zentralarchiv fur Empirische Sozialforschung (ZA), Cologne, Germany:) Aggregate 
File Distributors: Análisis Sociológicos Económicos y Políticos (ASEP) and JD Systems (JDS), Madrid, Spain/Tilburg 
University, Tilburg, The Netherlands/Zentralarchiv fur Empirische Sozialforschung (ZA) Cologne, Germany. 

www.reckon.co.uk  

49 

 
                                                 
Section 3: Econometric analysis of the VAT gap 

Variable 

Underlying factor(s) 
captured by the variable 

Source 

EU accession (dummy variable)  Countries might have stepped 
up enforcement on joining the 
EU. 

Effect of EU–10 accession on 
the EU–15 (dummy variable) 

Impact of an enlarged EU 

Takes the value 1 after accession, pro–rated 
for time as necessary, for the 10 countries 
that joined the EU in 2004; and 0 in all 
other cases 

Takes the value 1 after accession by the 
EU–10, pro–rated for time as necessary, for 
the 15 countries that joined the EU before 
2004; and 0 in all other cases 

Eurostat (national accounts) 

Relative size of the 
construction sector 

Gross fixed capital formation of 
construction products divided 
by GDP 

Household final consumption of 
hotel and restaurant services 
divided by GDP 

Proxy for the effect of tourism  Eurostat (national accounts) 

93. 

94. 

95. 

VAT gap estimates were available for all countries from 2000 to 2006 for 24 of the 25 
countries covered by this study.  There are no VAT gap estimates for Cyprus. 

Table  32  lists  the  candidate  explanatory  variables  that  we  have  considered,  a  short 
description  of  the  reason  for  their  inclusion  and  the  source  from  which  they  were 
obtained.  Several of these variables have been used in the Agha and Houghton (1996) 
and Christie and Holzner (2006) studies mentioned earlier.   

Two  of  the  variables  in  Table  32  are  not  available  on  a  time  series  basis.    The 
judicial/legal effectiveness index and the variable about how justifiable it is to cheat 
on taxes are both available for a single year only.  In our modelling we have assumed 
that these variables are constant over time for each country. 

96. 

Some of the other variables had missing values for some years and countries: 

(a)  The  Gini  coefficient  and  the  poverty  rate  both  had  missing  entries  for  some 
countries  and  years.    These  gaps  were  filled  in  using  the  fitted  values  of  a 
country–specific regression of the variable in question on time. 

(b)  Malta  has  no  data  for  the  corruption  perceptions  index  and  judicial/legal 

effectiveness index. 

(c)  Eurostat reported no Gini coefficient and poverty rate variables for Slovakia. 

97.  Where  there  were  missing  values  for  a  variable,  the  observations  affected  were  not 

included in the estimation of models using that variable. 

Econometric analysis — general considerations 

98. 

The general form of the models that we considered is as follows: 

VAT gap share(i,t) = a + b1X1(i,t)  + …+ bkXk(i,t) + e(i,t) 

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50 

 
 
Section 3: Econometric analysis of the VAT gap 

99. 

In this equation, i denotes the country, t the year, a is a constant, the b coefficients are 
the slopes on the explanatory variables X, and e is the model’s disturbance term.   

100.  The starting point for our analysis was to estimate a random effects model.  A model 
of  this  type  decomposes  the  disturbance  into  a  country-specific  component  (the 
random  effect)  that  is  fixed  over  time  and  an  unrelated  noise  component  that  is  not 
correlated over time or between countries.   

101.  We do not report results from fixed effects models, in which a specific intercept term 
would  be  included  for  each  country.    The  inclusion  of  such  intercept  terms  would 
mask the effect of any explanatory variables that are constant over time or very close 
to  being  constant  over  time  even  though  there  may  be  large  differences  between 
countries.    Given  that  we  are  interested  in  the  estimated  effects  of  such  variables,  a 
fixed effects model does not contribute to our analysis. 

102.  The estimated effect of the explanatory variables in a random effects model are only 
unbiased  if  the  random  effects  are  not  correlated  with  the  explanatory  variables.   A 
method that can be used to test whether this is the case is the Hausman test.  This test 
compares the estimated results of a fixed effects model and a random effects model.  
The more different the coefficients obtained by both methods are, the more likely it is 
that  the  assumption  of  no  correlation  between  random  effects  and  explanatory 
variable has been violated.   

103.  A further important assumption is that the  random effect  estimates are based on the 
hypothesis  that  the  noise  component  of  the  disturbance  term  in  the  model  is 
homoskedastic  (i.e.  has  a  constant  variance)  and  is  not  autocorrelated.    When 
hetereskedasticity or autocorrelation are present the estimated standard errors can lead 
to misleading inferences about statistical significance. 

104.  We have used a general-to-specific approach to identifying those variables that exhibit 
a  not-insignificant  relationship  with  the  VAT  gap  share.    This  approach  involves 
starting  with  the  most  general  model,  including  all  the  candidate  explanatory 
variables.  We then drop variables from the model, one at a time, starting with the one 
that has the highest p value.  We stop when each remaining variable is significant at a 
level of 95 per cent.   

Random effects estimates 

105.  Table 33 shows the results of estimating a random effects model over the period 2000-
2006 using  a  general-to-specific  approach.   It also includes the  results of diagnostic 
tests. 

106.  Data for only 23 countries are used, as: 

(a)  There were no VAT gap data for Cyprus. 

(b)  There was no judicial/legal effectiveness index for Malta. 

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51 

 
Section 3: Econometric analysis of the VAT gap 

107.  We  have  tried  fitting  models  without  the  explanatory  variables  for  which  data  are 
missing;  we  did  not  find  any  case  in  which  the  results  were  qualitatively  different 
from those in Table 33. 

Table 33  Random effects model results 

Dependent variable: 

Explanatory variables: 

Constant 

Judicial/legal effectiveness index divided 
by 100 (0–1) 

EU accession (dummy variable) 

Impact of EU enlargement in 2004 

VAT liability as a proportion of GDP 

Standard VAT rate 

Statistical indicators: 

Diagnostic tests 

VAT gap divided by the theoretical VAT liability (proportion) 

Coefficient 

Standard error 

0.152 

–0.212 

–0.044 

–0.012 

3.313 

0.008 

0.071 

0.042 

0.009 

0.006 

0.654 

0.003 

Number of observations: 161  Number of countries: 23 

Overall R-squared: 0.6308 

Hausman test statistic: 2.0 
(p = 0.7359) 

Wooldridge test for 
autocorrelation: 34.815 
(p = 0.000) 

Likelihood ratio test for 
heteroskedasticity: 115.95 
(p = 0.000) 

108.  For this model, the Hausman test was passed.  This suggests that there is low risk of a 
bias  in  the  estimated  coefficients  as  a  result  of  correlations  between  the  random 
effects and the explanatory variables.   

109.  One explanatory variable that might have been expected to display such correlation is 
the VAT liability as a proportion of GDP (i.e. the VAT burden).  This is because errors 
in  estimating  a  country’s  theoretical  VAT  liability,  and  therefore  its VAT  gap,  affect 
the  measure  of  the  VAT  burden  for  that  country.    This  is  what  is  termed  in 
econometrics an endogeneity problem. 

110.  The Hausman test however does not rule out the possibility of  correlations between 
the VAT burden and the noise component of the disturbance.  This issue is discussed 
later in this section. 

111.  We tested for heteroskedasticity and autocorrelation: 

(a)  We  carried  out  a  likelihood  ratio  test  in  which  the  null  hypothesis  is  that  the 
noise term has a constant variance across countries was rejected.  This suggests 
the presence of heteroskedasticity. 

(b)  We carried out the Wooldridge test, where the null hypothesis is of no first-order 
autocorrelation was rejected.  This suggests the presence of autocorrelation in the 
noise term. 

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52 

 
 
 
 
Section 3: Econometric analysis of the VAT gap 

112.  These  two  tests  indicate  that  assumptions  behind  the  random  effects  model  are  not 
satisfied in this case and that the estimated standard errors cannot be relied upon for 
statistical  testing.    Christie  and  Holzner  (2006)  reached  similar  findings  when  they 
carried out these tests on their estimated random effects model.   

113.  To take account of the detected heteroskedasticity  and  autocorrelation an alternative 
modelling  approach  is  required,  one  that  is  robust  to  these  features.    We  have  used 
two different estimation techniques to accomplish this: 

(a)  Panel corrected standard errors in conjunction with assuming a disturbance term 
with first-order autocorrelation.10  This was the approach adopted by Christie and 
Holzner (2006).  

(b)  Estimating the model by ordinary least squares  and then adjusting the standard 
errors so that they are “robust” to heteroskedasticity and autocorrelation.11  This 
approach  does  not  specify  the  form  of  the  autocorrelation  and  instead  uses  the 
correlation detected in the data itself to estimate the standard errors.   

114.  When  applying  these  techniques  we  again  took  a  general-to-specific  modelling 

approach.   

Panel corrected standard error estimates 

115.  Table  34  presents  the  results  of  our  modelling  using  the  panel  corrected  standard 

errors approach. 

Table 34   Panel corrected standard error modelling results 

Dependent variable: 

Explanatory variables: 

Constant 

Corruption Perceptions Index (0–
10, a lower score represents a 
higher perception of corruption)  

Gross fixed capital formation of 
construction products divided by 
GDP 

Theoretical VAT liability divided 
by GDP (proportion) 

Standard VAT rate 

Population (millions) 

Statistical indicators: 

VAT gap divided by the theoretical VAT liability (proportion) 

Coefficient 

Panel corrected standard error 

0.217 

–.024 

–0.399 

2.725 

–0.006 

0.522 

0.068 

0.002 

0.672 

0.697 

0.002 

0.186 

Number of observations: 161 

Number of countries: 23 

R-squared: 0.5671 

10 This method is implemented in the Stata statistical software package by using the xtpcse function.   
11 This method is implemented in the Stata statistical software package by using the regress function with the robust and 

cluster options. 

www.reckon.co.uk  

53 

 
 
 
                                                 
Section 3: Econometric analysis of the VAT gap 

116.  These results suggest the following: 

(a)  There  are  conflicting  signs  about  the  relationship  between  the VAT  burden  and 
the VAT gap.  A one percentage point rise in the theoretical VAT liability divided 
by  GDP  coincides  with  an  increase  in  the  VAT  gap  share  of  2.725  percentage 
points.  On the other hand, a one percentage point increase in the standard rate of 
VAT seems to coincide with a reduction in the VAT gap share of 0.6 percentage 
points.  

(b)  A  lower  perception  of  corruption  appears  to  reduce  the  VAT  gap  share.    An 
increase of 1 point in the Corruption Perception Index coincides with a reduction 
in the VAT gap share by approximately 2.4 percentage points. 

(c)  Countries with a larger population have a larger VAT gap share. 

(d)  Countries where construction services account for a greater share of GDP have a 

lower VAT gap share.   

117.  Our finding  about the influence of the VAT  burden seems inconsistent with Christie 
and  Holzner  (2006),  which  found  that  VAT  compliance  is  greater  in  countries  with 
lower weighted average VAT rates.   

118.  Our finding about the effect of lower perceived corruption is consistent with Christie 
and  Holzner  (2006),  which  found  that  VAT  compliance  is  higher  in  countries  with 
better  judicial/legal  effectiveness.    Both  perceived  corruption  and  judicial/legal 
effectiveness are measures of the effectiveness of the legal system and perceptions of 
corruption,  and  our  model  above  could  be  formulated  using  either  variable  without 
much change in explanatory power.  A feature in favour of the use of the Corruption 
Perceptions  Index  is  that  it  is  available  on  a  time-series  basis  whereas  the 
judicial/legal  effectiveness  index  is  only  available  for  2004.    Thus,  the  Corruption 
Perceptions Index might capture changes such as the affect of joining the EU for the 
accession countries whereas the judicial/legal effectiveness index cannot.   

119.  We  see  a  similar  effect  of  country  size  between  the  results  above,  which  found  that 
larger and richer economies have a greater VAT gap, and Agha and Houghton (1996), 
which found that countries with larger populations have less VAT compliance.   

Robust regression estimates 

120.  Very similar results to the panel corrected standard error model were obtained when 
the  “robust”  regression  procedure  was  implemented.    The  results  of  the  “robust” 
regression are shown in Table 35. 

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Section 3: Econometric analysis of the VAT gap 

Table 35  Robust regression modelling results 

Dependent variable: 

Explanatory variables: 

Constant 

Corruption Perceptions Index (0–10, a 
lower score represents a higher 
perception of corruption)  

GDP per capita (EUR 000 in 1995 
prices) 

Theoretical VAT liability divided by 
GDP (proportion) 

EU accession (dummy variable) 

Standard VAT rate 

Gross fixed capital formation of 
construction products divided by GDP 

VAT gap divided by the theoretical VAT liability (proportion) 

Coefficient 

0.446 

–0.029 

0.00009 

2.419 

–0.05 

–0.012 

–0.821 

“Robust” standard error 

.067 

0.003 

0.00002 

0.705 

0.02 

0.002 

0.22 

Statistical indicators: 

Number of observations: 161 

Number of countries: 23 

R-squared: 0.7318 

Root mean square error: 0.0437 

121.  The results in Table 35 above are similar to those in Table 34 reported earlier.     

Instrumental variable regression estimates 

122.  We noted above that even though the Hausman test was passed there may  still be  a 
possible  problem  of  “endogeneity”  which  could  be  attributed  to  the  use  of  the VAT 
burden as an explanatory variable. 

123.  The  relationship  between  VAT  gap  share  and  VAT  liability  as  a  proportion  of  GDP 
reported in Tables 34  and 35 is intuitively  and theoretically satisfying: it seems that 
attempts  at  capturing  a  greater  share  of  national  wealth  through  VAT  lead  to  more 
VAT evasion and avoidance. 

124.  But the endogeneity issues mean that there is a risk that the magnitude of this effect 
has been overstated.  As well as the underlying relationship between the VAT burden 
and the VAT gap share, the coefficients shown in Tables 34 and 35 might be capturing 
the effect of any errors in the estimation of the theoretical liability. 

125.  This  is  because  any  measurement  errors  in  the  estimation  of  the  theoretical  liability 
would tend to affect both the estimated tax gap and the burden measured by reference 
to the same estimate of VAT theoretical liability, and to affect them systematically in 
the same direction. 

126.  We  therefore  tested  for  endogeneity  using  the  Durbin-Wu-Hausman  test  to  decide 

whether it is necessary to use an instrumental variable. 

127.  Based  on  the  model  presented  in  Table  35,  the  VAT  burden  variable  was  tested  for 
endogeneity using the standard VAT rate and government final consumption divided 
by GDP as instruments for the VAT burden.  The requirement for the instruments is 

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Section 3: Econometric analysis of the VAT gap 

that they should be correlated with the VAT burden, but they must not be correlated 
with the disturbance term.  The test was failed: estimates from ordinary least squares 
may be biased.  A standard econometric approach to remove the bias arising from this 
endogeneity is to use instrumental variables.  

128.  Under this approach, in order to obtain an unbiased estimate of the effect of the VAT 
burden (the variable to be instrumented), a set of instruments needs to be used as part 
of the estimation.  We used the standard VAT rate and government final consumption 
divided by GDP as instruments for the VAT burden.   

129.  Table 36 shows the results when the model was estimated under this approach.  The 
estimated standard errors are “robust” to autocorrelation and heteroskedasticity, as in 
Table 35. 

Table 36  

Instrumental variable robust regression modelling results 

Dependent variable: 

Explanatory variables: 

Constant 

Theoretical VAT liability divided by GDP 
(proportion) 
[instrumented by the standard VAT rate and 
government final consumption divided by GDP] 

Corruption Perceptions Index (0–10, a lower 
score represents a higher perception of 
corruption)  

Gross fixed capital formation of construction 
products divided by GDP 

VAT gap divided by theoretical VAT liability (proportion) 

Coefficient 

“Robust” standard error 

0.583 

–1.309 

–0.035 

–0.987 

0.151 

1.388 

0.005 

0.335 

Statistical indicators: 

Number of observations: 161 

Number of countries: 23 

R-squared: 0.5432 

Root mean square error: 
0.056 

130.  Table  36  shows  that  once  the  VAT  burden  has  been  instrumented,  its  estimated 
coefficient  becomes  negative:  this  would  now  suggest  that  countries  with  a  higher 
VAT burden have a lower VAT gap share.  In other words, this model indicates that the 
positive correlation found in Tables 34 and 35 may be attributable to the bias arising 
from  correlations  between  the  error  in  estimating  the  VAT  gap  and  the  VAT  burden 
estimate. 

131.  The  instrumented  VAT  burden  explanatory  variable  is  also  no  longer  statistically 
significant.  We have checked that other combinations of instruments lead to the same 
findings. 

132.  Our  conclusion  is  therefore  that  there  is  no  reliable  statistical  evidence  of  any 

relationship between the VAT burden and the VAT gap. 

133.  Christie and Holzner (2006) found a relationship between the VAT burden measured 
by  a  weighted  average  VAT  rate  and  the  VAT  gap.    This  result,  like  our  results  in 
Tables 34 and 35, is vulnerable to a risk of bias due to correlations between VAT gap 

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Section 3: Econometric analysis of the VAT gap 

and VAT burden arising from possible errors in estimating the theoretical liability or 
weighted average VAT rate.  Christie and Holzner (2006) did not use an instrumental 
variable approach or any other way to address this risk of bias, and does not therefore, 
in our view, provide reliable statistical evidence of any relationship between the VAT 
burden and the VAT gap. 

Models without the VAT burden as an explanatory variable 

134. 

In the light of the above analysis, we focus on models that do not include a measure 
of the VAT burden as an explanatory variable.  This means that the general model in 
our general-to-specific modelling approach no longer includes the VAT burden as an 
explanatory variable.  All the other candidate explanatory variables presented earlier 
in this section are included.   

135.  We used a “robust” regression estimation technique for these models as it makes less 

restrictive assumptions about the form of the autocorrelation in the data.   

136.  Table  37  below  presents  the  results  of  a  model  without  the  VAT  burden  as  an 

explanatory variable. 

Table 37   Robust regression without VAT burden, with gross capital formation variable 

Dependent variable: 

Explanatory variables: 

Constant 

Corruption Perceptions Index (0–10, a 
lower score represents a higher 
perception of corruption)  

Gross fixed capital formation of 
construction products divided by GDP 

VAT gap divided by the theoretical VAT liability (proportion) 

Coefficient 

0.4347 

–0.032 

–0.8192 

“Robust” standard error 

0.0465 

0.004 

0.277 

Statistical indicators: 

Number of observations: 161 

Number of countries: 23 

R-squared: 0.6233 

Root mean square error: 0.051 

137.  The  regression  reported  in  Table  37  shows  that  gross  fixed  capital  formation  of 
construction  products  as  a  share  of  GDP  was  found  to  be  statistically  significant 
without the presence of the VAT burden variable.  Countries with 1 percentage point 
more  gross  fixed  capital  formation  of  construction  products  as  a  share  of  GDP  are 
estimated to have 0.82 percentage points less of a VAT gap.   

138.  There  are  three  possible  interpretations  for  the  apparent  relationship  between  gross 

fixed capital formation of construction products and the VAT gap share: 

(a)  Countries with large construction sectors have smaller VAT gaps suggesting that 
there may be relatively low levels of VAT losses associated with that sector.  

(b)  National  accounts  could  consistently  underestimate  the  value  of  economic 
activity related to the construction sector.  This might lead to estimates of gross 
fixed capital formation that are biased downwards, causing our estimates of the 

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Section 3: Econometric analysis of the VAT gap 

VAT liability (and gap) to be lower.  This bias would be correlated with the size 
of the construction sector relative to the economy as a whole. 

(c)  There  could  be  an  error  in  the  assumptions  we  have  used  to  calculate  the VAT 

gap related to the construction sector.     

139. 

It is not possible to test between these alternative theories.  To address the risk that b) 
and c) above is the case, we have re-run our modelling process  excluding the  gross 
fixed capital formation variable  

140.  The results of this process show that no variables other than the Corruption Perception 
Index become statistically significant once the gross fixed capital formation variable 
is  excluded  from  the  regression.    The  coefficients  of  the  other  variables  are  very 
similar to those in Table 36.  

Conclusions from the econometric analysis of the VAT gap 

141.  This section uses an econometric analysis of the VAT gap share, measured by our top-
down analysis, in an attempt at identifying possible causes of differences in the gap 
between countries and over time. 

142. 

If  the  VAT  liability  as  a  proportion  of  GDP  is  included  as  a  candidate  explanatory 
variable, then we find that it has a significant positive relationship with the VAT gap.  
This is in line with the literature on this topic, and with the theory that a higher tax 
burden should lead to higher levels of evasion. 

143.  However, we have identified a risk that this estimated relationship may be biased by 
measurement errors in the estimation of the theoretical liability.  If this risk is taken 
into account by using an instrumental variable regression, then there is no statistically 
significant  relationship  between  the  VAT  gap  share  and  the  VAT  liability  or  the 
standard VAT rate. 

144.  We do not find reliable statistical evidence of a relationship between the VAT burden 

and the VAT gap share. 

145.  The  variable  found  to  have  the  strongest  relationship  with  the  VAT  gap  was  the 
Corruption  Perceptions  Index.    The  relationship  implies  that  lower  perceived 
corruption is associated with a lower VAT gap share. 

146.  Our analysis has shown that countries where  gross capital formation in construction 
as a proportion of GDP is higher, tend to have a lower VAT gap share.  This apparent 
relationship could be due to errors, either in our method or in the coverage of national 
accounts.        

147.  Our  general-to-specific  approach  revealed  no  statistical  signs  of  a  relationship 
between  the  VAT  gap  share  and  many  of  our  other  candidate  explanatory  variables 
that  captured  the  effects  of  factors  that  included  the  standard  VAT  rate,  income 
inequality and poverty, size of the country and tourism. 

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Section 4: An outline of the top-down approach 

SECTION 4: AN OUTLINE OF THE TOP-DOWN APPROACH 

148.  Our  top-down  estimate  of  the  VAT  gap  is  based  on  a  comparison  of  accrued  VAT 
receipts  with  an  estimate  of  the  theoretical VAT  net  liability  that  would  have  arisen 
had all VAT due been remitted. 

149.  This section outlines the top-down approach we follow to compute the VAT gap and 
gives an overview of the main data sources used and the main assumptions made.  We 
keep the description set out here brief, and present in other sections a more detailed 
discussion of the data sources used, of the assumptions and adjustments made and of 
possible definitions of the VAT gap. 

An outline of the top-down approach  

150.  We estimate the VAT gap of a Member State in a given year as the difference between 
the net theoretical VAT liability associated with the economic activity in that year and 
the accrued VAT receipts of that Member State in that year.   

151.  The second of these components, accrued VAT revenues, refers to information that is 
readily  available  from  published  sources,  namely  from  Eurostat.    Data  on  VAT 
receipts obtained from Eurostat are prepared according to ESA 95 rules.  These rules 
require Member States to report revenues on an accrual basis, i.e. the VAT on taxable 
transactions  occurring  during  the  year  should  be  reported  as  part  of  that  year’s 
revenues, irrespective of when the VAT was actually paid. 

152.  As such, virtually all of the effort in computing a top-down estimate of the VAT gap 
lies in estimating the net theoretical VAT liability.  Most of this section is concerned 
with this.  

Identifying expenditure contributing to net theoretical VAT liability 

153. 

In broad terms, the method we have used to calculate the theoretical net VAT liability 
identifies and measures the categories of expenditure that make a net contribution to 
the  total  VAT  base,  for  each  Member  State  and  year.    The  main  categories  of 
expenditure that make such a contribution are: 

(a)  final  consumption  expenditure  by  households,  non-profit  institutions  serving 

households (NPISH) and by government on goods and services;  

(b)  intermediate  consumption  expenditure  that  attracts  irrecoverable  VAT,  such  as 
expenditure on inputs used in the supply of exempt goods and services; and 

(c)  gross  fixed  capital  formation  (GFCF)  that  attracts  irrecoverable VAT,  including 
those  that  can  be  allocated  to  the  supply  of  exempt  goods  and  services,  and 
purchases of valuables on which VAT cannot be reclaimed. 

154.  We  do  not  include  expenditure  on  intermediate  consumption  on  which  VAT  can  be 

recovered as this expenditure makes no net contribution to the VAT liability. 

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Section 4: An outline of the top-down approach 

155.  Our main source of data on final and intermediate consumption is the set of use tables 
prepared  by  each  Member  State  as  part  of  its  national  accounts  and  published  by 
Eurostat.  These  tables  report  the  value  of  expenditure  on  final  consumption  by 
households,  government  and  NPISH,  intermediate  consumption  by  59  industry 
groups,  total  gross  capital  formation  and  exports.    Each  of  these  consumption 
categories  are  further  split  into  59  product  groups  that  are  defined  according  to  the 
Classification of Products by Activity (CPA).  

156.  Use tables are usually published with a time lag of two or more years, and so we do 
not have published data for many Member States for more recent years.  Where use 
tables are not available, we estimate them by combining past trends in the pattern of 
consumption  with  aggregate  consumption  data  for  the  recent  years.    Section  5 
describes the approach followed in detail. 

157.  The data we use on GFCF is derived primarily from the national accounts domain of 
Eurostat.    This  dataset  allows  us  to  distinguish  between  capital  formation  by  each 
institutional  sector,  and  in  many  cases,  by  broad  industry  classifications.    We 
supplement these data with information obtained directly from Member States.  

We estimate the weighted average VAT rates for the defined product categories 

158.  Goods and services differ with respect to the VAT rate that they attract.  This needs to 

be recognised when estimating the theoretical net VAT liability.    

159.  The  use  tables  report  expenditure  on  59  product  groups  defined  by  the  CPA  and 
accordingly,  we  need  to  estimate  a  single  VAT  rate  for  each  of  these  59  groups  of 
products.   In many cases, a  group  contains products that attract different VAT  rates.  
For  these  mixed  product  groups,  a  weighted  average  rate  for  the  group  is  estimated 
based on the relative shares of consumption associated with the products within each 
group. 

160. 

In computing the weighted average rates, we take into account differences in the VAT 
rates based on who the consumer is.  For example, the relative share of consumption 
that different products within a given CPA group account for will vary depending on 
whether  we  are  considering  consumption  by  households  or  by  businesses.    To 
compute these weighted average rates, we rely on consumption data from household 
budget  surveys  (HBS)  and  production  data  from  the  Structural  Business  Statistics 
(SBS) dataset from Eurostat. 

Estimating the share of intermediate consumption on which VAT cannot be reclaimed 

161.  Each of the 59 consuming industries for which data are reported in the use tables can 
recover VAT paid by them on their intermediate consumption to varying extents.  The 
proportion of VAT that can be recovered depends on the nature of their activities, their 
output  and  their  size.   We  denote  this  proportion  by  propex;  the  value  of  propex  is 
defined at the industry level.  

162.  Some  industries  will  cover  a  mixture  of  exempt  and  non-exempt  activities.    In  this 

case, we estimate a weighted average propex for the industry as a whole. 

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Section 4: An outline of the top-down approach 

163.  National  accounts  data  do  not  allow  us  to  estimate  propex  directly.    Instead,  we 
estimate propex to be the proportion of the output of a sector that is exempt.  As with 
the  exercise  of  estimating  weighted  average  VAT  rates  for  product  categories,  we 
estimate  the  propex  for  an  industry  using  data  from  the  HBS  and  SBS  for  each 
Member  State.    This  relies  on  the  crucial  assumption  that  mixed  industries  are 
homogenous  in  their  inputs  and  outputs,  i.e.  we  assume  the  share  of  the  value  of 
exempt output in total output value is equal to the share of the value of inputs used in 
the production of exempt output in total input value consumed. 

164.  Data  on  the  relevant  categories  of  expenditure,  on  the  corresponding  weighted 
average  VAT  rates  and  on  propex  provide  the  basis  for  our  estimate  of  net  VAT 
liability resulting from the consumption of each sector. 

We consider a number of further adjustments to the net VAT liability  

165.  To  arrive  at  a  final  estimate  of  net  theoretical  VAT  liability  we  make  a  series  of 
adjustments  to  the  theoretical  liability  arising  from  the  expenditure  categories 
mentioned earlier.  These adjustments take account of special types of activities that 
impact on VAT liability.  Specifically, we seek to adjust for:  

(a) 

the restriction on the right to deduct VAT on business entertainment; 

(b)  the restriction on the right to deduct VAT on the purchase of company cars; 

(c) 

the exemption granted to small businesses, those whose level of activity is below 
the threshold for VAT registration; and 

(d)  “tank tourism” in Luxembourg, accounting for the consumption of road fuel by 
foreign business users on which VAT is paid in Luxembourg and not recovered 
later. 

166.  Discussion of these adjustments is provided in Section 7. 

The interpretation of the VAT gap 

167.  We obtain our final estimate of total net theoretical VAT liability by aggregating the 
estimated net VAT liability across all expenditure categories and across all products, 
subject to the adjustments described above.  We subtract from this the VAT revenue in 
the relevant year to compute our estimate of the VAT gap. 

168. 

In theory, the VAT gap, which is the difference between the theoretical VAT liability, 
—  what  should  have  been  paid  —  and  accrued  VAT  receipts  —  what  was  actually 
collected  —  could  be  caused  by VAT  fraud.    However,  considerable  care  should  be 
taken in interpreting these VAT gap estimates. 

169.  While fraud contributes to the VAT gap, it is not the only determinant of the gap.  The 
gap,  as  defined  by  the  construction  of  the  top-down  approach  outlined  above  is  a 
function of many variables, including each of the following items: 

(a)  fraud; 

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Section 4: An outline of the top-down approach 

(b)  legal avoidance not captured by our approach; 

(c)  unpaid VAT liability due to insolvencies; and 

(d)  the accuracy and completeness of national accounts. 

170.  Legal  avoidance  of  VAT  liabilities  and  unpaid  liabilities  due  to  insolvencies  both 
contribute  to  increase  the  VAT  gap.    On  the  other  hand,  any  failure  by  national 
accounts to detect and adequately incorporate elements of the shadow economy will 
lower the estimate of the VAT gap. 

171.  The  interplay  of  these  factors  means  that  a  high  VAT  gap  could  be  due  to  the 
prevalence  of  legal  avoidance  schemes,  while  a  low  gap  could  indicate  a  lack  of 
completeness in national accounts measuring activity, legal or illegal, in the economy.  

172. 

In  addition  to  these  complexities,  our  estimates  of  the  gap  are  also  affected  by  the 
assumptions we make in the light of data or other constraints.  While some of these 
assumptions  have  a  limited  impact  only,  others  have  a  considerable  impact  on  the 
final  results.    For  example,  we  assume  that  across  the  EU,  60  per  cent  of  the 
proportion  of  VAT  paid  by  financial  institutions  on  their  inputs  is  recoverable.  
Modifying  this  assumption  to  25  per  cent  can  have  a  significant  impact  on  the  gap, 
particularly  in  Member  States  where  the  financial  sectors  make  a  relatively  large 
contribution to output.   

173.  To present the impact on the estimated VAT gap of these assumptions in a systematic 
way, we have tested the sensitivity of our estimates to a set of significant assumptions.   

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Section 5: Data sources 

SECTION 5: DATA SOURCES 

Final and intermediate expenditure 

Use tables are the main source of data on expenditure 

174.  For  each  Member  State,  the  primary  source  of  data  on  expenditure  is  a  set  of  use 
tables.    Use  tables  report  “the  use  of  goods  and  services  by  product  and  by  type  of 
use,  i.e.  as  intermediate  consumption  (by  industry),  final  consumption,  gross  capital 
formation  or  exports.”12    Use  tables  are  typically  prepared  by  national  statistical 
offices and are a key component in preparing national accounts.  In principle, across 
all Member States they are prepared according to the European System of Accounts 
95 (ESA 95).13  Use tables are available for download from Eurostat.14 

175.  The  data  drawn  from  use  tables  are  at  the  heart  of  our  approach  and  so  we  outline 

with some care what information these contain. 

176.  The  use  tables  published  by  Eurostat  distinguish  between  59  separate  product 
categories;  these  are  reported  by  the  2-digit  Classification  of  Products  by  Activity 
(CPA) codes.  An example of one such product category is “Products of agriculture, 
hunting and related services”, CPA code 01. 

177.  A use table identifies the following types of use that can be made of each of the 59 

different categories of products: 

(a) 

intermediate consumption, by industry; 

(b)  final consumption by households; 

(c)  final consumption by non-profit institution serving households; 

(d)  final consumption by government; 

(e)  gross capital formation (this is often split between gross fixed capital formation, 

changes in inventories and changes in valuables); and 

(f)  exports. 

178.  For  each  of  the  59  2-digit  CPA  product  categories,  a  use  table  breaks  down  total 
intermediate  consumption  into  the  intermediate  consumption  by  types  of  industry.  
Industries  are  classified  by  2-digit  NACE  rev1.1  codes.    There  are  also  59  such 
categories of industry.  The fact that the number of industry categories is the same as 
the  number  of  product  categories  is  no  coincidence:  the  two  classifications  are 
purposefully aligned to each other.  Table 51 in the appendix lists the set of 59 2-digit 

12  ESA 95 paragraph 9.04, accessed from http://circa.europa.eu/irc/dsis/nfaccount/info/data/ESA 95/en/een00438.htm on 3 

August 2009. 

13  See http://circa.europa.eu/irc/dsis/nfaccount/info/data/esa95/en/esa95en.htm.htm.  Accessed on 3 August 2009. 
14  The table can be downloaded in Excel format from 

http://epp.eurostat.ec.europa.eu/portal/page/portal/esa95_supply_use_input_tables/data/workbooks , accessed on 3 August 
2009. 

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Section 5: Data sources 

CPA product categories — and therefore of the 59 2-digit NACE rev 1.1 industries — 
for which the use tables report intermediate and final consumption. 

179.  For the purpose of estimating the theoretical VAT liability, we are not concerned with 
all flows of expenditure recorded in a use table.  In particular, we are only concerned 
with  expenditure  which  gives  rise  to  an  irrecoverable  VAT  liability.    This  includes 
final  consumption  on  goods  or  services  subject  to  VAT  as  well  as  intermediate 
consumption and gross capital formation used in the production of exempt products.  
It excludes, on the other hand, intermediate consumption and gross capital formation 
that attract recoverable VAT.  

The values reported in use tables include non-deductible VAT 

180. 

181. 

In line with the principles set out in ESA 95, the values reported in use tables are at 
purchaser’s price.  That is to say, the value reflects the “price the purchaser actually 
pays  for  the  products;  including  any  taxes  less  subsidies  on  the  products  (but 
excluding deductible taxes like VAT on the products)”.15 

It  follows  from  this  that  if  we  observe  in  the  use  tables  that  household  final 
consumption on a product category that attracts a VAT rate of 20 per cent was EUR 
1,200 million, then we will estimate the theoretical VAT liability associated with this 
consumption to be EUR 200 million. 

Coverage of the publicly available use tables 

182.  Use tables are not available from Eurostat for all years in the period 2000-2006 and 

for all Member States as reported in Table 38.  

15  ESA 95 paragraph 3.06, accessed from http://circa.europa.eu/irc/dsis/nfaccount/info/data/esa95/en/een00122.htm.  

Accessed on 3 August 2009. 

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Section 5: Data sources 

Table 38   Eurostat use table availability as of 30 June 2008 

Country  Pre–2000 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

AT 

BE 

CY 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

(cid:57) 

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183.  No use tables are available for Cyprus, and because of this, Cyprus has been dropped 
from our analysis.  Greece and Latvia have produced use tables for some years before 
2000  (1995  through  to  1999  for  Greece,  1996  and  1998  for  Latvia)  and  we  have 
drawn on these in our analysis. 

184.  We have sought to fill in the gaps in the coverage.  We have, in short, predicted use 
tables  for  those  Member  States  for  years  that  are  not  available  from  Eurostat.    We 
have  done  so  using  the  use  tables  that  are  available  and  the  published  national 
accounts data for more recent years.  There were two steps to this exercise.  First, the 
data  from  the  available  use  tables  were  used  to  define  a  pattern  of  consumption  — 
how consumption by a given industry was shared across 59 product categories — and 
this pattern of consumption was then extrapolated to years for which use data are not 
available.    Second,  the  pattern  of  consumption  derived  was  applied  to  national 
accounts  of  the  corresponding  year  to  obtain  estimates  of  the  value  of  the  relevant 
consumption.   

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Section 5: Data sources 

185. 

It is worth stressing that our approach to filling in the gaps in the use tables does not 
involve forecasting the absolute values of consumption of any product.  Instead, our 
approach  forecasts  the  pattern  of  consumption,  and  applies  this  to  actual  total 
expenditure  reported  in  published  national  accounts.    For  example,  in  Slovakia  for 
2006, we do not forecast the value of consumption of food; we forecast the share of 
expenditure  on  food  in  total  household  expenditure  from  past  trends  and  apply  this 
share to actual total household expenditure for 2006 obtained from Eurostat.  

186.  We now set out in more detail the steps involved in our approach. 

Filling in the gaps in the use tables to cover the period 2000-2006 

187.  For each of the use tables that are available for the years since 2000, we computed the 
share  that  each  of  the  59  product  categories  contribute  to  the  total  intermediate 
consumption of each industry, and, similarly, to total intermediate consumption of a 
broader set of industries, to total intermediate and final consumption across the whole 
economy and finally to GDP.  We also computed the share that each product category 
contributed  to  the  total  final  consumption  of  households,  of  non-profit  institutions 
serving households, of government expenditure and to gross capital formation.   

188. 

In short, taking each column in the use table in turn, we computed the shares that a 
given  cell  in  that  column  contributed  to  total  consumption  at  various  levels  of 
aggregation.  

189.  Taking each of these shares separately, we carried out a simple econometric analysis 
to  predict  the  value  of  these  shares  in  each  of  the  years  from  2000  to  2006.    The 
analysis  consisted  of  a  standard  ordinary  least  squares  regression  of  the  share 
calculated as described above against a variable reflecting time.  The number of data 
points used to run each regression was given by the number of years since 2000 for 
which use tables are available.   

190.  We  used  the  results  of  the  econometric  analysis,  namely  the  predicted  values  of  the 
regression,  to  populate  the  cells  in  the  use  tables  for  each  of  the  Member  States  in 
each of the  years from 2000 to 2006 that are not held by Eurostat with a value that 
indicates  an  estimate  of  the  share  that  each  product  category  contributes  towards  a 
given class of consumption. 

191.  We  combined  our  estimates  of  these  shares  with  data  from  the  national  accounts 
domain  of  Eurostat.    The  national  accounts  domain  reports  aggregate  values  of 
intermediate consumption by some industry classification, of final consumption split 
by  households,  NPISH  and  government,  and  of  gross  fixed  capital  formation.    The 
level  of  aggregation  at  which  each  of  these  types  of  consumption  is  reported  varies 
across Member States: 

(a)  For some Member States, intermediate consumption is broken down by  NACE 
rev 1.1 classification sub-sections, e.g. it reports the total value of intermediate 
consumption  of,  say,  “DA  15  –  Manufacture  of  food  products,  beverages  and 
tobacco”. 

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(b)  For a few Member States, intermediate consumption is broken down at the level 
of  NACE  rev  1.1  classification  sections,  e.g.  it  reports  the  total  value  of 
intermediate consumption of, say, “D – Manufacturing”. 

(c)  Final  consumption  is  broken  down  by  final  consumption  by  households,  by 
NPISH  and  by  government,  though  for  some  Member  States  the  first  two  are 
grouped together. 

(d)  Gross capital formation is presented as a single figure, i.e. not broken down at all 
by  households,  NPISH  or  government.    We  discuss  gross  capital  formation 
further below. 

192.  Depending on the level of aggregation at which data are available for a Member State, 
we  applied  the  forecasted  share  corresponding  to  that  level  of  aggregation,  giving 
preference to shares computed on the basis of the most disaggregated total. 

193.  To illustrate the mechanics of this procedure we give a worked example below  

194.  Consider 

the 

the 

intermediate  consumption  by 

industry  defined  as  “DA15 
Manufacture  of  food  products  and  beverages”  (DA15  for  short)  of  the  products 
classified as “01 Products of agriculture, hunting and related services“.  Austrian use 
tables  are  available  for  2000,  2001,  2002,  2003  and  2004,  and  a  value  for  the 
consumption  of  those  products  by  DA15  is  reported  for  each  of  these  years.    The 
purpose of the extrapolation exercise is to estimate what this value would be for 2005 
and 2006.  This exercise involves the steps outlined above, more specifically: 

(a)  Drawing  on  the Austrian  use  tables  for  2000,  2001,  2002,  2003  and  2004,  we 
compute  the  share  formed  by  CPA  01  “Products  of  agriculture,  hunting  and 
related services“ in each of the years of each of the following:  

(i) 

(ii) 

the  intermediate  consumption  of  the  broader  manufacturing  sub-section 
DA (which also includes DA16 – Manufacture of tobacco products);  

the  intermediate  consumption  by  manufacturing  section  D  (covering  all 
manufacturing); 

(iii)  all intermediate consumption in the economy; and 

(iv)  gross domestic product of the economy. 

(b)  Taking  each  of  these  time  series  in  turn,  we  ran  an  OLS  regression  to  obtain 

predicted values of the relevant shares for 2005 and 2006. 

(c)  Data  from  the  Eurostat  national  accounts  domain  reveal  that  data  on  total 
intermediate  consumption  by  sub-section  DA,  which  combines  “DA15  – 
Manufacture  of  food  products  and  beverages”  and  “DA16  –  Manufacture  of 
tobacco  products”,  for  2005  and  2006  are  available.    This  is  the  most 
disaggregate level at which such data are available. 

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(d)  Finally, we multiply the appropriate predicted share described in (b) by the total 
intermediate consumption by DA 15 in 2005 and 2006 to obtain an estimate of 
the intermediate consumption of “01 Products of agriculture, hunting and related 
services“ by DA15. 

195.  For Greece and Latvia, the predicted value of the shares in the years 2000-2006 were 
estimated on the basis of use tables for 1995-1999, and 1997 and 1999 respectively, 
the most recent years for which use tables are available for these Member States.  No 
use  tables  are  available  at  all  for  Cyprus  and,  as  such,  this  Member  State  was  not 
included in our analysis. 

196.  For Ireland and Poland, we have needed to modify our approach slightly.  As shown 
in Table 41, use tables for Ireland are only available for 2000-2002 and in the case of 
Poland,  for  2000,  2002  and  2003;  data  for  Poland  in  2001  are  missing.   A  detailed 
visual  inspection  of  the  forecasted  numbers  has  uncovered  oddities  in  the  data  for 
these  two  countries.    For  example  in  Poland,    household  final  consumption  of  CPA 
“95 Products of private households” jumps from PLN 5 million in 2000 to PLN 5,182 
million  in  2002.    In  Ireland,  household  consumption  of  CPA  “34  Motor  vehicles, 
trailers  and  semi–trailers”  falls  from  EUR  2,569  million  in  2000  to  EUR  2,142  in 
2001.  Extending these trends to 2006 would result in obtaining either absurdly high 
or low numbers.  

197.  We have no reason to doubt that the numbers reported in the use tables of these two 
Member States accurately reflect consumption patterns in these countries.  Rather, the 
need  to  revise  our  method  for  these  Member  States  reflects  the  weakness  of  our 
forecasting  method,  namely  the  assumption  that  the  trend  observed  over  the  sample 
period  is  a  good  indicator  of  the  trend  expected  in  the  remaining  years  to  2006.  
Consequently, for Ireland and Poland, rather than predict shares based on a trend over 
time, we have computed the average share over the years for which we have data and 
used  that  share  to  predict  future  values  of  consumption  by  combining  these  with 
actual  aggregate  consumption  data  from  the  national  accounts  to  2006.    For  Malta, 
where use tables are available for only two  years, we did not compute a time trend; 
instead we used an average of the relevant shares. 

198.  By combining our predicted value of the shares  and the data from Eurostat national 
accounts we are able to improve considerably the coverage of our exercise.  However, 
this  method  of  forecasting  relies  crucially  on  the  relative  consumption  patterns  of 
industry groups following a predictable path based on past trends. We recognise this 
inherent weakness in our estimates for more recent years. 

Gross fixed capital formation 

199.  The  use  tables  report  the  value  of  gross  fixed  capital  formation  (GFCF)  associated 
with  each  of  the  59  2-digit  CPA  level  product  categories.   The  tables  do  not  report, 
however, how this aggregate GFCF figure is broken down by the industries that are 
carrying  out  the  capital  formation.   That  is  to  say,  we  know  what  type  of  capital  is 
being formed but not by whom.  For the purposes of estimating the net VAT liability 
base this matters as it is only the GFCF carried out by exempt persons that contributes 

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Section 5: Data sources 

to the theoretical net VAT liability.  We have turned to other data sources to address 
this. 

sectors:  households,  non-financial 

(a)  Eurostat’s  national  accounts  domain  gives  a  breakdown  of  the  GFCF  by 
institutional 
financial 
corporation, NPISH and government.  These data report who is carrying out the 
capital formation but gives no information on the type of assets associated with it  
These data are annual and coverage is reasonably good for the period 2000-2006, 
though  for  some  Member  States  and  for  some  years  (e.g.  Hungary,  Malta, 
Luxembourg) a breakdown is not given for all institutional sectors. 

corporations, 

(b)  Eurostat’s  national  accounts  domain  also  gives  a  breakdown  of  the  GFCF  in  a 
given Member State carried out by  each of the  31 branches of NACE.   It does 
not,  however,  identify  the  types  of  assets  with  which  the  GFCF  is  associated 
with.  Coverage of this dataset is reasonably good. 

(c)  We  have  compiled  data  on  GFCF  from  national  sources  that  are  generally 
produced  within  the  context  of  national  accounts.    We  have  contacted  the 
statistics offices of all Member States with requests for GFCF data broken down 
by institutional sector and by type of asset simultaneously.  The availability and 
fineness  of  the  data  made  available  to  us  vary  considerably  across  Member 
States.  The UK, for example, published a breakdown of GFCF by industry and 
by asset type for each of the years in the period 2000-2004 (though not in more 
recent  years)  and  a  further  breakdown  of  GFCF  by  type  of  asset  and  by 
institutional  sector.    Most  Member  States  do  not  make  such  detailed  data 
available.  

200.  The difference in the fineness of GFCF data available from national sources and the 
differences  in  the VAT  legislation  across  Member  States  impact  on  the  assumptions 
that  are  needed  to  estimate  the  contribution  of  GFCF  to  the  theoretical  net  VAT 
liability.  Section 6 spells these out. 

An outline of the approach to identify net VAT liability associated with GFCF 

201.  We  have  estimated  the  net  VAT  liability  associated  with  GFCF  by  estimating  the 
GFCF carried out by households, non-profit institutions serving households (NPISH), 
financial  corporations,  general  government;  and  exempt  non-financial  corporations.  
To estimate the contribution to the theoretical net VAT liability associated with GFCF 
of each of these groups, we: 

(a)  Identify  the  groups  of  product  for  which  the  estimated  weighted  average  VAT 
was  not  the  standard  rate  and  for  which  the  use  tables  recorded  some  positive 
amount of GFCF in the economy. 

(b)  Estimate  the  GFCF  carried  out  by  each  of  the  institutional  sectors  for  each  of 

these groups of products. 

(c)  Attribute the difference between a sector’s total GFCF and the GFCF relating to 
products  attracting  the  intermediate  or  reduced  rate  to  GFCF  associated  with 
products attracting the standard VAT rate. 

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Section 5: Data sources 

VAT rates 

202.  To  estimate  the  theoretical  net  VAT  liability,  it  is  necessary  to  match  each  of  the 
expenditure  numbers  to  the  corresponding VAT  rate.   As  we  draw  on  use  tables  for 
data on expenditure, and because the data on expenditure are reported at the level of 
2-digit  CPA  product  categories,  it  is  necessary  to  estimate  a  weighted  average  VAT 
rate to be applied to the consumption of each of the 2-digit CPA products.   

203. 

In many cases, the 2-digit CPA code is broader than the level at which VAT rates are 
defined.    For  example,  the  group  “15  Food  products  and  beverages”  consist  of 
processed  food  items,  alcoholic  beverages  and  non-alcoholic  beverages.  The 
applicable  VAT  rates  applicable  are  frequently  different  for  food  and  for  alcoholic 
beverages.  Because of this, it is necessary to estimate a weighted average VAT rate to 
set against this category of products.  We do so by: 

(a)  Identifying  the  VAT  rate  applicable  on  the  consumption  of  goods  and  services 

defined at the lower, narrower, 4-digit CPA level. 

(b)  Applying appropriate weights to these  rates to  estimate a weighted VAT rate at 

the broader 2–digit CPA level.  

204.  Taking  the  products  of  the  2-digit  CPA  food  and  beverages  sector  as  an  example, 
Table 39 illustrates the added “fineness” obtained by carrying out the first step of the 
exercise at the 4-digit CPA level. 

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Table 39  

 Breakdown of “CPA 15 – Manufacture of food and beverages”. 

15.11 

15.12 

15.13 

15.2 

15.31 

15.32 

15.33 

15.41 

15.42 

15.43 

15.51 

15.52 

15.61 

15.62 

15.71 

15.72 

15.81 

15.82 

15.83 

15.84 

15.85 

15.86 

15.87 

15.88 

15.89 

15.91 

15.92 

15.93 

15.94 

15.95 

15.96 

15.97 

15.98 

205. 

Fresh and preserved meat, except poultry 

Fresh and preserved poultry meat 

Meat and poultry meat products 

Processed and preserved fish and fish products 

Processed and preserved potatoes 

Fruit and vegetable juices 

Processed and preserved fruit and vegetables n.e.c. 

Crude oils and fats 

Refined oils and fats 

Margarine and similar edible fats 

Dairy products 

Ice cream and other edible ice 

Grain mill products 

Starches and starch products 

Prepared animal feeds for farm animals 

Prepared pet food 

Bread, fresh pastry goods and cakes 

Rusks and biscuits; preserved pastry goods and cakes 

Sugar 

Cocoa; chocolate and sugar confectionery 

Macaroni, noodles, couscous and similar farinaceous products 

Coffee and tea 

Condiments and seasonings 

Homogenized food preparations and dietetic food 

Other food products n.e.c. 

Distilled alcoholic beverages 

Ethyl alcohol 

Wines 

Cider and other fruit wines 

Other non–distilled fermented beverages 

Beer made from malt 

Malt 

Mineral waters and soft drinks 

In assigning a VAT rate for each of the 4-digit CPA products, we have distinguished 
between  the  VAT  that  would  be  applicable  if  the  product  were  to  be  consumed  by 
households,  by  the  healthcare  sector,  by  the  education  sector,  by  other  government 
activities  and  if  it  were  to  be  consumed  by  any  other  party.   These  distinctions  will 
matter  in  cases  where  legislation  envisages  that  the  applicable VAT  rate  depends  on 
who is doing the consumption.  The distinction also matters in those cases where we 
believe that there are important differences between the groups listed above in relation 

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Section 5: Data sources 

to  how  their  consumption  of  products  within  a  given  4-digit  CPA  category  is 
distributed and that these different products attract different VAT rates. 

206.  We assigned a VAT rate for each of the 4-digit CPA products, in each Member State 
and  for  each  year  from  2000-2006  on  the  basis  of  information  collected  primarily 
from: 

(a) 

the “Value added taxation in Europe” publication of the International Bureau of 
Fiscal Documentation; 

(b)  national VAT legislation; and 

(c)  various editions of the Commission document “VAT rates applied in the Member 
States of the European Community” published over the period 2000-2007. 

207.  While  it  is  more  straightforward  to  assign  a  single  VAT  rate  to  a  set  of  products 
defined at the 4-digit CPA products rather than at the broader 2-digit level, an element 
of  judgment  remains.    For  example,  in  the  UK,  chocolate  covered  biscuits  are 
standard rated, in contrast to food products in general which are reduced rated.  The 4-
digit CPA code does not allow us to uniquely identify chocolate covered biscuits, only 
that they are probably part of “15.84 Cocoa, chocolate and sugar confectionary”.  In 
most  cases,  we  have  not  found  a  source  of  information  that  would  allow  us  to 
construct weights at a level below the 4-digit CPA level.  Where this is the case, we 
make  an  assumption  based  on  our  view  of  the  likely  contribution  that  a  particular 
product makes to the overall consumption of the 4-digit CPA sector. 

208.  To  estimate  the  weighted VAT  rate  at  the  2-digit  CPA  level,  the VAT  rates  at  the  4-
digit  CPA  level  are  weighted  according  to  the  contribution  of  the  products  to  the 
higher 2-digit CPA level.  In most instances, we have constructed these weights on the 
basis of data from the more recent Household Budget Surveys of each Member State, 
where  available,  and  have  otherwise  used  data  from  Structural  Business  Statistics 
reported by Eurostat. 

VAT receipts 

209.  The  data  outlined  above  all  contribute  to  the  estimation  of  the  theoretical  net  VAT 
liability.    To  compute  an  estimation  of  the  VAT  gap,  it  is  necessary  to  compare  the 
estimate of that liability with the levels of VAT receipts that were actually accrued in 
the relevant year. 

210.  We have obtained data on these VAT receipts from Eurostat.  According to paragraphs 
1.57 and 4.26 of ESA 95, tax receipts are to be reported on an accruals basis, although 
they are collected on a cash basis.  Council Regulation 2516/2000 details the rules to 
be followed on the timing of recording and the amounts to be recorded according to 
the accrual method. 

211.  The regulation allows for two ways in which to prepare data on accrued receipts: 

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(a)  A  time-adjusted  cash  method,  where  cash  received  is  attributed  to  the  period 
when  the  activity  took  place;  this  is  generally  based  on  the  typical  time 
difference between when the activity took place and when the cash is received. 

(b)  A method based on declarations and assessments and adjusted for amounts that 

are declared or assessed but unlikely to be collected. 

212.  We were informed by Eurostat that the data on accrued VAT receipts prepared by most 
Member States are based on the time-adjusted cash method.  The reporting of the VAT 
receipts  on  an  accruals  basis  fits  in  with  the  purpose  we  have  at  hand:  we  wish  to 
compare the net VAT liability arising from the economic transactions taking place in a 
given year — as estimated on the basis of the use tables and other sources — with the 
VAT receipts accrued in that same year. 

213.  Eurostat  data  on  accrued  receipts  are  reported  net  of  input  VAT  refunds  to  VAT-
registered bodies eligible to recover their input VAT.  However, this does not apply to 
refunds that operate outside the scope of the VAT system.  For example, in the UK, 
local  authorities  can  claim  a  refund  of  VAT  incurred  on  the  purchase  by  them  of 
contracted-out  services.    This  refund  operates  outside  the  scope  of  the  VAT  system 
and, as such, we have assumed that Eurostat data on VAT receipts are reported gross 
of these refunds.       

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SECTION 6: ASSUMPTIONS 

214.  This section reviews the assumptions that our top-down analysis draws on.  We focus 
on the set of assumptions that, in general, are common to the analysis carried out for 
each Member State. 

215.  Assumptions are needed to bridge the gap between the intricacies of the VAT system 
and the fineness of available national accounts data.  The top-down approach attempts 
to  identify  all  those  supplies  of  products  which  give  rise  to  a  net VAT  liability  in  a 
given  year  in  a  particular  Member  State.    The  approach  cannot  hope  to  arrive  at  a 
precise  measurement  of  this  liability.   The  impossibility  of  doing  so  stems  from  the 
fact that data are not available to us, and indeed do not exist at all, that would allow 
all intricacies of the VAT systems to be picked up.   

216.  An exercise of judgement is necessary to identify to what level of detail one should 
carry out the analysis in order to ensure that those aspects of the VAT legislation that 
are  likely  to  have  a  material  impact  on  VAT  liability  are  adequately  captured.    We 
have had to make a number of simplifying assumptions.  We have aimed to strike the 
right balance between the need to be as accurate as possible, the availability of data 
and the time constraints of the study.  

Completeness of national accounts  

217.  We assume national accounts are complete in the sense that they capture all economic 
activity, including the shadow economy.  ESA 95 requires this to be the case but they 
do not spell out the method statistics offices should follow.  Our assumption is that the 
method  that  is  followed  be  each  statistics  office,  whatever  it  might  be,  is  one  that 
adequately captures the shadow economy. 

Proportion of intermediate consumption on which VAT is not recoverable 

218.  We  set  out  below  the  assumptions  we  make  to  determine  the  proportion  of 
intermediate  consumption  on  which  VAT  is  not  recoverable.    We  denote  this 
parameter as propex.  We first set out the assumption made in relation to estimating 
propex for industries in general, and then set out the assumptions that we make for the 
purpose  of  determining  propex  for  some  particular  cases,  namely  for  financial 
intermediation, for the education sector and for rental activities. 

The general case  

219.  One  component  of  our  top-down  method  is  the  estimation  of  the  proportion  of  the 
intermediate consumption by various industries that attracts irrecoverable VAT.  With 
a  few  exceptions  such  as  company  cars  and  entertainment  which  we  will  deal  with 
separately, this corresponds to the proportion of intermediate consumption used in the 
production  of  exempt  goods  and  services.    This  concept  is  best  explained  by  an 
example.    

220.  Consider the consumption, in Austria in 2003, by the 2-digit NACE sector “I.64 - Post 
and telecommunications” (I.64) of the products belonging to “CPA 22 Printed matter 
and recording media” which was approximately EUR138 million. 

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221. 

In  Austria,  as  in  other  Member  States,  the  activities  relating  to  national  post  are 
exempt whilst activities relating to telecommunications are not.  The activities of the 
national  post  are  classified  under  the  NACE  code  I.64.11.   This  sector,  in  turn,  is  a 
part of I.64.  Clearly, some proportion of the consumption of “CPA 22 Printed matter 
and  recording  media”  by  the  I.64  sector  relates  to  the  production  of  national  post 
activities.  It is this proportion of consumption by the I.64 industry that contributes to 
the VAT base, and which, therefore, we have to estimate.  

222.  We  have  attempted  to  estimate  the  value  of  propex  for  each  of  the  consuming 
industries of the use table.  In some cases, like household final consumption, propex 
will be equal to 1 as the entire consumption of this sector makes a contribution to the 
net VAT base.  In other cases, such as when considering I.64 as a consuming sector, 
this number needs to be estimated.  We estimate this number by setting it equal to the 
proportion of the value of output on national post out of the value of total output of 
the aggregate I.64 2-digit sector.  

223.  There is an implicit assumption behind this method: in particular, it is assumed that 
the share of output within a 2-digit NACE sector reflects the share of inputs used to 
produce that set of goods.   

224.  We use this method to compute propex for all exempt activities, with the exception of 
the financial services sector, the education sector and the rental sector.  Our approach 
to  devising  a  value  for  the  propex  parameter  for  these  three  sectors  is  described 
separately below. 

225.  We  should  also  note  that  for  sectors  other  than  that  classified  in  NACE  as  “J. 
Financial  intermediation”,  discussed  below,  we  assume  that  the  intermediate 
consumption  recorded  in  the  use  tables  relates  to  transactions  outside  VAT  groups.  
We  think  this  is  a  reasonable  assumption  for  those  sectors  whose  intermediate 
consumption contributes to the net theoretical VAT liability. 

Financial intermediation  

226. 

In  general,  most  activities  that  fall  within  the  NACE  group  “J.  Financial 
intermediation”  are  exempt  in  all  Member  States.    However,  a  few  Member  States 
allow  some  financial  services  firms  to  “opt  to  tax”,  thereby  waiving  their  exempt 
status and reclaiming some of their input VAT. 

227.  A  further  source  of  complexity  relates  to  the  treatment  of  “VAT  groups”,  a  matter 
which,  for  the  purpose  of  this  study,  impacts  on  firms  in  the  financial  sector  in 
particular.    The  issue  raised  by  VAT  groupings  relates  to  the  practice  of  allowing 
separate  entities  to  be  treated,  for  VAT  purposes,  as  a  single  entity  thereby  not 
charging  VAT  on  transactions  between  them.    The  use  tables  do  not  allow  us  to 
estimate the precise numbers that relate to “within VAT group” as opposed to “outside 
VAT group” transactions.   

228.  We have not found alternative sources to estimate propex as the actual proportion of 
irrecoverable VAT paid by financial service firms tends to be confidential and is not 
usually published. 

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229.  This  lack  of  information,  coupled  with  the  relatively  large  sums  of  VAT  revenue 
involved,  means  we  have  needed  to  make  an  important  assumption.    A  report 
published by the European Commission provides the results of a survey carried out of 
financial services firms across the EU.16  This survey reports a range of 0 to 74 per 
cent  for  the  proportion  of  VAT  that  is  actually  recovered.    We  have  settled  on  an 
assumption across the EU that 40 per cent of VAT is recovered.  We have tested the 
sensitivity  of  the  overall  VAT  gap  to  this  assumption;  the  results  are  presented  in 
Section 2.  

Education sector 

230. 

In general, the activities of the sectors classified in NACE as “M.80 Education”, are 
exempt  across  the  EU  as  far  as  they  relate  to  primary,  secondary  and  university 
education.    However,  services  such  as  the  provision  of  professional  training  and  of 
driving  lessons  are  generally  subject  to  VAT  and  these  too  are  part  of  the  category 
“M.80  Education”.    We  think  that  the  supply  of  these  education  services  that  are 
subject  to  VAT  account  for  a  significant  share  of  the  output  of  the  wider  “M.80 
Education”.    In  the  light  of  this,  it  is  necessary  to  form  an  estimate  of  the  propex 
associated with the “M.80 Education” sector.  

231.  We have not come across published data that would allow us to compute the value of 
the “propex” for “M.80 Education” directly.  We do, however, have data on the final 
consumption  of  education  services  by  the  government  and  by  NPISH,  and  we  also 
have data on the intermediate consumption by other industries of education services.  
By  making  the  rough  assumption  that  all  government  and  NPISH  consumption  of 
education services relate to exempt education and that all intermediate  consumption 
by  other  industries  and  direct  consumption  by  households  relate  to  “market” 
education services that are subject to VAT, we have arrived at an EU-wide assumption 
that 20 per cent of the input VAT of “M.80 Education” is recoverable.  We have tested 
the sensitivity of the overall VAT gap to this assumption; the results are presented in 
Section 2.  

Rental activities and the option to tax 

232.  Letting  and  leasing  activities  of  immovable  property  is  generally  exempt  with  an 
option to tax, if the lessor chooses.  Where that is the case, we have assumed that the 
lessor would choose to remain exempt when the lessee or tenant is an exempt entity, 
for example households.  Likewise, we assume that the lessor would opt to tax where 
the lessee or tenant is a business that can recover its input VAT.  

233.  We have computed the proportion of intermediate inputs of the rental sector that gives 
rise  to  irrecoverable  VAT  to  be  equal  to  the  share  of  the  final  consumption  by 
households,  government  and  NPISH  and  intermediate  consumption  by  financial 
services,  education  and  healthcare  in  the  total  output  of  the  rental  sector.    We  have 

16 Report prepared on behalf of DG TAXUD, available from 

http://ec.europa.eu/taxation_customs/resources/documents/common/publications/studies/Financial_Services_Study_Main
Report_en.pdf , accessed on 3 August 2009. 

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deducted imputed rents from both the numerator and denominator.  Data on imputed 
rents are available from Eurostat. 

Interpreting the data on final consumption 

234.  We turn to a discussion of assumptions made in the use of data on final consumption 
expenditure  by  households,  NPISH  and  government.    These  assumptions  are  re-
statements of some of the guidelines of ESA 95 and, as such, these assumptions are 
subsumed  within  the  earlier  assumption  that  national  accounts  are  prepared  in 
accordance with ESA 95.  All the same, we choose to set them out here explicitly. 

Purchases on the domestic territory by non-residents and by national residents abroad 

235. 

In  line  with  ESA  95  guidelines,  we  assume  that  the  data  reported  in  the  use  tables 
under “Final consumption by households” include the value of purchases made in the 
domestic territory, by both residents and non-residents, and exclude purchases made 
by residents abroad.  As an example, it is assumed that expenditure by French tourists 
staying  in  a  hotel  in  Malta  is  included  in  the  Maltese  use  tables  under  “Final 
consumption  by  households”  on  the  product  category  “H55  Hotels  and  restaurant 
services”.  

Purchase of goods by mail-order  

236.  We  assume  that  mail-order  goods  sold  to  final  consumers  based  in  the  EU  by  a 
supplier based in another EU Member State are recorded as household consumption 
in the Member State where the supplier is based. 

237.  On the other hand, our estimation assumes that if the mail-order goods are purchased 
from a supplier that is outside the EU then that purchase is reflected in the household 
consumption of the Member State where the consumer is located.   

Purchase of e-services 

238.  We assume that purchases of e-services by consumers based in the EU are recorded as 
household  final  consumption  in  the  use  tables  of  the  Member  State  in  which  the  e-
service  provider  is  registered  for  VAT  purposes.    For  example,  the  purchases  of  e-
services by a Danish resident from a provider registered for VAT in Luxembourg are 
assumed  to  be  included  in  the  value  of  household  final  expenditure  reported  in  the 
Luxembourg use tables and not in the Danish ones. 

239. 

If a supplier of e-services is in a country outside the EU and is not registered for VAT 
in any Member State, we assume that the VAT it remits to a single tax authority under 
the  simplified  scheme,  is  allocated  appropriately  to  each  Member  State  and  that  the 
associated  consumption  values  are  recorded  by  those  Member  States  in  their  use 
tables.  

Final consumption by households on alcohol and tobacco is accurately reported 

240.  The data on household final consumption reported in use tables are based, in part, on 
household  budget  surveys.    It  is  known  that  in  such  surveys,  consumption  of  goods 

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Section 6: Assumptions 

such  as  alcohol  and  tobacco  tends  to  be  significantly  under-reported,  and  that 
adjustments are made to this as a correction.   

241.  We assume that the data reported in the use tables for household final consumption of 
products  relating  to  “D15  Food  products  and  beverages”,  which  include  alcoholic 
drinks,  and  to  “D16  Tobacco  products”  reflect  the  consumption  of  these  products 
accurately so that any corrections for under-reporting and smuggling will have been 
made.  This is in keeping with the ESA 95 guidelines.   

242.  Consistent  with  our  definition  of  tax  losses,  we  assume  that  the  VAT  lost  on  the 
consumption of illicit alcohol or tobacco, is the VAT that would have been paid, had 
the same amount of money been spent on legal excise duty-paid tobacco or alcohol. 

Gross capital formation 

243.  ESA  95  defines  gross  capital  formation  (GCF)  as  gross  fixed  capital  formation 
(GFCF) plus changes in inventories plus net acquisitions of valuables.  GFCF is by far 
the  more  significant  component  of  GCF.    In  this  subsection  we  turn  to  a  review  of 
assumptions  relating  to  the  contribution  of  each  of  these  components  to  the  net 
theoretical VAT liability.  

We do not consider changes in inventories 

244.  The  purchase  of  products  underlying  any  recorded  change  in  inventory  would,  if 
carried  out  by  a  party  unable  to  reclaim  VAT,  make  a  contribution  towards  the  net 
VAT  liability.    However,  in  our  analysis  we  have  not  considered  this  contribution.  
This choice is grounded on the following reasoning: 

(a)  We  have  not  found  published  data  that  allow  us  to  break  down  changes  in 
inventory  by  both  type  of  asset  and  by  type  of  consuming  industry.    The  use 
tables  published  by  Eurostat  report  a  single  economy-wide  number  for  the 
change  in  inventory  associated  with  a  given  product  category;  information  on 
whose inventory it is that is changing is not provided and, consequently, we are 
unable  to  isolate  the  changes  in  inventory  carried  out  by  parties  that  cannot 
reclaim VAT. 

(b)  Our judgement is that stocks are by far more likely to be held by businesses that 
are not exempt rather than by exempt ones.  That is to say, we expect inventories, 
and changes in inventories, held by public administration, health and education 
institutions  and  financial  corporations  to  account  for  a  very  small  share  of  the 
changes in inventories in the economy as a whole. 

(c)  Typically, changes in inventories of a given product category account for a small 
share of the total intermediate consumption across the economy of that product 
category  and,  accordingly  we  think  the  impact  on  the  estimated  net  theoretical 
VAT liability to be small. 

245.  The  direction  of  the  (relatively  small)  error  in  our  estimate  caused  by  not  taking 
account  of  the  changes  in  inventories  carried  out  by  exempt  sectors  will  depend  on 
whether the observed change in inventory is positive or negative.  If, in a given year 

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the change in the inventories held by an exempt sector of a given product category is 
positive  then,  by  not  taking  account  of  changes  in  inventories,  we  will  be 
underestimating  the  theoretical  VAT  liability.    Conversely,  we  would  be  over-
estimating the liability when the observed change in inventory is negative.  Because 
changes  in  inventories  fluctuate  between  being  positive  and  negative  with  no  clear 
trend,  and  because  these  magnitudes  are  relatively  small,  we  do  not  expect  that  our 
approach  induces  us  in  any  consistently  significant  under-  or  over-estimation  of  the 
net theoretical VAT liability. 

We consider changes in valuables 

246.  As noted above, changes in valuables (ESA 95 code P.53) are one of the components 
of  GCF.    Valuables  are  defined  at  paragraph  3.125  of  the  ESA  95  manual  as  “non-
financial  goods  that  are  not  used  primarily  for  production  or  consumption,  do  not 
deteriorate (physically) over time under normal conditions and that are acquired and 
held  primarily  as  stores  of  value.”    Valuables  include  precious  stones  and  metals, 
precious  jewellery,  pearls,  antiques  and  art  objects  such  as  paintings  and sculptures.  
We note that agent or dealer margins associated with the acquisition and disposal of 
valuables will contribute to the value recorded against changes in valuables. 

247.  The  data  in  the  use  tables  reported  by  Eurostat,  and  in  published  national  accounts 
more generally, break down the changes in valuable by type of assets but not by the 
type of industry that is acquiring and disposing of these valuables.  All the same, we 
do  take  account  of  the  changes  in  valuables  for  the  purpose  of  estimating  the 
theoretical net VAT liability as we think that it is reasonable to assume that the VAT 
paid on these acquisitions of valuables cannot be reclaimed. 

248. 

In estimating the contribution to the theoretical net VAT liability we have set against 
the reported value of changes in values the standard VAT rate relevant to the Member 
State and year under consideration. 

249.  Finally,  we  note  that  some  Member  States  do  not  report  changes  in  valuables 

separately and, instead, include this within the figures for GFCF. 

A general assumption on the data on GFCF 

250.  The  figures  for  GFCF  reported  in  national  accounts  represent  net  flows.   That  is  to 
say, they represent the difference between acquisitions and disposals of fixed capital 
goods.  This raises a difficulty in identifying the value of transactions that may give 
rise to a VAT liability.  

251.  To see this, consider the case where the institutional sector defined as “government” 
sells EUR 1 million worth of existing office buildings to “non-financial corporations”, 
and  invests  in  EUR  11  million  worth  of  new  buildings.    In  the  sector  accounts 
reported in national accounts, in relation to office buildings, the government account 
would  report  a  GFCF  of  EUR  10  million,  and  the  account  of  the  “non-financial 
corporations” would report EUR 1 million.  The acquisition of existing buildings by 
businesses would not attract VAT, as they would be second-hand goods.  However, we 
have computed the VAT liability on the GFCF of EUR 10 million in buildings — the 
data recorded in national accounts — rather than on the EUR 11 million.  

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252.  On the other hand, we would not be able to distinguish between the purchase by the 
government  of  an  existing  building  previously  owned  by  a  “non-financial 
corporations”  and  the  acquisition  by  the  government  of  a  new  building.    Both 
expenditures would have the same impact on the government’s GFCF account. 

253.  We would have problems of a similar nature when dealing with Member States where 
a  significant  number  of  residential  dwellings  have  been  transferred  from  the 
Government to private non-financial corporations. 

254.  To correct this it would be necessary to access GFCF data that report acquisitions and 
disposals of capital separately and that identify the sectors between  which capital is 
acquired  from  or  disposed  to.    We  have  not  found  such  data  in  published  national 
accounts.  We have therefore been unable to estimate the impact on our calculation of 
the  net  theoretical VAT  liability  of  considering  net  GFCF  rather  than  separating  out 
the  acquisition  and  disposal  of  capital  of  each  of  the  institutional  sectors.    In  this 
respect, we think our VAT liability  estimate related to GFCF might be lower than it 
should be.   

Set of assumptions on the contribution to VAT liability from GFCF that are common 
across Member States  

255.  The differences in the nature of the data on GFCF available across Member States, as 
well as the differences across national VAT legislation, do not allow us to follow the 
exact  same  procedure  across  Member  States  when  estimating  the  contribution  of 
GFCF to net theoretical VAT liability.  But there are a number of assumptions that we 
do make which are common across all or most of the Member States.  We set these 
out below.  

1: GFCF associated with agricultural, forestry and fishing products are assumed not to 
attract VAT 

256.  Across  all  Member  States  the  use  tables  report  some  positive  value  for  the  GFCF 
associated with products of agriculture, forestry and fishing (corresponding to the 2-
digit CPA product categories codes “A.1 Products of agriculture, hunting and related 
services”, “A.2 Products of forestry, logging and related services” and “B.5 Fish and 
other fishing products; services incidental of fishing” respectively).  We assume that 
the  GFCF  associated  with  these  products  do  not  reflect  the  supply  of  products  on 
which VAT would be applied. 

257.  The assumption is based on our view that the GFCF associated with these products is 
likely to reflect the increase in the value of existing cultivated assets (orchards, forest 
trees, herds of cattle, flocks of sheep, shoals of fish used in fish farming) rather than 
the creation of new forms of capital that will have been acquired from other sectors.  
ESA  95  specifies  that  GFCF  should  incorporate  changes  in  the  value  of  such 
cultivated  assets.    Admittedly,  positive  values  of  the  GFCF  associated  with  these 
product  categories  could  also  come  about  through  the  importation  of  these  types  of 
fixed  capital.    Our  assumption  is  that  GFCF  through  the  development  of  existing 
domestic  capital  of  this  sort  is  likely  to  account  for  a  far  greater  proportion  of  total 
GFCF than that driven by imports. 

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258. 

In turn, the increase in the value of an existing orchard, herd of animals or shoal of 
fish used for production do not reflect any economic supply and so would not attract 
any VAT and therefore does not contribute to net VAT liability. 

2: GFCF associated with medical equipment 

259.  Medical equipment attracts a reduced or intermediary VAT rate in many, though not 
all  Member  States,  and  some  of  the  GFCF  recorded  against  the  product  category 
“DL33  Medical,  precision  and  optical  instruments,  watches  and  clocks”  relate 
precisely to such medical equipment.  Further, we expect that a significant fraction of 
the  GFCF  associated  with  medical  equipment  is  carried  out  by  parties,  namely 
providers of health services, that are VAT exempt.  Because of this, it is necessary to 
estimate the portion of GFCF associated with the purchase of these types of assets.  

260.  The  use  tables  available  from  Eurostat  do  not  allow  us  to  do  this  directly;  medical 
equipment  is  part  of  the  wider  2-digit  CPA  product  category  “DL33  Medical, 
precision and optical instruments, watches and clocks” and it is the total GFCF on this 
set of products that is reported.  However, more detailed national accounts for the UK 
and for France provide us with information on: 

(a)  For the UK for 2000-2004, the GFCF associated with the purchase of assets in 
the product category DL33 by the industry categorised as “hospitals and health 
care”. 

(b)  For France for 2000 and 2001, the GFCF associated with the purchase of assets 
classified  as  “Fabrication  de  materiel  medico_chirurgic”  by  “Le  secteur  des 
Administrations publiques” (APU). 

261.  We  think  it  is  reasonable  to  assume  that  the  data  described  above  for  the  UK  and 
France relate to the purchase of medical equipment and instruments by hospitals and 
health  care  providers.    For  the  remaining  Member  States,  for  which  we  have  been 
unable to find similarly detailed data, we estimate the value of the GFCF on medical 
equipment by hospitals and health care providers as follows: 

(a)  Using  the  data  available  for  France  and  the  UK,  we  compute  the  ratio  of  the 
GFCF on medical instruments by hospitals and health care providers to the value 
added  by  the  health  and  social  work  sector  (NACE  code  N)  as  reported  in  the 
Eurostat use tables.  We then compute an average of this ratio across these two 
Member States for the years for which data are available. 

(b)  For each of the other Member States and for each of the years in the period 2000-
2006,  we  estimate  the  value  of  GFCF  associated  with  the  purchase  of  medical 
equipments  by  hospitals  and  health  care  providers  by  multiplying  the  average 
ratio described above by the value added of the health and social work sector as 
reported in the relevant use table. 

262.  The  approach  outlined  assumes  that  ratio  between  GFCF  on  medical  equipment  by 
hospitals  and  health  care  providers  and  the  value  added  associated  with  health  and 
social care is (approximately) common across Member States. 

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3: GFCF associated with dwellings and other buildings 

263.  GFCF  associated  with  dwellings  is  a  substantial  component  of  the  GFCF  on  which 
VAT  sticks.    The  VAT  legislation  on  transactions  that  relate  to  GFCF  in  dwellings 
tends  to  be  particularly  complex  in  many  Member  States.    For  example,  a  different 
rate  applies  to  the  renovation  of  dwellings  in  Belgium  depending  on  the  age  of  the 
dwelling,  and  in  Italy  the  VAT  rate  on  the  purchase  of  new  dwellings  depends  on 
whether it is a first or second home, luxury or not.   

264.  Lack of relevant data does not allow us to examine these and all other intricacies of 
the relevant VAT law.  We attempt, however, to distinguish between, on the one hand 
investments in new dwellings, and on the other, expenditure on major improvements 
to existing dwellings.  Both of these expenditures contribute to GFCF associated with 
dwellings  and,  in  some  Member  States,  these  two  types  of  supply  attract  different 
VAT rates. 

265.  We have contacted the national statistics offices of those Member States for which the 
split  between  the  two  types  of  GFCF  on  dwellings  is  relevant  to  our  analysis  with 
requests for data that  would allow us to  compute the  relevant ratio.   Some Member 
States were able to provide us with these data.  

266.  For those Member States for which relevant data are not available to us, we assume 
that of the total GFCF in dwellings, half  relates  to investment in new buildings and 
half to major improvements in existing buildings.  This assumption is broadly based 
on figures for this share for the UK — where the split was estimated to be 42 per cent 
on new dwellings and 58 per cent on major improvements — and for France where 
the split over the period 1990-2000 is close to 50 per cent for each component. 

267.  We make this assumption in the estimate of the net theoretical VAT liability of those 
Member States where, on our interpretation, the VAT rate applicable on investment in 
new  dwellings  differs  from  that  applicable  to  major  improvements  to  existing 
dwellings  and  for  which  we  do  not  have  national  data  that  might  have  provided  a 
more precise estimate of the split.   

268. 

In  most  Member  States  the  supply  of  existing  buildings  —  buildings  that  are  not 
newly  built  —  does  not  attract  VAT,  but  in  some  (e.g.  Italy)  it  does.    We  have  not 
taken this contribution to VAT liability into account in our analysis as we do not have 
the data to identify the value of such transfers.  We note that the supply of non new 
buildings  do  not  contribute  to  gross  fixed  capital  formation  figures  in  national 
accounts, other than through the transfer costs that they originate (such costs we have 
taken into account). 

269.  Given  the  significance  of  GFCF  on  dwellings,  we  examine  the  sensitivity  of  our 
estimate of the overall VAT gap to the assumptions on how GFCF on dwellings is split 
between the two categories considered.  The results are presented in Section 2. 

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4: We have sought to take account of other taxes on transfer of real property 

270.  Taxes  on  the  transfer  of  real  property  (stamp  duty)  are  considered  to  contribute 
towards  GFCF.    Such  taxes  do  not  attract  VAT,  and  it  is  necessary,  therefore,  to 
exclude these amounts from the GFCF figure before applying the relevant VAT rate.   

271.  We  are  not  able  to  identify  the  relevant  tax  head  for  some  Member  States,  namely 
Estonia, Hungary, Lithuania, Luxembourg, Latvia and Malta.  We are also aware that 
stamp  duty  may  also  apply  to  the  transfer  of  goods  other  than  the  transfer  of  real 
property;  where  that  is  so,  and  where  data  relating  to  stamp  duty  on  real  property 
alone is not separately identified, we may have deducted an excessive amount.   

272.  For  the  purpose  of  our  analysis,  it  is  necessary  to  apportion  the  receipts  relating  to 
taxes  on  transfer  of  real  property  across  the  relevant  consuming  sectors/industries.  
This  is  usually  done  on  the  basis  of  the  GFCF  associated  with  new  dwellings  and 
other  buildings  carried  out  by  each  sector/industry  (where  such  information  was 
available)  or  on  the  basis  of  each  sector/industry’s  GFCF  on  construction  more 
generally. 

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Section 7: Adjustments 

SECTION 7: ADJUSTMENTS 

273. 

In  this  section  we  describe  a  number  of  adjustments  we  use  for  the  purpose  of 
capturing  particular  aspects  of  the  VAT  system  which  impact  on  estimates  of  the 
theoretical  net  VAT  liability.    These  adjustments  relate  to  business  entertainment 
expenditure, company cars, “tank tourism” in Luxembourg and exemption granted to 
small business. 

274.  We  also  identify  in  this  section  a  small  number  of  features  for  which  we  make  no 

adjustment.   

Business entertainment expenditure 

275. 

In  general,  across  the  EU,  the  VAT  on  expenditure  incurred  by  businesses  on 
entertainment is subject to restrictions on the right of deduction, even if the business 
is not engaged in an exempt activity.  We have not come across any data sources that 
allow us to estimate this expenditure.   

276.  These  restrictions  may  make  a  significant  contribution  theoretical VAT  liability.   To 
account  approximately  for  them  we  assume  that  the  VAT  on  all  intermediate 
consumption  of  the  product  category  “H.55  Hotels  and  restaurant  services”  is 
irrecoverable.  We exclude from this, self-supplies to the industry “H.55 Hotels and 
restaurants”, to the sector “I.63 Supporting and auxiliary transport activities; activities 
of  travel  agencies”  and  consumption  by  exempt  sectors  which  we  account  for 
elsewhere. 

Company cars 

277. 

In  general,  across  the  EU,  some  restrictions  apply  to  the  right  to  deduction  on VAT 
incurred on the purchase of company cars.  The scope and extent of these restrictions 
vary considerably across Member States.  The restrictions depend not only on who is 
making the purchase but also on the nature of use.  

278.  We  have  not  been  able  to  find  published  data  sources  on  the  precise  proportion  of 
VAT  incurred  on  such  expenditure  that  is  recoverable  in  any  Member  State.    In  the 
absence of this data, we are compelled to make a very rough assumption.  We assume 
that, across the EU, an amount equivalent to 50 per cent of the total gross fixed capital 
formation  (acquisitions  less  disposals)  in  the  economy  on  products  of  the  sector 
“DM34 Motor vehicles, trailers and semi-trailers” is subject to the restriction on the 
right to deduct input VAT. 

279.  We test the sensitivity of the overall gap estimates to this assumption and the results 

are presented in Section 2. 

 “Tank tourism” 

280. 

“Tank  tourism”  refers  to  the  practice  of  foreign  haulage  and  transport  businesses 
filling  up  their  trucks  in  a  Member  State  to  take  advantage  of  less  expensive  fuel 
there. 

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Section 7: Adjustments 

281. 

“Tank tourism” is a significant activity in Luxembourg.  A significant share of petrol 
and  diesel  sold  in  Luxembourg  is  accounted  for  by  the  consumption  of  foreign 
vehicles.  It is estimated that in 2006, around 80 per cent of diesel and 70 per cent of 
gasoline sold in Luxembourg was in fact “exported.17  Other than possible logistical 
reasons,  the  low  price  of  fuel  in  Luxembourg  relative  to  those  in  neighbouring 
countries will have been an important factor behind this.  From 2000 to 2006 prices at 
the  pump  for  automotive  diesel  were,  on  average,  around  30  per  cent  higher  in 
Germany than in Luxembourg, in the Netherlands and France they were around 25 per 
cent higher and in Belgium 16 per cent.18 

282.  Technically,  the  consumption  of  fuel  in  Luxembourg  by  private  cars  from  other 
Member  States  should  count  as  household  consumption  and  should  be  presented  as 
part  of  household  consumption  in  Luxembourg.    We  assume  that  this  is  indeed  the 
case and that there is therefore no need for a special adjustment in this regard. 

283.  Consumption  of  fuel  by  business  vehicles  from  other  Member  States,  on  the  other 
hand,  count  as  exports  from  Luxembourg,  and  should  be  treated  as  such  by  the 
Luxembourg use tables.  Such consumption attracts VAT in Luxembourg.  In theory, 
businesses registered in other Member States can apply for a refund of the VAT paid 
in  Luxembourg.    We  have  no  information  on  what  portion  of  this  VAT  goes 
unclaimed.  We assume that all exports of the products related to “DF23 Manufacture 
of coke, refined petroleum products and nuclear fuel” are subject to Luxembourg VAT 
that is not refunded.  This assumption potentially overstates the VAT liability arising 
from “tank tourism” in Luxembourg.  We do not make this adjustment for any other 
Member State.   

284.  We test the sensitivity of our assumption on the VAT gap for Luxembourg and we 

present the results in Section 2. 

Exemption granted to small businesses 

285.  The  VAT  Directive  allows  Member  States  to  exempt  taxable  persons  whose  annual 
turnover is below some threshold.  The value of these thresholds, as of May 2008, is 
set out in Table 40.  

17  Ministère de l’Environnement, Luxembourg (2008) “Luxembourg’s National Inventory Report 1990-2006 – Submission 
under the United Nations Convention on Climate Change and voluntary submission under the Kyoto Protocol”, Table 
3.44.  Available from 
http://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/4303.php, accessed on 
3 August 2009. 

18  The average relative prices are calculated as the average, across the 14 six-monthly observations covering the period 2000 
to 2006, of the relevant price ratio.  Price data relate to prices including all taxes.  The data were obtained from Eurostat’s 
“Energy Statistics” domain. 

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85 

 
                                                 
Section 7: Adjustments 

Table 40   Thresholds for application of special scheme for small businesses, May 2008 

Member State 

AT 

BE 

CZ 

DE 

DK 

EE 

ES 

FI 

FR 

GR 

HU 

IE 

Threshold 

€ 30,000 

€ 5,580 

€37,622 

€17,500 

€6,705 

€15,978 

None 

€8,500 

€76,300 or €27,000 

€9,000 or €4,000 

€19,700 

€70,000 or €35,000 

Member State 

Threshold 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

None 

€28,962 

€10,000 

€14,347 

€37,000, €24,300, or €14,600 

None 

€13,883 

€9,976 or €12,470 

None 

€25,000 

€44,642 

€87,098 

Source:.http://ec.europa.eu/taxation_customs/resources/documents/taxation/vat/traders/vat_community/vat_in_EC_annexI. 
pdf, accessed on 15 May 2008. 

286.  Taxable  persons  with  a  turnover  falling  below  the  applicable  threshold  are  exempt 

from VAT.  It follows that: 

(a)  The  sale  of  the  goods  or  services  produced  by  these  taxable  persons  does  not 

attract VAT. 

(b)  These taxable persons are not able to recover the input VAT that they will have 

paid. 

287.  To take account of the small business exemption on our estimation of the theoretical 
VAT liability it is necessary to deduct an element equal to product of the value added 
by  those  taxable  persons  falling  below  the  small  business  threshold  times  and  the 
relevant VAT rate. 

288.  We have not found data reporting the value added of business by level of turnover.  To 
overcome  this  we  have  contacted  all  relevant  Member  States  to  ask  for  data  on  the 
VAT  foregone  due  to  the  exemption  to  small  business.    Table  41  reports  the  2006 
values for those that responded.   

Table 41   Estimated VAT revenue foregone due to small business exemption 

UK (GBP million) 

Ireland (EUR million) 

Estonia (EEK million) 

Hungary (HUF million) 

Malta (EUR million) 

2000 

100 

42.61 

154.9 

n.a. 

5.87 

2001 

400 

25.27 

175.5 

2,047 

6.68 

2002 

400 

40.3 

196.9 

n.a 

7.14 

2003 

450 

37.3 

220.8 

2,659 

7.40 

2004 

300 

59.83 

243.3 

2005 

900 

41.96 

453.4 

10,607 

14,752 

9.53 

9.58 

2006 

950 

37.56 

358.7 

8,667 

10.03 

Source: UK HM Treasury”Chapter A: Budget Measures, of the "Financial Statement and Budget Report", several years; and 
data received from Irish Revenue and the Ministries of Finance of Estonia, Hungary and Malta. 

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86 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Section 7: Adjustments 

289.  For Ireland, Estonia, Malta and Hungary the values reported in Table 7 do not relate 
to all of the VAT foregone due to the exemption granted to small businesses.  Rather, 
the  values  are  those  that  were  submitted  as  part  of  the  relevant  VAT  own  resource 
calculations.   As  such,  the  values  relate  to  the  VAT  foregone  due  to  the  exemption 
granted  to  business  with  a  turnover  below  the  threshold  stipulated  by  national 
legislation — set out for 2006 in Table 44 — and above €10,000.  As such, the values 
used  for  these  Member  States  will  be  an  underestimate  of  the VAT  receipts  that  are 
foregone  due  to  the  exemption  granted  to  small  businesses.    Because  the  values  in 
question are relatively small, we do not consider this to have a material impact on our 
overall estimate of the VAT gap. 

290.  For  those  Member  States  for  which  we  were  not  able  to  collect  relevant  data,  we 
estimate the foregone VAT by applying a percentage to our estimated theoretical net 
VAT liability.  The percentages applied are informed by the data that we did receive 
from  some  of  the  Member  States  and  by  the  relative  size  of  the  thresholds.    For 
Belgium,  Denmark,  Finland,  Greece,  Luxembourg,  Poland  and  Portugal  we  assume 
the  foregone  VAT  to  be  0.1  per  cent  of  liability,  for  Austria,  Germany,  Latvia  and 
Slovenia  we  apply  0.3  per  cent,  and  for  the  Czech  Republic,  France,  Lithuania  and 
Slovakia a percentage of 0.5 per cent.  

Supplies in domestic territories with different VAT regimes 

291.  The VAT regimes applicable to the supply of goods and services in specific territories 
of  some  Member  States  differ  from  those  applicable  in  the  rest  of  the  national 
territory.  This is the case, for example, of the Portuguese archipelagos of the Azores 
and  Madeira  where  the  applicable  VAT  rates  are  lower  than  those  in  mainland 
Portugal.    Some  national  territories  are  outside  the  scope  of  the  VAT  directive 
altogether  and  no  EU  VAT  applies.    This  is  the  case,  for  example,  of  the  Canary 
Islands (Spain) and of French Guiana (France). 

292.  We  have  adjusted  our  estimate  of  the  theoretical  net  VAT  liability  of  the  relevant 
Member  States  to  take  account  of  the  special  regimes  that  apply  in  Madeira  and 
Azores (Portugal), Corsica and in the Overseas Departments (France) and in Lesbos, 
Chio,  Samos,  Dodecanese  and  the  Cyclades  and  on  the Aegean  Islands  of  Thassos, 
Northern  Sporades,  Samothrace  and  Skiros  (Greece).    We  have  also  adjusted  our 
estimate  of  the VAT  liability  of  Spain  to  take  account  of  the  fact  that VAT  does  not 
apply in the Canary Islands.  We have made no other territorial adjustment. 

293. 

In all cases the adjustment to the estimated liability was estimated on the basis of the 
VAT  rate  differentials  and  on  the  region’s  share  of  national  GDP.    Implicit  in  this 
computation  is  the  assumption  that  the  economy  of  each  of  these  territories  is  a 
“scaled down” version of the economy of the relevant Member State as whole.  In the 
adjustment  done  for  the  Canary  Islands  it  was  further  necessary  to  deduct  from  the 
VAT  receipts  figures  the  revenues  associated  with  the  Impuesto  General  Indirecto 
Canario (IGIC) as this is included within the VAT receipts reported by Eurostat.  Data 

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Section 7: Adjustments 

on  the  IGIC  receipts  were  obtained  from  the  “National  List  of  Taxes”  publication 
made available by DG TAXUD.19 

Adjustments that have not been carried out 

294.  We do not make adjustments to reflect the special schemes provided to farmers and 
others, purchases on the domestic territory by non-residents or mail order purchases 
from  suppliers  based  outside  the  EU.    We  set  out  below  the  reasoning  for  our 
approach. 

Special schemes provided to farmers and others 

295.  Across a number of Member States special schemes are in place for specific sectors of 
the economy.  The special flat rate scheme for farmers is perhaps the most common 
example  and  is  provided  for  where  the  application  to  farmers  of  the  “normal  VAT 
arrangements  or  the  special  scheme  [for  small  enterprises]  is  likely  to  give  rise  to 
difficulties.”20 

296.  A farmer under the flat rate scheme does not make a claim for the VAT he will have 
paid on his inputs.  Instead, the farmer is compensated by a percentage that is applied 
to  his  sales  of  agricultural  goods  and  services.    The  percentage  is  fixed  by  each 
Member  State  operating  such  a  scheme  and  the  VAT  Directive  specifies,  in Article 
298, that the percentage is to be “calculated on the basis of macro-economic statistics 
for  flat-rate  farmers  alone  for  the  preceding  three  years”  and,  in Article  299,  that  it 
“may  not  have  the  effect  of  obtaining  for  flat-rate  farmers  refunds  greater  than  the 
input  VAT  charged.”    In  addition,  the  directive  also  specifies,  in  Article  296  that 
“every flat rate farmer may opt […] for application of the normal VAT arrangements”.  

297.  On the basis of the above, we think it is reasonable to assume that the percentages that 
are fixed for flat-rate farmers are such as to render the scheme neutral in fiscal terms.  
That is to say, we think it is reasonable to assume that the compensation percentages 
are estimated such that flat-rate farmers are compensated for the VAT input that they 
will have paid.  Given this, flat-rate farmers, like all other non-exempt intermediary 
sectors in the economy, make no net contribution to the theoretical VAT liability and it 
is not necessary, therefore, to examine their activity with any greater attention. 

Purchases in the domestic territory by non-EU residents 

298.  We make no adjustment for the purchases made in the domestic territory by non-EU 
residents on which VAT has been reclaimed.  Again, we believe that what would be 
gained in terms of the accuracy of our estimates would be too small compared to the 
significant efforts required to source the data necessary to make these adjustments. 

19  Accessed from 

http://ec.europa.eu/taxation_customs/resources/documents/taxation/gen_info/economic_analysis/tax_structures/2009/2009
_NTL_en.xls on 3 August 2009. 

20  Article 296 Council Directive 2006/112/EC of 28 November 2006 on the common system of value added tax.  Available 
from http://eur-lex.europa.eu/LexUriServ/site/en/oj/2006/l_347/l_34720061211en00010118.pdf , accessed on 3 August 
2009.  

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Section 7: Adjustments 

Mail-order purchases from suppliers based outside the EU 

299.  We  make  no  adjustments  for  special  arrangements  that  might  apply  to  low-value 
consignments supplied from locations outside the EU.  For example, the UK operates 
a  low-value  consignment  relief  scheme  whereby  inbound  packages  valued  below  a 
threshold  set  by  HMRC  are  exempt  from  VAT  upon  importation.    We  do  not  have 
access to data that would allow us to make an adjustment for such schemes.   

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Section 8: Definitions of the VAT gap 

SECTION 8: DEFINITIONS OF THE VAT GAP  

300.  This section considers possible definitions of the VAT gap and sets out the reasoning 

for the definition adopted by this study.  

Possible definitions of the VAT gap 

301.  We  have  identified  two  issues  that  have  a  material  bearing  on  the  definition  of  the 

concept of the VAT gap: 

(a)  In  cases  where  tax  evasion  leads  to  an  increase  in  consumption  or  economic 
activity (albeit illegal activity), should the amount of tax that ought to have been 
levied  on  the  additional  activity  be  included  in  the  tax  gap  (even  though  that 
activity only occurs because tax is not collected)? 

(b)  When VAT has been evaded on a transaction, should the actual expenditure that 

is observed be deemed to be inclusive of VAT or exclusive of VAT? 

302.  We do not think it possible to construct a sufficiently robust estimate of tax losses that 
answers the first question in the affirmative.  The change in the behaviour of traders 
and  consumers  more  generally  that  results  from  there  being  tax  evasion  is  not 
observed.    To  estimate  such  a  change  requires  knowledge  about  the  responses  of 
suppliers  and  consumers,  e.g.  the  elasticity  of  demand.    There  are  also  further 
consequential  effects  of  compliance,  such  as  consumers  substituting  between 
products,  which  will  further  affect  the  tax  that  is  collected.    Our  assessment  is  that 
estimating  these  effects  across  the  whole  economy  is  too  vulnerable  to  missing  or 
inaccurate  information  about  the  consequences  of  compliance  making  such  an 
exercise unreliable and impractical.   

303.  This  leaves  only  the  second  identified  issue  remaining,  and  leads  to  three  possible 

definitions of tax losses: 

(a)  The  “tax  not  remitted”  definition  measures  the  tax  that  would  not  have  been 
remitted had all fraud taken the form of traders not submitting their VAT returns 
and payments.  This definition assumes that the fraudulent transaction amount is 
inclusive  of  VAT.    Thus,  observing  a  black  market  transaction  worth  120  on 
which 20 per cent VAT should have been charged will be deemed to correspond 
to a loss of 20, the VAT element of the 120 paid by the consumer. 

(b)  The  “tax  not  collected”  definition  measures  the  tax  that  would  not  have  been 
collected had all fraud taken the form of VAT not being charged on transactions 
where  it  should  be.    This  definition  assumes  that  the  fraudulent  transaction 
amount is exclusive of VAT.   Thus, observing a black market transaction worth 
120  on  which  20  per  cent  VAT  should  have  been  charged  will  be  deemed  to 
correspond  to  a  loss  of  24  —  because  the  consumer  should  have  paid  144,  of 
which 24 would have been VAT. 

(c)  The  “full  recovery”  definition  measures  the  amount  that  would  be  recovered 
through  enforcement  action  by  the  VAT  collection  agency  if  it  had  perfect 
information about past frauds, and if there were no losses due to insolvencies.   

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Section 8: Definitions of the VAT gap 

304.  This full recovery definition requires transaction specific information about the type 
of  fraud  that  has  been  committed  in  each  case.    The  tax  not  remitted  and  tax  not 
collected  definitions  do  not  require  this  transaction  specific  information.    Instead, 
these  definitions  only  need  data  on  consumption,  on  the  relevant  VAT  rate  and  on 
actual VAT receipts in order to estimate the VAT gap.   

Worked examples of the differences between the definitions for the VAT 
gap 

305.  We now describe how the three candidate definitions would be applied in four stylised 
examples each reflecting different types of tax losses.  This will, we think, clarify the 
above  definitions  and  clarify  the  reasoning  for  our  adopted  definition.    We  will  see 
that the estimate of the amount of tax lost may depend on the definition adopted. 

Worked example 1: VAT evasion with complicity of the final customer  

306.  The first worked example involves the following fraud scenario: 

A trader undertakes some work for a consumer.  The work is, in law, liable for 
VAT  at  20  per  cent.    At  the  instigation  of  the  consumer,  the  trader  does  not 
account for VAT and charges 120 on a cash-in-hand basis. 

307.  Under the tax not remitted definition, it is assumed that the transaction (120) includes 
VAT.  Under this definition the VAT associated with this transaction that ought to have 
been remitted is 20.  In contrast, under the tax not collected definition which assumes 
the fraudulent transaction is exclusive of VAT, the amount of VAT deemed not to have 
been collected is 24.  

308.  Assuming full recovery by the tax authorities, the consumer would have been made to 
pay  24  of VAT  to  the  authorities,  unless  there  was  no  evidence  that  he  initiated  the 
fraud.    If  no  such  evidence  were  available,  the  proceeds  of  enforcement  (excluding 
penalties) would only be 20 as a result of compulsory registration/assessment of the 
trader’s  liability.    This  illustrates  how  exact  knowledge  of  the  fraud  is  required  to 
assess the VAT loss under the full recovery definition.   

Worked example 2: VAT evasion by a trader, without complicity of the final customer 

309.  The second worked example involves the following fraud scenario: 

A wholesaler purchases goods worth 120 including 20 of VAT, and sells them on 
to retailers for 240 including a stated VAT amount of 40.  The wholesaler submits 
a fraudulent VAT return which does not include these transactions and does not 
pay  any  VAT  associated  with  the  goods.    Retailers  sell  the  goods  on  for  300, 
including VAT of 50, and remit VAT of 10 to the tax authorities in the ordinary 
way. 

310. 

In this example, the net VAT receipts in respect of these goods are 30. 

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Section 8: Definitions of the VAT gap 

311.  Under  the  tax  not  remitted  definition,  the  level  of  VAT  that  ought  to  have  been 
remitted is estimated to be 50.  Compared to the actual level of VAT receipts of 30, 
this would imply that the level of VAT not remitted was 20. 

312.  Under the tax not collected definition it would be estimated that, given the observed 
VAT  receipts  of  30,  final  consumption  should  have  been  180.    However,  given  that 
observed final consumption was actually 300, this would mean that there would have 
been  120  of  fraudulent  consumption  which  should  have  attracted  additional VAT  of 
24.  Under this definition, therefore, VAT receipts should have been 54 (24+30).  This 
gives estimated VAT losses of 24. 

313.  Under the full recovery definition, the level of VAT that ought to have been remitted 
is estimated to be 50.  Compared to the actual level of VAT receipts, this would imply 
that the level of VAT not remitted was 20. 

Worked example 3: MTIC fraud 

314.  The third worked example involves the following fraud scenario: 

A trader “A” acquires goods worth 120 from a legitimate trader registered for 
VAT  in  another  Member  State.    The  legitimate  trader  charges  no  VAT  on  the 
transaction.    The  acquirer  then  sells  the  goods,  possibly  via  intermediaries,  to 
another trader “B”, issuing a VAT invoice for 120 plus VAT of 24 (assuming the 
VAT  rate  is  20  per  cent).    Trader  “A”  goes  missing  without  settling  any  VAT.  
Trader “B” supplies the same goods to a trader based in another Member State 
for 150 plus zero VAT.  Trader “B” then applies for a refund of his input VAT of 
24  supposedly  paid  to  trader  “A”.      This  input  tax  claim  is  valid  under  the 
European 
Optigen 
Justice 
(http://www.reckon.co.uk/item/e225e8e4).  There is zero final consumption of the 
goods in this example. 

ruling 

Court 

in 

of 

315.  The VAT effects of the fraud are as follows: 

(a)  The  intra-community  acquirer,  trader  “A”,  pays  no  VAT  and  receives  no  VAT 

refund. 

(b)  The  intra-community  supplier,  trader  “B”,  receives  a  VAT  refund  of  24,  this 

being the VAT on the 120 that it paid on the goods to trader “A”. 

316.  Under  the  tax  not  remitted  definition,  net  VAT  receipts  that  ought  to  have  been 
remitted are zero, and therefore the estimated tax losses are 24.  The same estimates 
are produced under the “full recovery” definition.  

317.  Under the definition of tax not collected a different measure of tax loss is computed to 
be 28.8, the calculations underlying this figure are set out in Table 42.  Identifying the 
level  of  deemed  fraudulent  consumption  is  not  as  straightforward  as  the  previous 
examples.  In this MTIC example, as was the case in worked example 2, the measure 
of  tax  loss  associated  with  the  tax  not  collected  definition  is  one  that  we  cannot 
reconcile with any interpretable measure.  

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Section 8: Definitions of the VAT gap 

Worked example 4: Fraudulent consumer import 

318.  The fourth worked example involves the following fraud scenario: 

An individual acquires 120 worth of goods from another Member State, allegedly 
for business purposes and, accordingly, he provides his business’ VAT number.  It 
is  classified  as  an  intra-community  acquisition  and  does  not  attract  the  20  per 
cent VAT  that  would  otherwise  be  paid.   The  goods  are  in  fact  put  to  personal 
rather than business use and no VAT is declared.  

319.  Under  the  tax  not  remitted  definition,  it  is  estimated  that  there  should  have  been  a 
VAT  payment  of  20  associated  with  the  observed  final  consumption  of  120.    In  the 
context of this worked example, this measure of tax loss cannot easily be interpreted 
as the type of fraud does not fit with the specified definition.  

320.  Under  the  tax  not  collected  definition,  the  tax  loss  in  respect  of  the  fraudulent 
transaction  would  be  calculated  to  be  24.    This  corresponds  to  the  VAT  that  the 
consumer should have been charged had he not fraudulently claimed to be importing 
the goods for business purposes. 

321.  Using the full recovery definition, the estimated tax loss would also be 24. 

Tax losses calculations in the worked examples 

322.  Table 42 overleaf shows the different tax gap definitions for the worked examples. 

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Section 8: Definitions of the VAT gap 

Table 42   Calculation of different definitions of the VAT gap in worked examples 

Worked example 
1: 
Evasion with 
complicity 

Worked example 
2: 
Evasion without 
complicity 

Worked example 
3: 
MTIC carousel 
fraud 

Worked example 
4: 
Fraudulent 
consumer import 

V 
Applicable VAT 
rate 

A 
VAT receipts 

B 
Final consumption 
(including VAT) 

C = B*V/(1+V) 
Tax that ought to 
have been remitted 

D = C – A 
Tax not remitted 

E = A*(1+V)/V 
Consumption 
implied by VAT 
receipts 

F = B – E 
Deemed fraudulent 
consumption 

G = F*V 
Tax not collected 

H 
Full recovery 

20% 

20% 

30 

300 

50 

20 

180 

120 

24 

20 

0 

120 

20 

20 

0 

120 

24 

24 
(only 20 if no 
evidence of 
consumer 
complicity) 

20% 

–24 

0 

0 

24 

–144 

144 

28.8 

24 

20% 

0 

120 

20 

20 

0 

120 

24 

24 

323.  The  worked  examples  described  above  and  summarized  in  Table  42  illustrate  the 
limits  of  the  appropriateness  of  each  of  the  possible  definition  of  the  VAT  gap 
considered: 

(a)  Measure  D,  “tax  not  remitted”,  yields  estimates  that  are  not  meaningful  in  the 
case  of  fraud  where  goods  are  imported  allegedly  for  business  use  but  are 
actually  put  to  personal  use  (worked  example  4).    In  the  case  of  VAT  evasion 
where  the  consumer  is  complicit  (worked  example  1),  measure  D  gives  an 
indication of the tax loss that the tax authority would be able to recover from the 
trader without having to prove the complicity of the consumer himself.  This may 
understate the amount recoverable by the tax authorities. 

(b)  Measure G “tax not collected” yields estimates that are not meaningful and that 
cannot be interpreted in the case of VAT evasion by a trader (worked example 2) 
and in the MTIC case (worked example 3).   

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Section 8: Definitions of the VAT gap 

(c)  Measure H “full recovery” cannot be estimated on the basis of macroeconomic 

data alone.  They require transaction-specific information or assumptions. 

324.  We think that measure G “tax not collected” is not appropriate.  The case against this 

measure is the erroneous results that it gives in worked examples 2 and 3. 

Choice of definition for the study 

325. 

In  this  study  we  choose  to  focus  on  “tax  not  remitted”  estimates  based  on  macro-
economic data. 

326.  The “tax not remitted” definition provides a simple and clearly-defined measure, with 

the following features when used to analyse different types of fraud: 

(a)  It  provides  the  natural  measure  of  the  value  of  fraud  in  cases  of  MTIC  and 

wholesale evasion, and a reasonable measure in the case of retail evasion. 

(b)  It provides the correct measure of the value of fraud in cases of consumer fraud 
provided the data on final consumption incorporate adjustments made to reflect 
tax evasion where consumers are complicit (in the spirit of worked example 1).  

(c)  It does not provide a correct measure of the value of fraud in cases of consumer 

fraudulently importing goods for non-business purposes.  

327.  The “tax not remitted” definition appears to be the one used in estimates of the VAT 
gap by some national tax collection agencies.  However, it is not universally adopted 
by researchers in the area of tax fraud.  For example, the calculations carried out in 
Christie and Holzner (2006) imply a definition of tax losses in line with the “tax not 
collected” definition set out above.21 

21  Christie, E and Mario Holzner (2006) “What explains tax evasion?  An empirical assessment based on European data”, 

wiiw Working Papers 40, available from http://www.wiiw.ac.at/pdf/wp40.pdf , accessed on 3 August 2009. 

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

SECTION 9: REVIEW OF PUBLISHED ESTIMATES OF VAT 
FRAUD AND THE VAT GAP 

328.  This  section  provides  a  brief  review  of  estimates  of  VAT  fraud  and  VAT  losses  (or 
VAT  gap)  published  by  national  tax  authorities,  other  government  agencies  or 
departments,  and  research  institutes.    It  reviews,  in  turn,  top-down  estimates,  and 
bottom-up estimates which, by constructing an estimate of overall VAT fraud on the 
basis  of  the VAT  fraud  associated  with  different  types  of  fraudulent  activity,  offer  a 
characterisation of VAT fraud as a whole. 

329.  We  have  complemented  our  own  search  of  relevant  publications  by  contacting 
stakeholders in all Member States inviting them to point us to published studies that 
estimate VAT fraud.  

330.  This section is structured as follows.   First, we  present the  results of published top-
down  estimates  of  the  VAT  gap  for  some  Member  States.    Second,  we  review  two 
bottom-up  studies,  one  by  the  UK’s  HM  Revenue  and  Customs  and  another  by  the 
Swedish Skatteverket, which provide estimates of the contribution of different types 
of fraudulent activities to total VAT fraud.  Third, and closing this section, we review 
some  published  estimates  of  MTIC  fraud,  one  of  the  components  of VAT  fraud  that 
has attracted most attention in recent years. 

Published top-down estimates of the VAT gap 

331.  We have found published estimates of the VAT gap for some of the years overlapping 
with our period of analysis for Denmark, Germany, Italy, Sweden and the UK.  These 
have been prepared by Danmarks Statistik, the Ifo Institut for Economic Research in 
Munich,  the  Italian  Agenzia  delle  Entrate,  the  Swedish  NCB/NR  and  by  HMRC 
respectively.  The estimates of the VAT gap produced by these institutions are reported 
in Table 43, alongside our own  For the period between 2000 and 2006, we have not 
come across published top-down estimates for other Member States. 

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

Table 43   Other published estimates of the VAT gap 

Member 
state 

Source of 
estimate 

Alternative estimates 

Reckon estimates 

Units 

Year 

Liability 

Receipts  Gap 

Liability  Receipts  Gap 

DE 

EUR 
billion 

Ifo 
Institute 
for 
Economic 
Research 
Munich 

DK 

Danmarks 
Statistik 

DKK 
billion 

IT 

Agenzia 
delle 
Entrate 

SE 

NCB/NR 

UK 

HM 
Revenue 
& 
Customs  

EUR 
billion 

SEK 
billion 

GBP 
billion 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

2000 

2001 

2002 

2003 

2000 

2001 

2002 

2000 

2001 

2002 

2000 

2001 

2002 

2003 

2004 

2005 

2006 

155.6 

156.5 

156.5 

156.5 

154.2 

156.5 

157.9 

132.2 

136.6 

141.1 

144.3 

106.4 

110.2 

111.8 

198.7 

208.7 

217.3 

n.c. 

n.c. 

n.c. 

n.c. 

n.c. 

n.c. 

n.c. 

141.6 

138.5 

138.5 

138.5 

135.7 

138.5 

142.9 

123.8 

128.5 

132.4 

135.1 

73.5 

72.9 

76.7 

194.9 

204.6 

215.7 

n.c. 

n.c. 

n.c. 

n.c. 

n.c. 

n.c. 

n.c. 

14.0 

18.0 

18.0 

18.0 

18.5 

18.0 

15.0 

8.4 

8.0 

8.7 

9.2 

33.0 

37.4 

35.1 

3.8 

4.1 

1.6 

10.0 

11.3 

12.2 

9.9 

10.3 

13.4 

12.8 

158.6 

160.4 

157.7 

158.4 

159.4 

160.7 

164.1 

135.6 

140.9 

143.5 

145.2 

99.9 

103.4 

106.1 

208.0 

217.0 

225.2 

76.5 

80.4 

85.3 

90.3 

95.6 

101.6 

106.1 

140.0 

139.1 

136.8 

137.2 

137.4 

139.8 

147.1 

123.8 

128.5 

132.4 

135.1 

77.5 

78.1 

80.4 

194.9 

204.6 

215.7 

64.2 

67.1 

71.1 

77.3 

81.6 

83.4 

87.8 

18.5 

21.4 

20.9 

21.2 

21.9 

20.9 

17.0 

11.9 

12.4 

11.1 

10.1 

22.4 

25.3 

25.7 

13.2 

12.4 

9.5 

12.3 

13.3 

14.3 

12.9 

14.0 

18.2 

18.4 

Note: We believe receipts and liability figures of HMRC are not comparable (n.c.) principally because these are 
net of re-payments to eligible public bodies.  HMRC estimates for the UK are computed on the financial rather 
than calendar year.  The figures given in the table for the estimate by HMRC for the UK for, say, 2000 relate to the 
estimate for 2000/2001.  The figures from Danmarks Statistik are gross of deduction for debtors. 
Sources: Nam and Parsche (2007) “Trotz 19% Mehrwertsteuer  wird für 2007 ein weiteres, Absinken der Ausfallquote 
erwartet”,  ifo  Schnelldienst  10/200,  pp41-42;  Danmarks  Statistik  (2007)  “Danish  National  Accounts:  Sources  and 
Methods 2003”Table 3.71; Agenzia delle Entrate (2006) “Le basi imponibili IVA Aspetti generali e principali risultati 
per il periodo 1982-2002”; Swedish National Tax Agency (2008) "Tax Gap Map for Sweden: How was it created and 
how can it be used?", Report 2008:1B Table 6; HMRC (2007, 2005) “Measuring indirect tax losses”. 

Our estimates will differ from those published by national tax agencies and other 
institutions 

332.  We  understand  the  estimates  of  the  various  institutions  reported  in  the  table  above 
have  been  arrived  at  on  the  basis  of  a  top-down  approach,  broadly  similar  to  that 
which we ourselves have followed.  We are aware, all the same, of some differences 
between the approaches which will account for some of the differences between the 
set of estimates. 

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

333.  First, there will be differences in the set of national accounts data we used and that 
used by each of the above institutions, in terms of their coverage and detail.  Where 
data were not available to us in sufficient detail, we have made assumptions, some of 
which  have  a  material  impact  on  our  estimates  of  the  VAT  liability.    We  have 
described the set of assumptions in Sections 2 and 6, and report in Section 2 on the 
sensitivity of our results to the most significant of these.  Further, national accounts 
are often subject to  revisions by the national statistics agency and it is possible that 
the  “edition”  of  the  data  we  drew  on  is  not  the  same  as  that  which  underlies  the 
estimates of the institutions listed above. 

334.  Second,  other  than  for  Denmark,  our  data  series  on  VAT  receipts  are  also  different 
from those used by each of the other institutions in their estimations of the VAT gap.  
With the exception of the UK,  we do not have  an explanation for these  differences.  
We received written comments from the UK HMRC in June 2009 suggesting that the 
VAT  receipts  for  the  UK  reported  by  Eurostat  (and  reported  in  the  published  UK 
national accounts) are net of some refunds to National Health Service Trusts; we do 
not have a published source of information that would allow us to adjust our estimates 
to reflect this. 

335.  The  above  differences  come  about  from  differences  in  the  data  used  or  in  the  way 
estimates  are  reported,  and  do  not  reflect  differences  in  the  general  approach 
followed.    In  terms  of  differences  in  approach,  we  are  aware  of  just  the  following 
three points. 

336.  For  Germany,  we  understand  that  an  adjustment  is  made  to  the  receipts  figure  to 
reflect VAT that has not been remitted due to insolvencies.  Our own estimate of the 
VAT gap has not sought to exclude the effect of VAT not remitted due to insolvencies.  
For Denmark, Danmarks Statistik publishes estimates of the theoretical liability both 
with and without adjusting for deductions for debtors.  To make the comparison with 
our estimate on an equal footing, we report the estimate where such deductions have 
not  been  adjusted  for.    For  the  UK,  HMRC  does  not  include VAT  losses  associated 
with smuggled alcohol and tobacco in its estimate of the VAT liability.  To the extent 
that these illegal activities are captured by national accounts, these losses are included 
in our estimates of the VAT liability and VAT gap. 

337.  As reported in Table 43, our estimates of the German VAT gap are consistently higher 
than those reported by Ifo although the differences are relatively small.  The trend in 
our estimates of the VAT gap mirrors that of the estimates produced by Ifo.   

338.  Similarly,  our  estimates  of  the  VAT  gap  for  Denmark  are  consistently  higher  than 
those  published  by  Danmarks  Statistik.    Unlike Germany,  however,  the  trend  in  our 
estimated VAT gap does not mirror the trend in the estimates produced by Danmarks 
Statistik.   

339.  Our  estimates  of  the  VAT  gap  for  the  UK  are  also  higher  than  those  published  by 
HMRC.  For the reasons outlined above we are not able to compare the reported data 
on VAT receipts and estimated liability.     

340. 

In  contrast,  Table  43  reveals  that  our  estimates  of  the  VAT  gap  for  Italy  are 
consistently  lower  than  the  one  published  by  the Agenzia  delle  Entrate.    One  factor 

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

contributing to this is the fact that the series of VAT receipts used by the Agenzia delle 
Entrate  differs  from  the  one  we  obtained  from  Eurostat  and  used.    We  cannot 
reconcile  the  difference  in  the  receipts  data;  whilst  they  may  have  been  compiled 
differently they are both meant to reflect a notion of accrued receipts.  

341.  Lastly,  our  estimates  of  the  Swedish  VAT  gap  are  considerably  higher  than  those 
produced by NCB/NR.  We have not found a published description of the top-down 
approach used by NCB/NR to explore this further.  We do note, however, that these 
very  low  estimates  of  the  VAT  gap  by  NCB/NR  have  been  commented  on  by 
Skatteverket as appearing to be unreasonably low.22 

Bottom-up quantification of the components of VAT fraud 

342.  We have not carried out a bottom-up estimate of the VAT gap ourselves as we have 
not found a satisfactory way of estimating the relative contribution of different types 
of VAT fraud on the basis of publicly available data.  Producing a bottom-up estimate 
of the level of VAT fraud starts from identifying the different types of fraud and then 
proceeds to  estimate the size of each of these components.   Invariably, this requires 
operational  data  that  are  typically  only  held  by  national  tax  authorities  and  are 
confidential.   

343.  We focus, instead, on reviewing published bottom-up studies of VAT fraud.  Ahead of 
presenting  this  review  we  make  two  observations.    First,  we  have  found  only  two 
studies that attempt to estimate the overall level of VAT fraud by identifying the size 
of different categories of fraud; one was completed by the UK’s HMRC in 2002 and 
the second by the Swedish Skatteverket in 2008.  Second, other studies of relevance 
that we are aware of focus on estimating the value of a particular form of VAT fraud, 
MTIC which has attracted the most attention in recent years. 

Bottom-up estimates of VAT losses in the UK and in Sweden 

344.  We have found only two published studies that have sought to construct a bottom-up 
estimate of VAT losses.  The HM Customs & Excise (HMCE) in the UK published a 
note  of  their  estimate  on  this  in  2002  and,  more  recently,  the  Skatteverket  have 
published a very detailed report outlining the results of their bottom-up estimates.  We 
review each in turn. 

HMCE (2002) estimates of VAT losses in the UK 

345.  The note prepared in 2002 by the UK’s HM Customs & Excise, later HM Revenue & 
Customs  (HMRC),  on  “Measuring  indirect  tax  losses”  (HMCE,  2002)  presented 
estimates  of  VAT  losses  associated  with  (i)  missing  trader  (inter-community)  fraud 
(MTIC),  (ii)  avoidance  and  (iii)  general  non-compliance.23    The  first  two  are  self-
explanatory  and  the  third,  general  non-compliance,  is  described  as  “the  failure  of 

22  Skatteverket (2008) “Tax gap map for Sweden — How was it created and how can it be used?”, Report 2008:1B, p31.  
Available from  http://www.skatteverket.se/download/18.225c96e811ae46c823f800014872/Report_2008_1B.pdf, 
accessed on 3 August 2009. 

23  HM Customs & Excise (2002) “Measuring indirect tax losses”.  Available from http://www.hm-

treasury.gov.uk/d/admeas02-297kb.pdf, accessed on 3 August 2009. 

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businesses  to  pay  the  right  amount  of  VAT  at  the  right  time”  and  is  defined  at 
paragraph 2.37 of the note to include “errors and omissions on tax returns, including 
failure  to  submit  returns,  late  payment,  non-payment  or  incomplete  payment  and 
deliberate mis-declaration of input or output tax on tax returns”. 

346.  HMCE  drew  on  different  approaches  to  estimate  each  of  the  three  types  of  losses.  
Estimates of losses due to MTIC fraud were prepared on the basis of trade data.  The 
exercise  centred  on  comparing  the  data  on  exports  to  the  UK  declared  in  other 
Member  States  with  the  imports  from  other  Member  States  declared  in  the  UK.24  
Further  details  of  the  approach  is  described  in  the  section  on  MTIC  fraud  below.  
Estimates of the losses due to general non-compliance are based on operational data.  
Finally, losses due to avoidance were estimated by drawing on: 

(a)  An estimate of how much businesses spend on VAT avoidance schemes (between 

£250 and £300 million a year); 

(b)  An  assumption  that  this  spending  refers  to  the  fees  paid  to  accountancy  firms 

managing tax avoidance fees; and  

(c)  An assumption that accountancy firms’ fees for such services are 10 per cent of 

the tax saved. 

347.  The estimates put forward by HMCE against each of these types of VAT losses are set 
out  in  Table  44.    This  table  also  reports  the  estimate  for  overall  VAT  fraud  HMCE 
arrived  at  through  its  top-down  approach,  as  reported  in  its  2002  publication 
“Measuring indirect tax losses”. 

Table 44   HMCE bottom-up and top-down estimates of VAT losses 

Period covered 

Lower estimate 

Upper estimate 

Bottom-up estimate 

Missing trader fraud 

2001/2002 

Avoidance 

Annual — no specific year 

General non-compliance 

Annual — no specific year  

Total 

£1.75 billion 

£2.5 billion 

£2.9 billion 

£7 billion 

£2.75 billion 

£3 billion 

£4.5 billion 

£10 billion 

Top-down estimate 

2001/2002 

£10.4 billion 

Note:  The  top-down  estimate  of  £10.4  billion,  reported  in  HMCE’s  2002  publication,  has  been  revised  in  subsequent 
“Measuring indirect tax losses” publications. 
Source: HMCE (2002) “Measuring indirect tax losses” 

348.  Commenting  on  the  figures  presented  in  Table  44,  HMCE  (2002)  notes  that  the 
bottom-up  figures  provide  “a  useful  corroboration”  for  the  top-down  estimate.  
According to the bottom-up estimates, VAT losses are roughly between £7 billion and 
£10  billion.    These  are  in  the  same  neighbourhood  as  the  £10.4  billion  top-down 
estimate. 

24  HM Customs and Excise (2001) “Measuring indirect tax fraud”.  Available from http://customs.hmrc.gov.uk/ 
channelsPortalWebApp/downloadFile?contentID=HMCE_PROD_011638, accessed on 16 September 2009. 

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349.  Given the uncertainty surrounding its estimates, HMCE (2002) adds that a great deal 
of caution is necessary when interpreting the figures, particularly so for the estimates 
of the losses  due to general non-compliance and from avoidance.   

350.  HMCE/HMRC,  did  not  publish  estimates  of  the  fraud  associated  with  general  non-
compliance  (or  with  avoidance)  in  later  years  as  it  reasoned  that  it  was  “difficult  to 
construct  estimates  of  their  relative  contribution  to  overall  revenue  loss”  (HMRC, 
2005, page 6).25 

Skatteverket (2008) estimates of the VAT gap in Sweden 

351.  Skatteverket  published  in  2008  its  study  “Tax  gap  map  for  Sweden  —  How  was  it 
created  and  how  can  it  be  used?”  which  outlines  the  approach  and  results  of  its 
research into the tax losses in Sweden.26  The study adopts a different definition from 
the taxes not remitted definition we have adopted.  In particular, Skatteverket (2008) 
define the tax gap in the executive summary to the report: 

as  the  difference  between  the  tax  that  would  have  been  determined  if  all  those 
liable for tax reported all their business and their transactions correctly and the 
tax  that  actually  is  determined  after  the  efforts  of  the  National  Tax  Agency  to 
ensure compliance. 

352.  The  notion  of  tax  losses  used  reflects  in  part,  therefore,  the  knowledge  of  the  tax 
authority and does not consider whether taxes have actually been paid or not.   

353.  The study reports on losses across various types of tax, including VAT.  For each tax, 
the  study  estimates  the  losses  associated  with  three  broad  categories  of  activity: 
international,  undeclared  employment  and  other  national.    With  respect  to  VAT,  the 
activities found to contribute to the “international” component of the tax loss refer to 
ones involving import/export, trade with other EU Member States, carousel trade and 
trading of cars and boats.  The “undeclared employment” component of the VAT gap 
relates  primarily  to  the  VAT  losses  arising  from  the  non  reporting  of  sales  by 
companies and, to a less extent, to undeclared services and good provided to private 
individuals.    Finally,  the  activities  contributing  to  the  “national”  component  of VAT 
losses relate to errors in VAT reporting, including on the tax position of company cars. 

354.  Skatteverket  (2008)  draws  on  a  number  of  different  sources  to  derive  the  above 
estimates.  With regards to the VAT gap associated with international transactions, the 
estimates are based on calculations by Swedish customs and we understand that the 
estimates  on  the  tax  gap  associated  with  undeclared  employment  are  based  on  a 
number of sources including survey results and national accounts data, the latter being 
used, we understand, to develop a top-down estimate of the gap.  The sources of data 
used  to  estimate  the  “Other  national”  element  of  the  VAT  gap  include  inspection 
projects and outcomes of audit work by Skatteverket itself. 

25  HM Revenue & Customs (2005) “Measuring indirect tax losses”.  Available from 

www.hmrc.gov.uk/pbr2005/mitl2005.pdf accessed on 3 August 2009. 

26  Skatteverket (2008) Tax gap map for Sweden, Report 2008:1B.  Available from 

http://www.skatteverket.se/download/18.225c96e811ae46c823f800014872/Report_2008_1B.pdf , accessed on 3 August 
2009. 

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355.  Skatteverket  (2008)  estimates  the  total  VAT  tax  gap  to  be  SEK  35.3  billion.    As 
reported  in  Table  45,  VAT  losses  associated  with  transactions  associated  with 
undeclared employment account for almost half of the total VAT gap, and within this, 
it is the VAT gap relating to unreported sales in companies that is the most significant.  

Table 45  Swedish VAT tax gap reported in Skatteverket (2008) 

Nature of fraud 

International transactions 

    Import/export 

    EC trade 

    Carousel trade 

    Trade with cars, boats etc 

    Other 

Undeclared employment 

    Unreported sales in companies 

    Undeclared services between private individuals 

    Black market goods to private individuals 

    Other 

Other national 

    Micro-companies 

    Small and medium-sized companies 

    Large companies 

    Public sector associations etc 

Total 

Source: Skatteverket (2008, p. 62)  

Tax gap, SEK billion 

11.8 

3.6 

3.0 

1.0 

1.1 

3.2 

17.1 

13.6 

1.3 

0.8 

1.5 

6.4 

2.1 

2.0 

0.9 

3.8 

35.3 

356.  Skatteverket (2008) comments that its estimates of the international component of the 
VAT gap is subject to significant uncertainty and notes that it has been in relation to 
the  VAT  gap,  more  generally,  that  it  has  found  it  hardest  to  find  reliable  data  for 
estimation and that “the general opinion of those working on VAT controls is that the 
gap  is  great,  but  in  most  cases  it  has  been  hard  to  back  up  that  impression  with 
facts”.27 

Published estimates of MTIC fraud 

357.  Missing  trader  intra  community  (MTIC)  fraud  is  a  criminal  activity  that  takes 
advantage  of  the  zero-rating  of  cross-border  intra-EU  trade  to  evade  VAT  or  to 
fraudulently claim input VAT refunds.  

358.  There are broadly two types of MTIC fraud, acquisition fraud and carousel fraud.  In 
acquisition fraud, a VAT-registered trader purchases goods from a seller in another EU 
Member State.  This transaction attracts no VAT as cross-border transactions are zero-
rated for VAT purposes.  The trader then sells these goods onwards, charges VAT on 
them and disappears without remitting the tax collected to the tax authority. 

27  Skatteverket (2008), p.64. 

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359. 

In  its  initial  stages,  carousel  fraud  is  similar  to  acquisition  fraud.   A VAT-registered 
trader  imports  goods  from  another  EU  Member  State,  paying  no  VAT  on  this 
transaction.  The goods are sold onwards, possibly via a number of intermediaries or 
buffer traders, until the final link in this chain exports these goods to an intermediary 
based in another EU Member State.  This exporter submits an input VAT refund claim 
to the tax authority, to reclaim the VAT supposedly paid by him to his supplier.  The 
original  importer  disappears  without  paying  the  VAT  due  on  his  sales.    The  goods 
could  be  re-imported  by  the  original  importer  and  the  whole  cycle  repeated  many 
times before the importer disappears, thereby giving this type of fraud its name.  

360.  MTIC  fraud,  being  an  illegal  activity,  is  inherently  difficult  to  measure. Apart  from 
the  numbers  for  a  few  individual  EU  Member  States  covered  in  this  review,  we  are 
not aware of a single EU-wide estimate of MTIC. 

361.  To the best of our knowledge, the two most significant research efforts on measuring 
MTIC  fraud that have been published are those  led by the UK’s HMRC and by the 
Belgian Finance Ministry.  We review each in turn. 

HMCE and HMRC’s estimates of MTIC fraud in the UK 

362.  Since  2001,  HMCE,  and  later  HMRC,  in  the  UK  has  published  annual  estimates  of 
MTIC  fraud  in  the  UK.    (For  ease  of  exposition,  we  will  refer  in  this  section  to 
HMRC to mean both HMCE and HMRC, except when referencing publications.)  The 
documents  provide  few  details  on  what  lies  behind  the  calculations  although  an 
overview of the approach is provided.  The approach itself has changed over time. 

363.  From 2001 to 2005, HMRC estimated the extent of MTIC fraud on the basis of trade 
data.  The exercise centred on comparing the data on exports to the UK declared in 
other Member States with the imports from other Member States declared in the UK.  
Discrepancies between these two figures are regarded as an upper estimate of MTIC 
as it is thought that there would be other factors, such as the submission of incorrect 
information by traders that could drive a wedge between the two sets of numbers.   

364.  We  understand  that  in  its  2001  and  2002  calculation,  HMRC  produced  a  lower 
estimate of MTIC fraud by applying to the upper estimate a factor based on estimates 
of carousel fraud in Belgium and the Netherlands (HMCE, 2001, page 18).28  Because 
the  factor  only  reflected  carousel  fraud  and  because  it  did  not  take  into  account  the 
fact that VAT registration is easier in the UK than in those two Member States, HMCE 
regarded this as the lower estimate. 

365. 

In 2003, HMRC changed its approach to calculating the lower estimate.  It considered 
that  the  factor  applied,  based  on  estimates  of  carousel  fraud  in  Belgium  and  in  the 
Netherlands in the late 1990s, was unlikely to continue to produce sufficiently robust 
estimates.    Its  revised  methodology  used  a  subset  of  the  data  used  to  estimate  the 
upper  limit.    The  subset  of  data  used  was  such  that  HMRC  believed  that  all  that  it 
captured was MTIC fraud, but that it did not capture all MTIC fraud.  

28  HM Revenue & Customs (2001) “Measuring indirect tax losses”.   

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

366. 

In  2006,  HMRC  changed  its  approach  altogether.29    The  use  of  trade  data  was 
discontinued,  on  the  grounds  that  the  fraudsters’  new  modes  of  operation  rendered 
such an approach “unreliable”.  We share HMRC’s view that trade data are inadequate 
to identify the effect of MTIC: there is simply too much noise in trade data to allow 
this  to  be  the  case.      The  figures  put  forward  by  HMRC  in  2006  were,  instead, 
reported to be “based on operational evidence”; details of what evidence is used and 
how it is used are not provided. 

367.  Table 46 reports the estimates of MTIC for the period 2000/2001 to 2006/2007.   

Table 46  HMRC estimates of attempted MTIC fraud, 2000/2001-2005/2006 

Year 

2000/2001 

2001/2002 

2002/2003 

2003/2004 

2004/2005 

2005/2006 

2006/2007 

Lower Limit 
(£ billion) 

1.31 

1.72 

1.54 

1.06 

1.12 

3.50 (2.0) 

2.25 (1.0) 

Upper Limit 
(£ billion) 

2.47 

2.53 

2.34 

1.73 

1.90 

4.75 (3.0) 

3.25 (2.0) 

Note: All numbers pertain to attempted fraud. Figures in brackets are estimates of the impact on VAT receipts 
Source: HMRC (2005, 2006 and 2007) “Measuring indirect tax losses” 

368.  The  figures  reported  in  Table  46  relate  to  the  value  of  MTIC  fraud  attempted.    For 
2005/2006,  HMRC  estimated  that,  because  some  of  these  attempted  frauds  are 
stopped, the impact of MTIC fraud on actual receipts was between £2 and £3 billion.  
On  the  basis  of  HMRC’s  top-down  estimate  of  the  VAT  gap  for  that  year,  £12.4 
billion, the estimates for MTIC imply that this type of fraud accounted between 16 to 
24 per cent of total VAT fraud. 

Belgian Finance Ministry study using Eurocanet data 

369.  This  study  is  subtitled  “Spread  of  the  phenomenon  [MTIC  fraud]  in  the  EU”.    Its 
focus is on identifying differences between countries as to their vulnerability to MTIC 
fraud. 

370.  The  paper  reports  the  following  elements  as  being  part  of  a  “macro  economic 

approach”: 

(a)  The  larger  economies  (Germany,  UK,  France,  Italy  and  Spain)  are  likely  to  be 
more vulnerable to acquisition fraud since they have a large domestic market. 

(b)  There  is  a  correlation  between  economy  size  and  the  importance  of  electronic 
goods  in  the  economy  which  further  increases  the  risks  of  MTIC  fraud  in  the 
larger countries. 

29  HM Revenue & Customs (2006) “Measuring indirect tax losses”. 

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

(c)  Some  countries  appear  to  make  proportionately  much  greater  VAT  repayments 
than  others,  even  after  adjusting  for  the  importance  of  exports  to  the  national 
economy.    This  is  taken  to  mean  that  the  UK,  France  and  Spain  may  be 
particularly affected by MTIC fraud, over and above the factors noted above. 

(d)  A  Belgian  estimate  of  EUR  1.1  billion  for  the  cost  of  MTIC  fraud  in  2001 
(probably  based  on  operational  data  collected  during 
the  subsequent 
implementation of a carousel fraud prevention strategy, but no source or method 
is  given)  is  used  to  estimate  at  5.1  per  cent  the  proportion  of  intra-community 
trade in goods which is associated with MTIC fraud. 

(e)  The UK HMRC’s top-down estimate of the total VAT gap and estimate of MTIC 

fraud based on trade data. 

371.  These elements are presented as the backdrop for the main contribution of the paper, 
which  is  a  micro-economic  approach  based  on  an  extrapolation  of  data  collected 
through the Eurocanet information exchange network. 

372.  The micro-economic approach is based on the mirror flow method, which compares 
(for individual traders) the exports reported through EC sales list with the information 
provided  by  the  purchasers  about  the  acquisition  VAT  accounted  for.    A  possible 
broader  approach  which  considers  “profiles”  of  transactions  rather  than  individual 
traders is touched on but not described in detail. 

373.  To extrapolate these transaction-specific data to an estimate of carousel fraud for the 

whole economy, the paper relies on two hypotheses: 

(a)  All carousel fraud takes place through methods known to the tax authorities, and 
the proportion of fraud using different methods is the same in the sample of cases 
that have been investigated as for carousel fraud as a whole.  This hypothesis is 
justified by a review of operational experience, in particular the small number of 
modes of operation for carousel fraud that have emerged over the last 10 years. 

(b)  There is no correlation between the mode of operation of a carousel fraud and the 
time it takes to detect it.  This hypothesis is also supported by operational data. 

374.  Based  on  these  hypotheses  and  on  data  about  detected  carousel  fraud  from  the 
Eurocanet  network  (analysed  through  a  mirror  flow  method),  the  Belgian  Finance 
Ministry reports the following results. 

375.  The largest detected fraudulent transactions relate to UK carousel fraud using goods 
traded through Dubai.  These transactions were reported in Eurocanet with a variety 
of destination countries (in particular Spain) but according to the Ministry correspond 
to fraud against UK VAT only. 

376.  Other types of carousel fraud (which are spread across all EU countries) account for 
medium-size  detected  fraudulent  transactions.    The  minority  of  non-fraudulent 
transactions included in Eurocanet are at the smaller end. 

www.reckon.co.uk  

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Section 9: Review of published estimates of VAT fraud and the VAT gap 

377.  Omitting carousel fraud using good traded through Dubai, the VAT lost in the adjusted 
Eurocanet  dataset  is  distributed  as  follows  for  the  five  largest  EU  countries 
(2005/2006 data): 

(a)  The UK bears 25.4 per cent of total EU carousel fraud losses. 

(b)  Spain bears 17.3 per cent of total EU carousel fraud losses. 

(c)  Italy bears 15.7 per cent of total EU carousel fraud losses. 

(d)  Germany bears 13.2 per cent of total EU carousel fraud losses. 

(e)  France bears 10.2 per cent of total EU carousel fraud losses. 

378.  No other country is estimated to bear more than 5 per cent of total EU carousel fraud 

losses. 

379.  These  figures  are  consistent  with  the  review  of  factors  affecting  fraud  (size, 
importance of electronic goods, level of VAT repayments, etc.) reviewed above. 

380.  Adding back the fraud using goods traded through Dubai to the Eurocanet dataset also 
trebles  the  fraud  estimate  for  the  UK  (the  method  assumes  that  all  Dubai-method 
fraud is against UK VAT). 

381.  The  report  also  provides  illustrative  “ceiling”  financial  estimates  for  losses  due  to 
VAT carousel fraud.  These estimates are based on an extrapolation of the Eurocanet 
data using the assumptions that total non-Dubai carousel fraud amounted to EUR 14.8 
billion across the EU. 

382.  This figure is an average of four estimates or assumptions based for the most part on 
trade data matching; as a proportion of GDP, it is higher than HMRC’s estimate (even 
though HMRC’s estimate relates to total MTIC fraud and the EUR 14.8 billion figure 
relates only to non-Dubai carousel fraud).  These assumptions lead to an illustrative 
MTIC fraud loss figure of EUR 8.85 billion for the UK, of which: 

(a)  EUR 3.75 billion is non-Dubai fraud: a 25.4 per cent share of the assumed EUR 

14.8 billion EU-wide figure. 

(b)  EUR  5.1  billion  is  Dubai-method  fraud:  extrapolated  from  the  proportion  of 
Dubai and non-Dubai fraud in the Eurocanet dataset and the assumed EUR 14.8 
billion EU-wide figure for non-Dubai fraud. 

383.  This estimate is several times higher than HMRC’s estimates of MTIC fraud for the 
UK.  We  do  not  find  a  reasonable  basis  on  which  the  study  draws  to  estimate  the 
overall  estimate  of  the  amount  of  MTIC  fraud  that  is,  in  a  subsequent  step  of  the 
analysis, allocated to a set of Member States. 

www.reckon.co.uk  

106 

 
Appendix 

APPENDIX 

Table 47   EU-25 Member States 

Code 

Country 

AT 

BE 

CY 

CZ 

DE 

DK 

EE 

GR 

ES 

FI 

FR 

HU 

IE 

IT 

LT 

LU 

LV 

MT 

NL 

PL 

PT 

SE 

SI 

SK 

UK 

Austria 

Belgium 

Cyprus 

Czech Republic 

Germany 

Denmark 

Estonia 

Greece 

Spain 

Finland 

France  

Hungary 

Ireland 

Italy 

Lithuania 

Luxembourg 

Latvia 

Malta 

The Netherlands 

Poland 

Portugal 

Sweden 

Slovenia 

Slovakia 

United Kingdom 

www.reckon.co.uk  

107 

 
 
Appendix 

Table 48   List of 2-digit CPA products 

01-Products of agriculture, hunting and related services 

02-Products of forestry, logging and related services 

05-Fish and other fishing products; services incidental of fishing 

10-Coal and lignite; peat 

11-Crude petroleum and natural gas; services incidental to oil and gas extraction excluding surveying 

12-Uranium and thorium ores 

13-Metal ores 

14-Other mining and quarrying products 

15-Food products and beverages 

16-Tobacco products 

17-Textiles 

18-Wearing apparel; furs 

19-Leather and leather products 

20-Wood and products of wood and cork (except furniture); articles of straw and plaiting materials 

21-Pulp, paper and paper products 

22-Printed matter and recorded media 

23-Coke, refined petroleum products and nuclear fuels 

24-Chemicals, chemical products and man-made fibres 

25-Rubber and plastic products 

26-Other non-metallic mineral products 

27-Basic metals 

28-Fabricated metal products, except machinery and equipment 

29-Machinery and equipment n.e.c. 

30-Office machinery and computers 

31-Electrical machinery and apparatus n.e.c. 

32-Radio, television and communication equipment and apparatus 

33-Medical, precision and optical instruments, watches and clocks 

34-Motor vehicles, trailers and semi-trailers 

35-Other transport equipment 

36-Furniture; other manufactured goods n.e.c. 

37-Secondary raw materials 

40-Electrical energy, gas, steam and hot water 

41-Collected and purified water, distribution services of water 

45-Construction work 

50-Trade, maintenance and repair services of motor vehicles and motorcycles; retail sale of automotive fuel 

51-Wholesale trade and commission trade services, except of motor vehicles and motorcycles 

52-Retail  trade services, except of motor vehicles and motorcycles; repair services of personal and household 
goods 

55-Hotel and restaurant services 

60-Land transport; transport via pipeline services 

61-Water transport services 

62-Air transport services 

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108 

 
Appendix 

63-Supporting and auxiliary transport services; travel agency services 

64-Post and telecommunication services 

65-Financial intermediation services, except insurance and pension funding services 

66-Insurance and pension funding services, except compulsory social security services 

67-Services auxiliary to financial intermediation 

70-Real estate services 

71-Renting services of machinery and equipment without operator and of personal and household goods 

72-Computer and related services 

73-Research and development services 

74-Other business services 

75-Public administration and defence services; compulsory social security services 

80-Education services 

85-Health and social work services 

90-Sewage and refuse disposal services, sanitation and similar services 

91-Membership organisation services n.e.c. 

92-Recreational, cultural and sporting services 

93-Other services 

95-Private households with employed persons 

www.reckon.co.uk  

109 

 
 
VAT Gap in the EU-25 Member States – Report 2009 

GETTING IN TOUCH WITH THE EU 

In person 

All over the European Union there are hundreds of Europe Direct information centres. You can find the 
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Data can be downloaded and reused for free, for both commercial and non-commercial purposes. 

 
 
 
 
 
 
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