Benjamin Franklin says that nothing can be said to be confident in this world except death and taxes. From this point of view, we cannot say that this quote is entirely accurate. Tax evasion, tax avoidance, and fraud are spreading and becoming much more significant phenomena year by year. Especially while the most significant tax revenue is VAT, and the VAT gap is going to be a much critical issue for states, their governments, and policymakers.
34
According to Stavjaňová (2014), the VAT gap cannot be eliminated. The VAT gap has a broad concept that includes transactions and losses that would be impossible to detect. Therefore, states search for additional revenues to cover these losses. Namely, the problems caused by the VAT gap can be managed, and their effects would be reduced.For this, it is very crucial to develop and support research studies and future studies.
This thesis aimed to examine the effects of the determinants of the VAT gap in 2010 and 2018 and improve the solution of the problem and enrich the literature on the topic by discussing the importance of the determinants. The chosen determinants were real GDP growth, GDP per capita, GINI, final consumption expenditure of households, final consumption expenditure of households and nonprofit institutions serving households, population, VAT on GDP, number of VAT rate, standard VAT rate, share of shadow economy, research and development expenditure, and corruption perception index, whose impact is usually discussed. Based on the reasoning above, the purpose of this study was to answer the following research question:
How did the real GDP growth, GDP per capita, GINI, final consumption expenditure of households, final consumption expenditure of households and nonprofit institutions serving households, population, VAT on GDP, number of VAT rate, standard VAT rate, share of shadow economy, research and development expenditure, and corruption perception index influence on VAT gap in 2018?
The analysis of the influence of several independent variables on the dependent variable, which is the VAT gap, was performed by using the EViews package.
According to the multicollinearity test, which was applied on the cross-sectional regression model, some variables had to drop from the model except corruption perception index (CPI) for the year 2010 and GDP per capita, VAT on GDP for the year 2018 until p-value were significant at a minimum level of 0,05 or more. After applying histogram normality, serial correlation, and heteroscedasticity tests, all the
35
independent variables correlated to an acceptable level. The error terms were normally distributed and homoscedastic.
After running the cross-sectional regression model analysis for 2010, it is found that the CPI has a negative impact on the VAT gap. Namely, this negative correlation means that higher CPI causes the lower VAT gap and lower CPI increases the VAT gap.
According to previous studies, the expected influence of the Corruption Perception Index (CPI) was negative on the VAT gap, which means that the increasing value of the Corruption Perception Index (positive corruption perception) reduces tax evasion or vice versa. Therefore, compared with earlier studies, this thesis obtained no differential correlation between the CPI and VAT gap.
Regarding the cross-sectional regression model analysis for 2018, the VAT on GDP, and the GDP per capita were negatively related to the VAT gap. That means when these variables are decreasing, the influence on VAT GAP will be increasing effect.
The reason why GDP per capita decreases the VAT gap is, according to the literature, the fact that the more developed countries with higher GDP per capita have a better collection of taxes, and the citizen are not so prone to tax evasion. This result was entirely in line with previous research.
On the other hand, the negative relationship between the VAT gap and the VAT on GDP was unexpected as the tax burden should increase the VAT gap, according to some of the authors. However, some other authors, for instance Zídková, 2014, also mention the idea that countries with higher VAT on GDP are more focused on VAT collection and, therefore, have a lower VAT gap. They could be more efficient in VAT collection than the EU member states with a lower share of VAT on GDP.
In general, these results were shown for 2010 and 2018. It implies that the critical effects on the VAT gap could be different year by year. When the CPI is significant for 2010, GDP per capita, and VAT on GDP were crucial for 2018. Therefore, it is
36
meaningful to indicate the influences on the gap for the potential policies to solve the VAT gap issue every year.
Due to Covid-19, the VAT gap, which became more complicated to predict, is a problem that needs attention more than ever. According to European Commission estimation, the overall difference between the expected VAT revenue and the amount actually collected –will reach more than €164 billion ($195 billion) in 2020, up from
€140 billion in 2018. The pandemic, which causes unexpected expenditures in the economy and negatively affects GDP growth, may cause the economy to weaken and decrease tax revenues. Considering that the expenditures of households may decrease along with all these VATs, the expenditure-based tax will undoubtedly be adversely affected. In this respect, every research and analysis about the VAT gap is significant regarding the help to improve the situation.
37
Annex
1. CURRENT VAT’S: RATES, THRESHOLDS, AND REVENUES
VAT Rates (in per cent) Threshold
(in US Dollars) VAT Revenue
Date VAT
Introduced Standard rate Other rates Basic Services Per cent of tax revenue
Per cent of GDP
Belgium 1971 21 1 - 6 - 12 6300 15,1 6,9
Bulgaria 1994 20 30000 27,8 8,6
Czechia 1993 22 5 16000 18,7 7
Denmark 1967 25 1600 19,2 9,7
Germany 1968 16 7 60000 18,3 6,9
Estonia 1992 18 5 16500 23,4 8,5
Ireland 1972 21 0 - 3,6 - 10 -
12,5 64300 32000 22,2 7,2
Greece 1987 18 4 - 8 5700 1900 22,3 7,5
Spain 1986 16 4 - 7 341500 17,6 6,2
France 1968 20,6 2,1 - 5.5 100000 17 7,8
Italy 1973 19 4 - 10 - 16 587200 211380 12,5 5,4
38
Latvia 1992 18 18100 32,1 8,6
Lithuania 1994 18 12500 26,8 8,2
Luxembourg 1970 15 3 - 6 - 12 14800 13,8 5,8
Hungary 1988 25 12 8000 22 8,1
Malta 1995 15 17,5 4,8
Netherlands 1969 17,5 6 16,7 6,9
Austria 1973 20 10 - 12 - 32 8300 19,1 8,5
Poland 1993 22 7 - 12 23000 20,8 7,9
Portugal 1986 17 5 - 12 20600 23,3 8
Romania 1993 18 9 -11 7000 16,1 1,9
Slovenia 1999 19 8 20000 25,6 10,1
Slovakia 1993 23 6 9400 21,5 7,2
Finland 1994 22 6 - 12 - 17 9900 18,1 8,4
Sweden 1969 25 6 - 12 - 21 131000 13,9 7,2
UK 1973 17,5 0 82800 20,1 6,5
Sources: Ebrill, 2001/ National authorities and IMF staff estimates; IMF, World Economic Outlook. IMF, Fiscal Affairs Department database;
International Bureau of Fiscal Documentation (IBFD); International VAT Monitor; and Ernst and Young, VAT and Sales Taxes Worldwide, New York: (John Wiley).
39 2. COMPARING COUNTRIES WITH AND WITHOUT A VAT
Countries with VAT Countries without VAT1
GDP per capita (US Dollars) 7670 3430
Average population (millions) 38 27
Openness2 29 32
Literacy (per cent)3 79 70
Revenue (per cent of GDP):4
General government revenue and grants 29,4 28,8
Central government revenue 19,1 17,5
General government revenue 25,6 18,4
Source: Ebrill, 2001/ IMF staff calculations
1All countries without a VAT in September 1998.
2 Measured as (exports + imports)/(2 x GDP).
3 Data is available only for a subset of countries.
4 Figures are for the 99 countries with a VAT for which revenue data were available in early 2000
40
3. LIST OF SUPPLIES OF GOODS AND SERVICES TO WHICH THE REDUCED RATES REFERRED TO IN ARTICLE 98 MAY BE APPLIED
(1) Foodstuffs (including beverages but excluding alcoholic beverages) for human and animal consumption; live animals, seeds, plants, and ingredients usually intended for use in the preparation of foodstuffs; products normally used to supplement foodstuffs or as a substitute for foodstuffs;
(2) supply of water;
(3) pharmaceutical products of a kind normally used for health care, prevention of illnesses, and as a treatment for medical and veterinary purposes, including products used for contraception and sanitary protection;
(4) medical equipment, aids, and other appliances normally intended to alleviate or treat disability, for the exclusive personal use of the disabled, including the repair of such goods and supply of children's car seats;
(5) transport of passengers and their accompanying luggage;
(6) supply, including on loan by libraries, of books (including brochures, leaflets, and similar printed matter, children's picture, drawing or colouring books, music printed or in manuscript form, maps, and hydrographic or similar charts), newspapers and periodicals, other than material wholly or predominantly devoted to advertising;
(7) admission to shows, theatres, circuses, fairs, amusement parks, concerts, museums, zoos, cinemas, exhibitions, and similar cultural events and facilities;
(8) reception of radio and television broadcasting services;
(9) supply of services by writers, composers, and performing artists, or of the royalties due to them;
(10) provision, construction, renovation, and alteration of housing, as part of a social policy;
(11) supply of goods and services of a kind normally intended for use in agricultural production but excluding capital goods such as machinery or buildings;
41
(12) accommodation provided in hotels and similar establishments, including the provision of holiday accommodation and the letting of places on camping or caravan sites;
(13) admission to sporting events;
(14) use of sporting facilities;
(15) supply of goods and services by organisations recognised as being devoted to social wellbeing by the Member States and engaged in welfare or social security work, in so far as those transactions are not exempt according to Articles 132, 135, and 136;
(16) supply of services by undertakers and cremation services, and the supply of goods related there to;
(17) provision of medical and dental care and thermal treatment in so far as those services are not exempt under points (b) to (e) of Article 132(1);
(18) supply of services provided in connection with street cleaning refuses collection and waste treatment, other than the supply of such services by bodies referred to in Article 13.
42
43
Hungary 21,93 1,1 9960 24,1 53677,9 52587,4 10014324 1,14 8,5 3 25 23,3 4,7
Malta 39,23 5,5 16440 28,6 4512,6 3994,3 414027 0,61 7 2 18 26 5,6
Netherlands 4,89 1,3 38470 25,5 282121 290509 16574989 1,7 6,5 2 19 10 8,8
Austria 9,05
1,8 35390 28,3 158025 158310,
4 8351643
2,73 7,7 4 20
8,2 7,9 Poland 20,62
3,7 9400 31,1 219770,1 222709,
6 38022869
0,72 7,6 3 22
25,4 5,3 Portugal 13,15
1,7 16990 33,7 119946,9 118409,
3 10573479
1,53 7,5 3 20
19,2 6
Romania 41,27 -3,9 6200 33,5 78553,3 80345 20294683 0,46 7,6 2 19 29,8 3,7
Slovenia 9,54 1,3 17750 23,8 21248,3 20422,2 2046976 2,06 8 2 20 24,3 6,4
Slovakia 33,06 5,9 12560 25,9 38576,4 38943,4 5390410 0,62 6,1 2 19 16,4 4,3
Finland 7,12 3,2 35080 25,4 94100 99020 5351427 3,73 8,3 4 22 14 9,2
Sweden 2,61
6 39950 25,5 169848,6 175718,
1 9340682
3,21 9 4 25
15 9,2
UK 10,98
2,1 29830 32,9 1134388,7 1198418
,1 62510197
1,66 6,1 3 17,5
10,7 7,6
44
45 Ireland 2018 10,
6 9 58100 28,9 95850,7 99338,9 4830392 1,15 4,3 5 23 9,7 3,25
Greece 2018 30,
1 1,6 17400 32,3 135432,3 124429,7 10741165 1,18 8,5 3 24 20,81 2,51
Spain 2018 6 2,4 24910 33,2 727128 700310 46658447 1,24 6,6 3 21 16,61 2,14
France 2018 7,1 1,9 32890 28,5 1243065 1273391 67026224 2,2 7,1 4 20 12,32 1,17
Italy 2018 24,
5 0,9 27040 33,4 1077744,5 1066108,2 60483973 1,4 6,2 4 22 19,51 3,29
Latvia 2018 9,5 4 12180 35,6 17134,4 17199,3 1934379 0,63 8,4 2 21 20,24 2,95
Lithuania 2018 25,
9 3,9 13390 36,9 27934,8 27981,8 2808901 0,94 7,7 3 21 22,96 2,3
Luxembour
g 2018 5,1 3,1 83470 31,3 20003,5 17874 602005 1,24 5,9 4 17 7,94 2,32
Hungary 2018 8,4 5,4 12680 28,7 68018,7 67054,4 9778371 1,55 9,5 3 27 22,7 2,79
Malta 2018 15,
1 5,2 21690 28,7 6860,4 5835,7 475701 0,57 7,4 3 18 23,21 1,39
Netherlands 2018 4,2 2,4 41450 27,4 335967 341560 17181084 2,16 6,8 2 21 7,51 2,3 Austria 2018 9 2,6 37800 26,8 199827,8 200077,8 8822267 3,17 7,6 4 20 6,72 1,37 Poland 2018 9,9 5,4 12420 27,8 288325,9 290925,3 37976687 1,21 8,1 3 23 21,74 1,69 Portugal 2018 9,6 2,8 18190 32,1 140437,7 131871,3 10291027 1,37 8,7 4 23 16,13 2,14
Romania 2018 33,
8 4,5 8700 35,1 126731 130487,6 19533481 0,51 6,3 3 19 26,66 3,25
Slovenia 2018 3,8 4,4 20220 23,4 25097,9 23889,3 2066880 1,94 8,2 2 22 22,16 2,37
46
Slovakia 2018 20 3,7 15490 20,9 50056,6 50432,2 5443120 0,83 7,1 2 20 12,83 4,11 Finland 2018 3,6 1,1 36740 25,9 118018 123937 5513130 2,77 9,1 3 24 11,02 2,74 Sweden 2018 0,7 2 43760 27,0 205931,3 215084,9 10120242 3,34 9,2 3 25 11,63 2,02
UK 2018 12,
2 1,3 32640 33,5 1490658 1566265,8 66273576 1,72 7 2 20 9,8 2,03
47 6. ANALYSIS: HISTOGRAM NORMALITY TEST 2010
48 7. ANALYSIS: HISTOGRAM NORMALITY TEST 2018
49 8. ANALYSIS: SERIAL CORRELATION LM TEST 2010
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 1.092.573 Prob. F(2,22) 0.3529
Obs*R-squared 2.349.120 Prob. Chi-Square(2) 0.3090
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Sample: 1 26
Included observations: 26
Pre sample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
Constant -2.544.630 5.348.859 -0.475733 0.6390
CPI 0.406320 0.820890 0.494975 0.6255
RESID(-1) -0.203842 0.228729 -0.891197 0.3825
RESID(-2) -0.274344 0.207713 -1.320.789 0.2001
R-squared 0.090351 Mean dependent var -1.16E-14
Adjusted R-squared -0.033692 SD dependent var 6.905.343
SE of regression 7.020.708 Akaike info criterion 6.876.243
Sum squared resid 1.084.387 Schwarz criterion 7.069.797
Log likelihood -8.539.116 Hannan-Quinn criteria. 6.931.980
F-statistic 0.728382 Durbin-Watson stat 1.932.393
Prob(F-statistic) 0.545985
50 9. ANALYSIS: SERIAL CORRELATION LM TEST 2018
Breusch-Godfrey Serial Correlation LM Test:
F-statistic 4.010.638 Prob. F(2,21) 0.0335
Obs*R-squared 7.186.217 Prob. Chi-Square(2) 0.0275
Test Equation:
Dependent Variable: RESID
Method: Least Squares
Sample: 1 26
Included observations: 26
Pre sample missing value lagged residuals set to zero.
Variable Coefficient Std. Error t-Statistic Prob.
Constant -3.110.983 9.199.203 -0.338180 0.7386
GDP per capita 7.32E-06 7.72E-05 0.094856 0.9253
VAT on GDP 0.382942 1.051.736 0.364105 0.7194
RESID(-1) -0.535236 0.202203 -2.647.030 0.0151
51
RESID(-2) -0.395602 0.205029 -1.929.494 0.0673
R-squared 0.276393 Mean dependent var -6.08E-15
Adjusted R-squared 0.138563 SD dependent var 6.575.516
SE of regression 6.102.975 Akaike info criterion 6.626.471
Sum squared resid 7.821.724 Schwarz criterion 6.868.413
Log likelihood -8.114.412 Hannan-Quinn criteria. 6.696.142
F-statistic 2.005.319 Durbin-Watson stat 2.007.058
Prob(F-statistic) 0.130641
52 10. ANALYSIS: HETEROSKEDASTICITY TEST 2010
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 1.614.455 Prob. F(1,24) 0.2160
Obs*R-squared 1.638.756 Prob. Chi-Square(1) 0.2005
Scaled explained SS 1.479.360 Prob. Chi-Square(1) 0.2239
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Sample: 1 26
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
C 1.019.794 4.610.159 2.212.059 0.0367
CPI -8.909.468 7.011.949 -1.270.612 0.2160
R-squared 0.063029 Mean dependent var 4.584.977
Adjusted R-squared 0.023989 SD dependent var 6.806.295
SE of regression 6.724.163 Akaike info criterion 1.132.827
Sum squared resid 108514.5 Schwarz criterion 1.142.504
Log likelihood -1.452.675 Hannan-Quinn criteria. 1.135.613
F-statistic 1.614.455 Durbin-Watson stat 2.367.948
Prob(F-statistic) 0.216046
53 11. ANALYSIS: HETEROSKEDASTICITY TEST 2018
Heteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 0.627662 Prob. F(2,23) 0.5427
Obs*R-squared 1.345.619 Prob. Chi-Square(2) 0.5103
Scaled explained SS 1.389.693 Prob. Chi-Square(2) 0.4992
Test Equation:
Dependent Variable: RESID^2
Method: Least Squares
Sample: 1 26
Included observations: 26
Variable Coefficient Std. Error t-Statistic Prob.
Constant 1.000.762 1.046.538 0.956259 0.3489
GDP per capita -0.000972 0.000873 -1.113.924 0.2768
VAT on GDP -4.074.801 1.193.694 -0.341361 0.7359
54
R-squared 0.051755 Mean dependent var 4.157.444
Adjusted R-squared -0.030702 SD dependent var 6.888.140
SE of regression 6.993.079 Akaike info criterion 1.144.106
Sum squared resid 112477.2 Schwarz criterion 1.158.622
Log likelihood -1.457.337 Hannan-Quinn criteria. 1.148.286
F-statistic 0.627662 Durbin-Watson stat 1.766.527
Prob(F-statistic) 0.542736
55
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