1

Value Added Tax Effort

and the Influence of Governance

Paper presented at the

VAT in Developing Countries: Policy, Law and Practice Symposium

October 2016.

Marius van Oordt

Abstract

Many countries require additional tax revenues towards sustained development. Additional

revenue can be generated by increasing value-added tax capacity and/or value-added tax effort.

In this paper the regression approach to estimate tax effort is adopted in estimating value-added

tax effort. This is done by estimating a two-way fixed effects model for a panel of 131 countries

over a period of 11 years with control for economical and institutional value-added tax capacity

factors. The objectives of this approach is to provide the first empirical results on the influence

of governance on value-added tax performance and also to provide the first value-added tax

effort indexes in the literature.

African Tax Institute, University of Pretoria, South Africa The author is grateful for the inputs and comments of Marie Pallot (Inland Revenue, NZ), Roy Bahl (The Andrew Young School of Policy Studies, USA) and Phil Whittington (Inland Revenue, NZ).

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1. Introduction

Due to a global decline in corporate income tax revenues1 and decreases in aid to developing

countries, many countries may well need additional tax revenues to support their fiscus and

allow for further development. The value-added tax (VAT from hereon) is the broad base

consumption tax of choice in over 160 countries and for revenue raising purposes is “generally

regarded to be an easier tax handle and less detrimental to economic growth than the income

tax”.2 Many countries may therefore consider increasing their VAT performance and thereby

the amount of revenue generated by the VAT.

A country’s VAT performance, measured in terms of a VAT ratio,3 can be increased by

increasing the VAT capacity and / or the VAT effort of that country. The VAT capacity of a

country refers to the economic and institutional environment within which the VAT is applied.

VAT capacity can therefore be increased by changes in the economic and institutional

environment that benefit VAT performance. Although the economic environment of a country

may be difficult to change over the short term, e.g. the level of development or the size of the

informal sector4, the institutional environment may perhaps see for faster change, e.g.

decreasing corruption or increasing efforts to enforce the law.19

The VAT effort of a country can be increased by broadening the base of the VAT, increasing

the rate of the VAT or by increased efforts to improve compliance to the VAT. With many

countries VATs riddled with exemptions and zero rates that result in less efficient and neutral

VATs, broadening the base of the VAT would provide for additional benefits besides additional

revenue.5 Base broadening can therefore generally be preferred to increases in the VAT rate,

although such increases may also be required in certain countries towards additional revenue.

Towards increasing VAT performance, it is therefore of importance for policy makers to

understand how their country’s VAT effort compares to other countries.

1 SD Dyreng, M Hanlon, EL Maydew and JR Thornock, “Changes in Corporate Effective Tax Rates Over the Past 25 Years”, 2016, Journal of Financial Economics (forthcoming) show for instance that in the United States of America the effective corporate tax rates has significantly decreased over the past 25 years. 2 S Cnossen, “Mobilizing VAT revenues in African countries”, 2015 22(6), International Tax and Public Finance, at 1080. 3 There are other methods to measure VAT performance that the VAT ratio approach adopted in this paper. Section 2 provide additional discussion on this methodological decision. 4 For purposes of this paper, informal sector can be taken to mean the part of the economy not included in the gross domestic product of a country. 5 Exemptions and some zero rates provide for economic distortions, increased compliance and administrative costs, non-neutral treatment between certain goods and higher probability of evasion under the VAT.

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This contributions of this paper are twofold. The relationship between governance and VAT

performance has to the best of my knowledge not been empirically estimated in the literature.

In this paper I provide estimates of the influence of governance on VAT performance with the

use of a two-way fixed effects model applied to a dataset of 131 countries over a period of 11

years. The results are provided for a single measure of governance and also for six underlying

factors of governance. Further, the results are provided for all countries, developing countries,

and transitional and developed countries.

The second contribution of the paper is in providing ranked indexes of VAT effort for 131

countries. Although many tax effort studies have been published, no study could be found that

consider the tax effort of a specific tax. In other words, the VAT effort indexes provided are

the first of their kind. These indexes should be useful to policy makers in determining the

potential for additional VAT effort and thereby VAT revenues.

In the remainder of the paper I first discuss the measurement of VAT performance used,

together with providing a priori justifications for the VAT capacity factors included in the

paper. This is followed by a discussion of the methodology applied and the results of this paper,

including the VAT effort indexes. Hereafter the paper is concluded.

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2. VAT ratio, VAT capacity and VAT effort

For purposes of this paper the terms VAT ratio, VAT capacity and VAT effort are associated

with similar terms used in past tax effort studies. However, the unique aspects of the VAT

should be considered when defining and measuring these terms. The findings and methods of

previous tax efforts studies cannot be simply applied to a VAT effort study6 and no previous

VAT effort study has been published.

2.1 VAT ratio

For tax effort studies, the tax ratio is almost exclusively defined as

(1)

where is total tax revenue and is GDP. The idea behind this approach is to provide a

measure of the ratio of actual tax collected to potential tax, using GDP as the base. A

researchers may be inclined to use GDP as a proxy for the VAT base in calculating VAT ratios,

but this would not be ideal. In calculating national accounts, GDP is equal to total value added

in the economy plus indirect taxes less subsidies. Since VAT is not charged on the VAT

included in GDP, this approach would not provide an accurate proxy for potential VAT.

Another possible proxy for the VAT base is total value added in the economy (as used in

calculating GDP). Total value added is calculated as value added less intermediate

consumption. Since output VAT is charged on value added and input VAT can be claimed on

intermediate consumption, the VAT base seems to be captured on first appearance. The issue

in using this method lies in the standard calculation of total value added in national accounts;

investment type expenditure is excluded when calculating intermediate consumption. Under a

consumption type VAT, the type of VAT found in nearly all jurisdictions, 7 input VAT can be

claimed on investment expenditure, meaning total value added would not be an accurate proxy

for the VAT base.

However, when calculating total consumption expenditure in national accounts, investment

type expenditures are included in intermediate consumption (which is deducted). Further,

exported goods and services, which are zero rated under all VATs, are excluded from this

6 This is for instance incorrectly done by J-F Brun and M Diakite, “Tax Potential and Tax Effort : An Empirical Estimation for Non-resource Tax Revenue and VAT’s Revenue” Unpublished paper, the only other study that considers tax effort for the VAT. 7 The only exclusion to this is China, which is currently reforming its VAT to a consumption type VAT.

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calculation and imported goods and services, on which import VAT is charged, are included in

this calculation. The only issue in using total consumption expenditure as a proxy for the VAT

base is in the treatment of resident consumption of non-investment goods and services in other

jurisdictions. This is included in total consumption expenditure, but is not included in the VAT

base. It is however submitted that this is likely to be a small amount in national accounts and

total consumption expenditure is therefore preferred as a proxy for the VAT base. The VAT

ratio is therefore written as

(2)

Where is VAT revenue and is total consumption expenditure of households and

government. Government consumption is specifically included since many experts argue that

goods and services supplied by government entities, public sector bodies, non-profit

organisations, charitable organisations and similar tax-exempt bodies should be fully taxed

under the VAT.8 It should also be noted that the VAT ratio is not rate controlled, as would be

the case in determining a C-efficiency measure. This is so as the effective tax rate traditionally

forms part of tax effort and this is to be measured in this paper.

2.2 VAT capacity and VAT effort

The variance in the VAT ratios of countries can be theoretically divided into the variance

attributable to VAT capacity and the variance attributable to VAT effort .9 A model

to estimate VAT effort can therefore be written as

, , … , , (3)

where are independent variables that only affect VAT capacity and the residual is a

measure of VAT effort.10 This model therefore required an a priori justification for each

independent variable included since arguable measures of VAT capacity will significantly

decrease the validity of the estimation. The remainder of this section is therefore concerned

8 Refer to R de la Feria, “The EU VAT treatment of public sector bodies: Slowly moving in the wrong direction”, (2009) 37 Intertax 148 and P-P Gendron, “How should the United States treat government entities, non-profit organisations and other tax-exempt bodies under a VAT?”, (2010) 62(11) Tax Law Review 477 for further discussion on this. 9 RW Bahl, "A Regression Approach to Tax Effort and Tax Ratio Analysis”, (1971) 18(3) Staff Papers (International Monetary Fund) 570. 10 It is an important assumption of this model that included VAT capacity factors only influence VAT capacity and not also VAT effort. This assumption often do not hold and it is then assumed that the VAT capacity factors primarily influence VAT capacity. This assumption is also made for purposes of this paper.

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with providing the required a priori justifications for the independent variables included in this

paper.

2.2.1 Governance

When considering VAT capacity, a potential significant variable is governance.11 Due to the

often perceived regressivity of the VAT, the VAT carries a particular political sensitivity. This

becomes evident in the zero ratings or exemptions applied to basic foodstuffs and merit goods

in most VAT systems. As alluded to by Crawford, Keen and Smith:

“The survival of zero-rating of food and children’s clothing appears simply to reflect

politicians’ doubts of their ability to explain why a package involving its removal need

not have a regressive impact…”12

The relation between VAT capacity and performance is also noted by Bird and Gendron13

where they state that “the performance of the VAT in any country inevitably reflect politics”

and the nature of a country’s political institutions. Nearly every aspect of the design of a VAT

would be suspect to political influence.14 It is further not only in the design of the VAT system

that governance could influence, but also in the compliance to the VAT. As shown by Torgler,15

and Alm and Torgler16, among many others, there is a clear correlation between governance

factors and willingness to pay taxes.17 Improved governance should therefore results in

improved VAT performance.

Although the influence of governance on the design, compliance and overall performance of a

VAT have received much theoretical attention in the literature,18no study could be found that

empirically estimate the relationship between VAT performance and governance. Measures of

governance has, however, been included in a tax effort study by Bird and Martinez-Vazquez19.

In this study governance variables in the form of corruption and voice and accountability were

11 Governance for the purpose of this paper means the institutional environment within which the VAT is operated. 12 I Crawford, M Keen and S Smith, “Value Added Tax and Excises” in J Mirrlees, S Adam, T Besley et al, Dimensions of Tax Design: the Mirrlees Review, Oxford University Press, 2010, 275 at 300. 13 RM Bird and P-P Gendron, The VAT in Developing and Transitional Countries, Cambridge University Press, 2007 at 193. 14 L Ebrill, M Keen, J-P Bodin and V Summers, The Modern VAT, International Monetary Fund, 2001. 15 B Torgler, “Tax Morale and Direct Democracy”, (2005) 21 European Journal of Political Economy 525. 16 J Alm and B Torgler, “Culture Differences and Tax Morale in the United States and Europe”, (2006) 27 Journal of Economic Psychology 224. 17 This is also discussed in depth by B Torgler, Tax Compliance and Tax Morale: A Theoretical and Empirical Analysis, Edward Elgar, 2007. 18 One of the earlier theoretical contributions in this regard is WJ Turnier, “Designing an Efficient Value Added Tax”, (1984) 39 Tax Law Review 435. 19 RM Bird and J Martinex-Vazquez, “Tax Effort in Developing Countries and High Income Countries: The Impact of Corruption, Voice and Accountability”, (2008) 38(1) Economic Analysis & Policy 55 at 56.

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included as tax capacity factors and both these variables were found to be significant. This led

the authors to conclude that improving governing institutions would increase tax performance.

It is of interest in this paper to determine whether a similar statement can be made regarding

VAT performance.

2.2.2 Level of development and the size of the foreign trade sector

In tax effort studies, two factors have been nearly consistently used and showed to be

significant in explaining differences in tax performance. The first is the level of development

of a country, measured by per capita income in early studies20 and more recently measured as

per capita GDP.19 The level of development can also be expected to be a VAT capacity factor

since an increase in development should increase the capacity to pay and collect VAT and

thereby VAT performance.19 Further, it has been shown that the level of development is

associated with the size of the informal sector which is, as discussed extensively by Keen,21 a

VAT capacity factor. The level of development, measured as per capita GDP, is therefore

included in this paper as a VAT capacity factor.

The second factor predominantly found in the tax effort literature is the size of the foreign trade

sector (also called openness), measured as the ratio of exports plus imports to GDP. Since in

applying the destination principle, exports are zero rated under all VATs and imports are taxed,

the size of the foreign trade sector should be a significant VAT capacity factor. It should,

however, be noted that in using final consumption expenditure to determine VAT performance

(as discussed above), exports and imports are already controlled for. An additional measure for

the size of the foreign trade sector is therefore not included in this paper as a VAT capacity

factor.

2.2.3 Financial services

Financial services, which include deposits, lending, issuance of financial securities, long term

insurance, brokerage, advisory services and many other services “is the major remaining

frontier for the value added tax”.22 Although many methods has been suggested to tax the

intermediation services supplied by financial institutions under the VAT, the proposed methods

are either not conceptually correct or inefficient.13 Some methods such as the addition method

applied in Isreal, France and Denmark, or the subtraction method provide a fairly accurate

20 JR Lotz and ER Morss, “A Theory of Tax Level Determinants for Developing Countries”, 1970 18 Economic Development and Cultural Change, 328. 21 M Keen, “VAT Attacks!”, 2007 14 International Tax and Public Finance, 365. 22 P-P Gendron, “VAT treatment of financial services: Assessment and policy proposal for developing countries”, 2008 62 Bulletin for International Tax, at 494.

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proxy for the value of intermediation services, but cannot be applied on a transaction-by-

transaction basis. This means that these methods are not conceptually correct, since the VAT

is a transaction tax. Also, no deduction of input VAT would be available to businesses under

these methods, resulting in tax cascading.

Other methods, such as the cash-flow approach show promise in correctly taxing the value

added by intermediation services on a transaction-by-transaction basis, but is seems likely that

the administrative and compliance costs associated with this approach are large. The

New Zealand approach of zero rating business to business core financial services23 has the

desirable property of removing any tax cascading, but can potentially provide an administrative

challenge in distinguishing businesses from consumers, particularly for developing countries.

There may also be a loss of revenue, compared to exempting financial services, which may see

for difficult adoption by other countries.

Since identifying and separating the intermediation charge from the full margin of financial

services efficiently and practically remains problematic,24 in most instances it is advised to

exempt non-fee based financial services from the VAT.14 Since additional VAT effort cannot

see the inclusion of non-fee based financial services in the base of the VAT, such services are

VAT capacity factors. To capture this factor, domestic credit by financial institutions as a

percentage of GDP is included as a VAT capacity variable in the estimations performed in this

paper.25

2.2.4 Agriculture

From a theoretical perspective, agriculture should not be taxed any differently to other standard

rated supplies of goods and services under the VAT. In developing countries where collection

from and administration of rural farmers provide a challenge, efficiency concerns may provide

an argument for non-standard treatment of agriculture. This argument is, however, unlikely to

hold in most countries.14

Political considerations together with distributional concerns has however seen for an

exemption of agriculture under most VATs. This exemption is often accompanied by a zero

rate of typical agricultural inputs and also a zero rate of basic foodstuffs, primarily produced

23 Supplies of financial services to consumers are exempt under this approach. 24 R de la Feria and R Krever, Ending VAT exemptions: Towards a post-modern VAT. Oxford University Centre for Business Taxation, 2010. 25 It is not self-evident whether the inclusion of the exemption of financial services would see for an increase or decrease in VAT revenue and therefore VAT performance. It may possibly be that the non-deductible input VAT exceed the amount of VAT that would be collected if the intermediation service was charged with VAT.

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in the agricultural sector. Although strong arguments can and have been made against this

treatment of the agricultural sector26, likely due to political influence the agriculture sector

continue to avoide full taxation under the VAT. It is therefore argued for purposes of this paper

that increased VAT effort will not see for the inclusion of agriculture in the VAT base.

As previously discussed, the size of the informal sector is a VAT capacity factor. Since

insufficient data was available to include a specific measurement of the informal sector in the

analysis of this paper, it is argued that the size of the agricultural sector can serve as a proxy

for the size of the informal sector. To establish this argument I provide a Pearson correlation

between the value added by the agricultural sector to GDP and measurements of the informal

sector obtained from Schneider, Buehn and Montenegro27 for the period 2004 – 2007. The

results of this correlation are provided in Table 1.

Table 1: Pearson correlation between Agriculture to GDP and the Informal sector

Agriculture to GDP Informal sector

Pearson Correlation 1 0.575***

N 380 379

*** indicates significance at the 1% level.

From Table 1 it can be seen that Agriculture to GDP and the Informal sector is fairly strongly

positively correlated and that the correlation is significant. Agriculture to GDP should therefore

serve as an adequate proxy for the informal sector. It should also be noted that due to

multicollinearity it may not have been preferred to include a separate measure for the informal

sector in addition to Agriculture to GDP. This would likely further be the case due to an

expected negative correlation between GDP per capita and the size of the informal sector (as

discussed previously). It is therefore argued that Agriculture to GDP and GDP per capita

together capture the informal sector as a VAT capacity factor. Agriculture is therefore a VAT

capacity factor and it can be expected that VAT performance decrease as the size of the

agricultural sector increase.

26 The most convincing is perhaps that distributional concerns regarding the VAT should rather be addressed on the expenditure side of the budget. For an in depth study in this regard refer to ML van Oordt, A quantitative measurement of policy options to inform value-added tax reform in South Africa, PhD Thesis, University of Pretoria, Pretoria, 2016. 27 F Schneider, A Buehn and CE Montenegro, Shadow Economies All over the World: New Estimates for 162 Countries from 1999 to 2007, 2010, Available http://www.econ.uchile.cl/uploads/publicacion/ed606fb59ee288ea3f8bae853b853d81d832736e.pdf.

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2.2.5 Population growth

There are two possible argument for the inclusion of population growth as a VAT capacity

factor. The first is that population growth may see for an increase in VAT registered persons

who would have to be administered.28 An increase in VAT registered persons would likely

increase VAT performance, since more goods and services are subjected to VAT. The second

is that population growth will see for an increase in the number of consumers and a change in

behaviour of the average consumer.29 If the VAT is not applied to all goods and services in the

economy at a single rate, such behaviour changes could see for an increase or decrease in VAT

performance.

28 RW Bahl, Reaching the Hardest to Tax: Consequences and Possibilities in JR Alm, J Martinez-Vazquez and S Wallace (ed.) Taxing the Hard-to-tax: Lessons from Theory and Practice (Contributions to Economic Analysis, Volume 268) Emerald Group Publishing Limited, 2005 makes a similar argument for tax effort studies. 29 The average consumer would for instance be younger where a population has grown significantly in recent years.

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3. Methodology

In this paper a regression approach to tax effort analysis, as first used by Lotz and Morss30 and

further developed by Bahl,9 was adapted and applied in estimating the VAT effort and the

influence of governance for countries. A panel dataset consisting of 133 countries with a VAT,

over a period of 11 years (2004-2014) was used for this purpose. At the time of writing, the

VAT was applied in 161 countries around the world.31 A country was only excluded from the

sample if either that country did not have a VAT or insufficient data was available for that

country.

3.1 Fixed or random effects?

A random effects model or a fixed effects model is generally used in estimating tax effort with

the use of panel data. Since the panel dataset used for purposes of estimation only covers a

period of 11 years, the decision between these two models is important. As noted by Hsiao,32

the decision between these two models for a panel with a finite time period “can make a

surprising amount of difference in the estimates of the parameters”.

Many researchers have used the Hausman test as a model selection test, but this is not correct.

As stated by Baltagi,33 “(u)nfortunately, applied researchers have interpreted a rejection as an

adoption of the fixed effects model and a nonrejection as an adoption of the random effects

model.” This test is important in determining whether the assumptions of an error component

regression model holds, but if the test is not rejected it does not indicate that the random effects

model can be preferred.

One guideline towards selecting the correct model is the method of sample selection used.

Generally, a fixed effect model is appropriate if the study is focused on drawing inference on

a specific set of cases (i.e. only the countries included in the sample in the case of this paper).

Alternatively, if random sampling from a large population is applied the random effects model

is appropriate (i.e. to make inference of a larger population).3332

For purposes of this paper, however, the main determinant in model selection lies in

differentiating between VAT capacity and VAT effort. VAT capacity can be influenced by

30 JR Lotz and ER Morss, "Measuring "Tax Effort" in Developing Countries", (1967) 14(3) Staff Papers (International Monetary Fund) 478. 31 Refer to A Schenk, V Thuronyi and W Cui, Value Added Tax: A comparative approach, 2nd edition, Cambridge University Press, 2015, 531-535 for a list of countries with a VAT. The United Arab Emirates is expected to introduce a VAT in 2018. 32 C Hsiao, Analysis of Panel Data, 2nd edition, Cambridge University Press, 2003, at 41. 33 BH Baltagi, Econometric Analysis of Panel Data, 3rd edition, John Wiley & Sons Ltd, at 19.

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both fixed and variable factors. For instance, a country’s type of political and legal system is

likely to be fixed over the period of estimation, but the extent of corruption within the political

system is likely to vary. VAT effort is only variable, there are no fixed factors that influence

VAT effort. For instance, the amount of VAT audits is an indicator of effort, but the audit

system forms part of VAT capacity. Effort will include the improvement of the audit system,

but once in its improved state this system is a capacity factor. Also, the rate and base of the

VAT may be fixed over the period of estimation, but it is the effective VAT rate that is the

VAT effort variable (and is related to VAT performance).

Since any fixed effect can only be associated with VAT capacity, it would not be ideal to use

a random effects model. This is true since the residual that is a measure of VAT effort would

include some fixed factors associated with VAT capacity. Further, both country and year fixed

effects would be associated with VAT capacity. A two-way fixed effects model was therefore

estimated.

3.2 Data and variables used

Data on the variables used for estimation purposes was collected from multiple sources. These

include the International Monetary Fund’s (IMF) International Financial Statistics dataset,34 the

IMF Government Financial Statistics dataset, the IMF World Revenue Longitudinal dataset,

the World Bank’s (WB) World Development Indicators dataset,35 the WB Worldwide

Governance Indicators dataset,36 the United Nations’ (UN) National Accounts Official Country

dataset,37 the Organisation for Economic Cooperation and Development’s (OECD) National

Accounts datasets well as many countries published National Accounts and Public Finance

Reports (or similar reports). For the majority of the variables used, data from multiple sources

where compared to ensure accuracy and consistency within the data.

The dependant variable for purposes of analysis, VAT ratio, was calculated as the ratio between

VAT revenue and final total consumption expenditure (refer to section 2.1). VAT revenue data

was obtained from a comparison of the International Monetary Fund’s (IMF) Government

Financial Statistics dataset, 38 the IMF’s World Revenue Longitudinal dataset, 39 the

Organisation for Economic Cooperation and Development’s (OECD) Statistics Database40 and

34 Available at http://www.imf.org/en/Data. 35 Available at http://data.worldbank.org/data-catalog/world-development-indicators. 36 Available at http://info.worldbank.org/governance/wgi/index.aspx#home. 37 Available at http://data.un.org. 38 Available at http://www.imf.org/en/Data. 39 Available at http://data.imf.org/?sk=77413F1D-1525-450A-A23A-47AEED40FE78. 40 Available at stats.oecd.org.

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various countries’ Public Finance Reports (or similar reports).41 In the few instances where

discrepancies were identified in the value of VAT revenue, the most reasonable value, with

reference to the VAT ratio, was kept. If more than one value seemed reasonable, the lower

value was kept. Final consumption expenditure were obtained from the World Bank’s (WB)

World Development Indicators dataset, 42 the United Nations’ (UN) National Accounts Official

Country dataset,43 and countries’ published National Accounts. Table 2 provides summary

statistics for the VAT ratio variable.

Table 2: Summary statistics of VAT ratio variable

Variable N Mean Std Dev. Min Max

VAT ratio 120844 7.741 3.084 0.176 15.995

This variable of interest (refer to Section 2.2) is governance. The WB Worldwide Governance

Indicators dataset includes six variables on governance. These variables are obtained from the

perceptions of a large number of enterprises, citizens and expert survey respondents. Table 3

provides these variables of governance, including a description of each variable.

Table 3: Worldwide Governance Indicators variables45

Variable Description

Political stability and Absence of violence

Perceptions of the likelihood of political instability and/or politically motivated violence, including terrorism.

Control for corruption Perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as "capture" of the state by elites and private interests.

Regulatory quality Perceptions of the ability of the government to formulate and implement sound policies and regulations that permit and promote private sector development.

Government effectiveness

Perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government's commitment to such policies.

Rule of law Perceptions of the extent to which agents have confidence in and abide by the rules of society, and in particular the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence.

Voice and accountability

Perceptions of the extent to which a country's citizens are able to participate in selecting their government, as well as freedom of expression, freedom of association, and a free media.

41 These reports were only referred to in cases where the previously mentioned datasets did not contain any data for a specific country. 42 Available at http://data.worldbank.org/data-catalog/world-development-indicators. 43 Available at http://data.un.org. 44 Outliers were removed from the dataset taking into account their studentized residuals, leverage and Cook’s distance. An outlier was only removed in the case where it was apparent that an outlier resulted from a data error. 45 These descriptions are available from http://info.worldbank.org/governance/wgi/index.aspx#doc.

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Each of the variables in Table 3 can take a value between -2.5 and 2.5, with a higher value

indicating better governance. The variance inflation factors for the six variables in Table 3

showed a high degree of multicollinearity. For estimation purposes it was therefore required to

transform these six variables into a single variable, referred to as Governance. This was done

by sorting the six variables individually from lowest to highest, providing a rank, and

calculating an average rank over the six variables for each country. Table 4 provides the

summary statistics for the Governance variable.

Table 4: Summary statistics of Governance variable

Variable N Mean Std Dev. Min Max

Governance 1208 6.045 3.264416 0.06 11.975

GDP per capita, value added by the agricultural sector as a percentage of GDP, as well as

population growth variables were obtained from the WB’s World Development Indicators

dataset. The first two mentioned variables were significantly positively skewed and non-linear.

To improve the normality46 and the linearity of these variable, they were log transformed. The

population growth variable was not transformed.

Domestic credit provided by financial institutions were obtained from the WB’s World

Development Indicators dataset. GDP was obtained by combining data from the UN’s National

Accounts Official Country dataset and the IMF’s International Financial Statistics dataset. The

variable derived, domestic credit by financial institutions as a percentage of GDP, was slightly

positively skewed and non-linear. To correct for this, this variable was square root transformed.

The summary statistics of the log transformed GDP per capita variable (GDP per capita from

hereon), the log transformed value added by the agricultural sector as a percentage of GDP

variable (Agriculture to GDP from hereon), the log transformed population growth variable

(Population growth from hereon) and the square root transformed domestic credit by financial

institutions as a percentage of GDP variable (Domestic credit to GDP from hereon) are

provided in Table 5.

Table 5: Summary statistics of remaining variables.

Variable N Mean Std Dev. Min Max

GDP per capita 1208 8.700 1.488 5.086 11.666

Agriculture to GDP 1208 1.842 1.119 -1.272 4.022

46 Although this is not an estimation assumption, the T-test conducted assumes that the sample is drawn from a normally distributed population. Correcting the normality of the variable should therefore increase the reliability of the T-test.

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Domestic credit to GDP

1208 11.001 2.792 3.925 20.744

Population growth 1208 4.077863 1.067118 .3713436 8.321578

3.3 Estimation and post estimation

For purposes of this paper the two-way fixed effects model that includes the governance

variable can be written as

(4)

Where 1, … , ; 1,… , ; = Governance; = GDP per capita; =

Agriculture to GDP; = Domestic credit to GDP; = Population growth; =

unobservable country effects; = unobservable year effects and = the remainder error term

taken as VAT effort. is estimated by way of within transformation and by including binary

variables.47

(4) was estimated for all countries in the sample, only developing countries, and transitional

and developed countries. Country classification was based on the UN classification.48 From the

sample of 133 countries, 74 countries are classified as developing countries, 13 countries are

classified as transitional countries and 46 countries are classified as developed countries.

In addition to the estimation in (4), estimations were performed on the variables of Governance

as per the Worldwide Governance Indicators dataset (refer to Table X). Due to multicollinearity

between these variables, (4) was estimated, but exchanging the Governance variable for one

variable of Governance (i.e. control of corruption, regulator quality etc.). This was done to gain

an understanding of the specific aspects of governance that influence VAT performance

significantly. These estimations were again done for all countries, developing countries, and

transitional and developed countries.

Since tests for outliers, linearity and multicollinearity49 were performed pre-estimation, post

estimation included tests for normality of the error terms, homoscedasticity and serial

correlation. Although the error terms were normally distributed, there existed some

47 Although it is possible to perform within transformation of both effects in a two-way fixed effects model with an unbalanced panel (see TJ Wansbeek and A Kapteyn, “Estimation of the error components with incomplete panels”,1989 41 Journal of Econometrics, 341) this transformation is computationally difficult on would not provide for different results than the approach followed. 48 Available from http://www.un.org/en/development/desa/policy/wesp/. 49 The variance inflation factors for all variables in the models were tested and did not show significant multicollinearity.

16

heteroscedasticity in the error terms.50 Further, the Wooldridge test51 for serial correlation in

panel data indicated the presence of serial correlation in the error terms. To address the

violation of these assumptions of the fixed effects model, (4) was first estimated with

conventional standard errors and then estimated with country clustered robust standard errors.

For purposes of the estimations performed in this paper, the country clustered robust standard

errors address both the homoscedasticity and serial correlation violations and provide the most

efficient results.

50 Both these results were obtained by visually inspecting the residuals. 51 JM Wooldridge, Econometric Analysis of Cross Section and Panel Data. MIT Press, 2002.

17

4. Results

In this results section I first provide in Table 6 the results of equation (4) for all countries, with

conventional standard errors and country clustered robust standard errors.

Table 6: Two way fixed effect model for all countries

Variable All countries

CSE

All countries

CCRSE

Governance 0.241*** (0.067)

0.241** (0.115)

GDP per capita 0.412** (0.179)

0.412 (0.418)

Agriculture to GDP 0.025 (0.197)

0.025 (0.320)

Domestic credit to GDP 0.104*** (0.035)

0.104* (0.056)

Population growth 0.224*** (0.072)

0.224* (0.121)

N x T 1208 1208

*** indicates significance at the 1% level; ** indicates significance at the 5% level; * indicates significance at the 10% level; CSE is conventional standard errors and CCRSE is country clustered robust standard errors.

From Table 6 it is evident that Governance is a significant factor of VAT performance for the

131 countries in the panel. The VAT ratio can be expected to increase by 0.241 percent for

every additional point (between 0 and 12) increase in Governance. This means that on average,

for every additional point increase in Governance a country would increase VAT revenue by

4.81 percent, which is a significant amount of revenue.52 It can further be seen that this result

is robust and remains statistically significant at the 5% level after calculating country clustered

robust standard errors.

The result for GDP per capita is as expected. The more developed a country is, the higher their

VAT ratio. Agriculture to GDP is not significant and close to zero, indicating that when

controlling for the other variables in the estimation, the size of the agricultural sector does not

have a significant effect on VAT performance. This result is potentially due to the relation

between the level of development, the size of the informal sector and the size of the agriculture

sector.

The result for domestic credit to GDP indicates that increased domestic credit provided by

financial institutions will increase VAT performance. This result supports the hypotheses that

52 This is calculated by adding 0.241 to the VAT ratio of each country, recalculating the VAT revenue required to meet the adjusted VAT ratio and averaging this required VAT revenue for all countries.

18

a greater amount of VAT revenue is raised by exempting financial services and not allowing

input VAT deductions, than would be the case if the value added by the intermediation service

could be practically taxed. This of course does not mean that the exemption can and should be

preferred. This result is also robust and statistical significant at the 10% level when calculating

country clustered robust standard errors.

Population growth is also statistically significant and robust. This results indicates that the

greater the rate of population growth, the better the VAT performance of a country can be

expected to be. This is potentially due to a larger amount of persons registered for VAT and

changes in consumption patterns.

In Table 7 I provide the results of equation (4) for developing countries, and transitional and

developed countries.

Table 7: Two way fixed effects models for developing countries, and transitional and developed countries

Variable Developing countries Transitional and Developed countries

Governance 0.121 (0.084)

0.339*** (0.106)

GDP per capita -0.498** (0.238)

1.04*** (0.314)

Agriculture to GDP 0.082 (0.264)

-0.240 (0.311)

Domestic credit to GDP 0.289*** (0.065)

0.053 (0.046)

Population growth 0.134 (0.090)

0.200* (0.115)

N x T 648 560

*** indicates significance at the 1% level; ** indicates significance at the 5% level; * indicates significance at the 10% level.

From the results in Table 7, it is evident that Governance plays a larger role in the VAT

performance of transitional and developed countries than in developing countries. The results

in Table 8 below provide additional information in understanding this result.

Further, from Table 7 it can be seen that GDP per capita is negatively correlated with VAT

performance for developing countries. This means that when only considering developing

countries, a lower level of development is related to better VAT performance. It is interesting

to note that a similar result, however when considering tax effort and not VAT effort, was

obtained for developing and transitional countries by Bird and Martinez-Vazquez.19 Further

research may be needed to understand why less developed developing countries have better tax

19

and VAT performance than more developed developing countries. It is further clear that for

transitional and developed countries, more developed countries can be expected to have better

VAT performance.

Agriculture to GDP is again not statistically significant, however for transitional and developed

countries the result is not as close to zero than the same variable considered for all countries.

Agriculture to GDP is also negatively correlated with VAT performance for transitional and

developed countries, the a priori expected result. Domestic credit remains statistically

significant for developing countries and positively correlated with VAT performance.

Population growth also remains statistically significant for transitional and developed countries

and positively correlated with VAT performance.

Next, in Table 8, I provide estimated of equation (4), but after replacing the Governance

variable with the different variables of governance as indicated in the Worldwide Governance

Indicators (refer to Table X). These estimations are performed to obtain a greater understanding

of the specific aspects of governance that influence VAT performance in all countries,

developing countries, and transitional and developed countries.

Table 8: Two way fixed effects models for governance variables for all countries, developing countries, and transitional and developed countries

Variable All countries Developing countries Transitional and Developed countries

Political stability and Absence of violence

0.180* (0.111)

0.045 (0.124)

0.595*** (0.204)

Control for corruption 0.700*** (0.174)

0.242 (0.225)

1.009*** (0.261)

Regulatory quality 0.324* (0.190)

-0.042 (0.237)

0.497* (0.303)

Government effectiveness

0.463** (0.193)

0.352 (0.291)

0.436* (0.268)

Rule of law 0.362* (0.216)

0.646*** (0.266)

-0.016 (0.364)

Voice and accountability 0.849*** (0.203)

0.926*** (0.228)

0.381 (0.383)

N x T 1208 648 560

*** indicates significance at the 1% level; ** indicates significance at the 5% level; * indicates significance at the 10% level.

When considering all countries in the panel, each of the six factors of governance is positively

correlated with VAT performance and statistically significant at least at a 10% level. Control

of corruption and voice and accountability are, however, the most significant factors of

governance that influence VAT performance. This indicates that countries may do well in

20

attempting to improve governance with a focus on decreasing corruption and implementing

changes towards more democratic institutions.

It is further interesting to see that different factors of governance influence VAT performance

in developing, and transitional and developed countries respectively. For developing countries,

the statistically significant factors of governance that influence VAT performance are rule of

law and voice and accountability. With criminal activity more prevalent in developing

countries and generally less effective policing, it would make sense that rule of law would

influence VAT performance more in developing countries compared to other countries. With

nearly all non-democratic institutions in developing countries, it would also be sensible to

expect voice and accountability to significantly influence VAT performance in developing

countries.

For transitional and developed countries, the statistically significant factors of governance that

influence VAT performance are political stability and absence of violence, control of

corruption, regulatory quality, and government effectiveness. From these, political stability and

absence of violence, and control of corruption are the variables with the greatest influence on

VAT performance.

This results section is concluded by providing VAT effort indexes calculated with the use of

equation (4) when estimated with the Governance variable for all countries (refer to Table X).

Indexes are provided for ease of reference for all countries ranked by VAT effort,

alphabetically for only developing countries and alphabetically for only transitional and

developed countries. It should also be noted that the average VAT effort rank for developing

countries is 78, compared to 52 for transitional and developed countries. This indicates that

developing countries could increase VAT effort to better align with the VAT effort of other

countries. The indexed are provided in Table 9, 10 and 11.

Table 9: VAT effort by rank for all countries

Rank Country Year VAT introduced

VAT Ratio Actual

VAT Ratio Fitted

VAT Effort

1 Belarus 1992 15.05 6.18 2.11

2 Croatia 1998 13.05 8.11 1.86

3 Moldova 1992 12.27 6.15 1.82

4 China* 1994 13.28 7.01 1.79

5 Ukraine 1992 11.21 6.32 1.7

6 Seychelles* 2013 11.78 7.99 1.66

7 Georgia 1992 11.95 6.86 1.62

8 Serbia 2005 11.12 6.93 1.59

21

9 Azerbaijan 1992 10.72 6.5 1.55

10 Bulgaria 1994 11.34 7.43 1.53

11 Uruguay* 1968 13.01 8.12 1.47

12 Albania 1996 10.28 6.78 1.45

13 Estonia 1992 11.03 8.17 1.44

14 Venezuela, RB* 1993 12.51 6.5 1.43

15 Fiji 1992 13.02 7.09 1.41

16 Bolivia* 1973 12.38 6.49 1.4

17 Mongolia 1998 11.37 6.97 1.39

18 Morocco* 1986 11.28 7.13 1.39

19 Hungary 1988 10.05 8.35 1.36

20 Tajikistan 1992 9.27 4.98 1.35

21 Romania 1993 9.26 7.27 1.33

22 Macedonia, FYR 2000 9.91 6.98 1.3

23 Armenia 1992 10.65 6.43 1.3

24 Slovenia 1999 11.44 8.77 1.29

25 Jordan* 1994 9.72 7.81 1.29

26 Kyrgyz Republic 1992 9.93 5.68 1.29

27 Russian Federation 1992 9.69 6.45 1.28

28 Denmark 1967 10.01 10.37 1.26

29 Brazil* 1967 9.79 7.93 1.25

30 Peru* 1973 9.06 6.96 1.25

31 Vietnam* 1999 10.32 6.52 1.23

32 Norway 1970 10.31 10.19 1.23

33 Sweden 1969 10.45 10.12 1.22

34 Luxembourg 1970 8.68 10.72 1.21

35 Mozambique* 1999 9.44 6.32 1.21

36 St. Lucia 2012 9.42 8.57 1.2

37 Togo* 1995 11.32 5.73 1.19

38 Chile* 1975 10.08 8.98 1.19

39 Senegal* 1980 8.7 6.8 1.18

40 Lithuania 1992 8.32 7.85 1.18

41 New Zealand 1986 9.1 10.04 1.17

42 Poland 1993 9.52 8.21 1.15

43 Algeria* 1992 8.87 6.4 1.14

44 Finland 1994 10.29 10.05 1.14

45 Cabo Verde* 2004 8.41 7.76 1.14

46 Paraguay* 1993 10.28 6.35 1.14

47 Barbados* 1997 8.99 8.97 1.12

48 Benin* 1991 8.05 6.53 1.11

49 Czech Republic 1993 9.22 8.63 1.1

50 Latvia 1992 8.03 8.06 1.1

51 Argentina* 1975 9.35 7.21 1.1

52 Iceland 1990 9.02 10.38 1.09

22

53 Dominica 2006 7.64 8.13 1.08

54 Slovak Republic 1993 9.49 8.36 1.08

55 Tunisia* 1988 7.9 7.35 1.07

56 Austria 1973 9.46 9.85 1.06

57 Ecuador* 1970 6.7 6.42 1.05

58 Israel* 1976 7.29 9.01 1.05

59 Cyprus 1992 8.79 10 1.03

60 Portugal 1986 8.38 9.21 1.02

61 Ireland 1972 8.78 10.26 1.01

62 Burkina Faso* 1993 7.89 6.41 1

63 Malta 1995 7.22 9.52 1

64 Cameroon* 1999 7.3 5.96 0.99

65 South Africa* 1991 7.21 8.47 0.99

66 Jamaica* 1991 6.81 7.27 0.99

67 Burundi* 2009 7.18 5.42 0.97

68 Belgium 1971 7.93 9.58 0.96

69 Nicaragua* 1975 8.1 6.36 0.96

70 Germany 1968 6.75 9.56 0.94

71 El Salvador* 1992 7.8 7.04 0.94

72 Mauritius* 1998 7.1 8.42 0.94

73 Zambia* 1995 7.29 6.27 0.94

74 Mali* 1991 6.48 6.44 0.94

75 Netherlands 1969 6.43 10.14 0.93

76 Namibia* 2000 7.51 7.82 0.93

77 Kazakhstan 1992 7.33 7.02 0.92

78 Honduras* 1976 6.55 6.48 0.92

79 Colombia* 1975 7.14 7.23 0.92

80 Samoa 1994 6.5 7.75 0.92

81 France 1968 6.64 9.54 0.92

82 St. Kitts and Nevis 2010 6.63 8.87 0.91

83 Lesotho* 2003 7.37 6.15 0.91

84 Greece 1987 6.02 8.65 0.9

85 Nepal* 1997 5.9 5.79 0.9

86 Botswana* 2002 6.09 8.05 0.88

87 Turkey* 1985 5.58 7.79 0.85

88 Grenada 2009 5.99 8.04 0.85

89 Trinidad and Tobago* 1990 6.81 7.73 0.84

90 St. Vincent and the Grenadines

2007 6.79 8.09 0.84

91 Indonesia* 1985 6.27 6.59 0.83

92 Guatemala* 1983 5.24 6.69 0.82

93 India* 2005 5.9 6.77 0.81

94 Italy 1973 5.5 9.07 0.81

95 Swaziland* 2012 5.51 6.64 0.79

96 Kenya* 1990 6.13 6.27 0.78

23

97 Spain 1986 5.2 9.51 0.77

98 Costa Rica* 1975 5.26 8.2 0.76

99 United Kingdom 1973 5.23 9.85 0.76

100 Sri Lanka* 1998 5.49 6.72 0.75

101 Ghana* 1998 5.64 7.07 0.74

102 Thailand* 1992 4.9 7.42 0.74

103 Cambodia 1999 5.03 5.7 0.73

104 Pakistan* 1990 4.9 6 0.71

105 Dominican Republic* 1983 4.27 6.99 0.7

106 Korea, Rep.* 1977 4.56 8.97 0.69

107 Malawi* 1989 4.28 6.28 0.68

108 Uganda* 1996 4.29 6.16 0.67

109 Tanzania* 1998 4.15 6.43 0.67

110 Lebanon* 2002 4.26 7.66 0.66

111 Bangladesh* 1991 5.26 5.67 0.62

112 Mexico* 1980 4.15 7.57 0.6

113 Egypt, Arab Rep.* 1991 4.85 6.82 0.58

114 Panama* 1977 5.06 7.99 0.53

115 Switzerland 1995 4.92 10.36 0.51

116 Singapore* 1994 4.27 9.89 0.49

117 Australia 2000 3.95 10.14 0.49

118 Central African Republic* 2001 3.53 5.19 0.48

119 Sierra Leone* 2009 4.4 5.77 0.46

120 Canada 1991 3.48 10.14 0.43

121 Equatorial Guinea* 2004 2.7 6.81 0.4

122 Niger* 1986 2.63 6.06 0.39

123 Philippines* 1988 2.47 6.77 0.35

124 Japan 1989 2.37 10.02 0.35

125 Cote d'Ivoire* 1960 2.36 5.82 0.3

126 Madagascar* 1994 1.77 6.12 0.29

127 Congo, Dem. Rep.* 2012 1.89 5.11 0.26

128 Ethiopia* 2003 1.42 5.66 0.25

129 Malaysia* 2015 1.22 8.41 0.23

130 Suriname 1999 1.3 7.33 0.18

131 Nigeria* 1994 0.24 6.01 0.04

Developing countries are indicated by *.

Table 10: VAT effort by country for developing countries

Country VAT Ratio Actual

VAT Ratio Fitted

VAT Effort Rank Average Rank

Algeria 7.29 6.4 1.14 43 78

Argentina 7.9 7.21 1.1 51

Bangladesh 3.53 5.67 0.62 111

24

Barbados 10.08 8.97 1.12 47

Benin 7.22 6.53 1.11 48

Bolivia 9.06 6.49 1.4 16

Botswana 7.1 8.05 0.88 86

Brazil 9.95 7.93 1.25 29

Burkina Faso 6.43 6.41 1 62

Burundi 5.24 5.42 0.97 67

Cabo Verde 8.82 7.76 1.14 45

Cameroon 5.9 5.96 0.99 64

Central African Republic

2.47 5.19 0.48 118

Chile 10.65 8.98 1.19 38

China 12.53 7.01 1.79 4

Colombia 6.67 7.23 0.92 79

Congo, Dem. Rep. 1.3 5.11 0.26 127

Costa Rica 6.27 8.2 0.76 98

Cote d'Ivoire 1.74 5.82 0.3 125

Dominican Republic 4.9 6.99 0.7 105

Ecuador 6.75 6.42 1.05 57

Egypt, Arab Rep. 3.95 6.82 0.58 113

El Salvador 6.64 7.04 0.94 71

Equatorial Guinea 2.7 6.81 0.4 121

Ethiopia 1.42 5.66 0.25 128

Ghana 5.23 7.07 0.74 101

Guatemala 5.5 6.69 0.82 92

Honduras 5.99 6.48 0.92 78

India 5.51 6.77 0.81 93

Indonesia 5.47 6.59 0.83 91

Israel 9.44 9.01 1.05 58

Jamaica 7.18 7.27 0.99 66

Jordan 10.05 7.81 1.29 25

Kenya 4.9 6.27 0.78 96

Korea, Rep. 6.16 8.97 0.69 106

Lebanon 5.06 7.66 0.66 110

Lesotho 5.58 6.15 0.91 83

Madagascar 1.77 6.12 0.29 126

Malawi 4.26 6.28 0.68 107

Malaysia 1.89 8.41 0.23 129

Mali 6.02 6.44 0.94 74

Mauritius 7.93 8.42 0.94 72

Mexico 4.56 7.57 0.6 112

Morocco 9.91 7.13 1.39 18

Mozambique 7.64 6.32 1.21 35

Namibia 7.29 7.82 0.93 76

Nepal 5.2 5.79 0.9 85

25

Nicaragua 6.09 6.36 0.96 69

Niger 2.37 6.06 0.39 122

Nigeria 0.24 6.01 0.04 131

Pakistan 4.27 6 0.71 104

Panama 4.27 7.99 0.53 114

Paraguay 7.21 6.35 1.14 46

Peru 8.7 6.96 1.25 30

Philippines 2.36 6.77 0.35 123

Senegal 8.05 6.8 1.18 39

Seychelles 13.28 7.99 1.66 6

Sierra Leone 2.63 5.77 0.46 119

Singapore 4.85 9.89 0.49 116

South Africa 8.38 8.47 0.99 65

Sri Lanka 5.03 6.72 0.75 100

Swaziland 5.26 6.64 0.79 95

Tanzania 4.29 6.43 0.67 109

Thailand 5.49 7.42 0.74 102

Togo 6.81 5.73 1.19 37

Trinidad and Tobago 6.5 7.73 0.84 89

Tunisia 7.89 7.35 1.07 55

Turkey 6.63 7.79 0.85 87

Uganda 4.15 6.16 0.67 108

Uruguay 11.95 8.12 1.47 11

Venezuela, RB 9.27 6.5 1.43 14

Vietnam 8.03 6.52 1.23 31

Zambia 5.9 6.27 0.94 73

Table 11: VAT effort by country for transitional and developed countries

Country VAT Ratio Actual

VAT Ratio Fitted

VAT Effort Rank Average Rank

Albania 9.79 6.78 1.45 12 52

Armenia 8.34 6.43 1.3 23

Australia 4.92 10.14 0.49 117

Austria 10.45 9.85 1.06 56

Azerbaijan 10.09 6.5 1.55 9

Belarus 13.05 6.18 2.11 1

Belgium 9.22 9.58 0.96 68

Bulgaria 11.34 7.43 1.53 10

Cambodia 4.15 5.7 0.73 103

Canada 4.4 10.14 0.43 120

Croatia 15.05 8.11 1.86 2

Cyprus 10.28 10 1.03 59

26

Czech Republic 9.52 8.63 1.1 49

Denmark 13.02 10.37 1.26 28

Dominica 8.78 8.13 1.08 53

Estonia 11.78 8.17 1.44 13

Fiji 10.01 7.09 1.41 15

Finland 11.44 10.05 1.14 44

France 8.79 9.54 0.92 81

Georgia 11.12 6.86 1.62 7

Germany 9.02 9.56 0.94 70

Greece 7.8 8.65 0.9 84

Grenada 6.81 8.04 0.85 88

Hungary 11.37 8.35 1.36 19

Iceland 11.32 10.38 1.09 52

Ireland 10.32 10.26 1.01 61

Italy 7.33 9.07 0.81 94

Japan 3.48 10.02 0.35 124

Kazakhstan 6.48 7.02 0.92 77

Kyrgyz Republic 7.3 5.68 1.29 26

Latvia 8.87 8.06 1.1 50

Lithuania 9.26 7.85 1.18 40

Luxembourg 13.01 10.72 1.21 34

Macedonia, FYR 9.1 6.98 1.3 22

Malta 9.49 9.52 1 63

Moldova 11.21 6.15 1.82 3

Mongolia 9.72 6.97 1.39 17

Netherlands 9.46 10.14 0.93 75

New Zealand 11.72 10.04 1.17 41

Norway 12.51 10.19 1.23 32

Poland 9.42 8.21 1.15 42

Portugal 9.35 9.21 1.02 60

Romania 9.69 7.27 1.33 21

Russian Federation 8.25 6.45 1.28 27

Samoa 7.14 7.75 0.92 80

Serbia 11.03 6.93 1.59 8

Slovak Republic 8.99 8.36 1.08 54

Slovenia 11.28 8.77 1.29 24

Spain 7.37 9.51 0.77 97

St. Kitts and Nevis 8.1 8.87 0.91 82

St. Lucia 10.31 8.57 1.2 36

St. Vincent and the Grenadines

6.79 8.09 0.84 90

Suriname 1.3 7.33 0.18 130

Sweden 12.38 10.12 1.22 33

Switzerland 5.26 10.36 0.51 115

Tajikistan 6.7 4.98 1.35 20

27

Ukraine 10.72 6.32 1.7 5

United Kingdom 7.51 9.85 0.76 99

28

5. Conclusion

From the results of this paper it can be concluded that towards additional revenue of the VAT,

countries may do well to not only consider increasing VAT effort (by for instance an increase

in the standard rate), but also improving the economical and institutional environment in which

the VAT operates. One key aspect of this environment is governance.

It is empirically shown in this paper that governance is an important and statistically significant

VAT capacity factor towards increasing VAT performance. For developing countries,

improving rule of law and voice and accountability can lead to significant amounts of

additional revenue from the VAT. For transitional and developed countries a focus on

improved political stability and decreasing violence and corruption can also lead to significant

additional revenues from the VAT.

Although the VAT capacity factors in this paper are shown to be significant towards increased

VAT performance, it is likely that capacity factors are slow to change. Countries may therefore

look towards increasing VAT effort, preferably by broadening the base of the VAT. This paper

provides the first VAT effort indexes, for 131 countries. These indexes allows policy makers

to better understand the performance of their VAT, compared to the performance of other

countries’ VATs, controlling for VAT capacity factors, country fixed effects and year fixed

effects. These indexes can also be used by international organizations to determine advice and

support by way of aid, particularly for developing countries.