Determinants of countries’ tax structure: an empiri cal study

Bruno Teodoro Oliva Getulio Vargas Foundation

School of Economics Rua Itapeva, 474 – São Paulo – Brazil

E-mail: boliva@gvmail.br

Kátia Tiemi Saito

Getulio Vargas Foundation School of Economics

Rua Itapeva, 474 – São Paulo – Brazil E-mail: katiasaito@gvmail.br

Paulo Roberto Arvate Getulio Vargas Foundation

School of Business and Economics _CEPESP Rua Itapeva, 474 – São Paulo – Brazil

E-mail: parvate@fgvsp.br

Veronica Ines Fernandez Orellano Getulio Vargas Foundation

School of Economics – CEPESP Rua Itapeva, 474 – São Paulo – Brazil

E-mail:

Abstract

Developing countries face huge challenges in implementing efficient tax systems, given their political and economic characteristics. Several authors show that political and economic variables can determine the tax structure. These potential determinants, pointed out in the literature, were identified and tested through two ratios: the corporate income tax/personal income tax ratio, and the income tax/consumption tax ratio. An unbalanced panel with data on 35 countries was used for the 1976-1995 period. Tests were carried out taking care with possible endogeneity problems that may be caused by omitted variables, simultaneity or sample selection. Results show that the selected determinants explain the corporate income tax/personal income tax ratio much better than the other one. These determinants are not capable of explaining the differences observed in the income tax/consumption tax ratio between developed countries and other countries. However, many of the selected variables have a significant effect over the tax structure. The participation of labor income in the national income reduces the corporate income tax/personal income tax ratio and the rate of economic growth reduces the income tax/consumption tax ratio. Informality, economic growth and right-wing governments also affect the corporate income tax/personal income tax ratio, but with less significance. Informality, inequality and inflation have significant impact on the income tax/consumption tax ratio.

Key-words: Tax policy, Tax Structure, Developed and Developing countries. JEL Code: H20, H22

1. Introduction

The literature on tax systems has always found it difficult to determine the best tax policy

to be adopted in each country.1 As pointed out by Tanzi and Zee (2000), tax systems in

developing countries play difficult roles since they must, at the same time, raise enough funds to

finance essential expenditures without recourse to excessive public sector borrowing; raise the

revenue in ways that are equitable and that minimize its disincentive effects on economic

activities; and do so in ways that do not deviate substantially from international norms.23

According to this rationale, these authors point out the enormous challenges that

developing countries face in implementing a tax system. These challenges include the informal

structure of their economies, which hampers the collection of certain taxes; the limited capacity

of the tax administration; the lack of data; and the reticence politicians have regarding rational

tax policy. The main idea is that, with regard to the tax structure, governments do what they can,

which is not exactly what would be considered optimal in theory, assuming an ideal

environment. Thus, countries´ characteristics are essential in determining their respective tax

structures.

Although this is recognized, authors have not conducted empirical tests to address this

issue. They just show the differences in tax structures between OECD countries and developing

countries, pointing out the possible determining factors. Data presented by the authors reveal

quite clearly that the income tax/consumption tax ratio is much higher in OECD countries in

comparison with other countries.4 The corporate income tax/personal income tax ratio is much

lower in developing countries than in OECD countries.5

This paper aims to identify the variables that justify the differences between the tax

structures of developed countries (according to the World Development Indicators) and those of

other countries. To do that it is necessary to achieve another important goal, which is to

empirically confirm the causality put forth by Tanzi and Zee (2000): political and economic

characteristics determining countries´ tax structure. Tests must be carried out taking care with

endogeneity problems that may be caused by simultaneity, and also by omitted variables or

sample selection.

1 Heady(1993) states that “A close relationship between the prescriptions of optimal tax analysis and the tax systems that are actually implemented should not necessarily be expected.” (page 23) 2 See also Gordon and Li (2005) in this regard. 3 Alm (1996) performed a similiar analysis of existing tax structures. 4 The ratio for OECD countries in 1995-7 was 1.2, and the same ratio for developing countries for the same period was 0.3. 5 The ratio for OECD countries in 1995-7 was 0.5, and the same ratio for developing countries for the same period was 1.2. .

The paper comprises three additional sections, along with this brief introduction. In

section 2 the estimated equations and the variables are presented. The third section presents

the empirical results and is divided into two parts: first the data is presented and then estimation

results are discussed. In the last section, the main conclusions are presented.

2. The Model

As suggested by Tanzi and Zee (2000), the tax structure may be measured by the tax

revenue.6 Commonly two types of ratio are used as measures of tax structure: the corporate

income tax/personal income tax ratio, and the income taxation/consumption taxation ratio.

Since the same variables were used to explain each of the ratios, only one equation is

presented: 7

( ) ( ) ( ) ( ) ( ) ++∆++++= itititititit InflationGDPyInformalitNationalLaborGiniy 543210 / ββββββ( ) ( ) ( ) ( ) ( ) +++++∆+ ititititit ElectionRightLeftgimevenueTax 109876 ReRe βββββ( ) ( ) itiii uciesPoorcountriesOECDcountr ++++ 1211 ββ 8

where ity corresponds to one of the two taxation ratios, the t underscore indicates the year and

the i underscore indicates the country; ic is the non-observed fixed effect corresponding

to each country, and itu is the random term.

The dependent variable ( )PersonalCorporate/ is the ratio between corporate income tax

revenue and personal income tax revenue. The dependent variable ( )nConsumptioIncome / is the

ratio between income tax revenue and consumption tax revenue. The data source used to

construct these variables was the Government Finance Statistics Yearbook (GFSY).

The variable Gini represents the income inequities existing in each country. Although

income distribution is an important issue in designing a tax structure,9 there is much controversy

in the literature regarding its impact. 10

6 Tanzi and Zee (2000) and most of the studies reviewed in this paper adopt this procedure. 7 It is assumed that there is a causal relation between the dependent variable and the independent variables subject to empirical investigation. 8 Initially, an attempt was made to include in the model a measure of openness of the economy multiplied by a Latin American country dummy. The aim was to verify whether the degree of openness of the various Latin American economies had an effect (common to these countries) on the various dependent variables analyzed. Latin American economies were quite closed to trade in the 1980s. The openness of these economies tended to increase after this period. It was not possible to introduce a variable which represented the degree of openness of an economy because the constructed variable (sum of the exports and imports divided by GDP) presented serious multicolinearity problems. Regarding the importance of economic openness in determining tax structures see Tanzi and Zee (2000), Tozun (2002), Keen and Ligthart (2002), and Aizenman and Jinjarak (2006). 9 Since Adam Smith (1776: Book 5, Chapter 2) this has been a concern, for he pointed out that personal income tax should be proportional to personal income. 10 Tanzi and Zee (2000) also believe that the distributive issue in determining taxes is considered secondary in academic discussions because most researchers live in countries where this issue is not a serious problem (developed countries). This ended up orienting tax

Tanzi and Zee (2000) point out that inequities influence tax choices because: i) in certain

situations governments are tempted to impose higher taxes on individuals in the higher income

deciles (the rich); ii) on the other hand, the economic and political power of a society is generally

concentrated in the higher income deciles. If those who are richer have the political influence to

obstruct a tax reform that may go against their interests, this may explain why personal income

taxes and property taxes have not been better used in developing countries.

Considering this arguments, it may not be affirmed whether personal income tax revenue

tend to increase or decrease with an increase in inequity. It may only be affirmed that with

higher inequity we expect a lower property tax revenue. It is therefore not clear what the final

result would be with regard to an increase in inequities for the ( )PersonalCorporate/ and

( )nConsumptioIncome / ratios. The variable Gini was constructed based on the GINI indexes of

various countries which were obtained from the World Income Inequality Database11. Since

there are various methodologies used to construct these indices on a country basis, the

methodology adopted was the one that maximized the volume of data without incurring a data

consistency loss.12

Even when the effects of the tax structure on inequities are considered (a causality which

is inverse to that presented in the initial equation), there is not a consensus about the impacts of

a reform (that seeks greater tax progressiveness) on inequity. Taxation of consumption is a

good example. For a long time, this tax was considered regressive. Given this possibility, it was

recommended that countries with greater income inequities avoid this tax.13 As increasingly

adequate analytical instruments are used to address this issue, doubts were cast upon this

recommendation. The adoption of intertemporal optimization models made it difficult to conclude

that consumption taxes would be more regressive than income taxes. Certain consumption

taxes, which are in principle regressive in static models, would not be so in intertemporal

models.14 An empirical example of this was the distributive evaluation of value-added taxes

(VAT) in intertemporal models.15 16

research towards issues related to efficiency (well-being). An example of this is the work of Atkinson and Sandmo (1980). Nevertheless, this may change due to the fact that inequities have worsened in developing countries. See also Alesina and Angeletos(2003). 11 Organized by the United Nations University. 12 There were many missing values in the database used. To minimize this problem, data were imputed through geometric interpolation between two years presenting the original information. 13 Generally, in static models, this heroic hypothesis is assumed, according to which the tax burden falls completely on the consumer. See Musgrave, Case and Leonard (1974), and Pechman(1985). 14 In life cycle models, individuals with low income are not necessarily poor, being either young or old. The former plan their consumption based on their income over their life time. In order to determine whether a tax is progressive or regressive, it is necessary to work with income plus present value transfers. The problem may also be designed differently, considering the possiblity of inheritances, for example. 15 See Metcalf (1994). 16 The a priori assumption regarding the elasticities of the supply of labor and the supply of savings is another issue to be considered before any recommendation is made. On the importance of these elasticities in tax choices, see the work of Mirrlees (1971), Atkinson (1973), Atkinson and Stiglitz(1976), Feldstein(1978), and Atkinson and Sandmo (1980).

The variable ( )NationalLabor / represents the labor income divided by the National

Income of each country. For Tanzi and Zee (2000), the share of labor income in the national

income is a determinant of the tax structure. If this share is too small, it would be difficult to

increase the tax revenue through an increase in personal income tax. In addition, this potential

revenue is limited by the fewer number of individuals subject to taxation (revenue base),

especially those liable to higher income tax. Thus, it is expected that the higher the share of

labor income in the National Income, the lower the ( )PersonalCorporate/ ratio, and the higher

the ( )nConsumptioIncome / ratio. The variable ( )NationalLabor / was calculated from the ratio

between “workers’ compensation” and the GDP of the various countries. This variable was

constructed from the UN national accounts database.17

The variable yInformalit represents the level of informality in different countries. Tanzi

and Zee (2000) affirm that differences in informality may cause differences in tax structures.

Higher levels of informality lead to increases in corporate income and consumption tax revenue,

due to the difficulties in collecting personal income taxes. However, the causality between

informality and tax revenue may run in the opposite direction, since countries’ tax burden is one

of the main arguments used to explain the increase in informality in recent years (Scheneider

and Enste, 2000). Assuming the direction of causality from informality to tax structure, it is

expected that higher levels of informality would lead to a greater ( )PersonalCorporate/ ratio, and

a lower ( )nConsumptioIncome / ratio. Informality data are not available for most countries and,

therefore, the variable yInformalit was defined by the share of rural population in the country’s

total population. 18 The data were extracted from the World Development Indicators (WDI).

According to Ashworth and Heyndels (2002), real economic growth and inflation affect

the share of taxes in the tax structure. This is because each tax responds with different force to

either economic growth or inflation. In order for the tax structure to remain constant it is

necessary that the reaction of different taxes in response to growth and inflation (elasticities)

remain the same; which is highly improbable. For this reason, economic growth and inflation

must be controlled in the model. The variable GDP∆ represents the real rate of growth of the

Gross Domestic Product and the variable Inflation represents the variations in prices in the

different countries.

Some studies suggest that the tax structure affects the rate of economic growth (opposite

direction of causality). Lee and Gordon (2004), based on a sample of 70 countries between

1970 and 1997, reached the conclusion that increases in corporate income taxes lead to lower

17Data were organized by Professor Adalmir Marquetti, available at: http://homepage.newschool.edu/~foleyd/epwt/ 18 Diwan (2000) used this same procedure.

economic growth in the long run. Wildmam (2001), using data on 23 countries between 1965

and 1990, reached the conclusion that a higher tax/income ratio would have a negative impact

upon growth. Other types of taxes (taxes on salaries, consumption and profits) would not have a

negative impact upon the rate of growth. Therefore, it is expected that the variable GDP∆ will

be related to the countries’ tax structures, but both directions of causality are foreseen and this

endogeneity problem must be faced. The variable GDP∆ was constructed based on the data

obtained from the United Nations database 19.

The variable Inflation represents the variations in prices in the different countries. For

Messere (1993), income taxes increase with inflation, while consumption taxes remain

unchanged. Kemmerling (2003), using data on OECD countries, also reached the conclusion

that inflation is positively related to income taxes, and negatively related to indirect taxes. Thus,

it is expected that the higher the level of inflation during the period, the higher the

( )nConsumptioIncome / ratio will be. Inflation may also be related to the ( )PersonalCorporate /

ratio, although this effect may not be determined since the studies mentioned do not specify the

types of income tax (personal or corporate) which are affected by inflation. The data on inflation,

organized by the IMF,20 refer to the variation in consumer price indexes of the different

countries.

The variable venueTaxRe∆ represents the rate of variation of the total real tax revenue in

each country. According to Ashworth and Heyndels (2002), one of the main reasons for

variations in the tax structure is pointed out by Hettich and Winer (1999). According to these

authors, politicians choose a tax structure which also minimizes the political cost in relation to

the Total Revenue needs. The necessity to raise total tax revenue commonly leads to a shift in

the tax structure, given the political costs of raising some types of taxes. This kind of rationale

contradicts the idea of tax smoothing. Following Ashworth and Heyndels (2002), it is expected

that the variable venueTaxRe∆ be related to the countries’ tax structure. However, it is not

known how this variable would affect the ratios used in this study. The rate of variation of the

real tax revenue was calculated based on nominal data obtained from the WDI. The consumer

price index was used as a deflator.

The variable gimeRe classifies countries in two distinct groups: those that have a “strong

president” in relation to the others. 21 A “strong president” is defined here as a president who

was elected by the Congress, and not through direct vote. At least in theory, for being elected by

the Congress, he or she would find it easier to implement reforms or tax choices. Ashworth and 19 Available at: http://unstats.un.org/unsd/default.htm 20 Available at: http://www.imf.org. 21 Tests were conducted regarding the type of government (presidentialism and parlamentarism) and the results obtained were not satisfactory. Thus, these variables were excluded from the final model.

Heyndels (2002) had already pointed out that the dispersion of political power may reduce the

variation in the tax structure. Lavigne (2006), based on an analysis of 61 countries, concludes

that political factors have a significant effect upon the need and willingness of governments to

implement major fiscal adjustments. In developing countries, the successful implementation of

fiscal adjustments would be associated with strong majority governments, low levels of

subsidies and transfers, and relatively weak institutions. In these countries, weak institutions

would allow governments more freedom to adopt non-orthodox policies, including measures that

conflict with current legislation. According to this author, this is corroborated by several

countries that have autocratic governments, or by countries that had autocratic governments

during periods of fiscal adjustment. In this regard, it is expected that the variable gimeRe be

related to the countries’ tax structures. However, it is not known how the variable affects the

ratios analyzed in this study. In this paper, the existing regime is captured by a dummy variable

which is equal to 1 when the regime has a “strong president”, and 0 if it does not. The countries

were classified according to the World Bank’s Database of Political Institutions.

The variables Left and Right are dummy variables that indicate the ideologies of the

governments, in contrast with centrist governments. Messere (1993) has argued that center-

right governments generally tend to choose a lower total tax burden, orienting the composition

towards more consumption taxes as opposed to income taxes. On the other hand, left-wing

governments tend towards a higher tax burden, in which income taxes predominate over

consumption taxes. Pommerehne and Scheneider (1983) analyzed Australia during the 1970s

and concluded that right-wing governments tend to have less direct taxes and a lower tax

revenue/GDP ratio, while left-wing governments tend to have more indirect taxes and a higher

tax revenue/GDP ratio. So it is expected that the ( )nConsumptioIncome / ratio will be higher for

left-wing governments and lower for right-wing governments. It is not certain how the

( )PersonalCorporate / ratio would be affected. The governments’ ideology classification was

obtained from the World Bank’s Database of Political Institutions. There are certain countries for

which the World Bank was unable to make a classification. These countries were not removed

from the sample. Thus, right-wing and left-wing governments are not being contrasted solely

with centrist governments, but also with government whose ideologies were not classified.

The variable Election represents the election years in the different countries. For

Pommerehne and Scheneider (1983), an important instrument used to influence the popularity

of a government, which influences its chances of being reelected, is the composition of the tax

system. The authors analyzed data for Australia and concluded that a government’s popularity

is negatively influenced by the tax burden, and by the proportion of indirect taxes in the total

income. Geys and Veirmer (2005), analyzing data between 1959 and 2004 for the United

States, argued that both the tax burden and changes in the tax structure reduced the presidents’

approval rates.

Rogoff (1990) outlines a signaling model according to which politicians change the

spending structure in election years to signal their competence (expenditures which are more

visible to voters, thus influencing their choice of candidates). In this model, voters, in spite of

their rationality, have incomplete information, and do not observe that budget restrictions in an

election year will certainly be corrected afterwards. There are no incentives for policymakers to

overcome this restriction in the long run. Following this reasoning, changes in the tax structure

may be introduced for electoral ends. Before the elections, those taxes which are more visible to

voters may be reduced, while others may be increased. Ashworth and Heyndels (2002)

exemplify this type of situation based on data for 18 countries between 1965 and 1995,

indicating a reduction in consumption and personal income taxes, and an increase in corporate

taxes during an election period. Therefore, it is expected that elections provoke an increase in

the ( )PersonalCorporate / ratio, and a reduction in the ( )nConsumptioIncome / ratio.22 In order to

observe the effect in an election year, a dummy variable was included. In an election year it

assumes the value of 1, and in the other years 0. These data were also extracted from the

World Bank database.

In order to facilitate the analysis of the results, a table of the expected results was

prepared, based on the arguments outlined above:

Table 1: Expected effect of the independent variabl es on the tax ratio

Expected effect on the tax ratio

Independent variables ( )PersonalCorporate / ( )nConsumptioIncome /

Gini + or - + or -

( )NationalLabor / - +

yInformalit + -

GDP∆ + or - + or -

Inflation + or - +

venueTaxRe∆ + or - + or -

gimeRe + or - + or -

yIdeo log

Left + or - +

Right + or - -

Election + -

22 Even if a corporate income tax increase is offset by a decrease in personal income tax, a drop in the consumption tax is also expected, leading to a decrease in this ratio.

Besides the economic and political variables, two dummies were included – one to

separate the developed countries (high income OECD countries)23 , and the other to separate

the poor countries24. This was done in order to contrast developing countries with developed

countries, given the tax composition differences pointed out by Tanzi and Zee (2000). The first

variable, iesOECDcountr , is equal to 1 when the country is an OECD country and zero otherwise;

the second, iesPoorcountr , is equal to 1 when the country is poor and 0 otherwise.25 The idea is

to verify whether these dummies remain significant after controlling for relevant country

characteristics indicated in the literature.

3. Empirical Results 3.1. The data

The data survey was conducted for 48 countries. The period observed was 1975 to 1995.

In practice, the 1976-1995 period was studied (a total of 20 years), for when the tax revenue

variations were calculated, the first year was lost.

The table below presents the descriptive statistics of the explanatory economic variables.

Table 2: Descriptive statistics of the explicative economic variables

Variables Average Standard deviation

Minimum Maximum

Gini 0.366 0.087 0.191 (Sweden – 1981)

0.637 (S. Leoa – 1989)

( )NationalLabor / 0.476 0.109 0.128 (S. Leoa – 1989) 0.706 (India – 1977)

yInformalit 0.413 0.240 0.03 (Belgium – 1995) 0.903 (Ethiopia – 1976)

GDP∆ 0.034 0.038 -0.136 (Chile – 1982) 0.182 (Papua – 1993)

venueTaxRe∆ 0.044 0.111 -0.516 (S. Leoa – 1983)

0.732 (S. Lanka – 1978)

Inflation 0.142 0.251 -0.098 (Guatemala – 1987)

3.698 (Israel – 1984)

If all the information were available for the all countries for all years, there would be a

total of 960 observations (20 for each one of the 48 countries). However, the lack of information

was quite frequent, especially with regard to the variables Gini and ( )NationalLabor / .26

23 According to the WDI, the per capita income of a high income OECD country is greater than US$ 9,386.00 (2003). 24 According to the WDI, the per capita income of a low income country is under US$ 765.00 (2003) 25 The 24 high income OECD countries were: Germany, Australia, Austria, Belgium, Canada, South Korea, Denmark, Spain, United States, Finland, France, Greece, Holland, Iceland, Ireland, Italy, Japan, Luxembourg, New Zealand, Norway, Portugal, United Kingdom, Sweden and Switzerland. The seven poor countries were: Cameroon, Ethiopia, Gambia, Ghana, India, Papau – New Guinea and Serra Leoa. The seventeen developing countries in the sample were (OECD rich countries minus the poor countries) were: South Africa, Chile, El Salvador, Philippines, Fiji, Guatemala, Indonesia, Iran, Israel, Malaysia, Malta, Mauritius, Mexico, Sri Lanka, Thailand, Tunisia and Turkey. 26 In spite of the interpolations performed for the variable Gini as mentioned above.

Comentário: É isso mesmo, a Índia tem o maior WS.

The table below summarizes the total observations available for each variable. This was

done only for the economic variables, since the greatest problem posed by the lack of

information was associated with these variables.

Table 3: Total observations available per variable

VARIABLES TOTAL NUMBER OF OBSERVATIONS

( )PersonalCorporate / 903

( )nConsumptioIncome / 947

Gini 553

( )NationalLabor / 656

yInformalit 960

GDP∆ 959

venueTaxRe∆ 891

Inflation 960

If information on only one variable for a certain year was missing, all the information for

that year was lost. As a result, the total number of observations of the sample dropped sharply,

and the number of observations for each country varied greatly.

Next subsection presents information on panel balancing specific for each of the two

models studied, since the two dependent variables analyzed in this study were not exactly equal

with regard to the available information.

3.1.1. Characterization of the observations: model for the ( )PersonalCorporate / ratio In this model, a total of 33 countries remained in the sample. Fifteen countries were left

out for not having complete information available for any of the years. Of the total of 33

countries, 22 were high-income OECD countries (66.7%).

Only four countries were observed during the entire 20-year period. However, 26 of the

33 countries of the sample had more 10 observations in time. The table below presents the

characterization of the observations:

Table 4

Characterization of the observations in the model f or the ( )PersonalCorporate / ratio Number of years observed

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Frequency of countries

3 1 1 0 1 0 0 2 0 0 3 5 1 2 4 4 1 1 1 4

The total number of observations was 403, and the average number of years observed

per country was 12.21. Besides the higher number of OECD countries in the sample, the

number of years observed per country also tended to be higher for OECD countries. The

average number of years observed per country was 13.72 for OECD countries, and 9.8 for the

other countries. Thus, of a total of 403 observations, 302 (74.9%) were of high-income OECD

countries.

3.1.2. Characterization of the observations: model for the ( )nConsumptioIncome / ratio In this model, a total of 35 countries remained in the sample. Thirteen countries were

excluded for not having complete information for any given year. In the total of 35 countries, 22

were OECD countries (62.8%). Only four countries were observed for the entire 20-year period.

However, 26 of the 35 countries comprising the sample had more than ten observations in time.

The table below presents the characterization of the observations:

Table 5 Characterization of the observations in the model f or the ( )nConsumptioIncome / ratio Number of years observed

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Frequency of countries

3 1 0 1 1 0 0 2 1 0 4 5 1 2 4 4 1 0 1 4

The sample size reached 424 observations, and the average number of years observed

per country was 12.11. Once again, there were many OECD countries in the sample, and the

number of years observed per country tended to be higher for these ones. The average number

of years observed per country was 13.77 for the OECD countries, and 9.3 for the remaining

countries. Thus, of the total of 424 observations, 303 (71.5%) were of high-income OECD

countries.

3.1.3. Unbalanced panel and selection bias

When working with an unbalanced panel, it is necessary to evaluate if the missing values

– which corresponds to a type of sample selection – may lead to inconsistent or biased

parameter estimates.

An important point here is that the sample selection based on explanatory variables does

not necessarily imply inconsistent parameters. If the explanatory variables are exogenous, and

if the selection pattern is not correlated with the residuals of the regression, then the estimates

made with ordinary least squares (OLS) are consistent.

The sample used in this study was clearly selected based on one or more explanatory

variables, given that there were more observations for OECD countries. However, as will be

seen, we found strong evidence of weak (or contemporaneous) exogeneity of the explanatory

variables for the ( )PersonalCorporate / ratio model, and of strict exogeneity for the

( )nConsumptioIncome / ratio model.

Nevertheless, it is still necessary to ensure non-correlation between the sample’s

selection pattern and the residuals of the regressions. In OLS estimations, it is necessary to

ensure that the residual in t, given by iti uc + , is not correlated with the selection pattern. On the

other hand, in estimations with the fixed effect model (FE), it is necessary to guarantee that the

sample’s selection pattern is not correlated with the itu error component in any period of time.

Lastly, for the estimation with the random effect (RE) model, the sample’s selection pattern may

not be correlated with the fixed effect ic , nor with the error component itu for any period of time.

Wooldridge (2002: pg. 581) proposed a very simple method to test for selection bias in

the fixed effect model. A selection indicator, its , is constructed, where 1=its if the dependent

and the explanatory variables are observed for country i in t, and 0=its if any of these variables

are not observed for country i in t. Wooldridge proposes the inclusion of the lagged selection

indicator 1−its 27 in the equation, and then the inclusion of this lagged indicator in the fixed effect

model estimation. If the selection pattern is not correlated with itu in none of the periods, then

1−its must not be significant in the equation. Another suggestion is the inclusion of 1+its instead of

1−its .

An analogous test may be implemented with both an OLS (pooled) estimation, as well as

with an estimation with a random effect model (RE). In these cases, the non-significance of the

dummy 1−its (or 1+its ) would indicate non-correlation between the selection pattern and the

compound residual iti uc + , i.e., indicating that the selection pattern is also not correlated with

the fixed effect ic 28. Another possibility for these cases would be the inclusion of a temporal

average of its in the equation, instead of 1−its or 1+its , since the pooled OLS and RE models

admit explanatory variables which are invariant over time.

These simple selection bias tests were implemented in both models analyzed in this

study, both with regard to pooled OLS estimations, as well as estimations with RE and FE. All

the selection indicators suggested above were tested. In the RE and FE models, none of the

tested indicators was statistically significant. However, in the pooled OLS estimation the

temporal average of its had statistical significance29, indicating that the selection pattern,

27 The inclusion of its was not possible because 1=its exactly when the observation took place; thus the inclusion of the lagged its . The

correlation of the constructed dummy its was greater than 0.8. 28 More precisely, with regard to the random effect model, this would indicate that the selection pattern is not correlated with one part of the fixed effect, since the transforming of the random effects eliminates a fraction of the fixed effect (a quasi time-demeaning transformation). 29 These results are listed in Annex 1. Table A.1 only presents the coefficients estimated for the variable which indicated selection for each case. However, the inclusion of variables indicating selection practically did not alter the coefficients estimated for the other variables, nor did it alter their signs.

besides being correlated with at least some of the explanatory variables, was also correlated

with the fixed effect ic 30. Thus, the pooled OLS estimations were not considered, and were not

included in the results subsection.

The statistical significance of the temporal average of its is not only indicative of a

selection bias in the pooled OLS model, but also shows that there is some non-controlled

variable (fixed in time) which helps to explain the countries´ tax structure. In other words, this

result indicates that the non-observed fixed effect ic is relevant in determining the tax structure.

This non-controlled variable is correlated with the sample’s selection pattern.

3.2. Empirical Results

3.2.1 Determinants of the ( )PersonalCorporate / ratio

The first six columns of coefficients in the table below summarize the results obtained for

the first difference (FD), fixed effects (FE) and random effect (RE) models, with and without the

inclusion of year dummies. The standard deviations were calculated with the robust variance

matrix, since the modified Wald test indicated heterocedasticity of the residuals.

Table 6(*): Results obtained for the dependent vari able ( )PersonalCorporate /

Variable FD FD FE FE RE RE Arellano Bond (**)

Arellano Bond

Gini -0.0096 (-1.02)

-0.0172 (-1.41)

-0.0043 (-0.62)

-0.0014 (-0.17)

-0.0023 (-0.33)

0.0023 (0.23)

0.0011 (0.14)

-0.0049 (-0.59)

(Labor/National) -1.5532 (-1.94)

-1.6515 (-1.96)

-0.8990 (-2.43)

-1.5088 (-3.41)

-1.1991 (-2.67)

-1.9395 (-2.78)

-1.1126 (-3.01)

-1.2494 (-3.20)

yInformalit -0.7828 (-0.43)

-0.1247 (-0.07)

0.9966 (2.83)

0.3277 (0.51)

0.8024 (2.08)

0.2338 (0.43)

0.7370 (1.30)

1.1134 (1.90)

GDP∆ 0.3215 (1.09)

0.5686 (1.57)

1.7429 (2.69)

2.1131 (2.73)

1.7238 (2.59)

2.2374 (2.70)

1.1575 (2.67)

1.5632 (3.43)

venueTaxRe∆ -0.1915 (-1.13)

-0.1834 (-1.10)

-0.1725 (-0.58)

-0.1609 (-0.55)

-0.0772 (-0.25)

-0.0227 (-0.07)

-0.1784 (-1.23)

-0.1852 (-1.26)

Inflation -0.0229 (-0.96)

-0.0258 (-1.08)

0.0520 (1.19)

0.0295 (1.03)

0.0522 (1.10)

0.0179 (0.54)

0.0224 (0.49)

0.0276 (0.60)

iesOECDcountr ------- ------- ------- ------- -0.2447 (-0.73)

-0.2378 (-0.92)

------- -------

iesPoorcountr ------- ------- ------- ------- 2.7920 (1.60)

2.8097 (1.65)

------- -------

Election -0.0064 (-0.56)

-0.0039 (-0.34)

-0.0101 (-0.46)

-0.0073 (0.04)

-0.0124 (-0.56)

-0.0157 (-0.67)

-0.0046 (-0.21)

-0.0007 (-0.03)

Left -0,0212 (-0,63)

0,0143 (0,38)

-0,0333 (-0,97)

0,0017 (-0,97)

-0,0740 (-1,73)

-0,0675 (-1,10)

-0,0356 (-0,71)

-0,0146 (-0,29)

Right 0,0160 (0,36)

0,0564 (1,17)

0,0898 (2,14)

0,1159 (2,33)

0,0494 (1,04)

0,0447 (0,67)

0,0602 (1,14)

0,0858 (1,66)

Regime 0,0240 (0,25)

-0,0033 (-0,04)

-0,3529 (-3,00)

-0,3283 (-3,27)

-0,3509 (-3,57)

-0,3247 (-3,97)

-0,1488 (-1,48)

-0,1284 (-1,27)

30 These tests allowed us to determine, for the 33 countries observed, whether the lack of information for certain countries in certain periods implicated bias or inconsistencies. It was not possible to test whether the selection of these 33 countries, of the total of countries, implicated bias or inconsistencies.

Year Dummies No Yes No Yes No Yes No Yes

Lagged (Corporate/Personal)

No No No No No No 0,4503 (10,01)

0,4565 (9,94)

Overall R2 ---------- ---------- 0,2971 0,2393 0,4356 0,4243 --------- --------- (*) t statistics are between parenthesis, and non-significant variables at 10% are shaded. Breusch and Pagan LM test for random effects→ Test: Var(u) = 0; chi2(1)=345.93; Prob > chi2 = 0.0000 Modified Wald test for groupwise heteros.→H0: sigma(i)^2 = sigma^2 for all i; chi2 (33) = 3.4e+31; Prob>chi2 = 0.0000 (**) Sargan test of over-identifying restrictions: chi2(397) = 401.56 Prob > chi2 = 0.4266 // Arellano-Bond test that average autocovariance in residuals of order 2 is 0: H0: no autocorrelation z = -1.43 Pr > z = 0.1527

The Breusch-Pagan test indicated the presence of a fixed effect, but the proximity of the

FE and RE coefficients suggests that there is no correlation between the compound error in t

and the model’s explanatory variables in t. This suggests that 0)( =itit xeE , where itiit uce += ,

and thus the fixed effect is not correlated with the observed explanatory variables.

The standard Hausman test assumes noncorrelation and homocedasticity of the

residuals, but the results indicated heterocedasticity. Therefore, a robust Hausman test was

conducted in order to compare the FE and RE coefficients, where no pattern was previously

assumed with regard to the error (see Wooldridge (2002): page 291)31. The results, listed in

Annex 2, revealed that all the coefficients obtained from the fixed effect model were statistically

equal to the coefficients obtained from the random effect model, indicating that there was no

contemporary correlation between the compound error and the explanatory variables.

Assuming that the error is strictly exogenous, and not only contemporarily exogenous,

both the FE and RE parameters could be considered reliable. Unfortunately, doubts may be

raised regarding the assumption of strict exogeneity. As mentioned in section 2, it may be

argued that a higher ( )PersonalCorporate / ratio in a given year may affect the rate of growth of

the product. Thus, the dependent variable in t would be correlated with one of the explanatory

variables in (t+1), causing error correlation in t with this variable in (t+1).

Wooldridge (2002: pgs 284 and 285) suggests a way to test for strict exogeneity, by

testing equivalence between the fixed effect (FE) and first difference (FD) parameters. If the

differences between the FD and FE estimates cannot be attributed to sampling error, this casts

doubt on the strict exogeneity assumption. A robust Hausman test was conducted comparing,

one by one, the FD and FE estimated coefficients. This test, outlined in Annex 2, focused on the

six economic variables, for it is upon these variables that doubts regarding strict exogeneity are

cast32.

Only two of the six FD and FE coefficients compared may be considered statistically

equal – the coefficients of the variables GDP∆ and Inflation . Therefore, it may not be assumed

31 In order to implement this and other robust Hausman tests, it was necessary to write small program in Stata, which may be requested from the authors. 32 Although it is possible to determine institutions through economic variables, the political variables included in the model estimated in this study (election year, strong president, and government ideology) were considered exogenous.

that all the economic variables are strictly exogenous. For this reason, the dynamic Arellano and

Bond model was also estimated, in which the lag of the dependent variable was included on the

right side of the equation, and the variable GDP∆ was considered predetermined, since this is

the main variable for which endogeneity was suspected. The lags of the predetermined

variables were used as instrumental variables. The Sargan test did not reject the null hypothesis

of exogeneity of the instruments matrix. The results of the Arellano and Bond model, with and

without year dummies, are presented in the last two columns of Table 6.

The results obtained reveal that the share of labor income in national income and the rate

of growth of the product are the main variables that help to explain the ( )PersonalCorporate /

ratio.

Countries with higher labor income share had a higher share of personal income tax in

the total income tax revenue. The results also suggest that, in periods of growth, the

corporate/personal income tax ratio tends to increase.

The other results are not as significant. Of the other controlled economic variables,

informality is the only one which appears to be relevant, with the expected sign. This suggests

that countries with higher informality tend to collect relatively more corporate income taxes.

Considering the variables related to political aspects, only right-wing governments

increase the share of corporate income tax in relation to personal income tax. This result was

not expected.

Lastly, it must be mentioned that the results of the RE model (for the OECDhigh dummy)

suggest that the labor income/national income ratio and the informality explain quite well the

differences observed for the ( )PersonalCorporate / ratio between developed countries and the

other countries (poor and developing). The dummy variable indicating high income OECD

countries was not significant. However, the dummy variable indicating poor countries remained

significant for the estimates with year dummies, suggesting that the selected variables do not

totally explain the difference observed for this ratio between poor countries and the rest.

Another point which deserves mentioning is that the non-observed fixed effect ci is very

important to explain the ( )PersonalCorporate / ratio for each country. This was detected in a test

similar to that performed by Burgess, Lane and Stevens (2000). An OLS regression was

estimated for the ( )PersonalCorporate / ratio, in which the independent variables were only the

fixed effect 33 and the year dummies. This yielded a R2 value of 0.81. The estimation of the

original model with OLS – which included the iesOECDcountr , iesPoorcountr and year dummies,

but not the fixed effect – yielded a R2 result of 0.51.

33 The individual fixed effect for each country were recovered from the estimation of the fixed effect model.

3.2.2. Determinants of the ( )nConsumptioIncome / ratio The table below presents a summary of the results obtained for the first difference (FD),

fixed effect (FE), and random effect (RE) models, with and without the year dummies. The

standard deviations were all calculated from the corresponding robust variance matrix, since the

modified Wald test indicated error heterocedasticity.

Table 7(*): Results obtained for the ( )nConsumptioIncome / ratio

Variables FD FD FE FE RE RE

Gini 0.0017 (0.15)

-0.0094 (-0.66)

0.0175 (1.89)

0.0083 (0.92)

0.0198 (2.28)

0.0141 (1.62)

(Labor/National) -0.4814 (-0.77)

-0.2201 (-0.36)

-0.3811 (-1.08)

0.0669 (0.13)

-0.2387 (-0.68)

0.1768 (0.34)

yInformalit 0.3925 (0.22)

0.5190 (0.29)

-0.6169 (-1.56)

0.4192 (0.82)

-0.7312 (-2.07)

-0.1486 (-0.34)

GDP∆ -0.9023 (-2.21)

-0.7095 (-1.84)

-1.0436 (-1.71)

-1.4102 (-2.27)

-0.9736 (-1.72)

-1.3230 (-2.26)

venueTaxRe∆ 0.0138 (0.13)

-0.0372 (-0.28)

0.1439 (0.71)

0.1648 (0.69)

0.1391 (0.76)

0.1581 (0.76)

Inflation 0.0126 (0.41)

0.0251 (0.78)

0.0112 (0.33)

0.0601 (1.46)

0.0133 (0.43)

0.0594 (1.64)

iesOECDcountr ------- ------- ------- ------- 1.0222 (2.39)

1.0210 (2.74)

iesPoorcountr ------- ------- ------- ------- 0.2051 (0.37)

0.0858 (0.14)

Election -0.0324 (-1.42)

-0.0315 (-1.22)

-0.0500 (-1.34)

-0.0435 (-1.07)

-0.0504 (-1.40)

-0.0442 (-1.09)

Left 0.0746 (1.21)

0.0688 (0.96)

0.0641 (1.47)

0.0734 (1.48)

0.0663 (1.54)

0.0738 (1.49)

Right -0.0767 (-0.88)

-0.0852 (-1.03)

-0.0836 (-1.48)

-0.0646 (-1.10)

-0.0792 (-1.36)

-0.0665 (-1.10)

Regime 0.0616 (0.55)

0.0818 (0.56)

-0.1214 (-1.25)

-0.1077 (-0.82)

-0.1268 (-1.33)

-0.1119 (-0.90)

Year Dummies No Yes No Yes No Yes

Overall R2 ---------- ---------- 0.016 0.0132 0.1253 0.1052

(*) T statistics between parenthesis, and the non-significant variables at 10% are shaded. Breusch and Pagan LM test for random effects→ Test: Var(u) = 0; chi2(1)= 3079.18; Prob > chi2 = 0.0000 Modified Wald test for groupwise heteros.→H0: sigma(i)^2 = sigma^2 for all i; chi2 (35) = 3.2e+31; Prob>chi2 = 0.0000

The Breush-Pagan test indicated the presence of a fixed effect, but the proximity

between the FE and RE coefficients once again suggests that there is no correlation between

the compound error in t and the models’ explanatory variables in t – and therefore non-

correlation between the fixed effect and the observed explanatory variables. Since the standard

Hausman test assumes non-autocorrelation and homocedasticity of the residuals, a robust

Hausman test was implemented once again (see Wooldridge(2002): pg. 291). The results,

which may be seen in Annex 3, reveals that all the coefficients obtained with the fixed effect

model are statistically equal to the coefficients obtained with the random effects model – as was

the case for the ( )PersonalCorporate / ratio model. Again, there was no evidence of correlation

between the fixed effect and the explanatory variables. Thus, 0)( =itit xeE , where itiit uce += .

As mentioned earlier, it is also necessary to consider the possibility of the error not being

strictly exogenous, in spite of the strong evidence of contemporaneous exogeneity. In the model

for the ( )nConsumptioIncome / ratio, it also may be argued that an increase in this ratio for a

given year may affect the growth of the product, provoking correlation of the error in t with one

of the explanatory variables in (t+1).

Following Wooldridge (2002: pgs 284 and 285), the assumption of strict exogeneity was

formally tested by comparing the FD and FE estimated coefficients for the six economic

variables. The test was a robust one, then no assumption was made regarding the error. As

may be seen in Annex 3, the result indicated that the six FE and FD coefficients compared were

statistically equal. Thus, it may be concluded that the estimators FD, FE and RE are consistent.

Unfortunately, the lack of endogeneity problems does not guarantee a satisfactory

explanatory power of the covariates. Most of the selected explanatory variables were not

statistically significant with regard to the ( )nConsumptioIncome / ratio. None of the four political

variables proved to be relevant, and only one of the economic variables ( GDP∆ ) had a

significant marginal effect in all the estimations. The data indicate that in periods of growth, the

countries’ ( )nConsumptioIncome / ratio tends to drop.

From the results obtained with the RE model, it may be observed that, in spite of

controlling the political and economic variables pointed out in the literature, the dummy for

OECD countries continued having a significant marginal effect, revealing that the selected

independent variables were not able to explain the differences observed in the

( )nConsumptioIncome / ratio between developed countries and the rest.

Everything indicates that there is some variable, not mentioned in the literature nor

included in the regression, whose importance in explaining the ( )nConsumptioIncome / ratio is

expressive. By estimating an OLS regression in which the independent variables were only the

fixed effect34 and the year dummies, it was obtained an R2 value of 0.96. On the other hand, the

estimation of the original model with OLS – which included the iesOECDcountr , iesPoorcountr ,

as well as the year dummies, but did not include the fixed effect –, produced an R2 of only 0.37.

By analyzing the marginal effects which were significant in at least one of the

estimations, it was observed that the results do not corroborate the idea that countries with

higher inequities would tend to impose relatively higher taxes on consumption than on income,

34 The individual fixed effects for each country were recovered with the estimation of the fixed effect model.

thus favoring the smaller rich population. On the contrary, what was verified is that countries

with higher inequities tend to impose relatively higher taxes on income.

Moreover, the results corroborate the idea that countries with higher levels of informality

tend to establish tax structures that impose relatively more taxes on consumption. As observed

in Kemmerling (2003), inflation is positively related to income tax revenue, and thus tends to

increase the ( )nConsumptioIncome / ratio.

4. Conclusions

This study sought to identify the variables that explain the differences between the tax

structures of developed countries and other countries, based on the determinants pointed out in

the literature. These potential determinants were tested through two ratios: the corporate

income tax/personal income tax ratio, and the income tax/consumption tax ratio. An unbalanced

panel with data on 35 countries was used for the 1976-1995 period. Tests were carried out

taking care with endogeneity problems that may be caused by simultaneity, omitted variables

and sample selection.

Results show that the determinants pointed out in the literature explain much better the

differences between countries in relation to the first ratio (corporate income tax/personal income

tax). Regarding the second ratio (income tax/consumption tax), the selected characteristics

certainly were not capable of explaining the differences observed between the developed

countries and other countries. There is some variable, not mentioned in the literature and not

included in the regressions, whose importance in explaining the income tax/consumption tax

ratio is expressive.

There are two variables that are strong determinants of the corporate income

tax/personal income tax ratio: the share of labor income in national income, and the product

growth rate. As expected, the corporate income tax/personal income tax ratio is greater in

developed countries due to the larger share of labor income in the national income. This

ensures significant revenue via personal income taxes. On the other hand, in periods of higher

rate of growth, this ratio falls.

Moreover, though less significant and robust, informality and right-wing governments also

influence the corporate income tax/personal income tax ratio. As predicted in the literature,

countries with higher levels of informality tend to collect more taxes from businesses with regard

to overall income tax revenue. Contradicting expectations, right-wing governments collect more

corporate income taxes.

In spite of the low explanatory power of the income tax/consumption tax ratio model,

results show that during periods of growth this ratio tends to decrease. There is evidence, which

is not as significant, indicating that countries with higher levels of informality actually tend to set

up tax structures with a higher proportion of consumption taxes. Greater income inequity is a

factor that tends to increase the share of income taxes, a result that was not expected. The rate

of inflation is a factor that triggers an increase in the proportion of income taxes in the overall tax

structure.

It was not detected any relation between the political variables and the income

tax/consumption tax ratio.

References Aizenman, J; Jinjarak, Y. (2006). Globalization and Developing Countries - A Shrinking Tax Base?. NBER Working

Papers 11933, National Bureau of Economic Research, Inc. Alm, J. (1996) What is an “optimal” tax system. National Tax Journal, vol 49, number 1 pp 117-33. Alm,J. Bahl,R. and Murray, M.N. (1990) Tax structure and tax compliance. The Review of Economics and

Statistics, vol 72, number 4, pp 603-613. Alesina, A; Angeletos, G. (2003). Fairness and Redistribution: U.S. versus Europe. NBER Working Papers 9502,

National Bureau of Economic Research, Inc. Ashworth, J; Heyndels, B (2002). Tax structure turbulence in OECD countries. Public Choice 111: 347–376. Atkinson (1973) Worker Management and the Modern Industrial Enterprise. The Quarterly Journal of Economics,

MIT Press, vol. 87(3), pages 375-92 Atkinson, A. B; Sandmo, A. (1980). Welfare Implications of the Taxation of Savings. Economic Journal Royal

Economic Society, vol. 90(359), pages 529-49 Atkinson, A. B., Stiglitz, J.E. (1976). The design of tax structure: direct versus indirect taxation. Journal of Public

Economics 6, 55–75. Burgess, J.R.;Stevens,L. (2000) Job flows, worker flows and churning, Journal of Labor Economics, v.18, n.3,

p.473-502. Chamley, C. (1986) Optimal Taxation of Capital Income in General Equilibrium with Infinite Lives. Econometrica 54

No. 3 pages 607–22. Diwan, I. (2000). Labor Shares and Globalization, World Bank, Washington. (November) Eaton, J.; Rosen, H. S. (1980). Taxation, Human Capital and Uncertainty. The American Economic Review, volume

70, number 4 (september), pages 705-15. Feldstein, M. S. (1978) The Rate of Return, Taxation and Personal Savings. Economic Journal, Royal Economic

Society, vol. 88(351), pages 482-87. Feldstein, M. S. (1978). The Welfare Cost of Capital Income Taxation. Journal of Political Economy, University of

Chicago Press, vol. 86(2), pages S29-51. Geys,B.; Veirmer,J. (2005). Taxation and presidential approval: separate effects from tax burden and tax structure

turbulence? EPCS Conference Durhan, UK. Preliminary Version. Heady, C. (1993) Optimal taxation guide as a guide to tax policy: a survey. Fiscal Studies. Volume (Year): 14, Issue

(Month): 1 (February), Pages: 15-41 Hettich,W.; Winer, S. (1999). Democratic choice and taxation: A theoretical and empirical analysis. Cambridge:

Cambridge University Press. Keen, M.; Ligthart, J. E. (2002). Coordinating Tariff Reduction and Domestic Tariff Reform,” Journal of International

Economics 56 (2002):489–507. Kemmerling, A (2003). The political economy of tax mixes in OECD countries. Paper presented at the 2nd ECPR

General Conference, Marburg, 18-21th September, 2003. Lavigne, R. (2006). The Institutional and Political Determinants of Fiscal Adjustment. Bank of Canada Working

Paper 2006 - 1 Lee, Y; Gordon, R. H. (2005). Tax Structure and Economic Growth. Journal of Public Economics, Elsevier, vol.

89(5-6), pages 1027-1043, June. Lucas Jr. , R.(1990) Why doesn´t capital flow from rich to poor countries? The American Economic Review, Vol.

80, No. 2, pages: 92-96. Messere, K. (1993). Tax Policy in OECD Countries: Choices and Conflicts. The Netherlands: IBFD Publications BV. Metcalf, G. E. (1994). Life-Cycle Versus Annual Perspective on the Incidence of a Value-Added Tax. Tax Policy

and the Economy 8, edited by James M. Poterba. Cambridge: The MIT Press, 1994. Mirrlees, J. A. (1971). An Exploration in the Theory of Optimum Income Taxation Review of Economic Studies, vol.

38(114), pages 175-208.

Musgrave, R. A.; Case, K. E. Leonard H. (1974) The Distribution of Fiscal Burdens and Benefits. Public Finance Quarterly, vol. 2, pages: 259-311.

Nijman, T., Verbeek, M. (1992) “Nonresponse in panel data: The impact on estimates of a life cycle consumption function”, Journal of Applied Econometrics, vol.7, num.3, jul-sep, pp.243-257.

Pechman, J. (1985) Who Paid the Taxes: 1966-85? Washington DC: Brookings Pommerehne, W.W. and Schneider, W. (1983). Does government in a representative democracy follow a majority

of voters’ preferences: An empirical examination. In H. Hanush (Ed.), Anatomy of government deficiencies, 61–88. Berlin-Heidelberg: Springer Verlag.

Rogoff, K. (1990). Equilibrium political budget cycles. American Economic Review 80: 21–36. Scheneider, F.; Enste, D (2000). Shadow Economies: Size, Causes, and Consequences. Journal of Economic

Literature Vol. XXXVIII (March 2000) pp. 77–114. Smith, A. (1776). An Inquiry into the Nature and Causes of the Wealth of Nations. Tanzi, V.; Zee, H. (2000). Tax policy for emerging markets: Developing Countries. National Tax Journal, volume 53,

number 2, pages 299-322. Tosun, M. S. (2003). An Analysis of Tax Structure Changes in the MENA Region in Response to Trade

Liberalization. West Virginia University. Tosun, M. S.; Abizadeh, S. (2005). Economic Growth and tax components: an analysis of tax changes in OECD.

Applied Economics, volume 37, páginas 2251 a 2263. Tozun, M (2002) Trade Liberalization and Tax Exporting Incentives. Working Paper No.4. West Virginia Public

Finance Program, Morgantown, WV. Trostel, P. A. (1993).The effect of taxation on human capital. The Journal of Political Economy, Vol. 101, número 2,

páginas 327 a 350. Uzawa, H. (1965) Optimal Technical Change in an Agregative Model of Economic Growth? International Economic

Review, 6, pp:18-31 Volkerink, B.; de Haan, J. (1999). Political and institutional determinants of the tax mix: An empirical investigation

for OECD countries. SOM research Report 99E05. Wildmam, F (2001). Tax Structure and Growth: Are Some Taxes Better Than Others? Public Choice 107, pages

199-219. Wooldridge, J.(2002) Econometric Analysis of cross section and panel data, The MIT Press, Cambridge,

Massachussets.

ANNEX 1 – Variable addition tests for selection bias Table A.1 (*) –Variable addition tests for selection bias Variable indicating selection

Pooled OLS: [1]

FE Model: [1]

RE Model: [1]

Pooled OLS: [2]

FE Model: [2]

RE Model: [2]

sit-1 -0,0859 (-0,61)

-0,0751 (-0,87)

-0,0803 (-0,96)

0,1741 (0,74)

-0,0303 (-0,45)

-0,0314 (-0,55)

sit+1 0,0212 (0,17)

0,0574 (0,84)

0,0555 (0,85)

0,3654 (1,59)

-0,0129 (-0,27)

-0,0109 (-0,23)

Average temp.sit

-0,3462 (-2,87)

------------- -0,0202 (-0,06)

3,2980 (7,18)

------------- 1,3641 (1,44)

(*) T statistics between parenthesis, and the non-significant variables at 10% are shaded.

[1] ( )PersonalCorporate / , [2] ( )nConsumptioIncome /

ANNEX 2 – Model for the Corporate Income Tax/Personal Income Tax ratio A.2.1 Equivalency test for the parameters obtained with the FE and RE models The results of these models were estimated with the command “reg” and stored with the command “estimates store” before the following programming. suest fixed random test [fixed_mean]fe_gini=[randon_mean]re_gini → chi2( 1) = 0.22; Prob > chi2 = 0.6423 test [fixed_mean]fe_labor/national=[randon_mean]re_labor/national → chi2( 1) = 0.83; Prob > chi2 = 0.3620 test [fixed_mean]fe_informality=[randon_mean]re_informality → chi2( 1) = 0.33; Prob > chi2 = 0.5631 test [fixed_mean]fe_varGDP=[randon_mean]re_varGDP → chi2( 1) = 0.01; Prob > chi2 = 0.9061 test [fixed_mean]fe_varRevenue=[randon_mean]re_varRevenue → chi2( 1) = 1.04; Prob > chi2 = 0.3070 test [fixed_mean]fe_inflation=[randon_mean]re_inflation → chi2( 1) = 0.00; Prob > chi2 = 0.9914 test [fixed_mean]fe_left=[randon_mean]re_left → chi2( 1) = 1.95; Prob > chi2 = 0.1622 test [fixed_mean]fe_right=[randon_mean]re_right → chi2( 1) = 1.92; Prob > chi2 = 0.1657 test [fixed_mean]fe_election=[randon_mean]re_election → chi2( 1) = 0.11; Prob > chi2 = 0.7372 test [fixed_mean]fe_regime=[randon_mean]re_regime → chi2( 1) = 0.01; Prob > chi2 = 0.9341 A.2.2 Equivalency test for the parameters obtained with the FD and FE models

The results of these models were estimated with the command “reg” and stored with the command “estimates store” before the following programming. suest firstdiff fixed test [fixed_mean]fe_gini=[firstdiff_mean]d.gini → chi2( 1) = 0.31; Prob > chi2 = 0.5787 test [fixed_mean]fe_labor/national=[firstdiff_mean]d.labor/national → chi2( 1) = 0.74; Prob > chi2 = 0.3908 test [fixed_mean]fe_informality=[firstdiff_mean]d.informality → chi2( 1) = 0.95; Prob > chi2 = 0.3295 test [fixed_mean]fe_varGDP=[firstdiff_mean]d.varGDP → chi2( 1) = 5.64; Prob > chi2 = 0.0176 test [fixed_mean]fe_varRevenue=[firstdiff_mean]d.varRevenue → chi2( 1) = 0.00; Prob > chi2 = 0.9441 test [fixed_mean]fe_inflation=[firstdiff_mean]d.inflation → chi2( 1) = 3.02; Prob > chi2 = 0.0820

ANNEX 3 – Model for the Income Tax/Consumption Tax ratio A.3.1 Equivalency test for the parameters obtained with the FE and RE models The results of these models were estimated with the command “reg” and stored with the command “estimates store” before the following programming. suest fixed randon test [fixed_mean]fe_gini=[randon_mean]re_gini → chi2( 1) = 0.47; Prob > chi2 = 0.4947 test [fixed_mean]fe_labor/national=[randon_mean]re_labor/national → chi2( 1) = 1.85; Prob > chi2 = 0.1739 test [fixed_mean]fe_informality=[randon_mean]re_informality → chi2( 1) = 1.84; Prob > chi2 = 0.1748 test [fixed_mean]fe_varGDP=[randon_mean]re_varGDP → chi2( 1) = 0.17; Prob > chi2 = 0.6830 test [fixed_mean]fe_varRevenue=[randon_mean]re_varRevenue → chi2( 1) = 0.01; Prob > chi2 = 0.9366 test [fixed_mean]fe_inflation=[randon_mean]re_inflation → chi2( 1) = 0.07; Prob > chi2 = 0.7907 test [fixed_mean]fe_left=[randon_mean]re_left → chi2( 1) = 0.03; Prob > chi2 = 0.8649 test [fixed_mean]fe_right=[randon_mean]re_right → chi2( 1) = 0.03; Prob > chi2 = 0.8614 test [fixed_mean]fe_election=[randon_mean]re_election → chi2( 1) = 0.00; Prob > chi2 = 0.9882 test [fixed_mean]fe_regime=[randon_mean]re_regime → chi2( 1) = 0.02; Prob > chi2 = 0.8820 A.3.2 Equivalency test for the parameters obtained with the FD and FE models The results of these models were estimated with the command “reg” and stored with the command “estimates store” before the following programming. suest firstdiff fixed test [fixed_mean]fe_gini=[firstdiff_mean]d.gini → chi2( 1) = 0.84; Prob > chi2 = 0.3597 test [fixed_mean]fe_labor/national=[firstdiff_mean]d.labor/national → chi2( 1) = 0.02; Prob > chi2 = 0.8798 test [fixed_mean]fe_informality=[firstdiff_mean]d.informality → chi2( 1) = 0.26; Prob > chi2 =0.6117 test [fixed_mean]fe_varGDP=[firstdiff_mean]d.varGDP → chi2( 1) = 0.03; Prob > chi2 = 0.8558 test [fixed_mean]fe_varRevenue=[firstdiff_mean]d.varRevenue → chi2( 1) = 0.43; Prob > chi2 = 0.5128 test [fixed_mean]fe_inflation=[firstdiff_mean]d.inflation → chi2( 1) = 0.08; Prob > chi2 = 0.7840