Efficiency in tax collection: evidence from Brazilian municipalities

Paulo Arvate1 and Enlinson Mattos

2

This paper assesses and tests the efficiency in tax collection in 3,359 Brazilian

municipalities. First, applying a non-parametric methodology - Free Disposable Hull,

we compute comparative efficiency scores for each municipality. In particular, given an

individual amount of capital an, labor treated as inputs, we evaluate efficiency in

producing two outputs: amount of per capita local tax collected (tax revenue) and the

size of local informal economy (tax evasion). Second, controlling for spatial

interaction, the paper also investigates the factors that influence that efficiency score.

The results suggest that the federal and state transfer to municipalities (flypaper effect)

and local public expenditure per-capita are negatively associated with their ranking.

Keywords: Efficiency, Tax collection, Informal economy, FDH, spatial econometrics

JEL: H20, H21, C67, C14, C31.

1 CEPESP and EESP - Getulio Vargas Foundation, e-mail: parvate@fgvsp.br. 2 CEPESP and EESP - Getulio Vargas Foundation, e-mail:enlinson@fgvsp.br..

1. Introduction

One of the main problems of tax implementation faced by developing countries

is the size of informal economy. The tax burden falls over a relative small fraction of

the economy, while many individuals resist contributing to the government tax

collection by opting out to the formal sector. It is true that, sometimes these individuals

do not find work elsewhere, but it may also happen that skilled workers want to pay less

tax.3 This phenomenon is also noticed by the media. In May of 2006 in the Business

Weak Magazine, Diana Farrel, Mackinsey´s consultant, argues that:

“Informal business, even large ones, choose you stay that way if there

is a change in the factors that generally drive them into the informality:

high corporate tax and the bureaucratic burden of operating formally.

In developing countries, only about half of total tax revenue is paid by

registered businesses (with the rest contributed by individuals).” 4

Usually, the issue of the tax collection efficiency is concerned with the

maximization of tax revenue instead of minimizing the size of informal economy.

There is an obvious trade-off between maximizing tax revenue and minimizing the size

of informal economy. Since tax collection comes from the formal sector, governments

are willing not to sacrifice this sector. Therefore the optimal alternative to finance

expenditures is to reduce the shadow economy.

Although the relation between tax collection efficiency and size of informal

economy has not been accurately explored, Bajada (2007), Schneider and Klinglmair

(2004), Dell´Anno and Schneider (2003), Schneider and Enste (2000), Giles and

Caragata (1998) and Giles (1998) explore the relation between tax burden and informal

(shadow) economy. However, Giles (1999) estimating the New Zealand’s Tax-Gap,

attempts to measure the loss in tax collection on the part of that government due to the

growth of the informality in that country.

In contrast, the objective of the paper is twofold. First, to investigate the

efficiency scores of tax collection in the municipalities in terms of amount of per-

capita local tax collected (tax revenue) and the size of informal economy (tax evasion).

Second, we attempt to verify the factors that are associated with that efficiency score.

This work contributes to the literature when includes the size of informal economy as

3 This decision is of course dependent on the size of the government and consequently the tax imposed as well the individual’s earnings. See also Strand (2001) for informality only at the top of income distribution. 4 See also Kenyon and Kapaz (2005).

an item in the tax collection efficiency score.5. It also advances to test the factors that

are correlated with that score, in particular, municipality’s characteristics (ideology of

the mayors, technology, expenses) and residents’ characteristics (age, income and

wealth), taking into consideration spatial effects.

The results suggest that the federal and state transfers to municipalities are negatively

associated with their ranking. This leads to a reinterpretation of the flypaper effect. Higher

transfers are associated with less efficiency in local tax collection and reduction of local

informal sector. Also, technology implementation improves efficiency. In addition, we find

that higher income per-capita is related to more efficiency in informal sector reduction but also

to less efficiency in local tax collection per-capita

Brazilian municipalities represent an interesting sample. First, Brazil is in the

“eye” of the hurricane in terms of size of the tax revenue and the informal economy.

The total tax burden has approached to forty percent (40%) of the GDP in the 2006

(Ipeadata, 2006) and the size of the shadow economy, represents more than thirty five

percent (35%) of the official GDP in 2000 (Schneider and Klinglmair, 2004). Second,

the Brazilian municipalities present a uniform tax, contract worker and company

openings legislation.6

The next section lays out the technique used to measure the efficiency score

(Free Disposable Hull) and the empirical model necessary to estimate the factors

correlated with the two measures of efficiency used to rank the municipalities (tax

revenue of the formal sector and the size of formality). Section 3 presents the results

and the last section concludes.

2. Empirical Implementation

2.1 Efficiency Scores

The first step is to compute the efficiency scores of each municipality. To obtain

such efficiency indicator is necessary to determine the inputs and the outputs of the

municipality and then compare them (both input and output) to each other. That

produces a score of the position for each unit. They are input and output efficiency

5 The efficiency score is usually computed for public expenditures. For instance ,see Gupta e Verhoeven (2001), Afonso e Aubyn (2004), Herrera e Pang(2005), Afonso, Schuknecht e Tanzi(2005), Tandon (2005), Afonso e Fernandes (2003), Brunet (2006) e Souza, Cribari-Neto e Stosic (2005). 6 Although the municipalities could decide on tax rate, fines and exemptions, all municipalities have basically two taxes by the federal law: the service tax (ISS) and the residential property tax (IPTU). In addition, the legislation for contract workers and opening of companies is federal.

scores whose range goes from 0 to 1. Every municipality on the Production Possibility

Frontier receive the maximum score 1. These are relative efficiency scores. For

instance, the input efficiency score of a unit means how much less input could be used

to obtain the same level of output. Similarly, the output efficiency score calculates how

much more output could be produced given the amount of input.

This paper utilizes Free Disposable Hull (FDH) methodology to compute those

scores7. FDH is a non-parametric technique proposed by Deprins, Simar e

Tulkens(1984). The major advantages of FDH analysis is that it imposes only weak

assumptions on the production technology but still allows for comparison of efficiency

levels among producers. It is necessary to assume that reduction of the inputs (outputs)

with the same technology maintaining the output (input) fixed cross municipalities are

made. The production set is not necessarily convex.

That guarantees the existence of a continuous FDH which is going to be used as

a dependent variable to identify the best practices in government tax collection to asses

what are the factors that increase (relative) efficiency.

Therefore, to determine the efficiency score using FDH analysis, assume n

municipalities, m products/services produced by those governments with k inputs. In

terms of production function

(1)

where 1mxy is the ouput vector and 1kxx corresponds to the input vector. One can rank

the municipality i if it is not the most efficient in terms of input

(2)

and lnn ,.....,1 are l municipalities more efficient than municipality i.

Similarly, in terms of output, municipality i can be ranked in relation to the most

efficient

7 Two other methodologies are also used in the literature. First, Data Envelopment Analysis (DEA) is also non-parametric and builds envelops from the efficient points on the frontier differently from the FDH explained above. See, for instance Afonso and St. Aubyn (2004) and Herrera and Pang (2005) and Sousa, Cribari-Neto, Stosic and Borko (2005). Second, a parametric approach denominated stochastic frontier computes the frontier using regression techniques. This method assumes error distributions. See Greene (2003).

)(

)(,....,1,....,1

ix

nxMAXMIN

j

j

mjnlni ==

nixFy ii ...1),( ==

(3)

The procedure can be summarized as follows. First a producer is selected. Then

all producers that are more efficient than it are marked. For every pair of producers

containing the unit under analysis and the more efficient one is computed a score for

each input (dividing the input of the unit under analysis and the more efficient one).

Then select the more efficient producer that bring the unit under analysis closest to the

frontier. The calculation of the input efficiency score can be illustrated with an example.

Suppose 3 producers with a 2-input 2-output case. A(20, 33; 15, 10), B(19, 30, 16,12),

C(25, 32 ; 16, 11). The first two numbers denotes inputs while the last two numbers

yield outputs. A is less efficient than B -A uses more of both inputs while its outputs is

smaller. However C is not more efficient than A. The input score for A can be calculated

in the table below. Observe that since C is not compared to neither A and B, it gets

score equal to 1. B also receives 1 because it is more efficient than A and there is no

other municipality more efficient than it is.

Table 1 – Example

Several studies have used FDH analysis to asses the government spending

efficiency. Vanden Eeckaut, Tulkens and Jamar (1993) establish the relative efficiency

municipalities for Belgium. Gupta and Verhoeven (2001) consider the efficiency in

education and health expenditures for Africa countries. In contrast this paper studies the

tax collection efficiency for Brazilian municipalities and what are the factors the

influence such ranking.

2.2 Regression Analysis

In fitting a regression plane using municipalities’ data, one has to take into

consideration the possible spatial interaction of the variables. The most intuitive

criterion for selecting neighbors within a local government context is based upon

geographical proximity, and this paper is going to focus on contiguity (rook criteria).8

Within our model this is particularly reasonable since the municipalities are sensitive to

neighboring jurisdictions for two reasons (as argued in Besley and Case 1995). First,

8 Results for Euclidian distance of the municipalities and queen criteria are available upon request.

)(

)(,....,1,....,1

iy

nyMINMAX

j

j

mjnlni ==

they experience similar shocks in the economy and second, the information about the

nearby jurisdictions is likely to spread out.9

Therefore our base model is

uXWYY ++= βδ (4)

where Y is a (nx1) vector of municipal efficiency scores. X is (nxk) matrix. In our case

k=17 (with the constant). W is a (nxn) matrix with the weights of neighbors’

municipalities. The equation (4) also points out that any estimation by OLS will

represent a biased estimation of the coefficients.

Neighbors may also be subject to correlated random shocks that cannot be attributed to

the spillover causality which can imply wrong inferences. Therefore, I also check the

possibility of a model that allows for possible correlation among the errors of

neighboring.

ερ

β

+=

+=

Wuu

uXY (5)

where 2~ (0, )Nξ σ .

Even though estimation of (5) by OLS is not biased, it is not consistent. Then a

correction of the covariance matrix will improve the estimation.

2.3 Data

2.3.1 FDH data

Inputs are defined as capital, labor and the combination of them. 10 As “proxy”

of the capital (K), capital investments per-capita from 1980 and 2004 are accumulated

and depreciated by the rate of 3% at each year. 11 As “proxy” of the labor (L) we use

the number of both indirect and direct public works per capita in the municipalities. We

use two measures of output of the ´´production function´´ of the government: local tax

revenue (T) and the proportion of formal sector (inf). The amount of local tax per-capita

collected is used for the first variable while the proportion of formal workers in the total

workers (excluding self-employers and employers) as a “proxy” of the size of the

formal sector. 12 With this choice, we aim to see the two sides of the same coin: efficient

9 See also Anselin (1988). 10 In addition, the total municipality’s expenditures per capita is used as input. The results are worse than shown here and are available upon request. 11 We test alternatives rates of depreciation: 5% e 8%. The results are similar. 12 There is a distinction between formal (CLT) and informal workers in Brazil. The informal workers do not have the legal right of job tenure. We could say that their job tenure is more precarious than that of formal workers. The expression ‘CLT’ has its origin in Law 5452 of May of 1943, entitled the

municipalities will be the ones that have more tax revenue but also a higher

participation of the formal sector in the total municipality economy.13

2.3.2 Regression Data

To identify possible variable associated with the differences in efficiency of tax

collected among the municipalities we select:

a. Ideology – Despite the literature mention the effect of the ideology of the

governments on the taxation, that is not done relating ideology and tax revenue

efficiency and informality together. Messere (1993) argues that center-right

governments generally tend to choose a total tax burden lower directing it for a

composition where more consumer taxes predominate of what income taxes. On the

other hand, left-wing governments tend to favor a higher size of the government which

implies a higher tax burden, where more income tax predominates over consumption

tax. Pommerehne and Scheneider (1983) analyzes Australia during the decade of 70

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

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

higher tax/GDP ratio.

We use the ideological classification of the parties of the mayors for 2004

(Pesquisa de Informações Básicas Municipais of the IBGE) following the classification

proposed by Coppedge (1997). Two dummies are used to represent the ideology. The

parties classified as center-left and left are denominated by the variable left (left) and

the parties from center-right and right are denoted as right (right).

b. Technology - As Sousa et al (2005) argue from the expenditure view, the technology

helps to increase efficiency. We use two dummy variables as “proxy” of the existence

of technology: tax service data set computerized (ISSinform) and the services from

municipalities to contributors through Internet, portal or web-page (serint, source:

Pesquisa de Informações Básicas Municipais, IBGE , 2004 )14.

c. Fiscal impacts - Certainly a municipality that has an expense level higher searches for

a higher level of tax revenue. That could lead to higher tax collection efficiency.15 On

the other hand, more transfers to the municipalities from either the federal or state

Consolidation of Labor Laws (CLT in Portuguese). This law establishes the rules of labor relations in the private sector. 13 The data used to build the variable tax-collect was taken from Ipeadata (2004). The variable that captures informality (inf) is taken from the CENSO (2000). 14 We also test the possibility of residential property tax data set computerized (IPTUinform) and the results show that this variable is not significant. 15 The literature shows only that higher governments are more inefficient on expenditures. See Herrera and Pang (2005) and Afonso, Schuknecht and Tanzi (2005).

government, might imply in more incentives to spend (flypaper effect). Consequently,

lower is the incentive to search for efficiency. We construct two variables to capture

these effects. We consider the local expenditure per capita (exp). To observe the effect

of the transfers into the model, we include the transfers per capita of both the state and

municipal governments (transf). The data of expenditure and transferences are taken

from Ipeadata (2004)16 and the population data is extracted from the Pesquisa de

Informações Básicas Municipais (IBGE, 2004).

d. Characteristics of the municipalities – To control for territorial differences in the

municipalities, we use the followings variables: the percentage of urban population over

resident population (urb), the population density (density) and percentage of people

with electric energy in their residence (eletr), the percentage of people employed

(Economically Active Population divided by the Working Age Population - emp), the

percentage of resident doctors for a thousand inhabitants (doctor) and the cost of

transport of the Municipal Headquarters until the nearest State Capital (transport). With

exception of the transport cost (Ipeadata, 1995), the data are taken from Ipeadata (2000).

e. Characteristics of the residents – To cite as example of how these characteristics

matter in Brazil, it is very common to observe pensioner exemption in the Imposto

Predial e Territorial Urbano (IPTU - the most important urban territorial tax collected

from the municipalities). Or as Rodríguez (2004) argues: ´´the bargain between groups

of interest and politicians on exemptions taxes implies that individuals with high

income do not pay taxes” (p.957). To identify these characteristics of the contributors

we use the percentage of people with more than sixty five years in the municipality

living alone (old), percentage of residents in the municipality with a computer (compu),

percentage of poor people in the municipality (poverty) and income per capita (Inc.).

The data are taken from Ipeadata, 2000. The Table 2 summarizes the Descriptive

Statistics.

3. Results

3.1. Efficiency score results

Table 3 presents the summary statistics of the efficiency score for each state17. As

explained above, we compute six efficiency scores: three input-related and three output-

16 Site: www.ipeadata.gov.br 17 The full table is available upon request. Results concerning administrative costs instead of the pair capital and labor as input are also available upon request.

related. The difference among them is the output. In the first two columns the output

considered is local tax collected percapita (T), the next two columns regard the output

equal to the size of formal economy, and the oher two columns take into consideration

both outputs. The last column presents the municipalities that are efficient in, at least,

one criterion.

There are some characteristics interesting in the data. The results suggest that a

large number of efficient cities in the South of Brazil (Sao Paulo, Minas Gerais, Espirito

Santo, Rio de Janeiro, Parana, Santa Catarina and Rio Grande do Sul). Also, 82% of the

states that have efficient cities include their capital as one of them. Sao Paulo state, the

richest and more developed one, has 25 cities classified as efficient, while Rio Grande

do Sul has 18 and Santa Catarina 15. In most of the cases, when states out of the South

region allocate an efficient city, that one is the capital. (approximately (70%)). Piaui, the

poorest state, has no efficient city while Maranhao, the second poorest, has two, and one

of them is the capital, Sao Luis.

For instance, the results show that ninety five (95) municipalities present at least

one type of efficiency (input or output and three different outputs: tax collection, size of

local informal economy and both). Almost fifteen per cent (13 out of 95) are capitals of

the states. Among those, only four can be considered efficient in all criterions used (Rio

Branco, Belém, Salvador and Porto Alegre). Other municipalities such as Manacapuru

(Amazonas), Rorainópolis (Roraima), Bacabal (Maranhão), Vila Velha (Espirito Santo)

and São João de Miriti (Rio de Janeiro) are also efficient in all criterions.18

In order to better design public policies to improve tax collection efficiency, we

relate the computed efficiency rankings with possible explanatory variables, described

below.

3.2. Regression Results

Before using traditional regression methods, it is necessary to check if there

exists spatial interaction between the computed efficiency scores. For instance, the fact

that one jurisdiction is relatively efficient, that might influence the behavior and

practices of neighborhood jurisdictions in order to improve tax collection methods.

Table 4 presents unconditional Moran´s I statistics for these scores and confirms

that suspect, i.e., we cannot accept the hypothesis of non-spatial covariance among the

18 See table 2.

efficient scores. Tables 5-8 in the linear regression columns also reinforce that, even

conditional; the above hypothesis cannot be accepted (See Moran´s I statistic, LMerror

and LMlag tests). In particular, the tests suggest a positive spatial correlation among

municipalities efficiency scores.

We divide the regressions results according to assumptions on which variables

are input and which ones are output. Cases 1-3 assume that the Inputs are capital and

labor while case 4 considers the average score of inputs, outputs or both. Moreover,

Case 1 considers only per-capita local tax collection, Case 2 takes into consideration the

size of formal local economy as the output and Case 3 combines both outputs.19

Case 1 – Output (T): Local tax collected per-capita.

Table 5 presents the result for the Case1. It shows that the likelihood of the

spatial error model is higher in both input and output-oriented scores. Therefore we

focus the analysis in those columns. Not surprisingly, the variables that capture

technology (ISSinf and servint) in the process of tax collection are positively associated

with relative the efficiency scores. More importantly, the level of local expenditure per-

capita (exp) and transfers (transf) to municipalities (federal and state) is negatively

related with their efficiency ranking. In particular, a one real (R$) increase in per-capita

terms is associated with a 0,00003 decrease in the relative efficiency score. This last

outcome can be reinterpreted as the ´new flypaper effect´, as it says that higher transfers

are associated with less efficiency. Note that this result holds for every possibility of

input and output.

Income per-capita (inc) and poverty also relates negatively with tax collection

efficiency. This might reflect the fact that when there is a large fraction of the

population below the poverty line, they also have less information about government

spending. Then, richer individuals could benefit from more inefficient methods of tax

collection, since they are the ones who pay the tax. In addition to that, the inclusion of

political variables suggests that center mayor’s party is more efficient than right or left-

wing ones.

Last, municipalities located too far from the its state´s capital (transpo) tend to be

less efficient (coefficient equals to -0,00005).

19 Selected models are dashed and statistically significant variables are in bold letters.

Case 2 – Output(I): Size of Formal Local Economy

When we consider a different output, which is the size of local formal economy,

we find surprising results. As opposed to the Case 1, the likelihood of the spatial error

model is lower in both input and output-oriented scores than spatial lag modes. We only

discuss the results concerning the last ones.

First, table 6 shows that the variables income (inc) and poverty change theirs

signs. Now they are positively associated with the computed efficiency score. That

means that richer municipalities (high per -capita income) relates to higher efficiency in

universalizing tax payment (reduction in informal economy) rather than to increase tax

collection. Similarly, higher levels of local public expenditures per-capita (exp), now is

related to higher efficient scores. Municipalities that spend a lot, try to increase the

efficiency to collect from the informal sector.

Second, as expected, urbanization (urb) and residence density (density) is

correlated with higher efficiency in reduction the informal sector. And last, technology

(+), transfers (-) and distance from the state´s capital (-) have the same sign.

Case 3 – Outputs(T+I): Local tax collected per-capita and Size of Formal Local

Economy.

Table 7 concerns the result where the local governments have multiple

objectives: increase local tax collection per-capita and decrease local informal sector.

The spatial lag model is chosen for the input-oriented case while the spatial error model

is selected for the output-oriented one.

The variables concerning technology (ISSinf and servint), transfers (transf),

urbanization (urb), distance from state´s capital (transpo) have the same sign as before

(+,-,+,-). The difference here is that neither income per-capita (inc) nor local public

expenditure per-capita (exp) is statistically significant, while poverty is negative and

significant only for the output-oriented situation. This might reflect the conflict between

the two objectives and their relation to these variables.

Case 4 - Average Input Scores, Average Output Scores, Total Average Scores

This case attempts to compute how the explanatory variables are related to the

average input, average output and total average scores. We find similar results

concerning the following variables: technology (ISSinf and servint), transfers (transf),

urbanization (urb), distance from state´s capital (transpo). Income per-capita (inc) is not

significant while poverty is significant only in the last two models. Local public

expenditure per-capita (exp) is statistically significant (and negatively related to the

efficiency score) only in the average input score model. Political variables, in particular

right-wing mayor´s party (right), is related to less efficient average output and total

average scores. Similarly, the proportion of old-age persons in the community (old) is

negatively related to the average output and total average efficiency scores.

4. Conclusion

This paper assesses and tests the efficiency in tax collection in 3,359 Brazilian

municipalities. First, applying a non-parametric methodology - FDH (Free Disposable Hull), we

compute the efficiency scores for each municipality comparing to each other. Second,

controlling for spatial interaction, the paper also investigates the factors that influence that

efficiency score. The results suggest that the federal and state transfers to municipalities are

negatively associated with their ranking. This leads to a reinterpretation of the flypaper effect.

Higher transfers are associated with less efficiency in local tax collection and reduction of local

informal sector. Also, technology implementation improves efficiency. In addition, we find that

higher income per-capita is related to more efficiency in informal sector reduction but also to

less efficiency in local tax collection per-capita.

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Tables Table 1: Descriptive Statistics

Input: K,L (T) output (Tax) Input: K,L(I) output (I) Input: K,L(I,T) output (I,T) Input (Avg) output (Avg)

Average score

Min. 0.017 0.010 0.017 0.000 0.017 0.015 0.017 0.014 0.040

1st Q 0.150 0.299 0.159 0.037 0.170 0.312 0.164 0.222 0.212

Median 0.248 0.468 0.259 0.066 0.273 0.488 0.264 0.346 0.295

Mean 0.282 0.483 0.294 0.126 0.322 0.504 0.299 0.371 0.335

3rd Q 0.365 0.649 0.383 0.135 0.412 0.680 0.387 0.493 0.415

Max. 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Exp transf old urb density eletr compu emp inc

Min. 102.100 193.100 0.056 0.000 0.082 17.430 0.002 0.250 30.430

1st Q 436.600 533.700 10.336 43.200 12.920 87.920 0.998 0.508 107.510

Median 582.700 689.000 12.986 62.740 26.030 96.580 2.621 0.562 186.530

Mean 677.100 819.700 13.054 61.410 119.500 90.340 3.962 0.561 192.240

3rd Q 806.200 967.900 15.671 81.090 54.040 99.250 5.445 0.606 250.050

Max. 6327.100 7775.900 28.698 100.000 12700.000 100.000 41.405 0.932 954.650

ISSinform IPTUinform servint left poverty doctor transport right

Min. 0.000 0.000 0.000 0.000 0.639 0.000 0.000 0.000

1st Q 0.000 1.000 0.000 0.000 6.992 0.000 221.300 0.000

Median 1.000 1.000 0.000 0.000 14.024 0.000 376.000 0.000

Mean 0.696 0.885 0.300 0.353 20.807 0.325 428.700 0.401

3rd Q 1.000 1.000 1.000 1.000 34.198 0.526 542.400 1.000

Max. 1.000 1.000 1.000 1.000 75.621 7.273 5949.000 1.000

Table 2: Efficient Scores by both Total and State sample

Sample (observations) Input: K,L (I) Output (I) Input: K,L (T) Output (T) Input: K,L (T+I) Output (T+I)

Total (3359) min 0.017 0.010 0.017 0.000 0.017 0.015

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.282 0.483 0.294 0.126 0.322 0.504

std 0.185 0.236 0.189 0.168 0.215 0.243

Amapá(37) min 0.063 0.091 0.063 0.009 0.063 0.108

max 0.930 0.882 0.930 0.738 0.930 0.882

mean 0.327 0.466 0.300 0.095 0.333 0.472

std 0.202 0.192 0.193 0.137 0.206 0.191

Acre (15) min 0.065 0.034 0.065 0.001 0.065 0.038

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.198 0.350 0.198 0.081 0.198 0.352

std 0.231 0.232 0.231 0.255 0.231 0.231

Amazonas (42) min 0.048 0.045 0.048 0.008 0.048 0.057

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.183 0.251 0.199 0.077 0.199 0.257

std 0.166 0.182 0.184 0.195 0.184 0.184

Roraima (9) min 0.103 0.216 0.135 0.049 0.135 0.216

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.395 0.470 0.402 0.282 0.402 0.521

std 0.268 0.246 0.261 0.339 0.261 0.254

Pará (22) min 0.027 0.034 0.040 0.005 0.040 0.039

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.228 0.335 0.252 0.117 0.256 0.351

std 0.206 0.241 0.202 0.205 0.201 0.238

Amapá (3) min 0.265 0.182 0.265 0.088 0.265 0.219

max 0.657 0.714 0.657 0.115 0.657 0.733

mean 0.441 0.488 0.424 0.100 0.441 0.506

std 0.199 0.275 0.206 0.014 0.199 0.263

Tocantins (50) min 0.017 0.065 0.017 0.001 0.017 0.081

max 0.561 0.831 0.594 0.524 0.594 0.842

mean 0.174 0.263 0.202 0.078 0.206 0.286

std 0.118 0.146 0.135 0.089 0.142 0.148

Maranhão (47) min 0.047 0.015 0.047 0.000 0.047 0.031

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.348 0.251 0.367 0.106 0.367 0.263

std 0.225 0.210 0.251 0.214 0.251 0.217

Piauí (85) min 0.028 0.011 0.028 0.007 0.028 0.032

max 0.614 0.739 0.834 0.538 0.834 0.850

mean 0.222 0.201 0.236 0.043 0.236 0.211

std 0.152 0.135 0.171 0.062 0.171 0.138

Ceará (115) min 0.036 0.013 0.036 0.006 0.036 0.029

max 0.572 0.794 0.799 0.762 0.960 0.831

mean 0.228 0.254 0.239 0.059 0.241 0.264

std 0.126 0.137 0.139 0.082 0.145 0.135

Rio Grande do Norte (93) min 0.032 0.071 0.032 0.009 0.032 0.080

max 0.768 0.984 1.000 1.000 1.000 1.000

mean 0.226 0.384 0.240 0.068 0.243 0.396

std 0.121 0.162 0.140 0.107 0.138 0.166

Paraíba (105) min 0.024 0.012 0.030 0.006 0.030 0.029

max 0.555 0.839 0.722 0.511 0.722 0.839

mean 0.246 0.315 0.256 0.052 0.258 0.324

std 0.098 0.170 0.103 0.054 0.103 0.167

Pernambuco (122) min 0.034 0.015 0.034 0.005 0.034 0.043

max 0.988 0.966 1.000 1.000 1.000 1.000

mean 0.327 0.382 0.325 0.074 0.335 0.390

std 0.152 0.216 0.146 0.113 0.157 0.216

Alagoas (73) min 0.066 0.023 0.066 0.001 0.066 0.029

max 0.676 0.824 0.676 0.594 0.676 0.848

mean 0.286 0.364 0.291 0.048 0.293 0.369

std 0.118 0.155 0.117 0.075 0.118 0.155

Sergipe (45) min 0.024 0.104 0.045 0.008 0.045 0.114

max 1.000 1.000 0.621 0.448 1.000 1.000

mean 0.251 0.403 0.249 0.070 0.267 0.419

std 0.203 0.198 0.150 0.085 0.205 0.205

Bahia (154) min 0.024 0.057 0.055 0.005 0.055 0.068

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.290 0.366 0.316 0.095 0.317 0.383

std 0.146 0.176 0.160 0.140 0.160 0.187

Minas Gerais (503) min 0.022 0.010 0.031 0.000 0.031 0.015

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.302 0.453 0.305 0.090 0.325 0.469

std 0.166 0.229 0.154 0.120 0.184 0.235

Espírito Santo (58) min 0.023 0.182 0.023 0.015 0.023 0.187

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.298 0.537 0.309 0.131 0.338 0.554

std 0.162 0.186 0.160 0.176 0.192 0.195

Ro de Janeiro (62) min 0.030 0.297 0.051 0.027 0.051 0.297

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.328 0.652 0.411 0.305 0.436 0.710

std 0.257 0.172 0.264 0.287 0.281 0.178

São Paulo (460) min 0.020 0.170 0.024 0.008 0.024 0.187

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.332 0.640 0.416 0.261 0.438 0.677

std 0.202 0.180 0.246 0.251 0.258 0.194

Paraná (308) min 0.019 0.102 0.024 0.014 0.024 0.122

max 0.982 1.000 0.868 0.812 1.000 1.000

mean 0.228 0.532 0.227 0.092 0.245 0.548

std 0.152 0.168 0.142 0.091 0.167 0.169

Santa Catarina (252) min 0.029 0.045 0.041 0.018 0.041 0.067

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.351 0.664 0.305 0.144 0.399 0.688

std 0.231 0.205 0.185 0.162 0.261 0.207

Rio Grande do Sul (388) min 0.023 0.068 0.023 0.013 0.023 0.082

max 1.000 1.000 1.000 1.000 1.000 1.000

mean 0.285 0.633 0.229 0.121 0.318 0.652

std 0.230 0.206 0.180 0.137 0.250 0.205

Mato Grosso do Sul (69) min 0.042 0.036 0.045 0.017 0.045 0.042

max 0.504 0.822 0.866 0.457 0.866 0.833

mean 0.189 0.378 0.248 0.121 0.249 0.406

std 0.106 0.155 0.136 0.081 0.137 0.154

Mato Grosso (72) min 0.020 0.091 0.020 0.014 0.020 0.100

max 0.539 0.871 0.782 0.647 0.782 0.924

mean 0.160 0.393 0.206 0.119 0.208 0.420

std 0.108 0.166 0.141 0.103 0.144 0.168

Goiás (146) min 0.039 0.023 0.039 0.020 0.039 0.029

max 0.690 0.896 1.000 1.000 1.000 1.000

mean 0.265 0.356 0.329 0.165 0.331 0.395

std 0.136 0.181 0.175 0.157 0.178 0.192

Table 3 - Efficient municipalities by state - Brazil

States Efficient Municipalities in each state (type of score)

Amapá -

Acre Rio Branco ( I,T,T+I)

Amazonas Manacapuru ( I,T,T+I)

Roraima Rorainópolis ( I,T,T+I)

Pará Belém ( I,T,T+I)

Amapá

Tocantins

Maranhão Bacabal ( I,T,T+I) and São Luiz ( T,T+I)

Piauí

Ceará

Rio Grande do Norte Natal ( T,T+I)

Paraíba

Pernambuco Recife ( T,T+I)

Alagoas

Sergipe Nossa Senhora do Socorro ( I,T+I)

Bahia Salvador ( I,T,T+I)

Minas Gerais Belo Horizonte ( T,T+I), Betim ( I,T+I), Itajubá ( T+I), Juiz de Fora ( T+I), Patos de Minas (T,T+I), Santa Luzia ( I,T+I), Santa Rita do Sapucaí ( I,T+I) and.

São Gonçalo do Rio Abaixo (T,T+I).

Espírito Santo Vila Velha ( I,T,T+I) and Vitória ( T+I)

Rio de Janeiro Duque de Caxias ( T,T+I), Itaperuna (T,T+I), Niterói ( T,T+I), Nova Iguaçu ( I,T+I) and São João de Meriti ( I,T,T+I).

São Paulo São Paulo ( T,T+I), São Vicente (T,T+I), Sorocaba ( T,T+I), Várzea Paulista ( I,T+I), Votorantim ( I,T+I), Santana de Parnaíba ( I,T,T+I), Santos ( I,T,T+I), São Bernardo do Campo ( T,T+I), São Caetano do Sul

(T+I), Arujá ( T,T+I), Bertioga (T,T+I), Botucatu ( T+I), Caçapava ( T+I), São João da Boa Vista ( I,T+I), São José do Rio Preto ( T,T+I), Campinas ( T,T+I), Cerquilho ( T+I), Diadema ( T,T+I), Embu-Guaçu (

T,T+I), São Lourenço da Serra (T,T+I), Franca ( T,T+I), Franco da Rocha ( T+I), Guarujá ( T,T+I), Ibaté ( I,T+I), Itaquaquecetuba (T,T+I), Jandira ( T+I), Jaú ( I,T+I), Jundiaí ( T,T+I), Mogi das Cruzes (

I,T,T+I), Piracicaba ( I,T,T+I), Salto ( I,T,T+I)and Santa Lucia ( I,T,T+I).

Paraná Curitiba ( I,T+I) and Pinhais ( T+I)

Santa Catarina Rio Sul ( T+I), Schroeder ( I,T+I), Timbó ( T+I), Biguaçu (T+I), Blumenau ( T,T+I), Botuvará ( T,T+I), Campos Novos ( T,T+I), Cordilheira Alta ( I,T+I), Cunhataí

(T,T+I), Florianópolis ( T+I), Gaspar ( I,T+I), Jaraguá do Sul ( T+I), Mafra ( I,T,T+I) and Nova Trento ( T+I), Piratuba ( I,T+I).

Rio Grande do Sul Santa Cruz do Sul ( T+I), Santa Maria ( I,T+I), Vianão ( I,T+I), Alvorada ( I,T+I), Bento Gonçalves ( I, T+I), Casca ( I,T+I),

Caxias do Sul ( I,T,T+I), Dois Irmãos ( I,T+I), Esteio ( T+I), Farroupilha ( I,T+I), Fazenda Vilanova ( T+I), Gramado ( T+I), Lagoa Vermelha (T,T+I), Lajeado ( I,T+I), Novo Hamburgo ( T,T+I), Porto Alegre (

I,T,T+I), Rio Grande (T+I), Rio Pardo ( I,T+I) and Roca Sales ( I,T+I).

Mato Grosso do Sul

Goiás Goiânia ( T,T+I) and Rio Quente ( T,T+I)

Note: Bold Letter in Efficient Municipalities is the capital of State

Table 4: Preliminary tests for spatial presence

Unconditional Spatial Dependence

Weight matrix - rook

Variables Moran´s I statistic

SI 25.4737

p-value 2.20E-16

SP 55.9222

p-value 2.20E-16

SI3 32.4531

p-value 2.20E-16

SP3 57.4308

p-value 2.20E-16

SI2 30.7199

p-value 2.20E-16

SP2 40.0036

p-value 2.20E-16

Sim 29.7062

p-value 2.20E-16

SPm 55.8353

p-value 2.20E-16

Ipm 43.3889

p-value 2.20E-16

Dependent - Input: K , L (tax collec) Dependent - output (tax collec)

Lin Reg. Spatial Lag Spatial Error Lin Reg. Spatial Lag Spatial Error

(Intercept) 0.41960 0.31185 0.41855 0.54800 0.28098 0.56473

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

right -0.00597 -0.00781 -0.00940 -0.01124 -0.01201 -0.01380

p-value 0.35501 0.21001 0.13216 0.08959 0.04362 0.01731

left -0.01004 -0.01003 -0.00789 -0.02151 -0.01819 -0.01007

p-value 0.12953 0.11725 0.21978 0.00155 0.00289 0.09206

ISSinform 0.01182 0.01311 0.01328 0.04757 0.03639 0.02767

p-value 0.06053 0.03092 0.03131 0.00000 0.00000 0.00000

servint 0.01805 0.01754 0.01626 0.01614 0.01978 0.01999

p-value 0.00279 0.00260 0.00526 0.00910 0.00038 0.00022

exp -0.00016 -0.00015 -0.00015 -0.00005 -0.00006 -0.00006

p-value 0.00000 0.00000 0.00000 0.00613 0.00022 0.00198

transf -0.00003 -0.00003 -0.00003 -0.00003 -0.00003 -0.00003

p-value 0.10294 0.08967 0.07677 0.05192 0.07381 0.02514

old -0.00078 -0.00077 -0.00079 -0.00496 -0.00419 -0.00290

p-value 0.22799 0.23499 0.27046 0.00000 0.00000 0.00005

urb 0.00015 0.00025 -0.00031 0.00045 0.00058

p-value 0.27069 0.10859 0.05890 0.00239 0.00051

density 0.00003 0.00002 0.00003 0.00001 0.00000 0.00001

p-value 0.00000 0.00000 0.00000 0.03607 0.82578 0.22700

eletr 0.00149 0.00097 0.00127

p-value 0.00000 0.00028 0.00029

compu 0.01777 0.01426 0.01705 0.02788 0.01819 0.02037

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

emp -0.02495 -0.01364 -0.12928

p-value 0.53871 0.70921 0.00178

inc -0.00021 -0.00017 -0.00030 -0.00022 -0.00013 -0.00013

p-value 0.00534 0.02095 0.00030 0.00717 0.06919 0.09408

poverty -0.00106 -0.00079 -0.00124 -0.00486 -0.00247 -0.00455

p-value 0.00041 0.00806 0.00032 0.00000 0.00000 0.00000

doctor 0.01534 0.02385 0.01861 -0.01254 0.00930 -0.00529

p-value 0.00876 0.00003 0.00118 0.03631 0.08857 0.32714

transport -0.00005 -0.00004 -0.00005 -0.00007 -0.00004 -0.00007

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

spatial error 0.34709 0.57974

p-value 0.00000 0.00000

spatial Lag 0.28795 0.44298

p-value 0.00000 0.00000

LR 176.67000 178.65000 564.20000 602.35000

p-value 0.00000 0.00000 0.00000 0.00000

Wald 181.10000 196.73000 643.71000 870.79000

p-value 0.00000 0.00000 0.00000 0.00000

Llikel 1810.36300 1811.35400 1933.18100 1952.25500

AIC: -3586.70000 -3588.70000 -3826.40000 -3864.50000

Adj R 0.39110 0.60570

F-statistic: 165.70000 302.20000

Moran´s I 14.65210 27.11460

p-value 0.00000 0.00000

LMerr 208.86330 718.25780

p-value 0.00000 0.00000

LMlag 210.8522, 667.19430

p-value 0.00000 0.00000

Table 5 - Regression Score Efficiency for Tax Collection

Dependent - Input: K , L (inf. ) Dependent output (inf. )

Lin. Reg. Spatial Lag Spatial Error Lin. Reg. Spatial Lag Spatial Error

(Intercept) 0.38740 0.24107 0.35596 0.05108 -0.00995 0.05400

p-value 0.00000 0.00000 0.00000 0.18507 0.78685 0.19931

right -0.00519 -0.00734 -0.00966 0.00129 -0.00043 -0.00275

p-value 0.42017 0.23358 0.11951 0.81313 0.93444 0.59742

left 0.00195 -0.00003 0.00115 0.01541 0.01352 0.01465

p-value 0.76749 0.99668 0.85720 0.00583 0.01078 0.00637

ISSinform 0.01038 0.01123 0.01347 0.00336 0.00263 0.00473

p-value 0.11583 0.06176 0.02811 0.52693 0.60265 0.35881

servint 0.01983 0.01866 0.01738 0.01604 0.01502 0.01308

p-value 0.00100 0.00119 0.00267 0.00161 0.00186 0.00710

exp 0.00002 0.00001 0.00001 0.00013 0.00010 0.00011

p-value 0.27359 0.62363 0.68304 0.00000 0.00000 0.00000

transf -0.00013 -0.00012 -0.00012 -0.00010 -0.00008 -0.00008

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

old 0.00176 0.00121 0.00076 0.00157 0.00115 0.00089

p-value 0.00908 0.06172 0.29065 0.00583 0.03366 0.14312

urb 0.00075 0.00069 0.00065 0.00084 0.00069 0.00058

p-value 0.00000 0.00001 0.00010 0.00000 0.00000 0.00005

density 0.00004 0.00003 0.00003 0.00006 0.00004 0.00005

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

eletr -0.00078 -0.00055 -0.00045 -0.00121 -0.00086 -0.00080

p-value 0.00731 0.04788 0.18039 0.00000 0.00021 0.00511

compu 0.01529 0.01168 0.01412 0.01272 0.01000 0.01297

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

emp -0.12590 -0.06723 -0.09098 -0.05865 -0.02540 -0.04309

p-value 0.00145 0.07649 0.02975 0.07899 0.42384 0.22357

inc 0.00008 0.00010 0.00007 0.00034 0.00028 0.00026

p-value 0.31020 0.17689 0.42927 0.00000 0.00001 0.00022

poverty 0.00038 0.00064 0.00027 0.00081 0.00112 0.00045

p-value 0.29713 0.06399 0.51004 0.00808 0.00012 0.19466

doctor 0.00724 0.01934 0.01179 0.00253 0.01763 0.00890

p-value 0.21423 0.00059 0.03881 0.60727 0.00021 0.06355

transport -0.00006 -0.00004 -0.00006 -0.00004 -0.00003 -0.00004

p-value 0.00000 0.00000 0.00000 0.00000 0.00002 0.00001

spatial error 0.35644 0.38337

p-value 0.00000 0.00000

spatial Lag 0.30621 0.32843

p-value 0.00000 0.00000

LR 217.46000 183.05000 262.37000 210.11000

p-value 0.00000 0.00000 0.00000 0.00000

Wald 217.39000 210.40000 265.52000 253.87000

p-value 0.00000 0.00000 0.00000 0.00000

Llikel 1850.01300 1832.81200 2432.81500 2406.68800

AIC: -3662.00000 -3627.60000 -4827.60000 -4775.40000

Adj. R 0.41970 0.47780

F-statistic: 142.80000 191.60000

Moran´s I 14.61770 15.49310

p-value 0.00000 0.00000

LMerr 206.37460 232.23870

p-value 0.00000 0.00000

LMlag 266.52700 316.65520

p-value 0.00000 0.00000

Table 6 - Regression Score Efficiency for Informal Sector Size

Dependent - Avg. score input Dependent - Avg score output Dependent - total score average

Lin. Reg. Spatial Lag Spatial Error Linear Reg. Spatial Lag Spatial Error Lin. Reg. Spatial Lag Spatial Error

(Intercept) 0.40550 0.27674 0.39385 0.38520 0.20402 0.39545 0.39540 0.24914 0.39092

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

right -0.00535 -0.00737 -0.00945 -0.00705 -0.00823 -0.01048 -0.00620 -0.00782 -0.01014

p-value 0.38692 0.21674 0.11540 0.16902 0.08067 0.02373 0.21180 0.09711 0.03102

left -0.00477 -0.00553 -0.00378 -0.00894 -0.00767 -0.00201 -0.00685 -0.00677 -0.00337

p-value 0.45197 0.36589 0.54123 0.08923 0.11228 0.67345 0.17850 0.16128 0.48637

ISSinform 0.01107 0.01225 0.01372 0.03211 0.02506 0.02060 0.02159 0.01940 0.01858

p-value 0.06651 0.03525 0.02064 0.00000 0.00000 0.00001 0.00001 0.00002 0.00006

servint 0.02140 0.02043 0.01912 0.01695 0.01843 0.01753 0.01917 0.01923 0.01803

p-value 0.00021 0.00024 0.00064 0.00040 0.00003 0.00005 0.00004 0.00001 0.00004

exp -0.00005 -0.00005 -0.00005 0.00003 0.00001 0.00002 -0.00001 -0.00002 -0.00002

p-value 0.00307 0.00176 0.00370 0.04113 0.46386 0.21867 0.42930 0.13962 0.25924

transf -0.00009 -0.00008 -0.00008 -0.00006 -0.00005 -0.00006 -0.00008 -0.00007 -0.00007

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

old 0.00041 0.00019 -0.00005 -0.00255 -0.00233 -0.00170 -0.00107 -0.00111 -0.00104

p-value 0.52258 0.76503 0.94220 0.00000 0.00000 0.00240 0.04050 0.02493 0.06035

urb 0.00046 0.00053 0.00053 0.00013 0.00049 0.00049 0.00029 0.00048 0.00047

p-value 0.00252 0.00035 0.00101 0.32181 0.00003 0.00020 0.01670 0.00004 0.00026

density 0.00003 0.00002 0.00003 0.00003 0.00001 0.00002 0.00003 0.00002 0.00002

p-value 0.00000 0.00000 0.00000 0.00000 0.00031 0.00002 0.00000 0.00000 0.00000

eletr -0.00068 -0.00052 -0.00045 0.00048 0.00034 0.00057 -0.00010 -0.00006 0.00009

p-value 0.01418 0.05368 0.15348 0.03723 0.10663 0.03655 0.65110 0.76148 0.72490

compu 0.01813 0.01455 0.01721 0.02305 0.01639 0.01876 0.02059 0.01587 0.01835

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

emp -0.03818 -0.00470 -0.03435 -0.04356 -0.02177 -0.09105 -0.04087 -0.01313 -0.05578

p-value 0.31408 0.89807 0.39249 0.16593 0.45217 0.00523 0.17970 0.65048 0.08315

inc -0.00002 0.00000 -0.00007 -0.00001 0.00001 0.00000 -0.00001 0.00000 -0.00004

p-value 0.83725 0.98342 0.38895 0.87696 0.89949 0.98994 0.83540 0.94612 0.51990

poverty -0.00028 0.00003 -0.00046 -0.00293 -0.00141 -0.00303 -0.00160 -0.00081 -0.00178

p-value 0.41340 0.92473 0.23423 0.00000 0.00000 0.00000 0.00000 0.00258 0.00000

doctor 0.00800 0.01910 0.01226 -0.00907 0.01013 -0.00164 -0.00054 0.01353 0.00493

p-value 0.15299 0.00045 0.02596 0.05062 0.01980 0.70296 0.90510 0.00180 0.25538

transport -0.00006 -0.00004 -0.00006 -0.00006 -0.00004 -0.00006 -0.00006 -0.00004 -0.00006

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

spatial error 0.32686 0.52219 0.41500

p-value 0.00000 0.00000 0.00000

spatial Lag 0.27628 0.38409 0.31182

p-value 0.00000 0.00000 0.00000

LR 177.38000 151.87000 455.76000 468.65000 274.34000 263.87000

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

Wald 178.11000 169.43000 492.86000 613.99000 283.49000 313.64000

p-value 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000

Llikel 1965.23900 1952.48400 2730.16300 2736.60700 2745.27400 2740.03600

AIC: -3892.50000 -3867.00000 -5422.30000 -5435.20000 -5452.50000 -5442.10000

Adj. R 0.45700 0.65530 0.60020

F-statistic: 176.40000 397.10000 313.80000

Moran´s 13.33690 24.01020 17.74260

p-value 0.00000 0.00000 0.00000

LMerr 171.44820 562.37740 305.47400

p-value 0.00000 0.00000 0.00000

LMlag 211.94650 531.47020 322.55320

p-value 0.00000 0.00000 0.00000

Table 7 - Regression Score Efficiency - Average