Efficiency in tax collection: evidence from Brazilian municipalities
Paulo Arvate1 and Enlinson Mattos2
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
2
CEPESP and EESP - Getulio Vargas Foundation, e-mail: [email protected].
CEPESP and EESP - Getulio Vargas Foundation, e-mail:[email protected]..
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 percapita 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
y i = F ( xi ), i = 1...n
(1)
where y mx1 is the ouput vector and x kx1 corresponds to the input vector. One can rank
the municipality i if it is not the most efficient in terms of input
MIN i = n1,....,nl MAX j =1,....,m
x j (n)
x j (i )
(2)
and n1 ,....., nl 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).
MAX i = n1,....,nl MIN j =1,....,m
y j (n)
y j (i )
(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.
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
Y = WYδ + Xβ + u
(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.
Y = Xβ + u
u = ρWu + ε
(5)
where ξ ~ N (0, σ 2 ) .
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).
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
10
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 output16
Site: www.ipeadata.gov.br
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.
17
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 percapita (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 leftwing 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
Min.
1st Q
Median
Mean
3rd Q
Max.
Min.
1st Q
Median
Mean
3rd Q
Max.
Min.
1st Q
Median
Mean
3rd Q
Max.
Input: K,L (T)
output (Tax)
Input: K,L(I)
output (I)
Input: K,L(I,T)
output (I,T)
Input (Avg)
output (Avg)
0.017
0.150
0.248
0.282
0.365
1.000
Exp
102.100
436.600
582.700
677.100
806.200
6327.100
ISSinform
0.000
0.000
1.000
0.696
1.000
1.000
0.010
0.299
0.468
0.483
0.649
1.000
transf
193.100
533.700
689.000
819.700
967.900
7775.900
IPTUinform
0.000
1.000
1.000
0.885
1.000
1.000
0.017
0.159
0.259
0.294
0.383
1.000
old
0.056
10.336
12.986
13.054
15.671
28.698
servint
0.000
0.000
0.000
0.300
1.000
1.000
0.000
0.037
0.066
0.126
0.135
1.000
urb
0.000
43.200
62.740
61.410
81.090
100.000
left
0.000
0.000
0.000
0.353
1.000
1.000
0.017
0.170
0.273
0.322
0.412
1.000
density
0.082
12.920
26.030
119.500
54.040
12700.000
poverty
0.639
6.992
14.024
20.807
34.198
75.621
0.015
0.312
0.488
0.504
0.680
1.000
eletr
17.430
87.920
96.580
90.340
99.250
100.000
doctor
0.000
0.000
0.000
0.325
0.526
7.273
0.017
0.164
0.264
0.299
0.387
1.000
compu
0.002
0.998
2.621
3.962
5.445
41.405
transport
0.000
221.300
376.000
428.700
542.400
5949.000
0.014
0.222
0.346
0.371
0.493
1.000
emp
0.250
0.508
0.562
0.561
0.606
0.932
right
0.000
0.000
0.000
0.401
1.000
1.000
Average
score
0.040
0.212
0.295
0.335
0.415
1.000
inc
30.430
107.510
186.530
192.240
250.050
954.650
Table 2: Efficient Scores by both Total and State sample
Sample (observations)
Total (3359)
Amapá(37)
Acre (15)
Amazonas (42)
Roraima (9)
Pará (22)
Amapá (3)
Tocantins (50)
Maranhão (47)
Piauí (85)
Ceará (115)
Rio Grande do Norte (93)
Paraíba (105)
Pernambuco (122)
Input: K,L (I)
Output (I)
Input: K,L (T)
Output (T)
Input: K,L (T+I)
Output (T+I)
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
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
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
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
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
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
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
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
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
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
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
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
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
min
0.034
0.015
0.034
0.005
0.034
0.043
Alagoas (73)
Sergipe (45)
Bahia (154)
Minas Gerais (503)
Espírito Santo (58)
Ro de Janeiro (62)
São Paulo (460)
Paraná (308)
Santa Catarina (252)
Rio Grande do Sul (388)
Mato Grosso do Sul (69)
Mato Grosso (72)
Goiás (146)
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
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
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
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
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
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
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
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
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
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
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
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
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
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
Table 3 - Efficient municipalities by state - Brazil
States
Efficient Municipalities in each state (type of score)
Amapá
Rio Branco ( I,T,T+I)
Acre
Manacapuru ( I,T,T+I)
Amazonas
Rorainópolis ( I,T,T+I)
Roraima
Belém ( I,T,T+I)
Pará
Amapá
Tocantins
Bacabal ( I,T,T+I) and São Luiz ( T,T+I)
Maranhão
Piauí
Ceará
Natal ( T,T+I)
Rio Grande do Norte
Paraíba
Recife ( T,T+I)
Pernambuco
Alagoas
Nossa Senhora do Socorro ( I,T+I)
Sergipe
Salvador ( I,T,T+I)
Bahia
Belo Horizonte ( T,T+I), Betim ( I,T+I), Itajubá ( T+I), Juiz de Fora ( T+I),
Minas Gerais
Espírito Santo
Rio de Janeiro
São Paulo
Paraná
Santa Catarina
Rio Grande do Sul
0.192
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).
Vila Velha ( I,T,T+I) and Vitória ( T+I)
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 ( 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).
Curitiba ( I,T+I) and Pinhais ( T+I)
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).
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
Goiânia ( T,T+I) and Rio Quente ( T,T+I)
Goiás
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
SI
p-value
SP
p-value
SI3
p-value
SP3
p-value
SI2
p-value
SP2
p-value
Sim
p-value
SPm
p-value
Ipm
p-value
Moran´s I statistic
25.4737
2.20E-16
55.9222
2.20E-16
32.4531
2.20E-16
57.4308
2.20E-16
30.7199
2.20E-16
40.0036
2.20E-16
29.7062
2.20E-16
55.8353
2.20E-16
43.3889
2.20E-16
(Intercept)
p-value
right
p-value
left
p-value
ISSinform
p-value
servint
p-value
exp
p-value
transf
p-value
old
p-value
urb
p-value
density
p-value
eletr
p-value
compu
p-value
emp
p-value
inc
p-value
poverty
p-value
doctor
p-value
transport
p-value
spatial error
p-value
spatial Lag
p-value
LR
p-value
Wald
p-value
Llikel
AIC:
Adj R
F-statistic:
Moran´s I
p-value
LMerr
p-value
LMlag
p-value
Table 5 - Regression Score Efficiency for Tax Collection
Dependent - Input: K , L (tax collec)
Dependent - output (tax collec)
Lin Reg.
Spatial Lag
Spatial Error
Lin Reg.
Spatial Lag
Spatial Error
0.41960
0.31185
0.41855
0.54800
0.28098
0.56473
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.00597
-0.00781
-0.00940
-0.01124
-0.01201
-0.01380
0.35501
0.21001
0.13216
0.08959
0.04362
0.01731
-0.01004
-0.01003
-0.00789
-0.02151
-0.01819
-0.01007
0.12953
0.11725
0.21978
0.00155
0.00289
0.09206
0.01182
0.01311
0.01328
0.04757
0.03639
0.02767
0.06053
0.03092
0.03131
0.00000
0.00000
0.00000
0.01805
0.01754
0.01626
0.01614
0.01978
0.01999
0.00279
0.00260
0.00526
0.00910
0.00038
0.00022
-0.00016
-0.00015
-0.00015
-0.00005
-0.00006
-0.00006
0.00000
0.00000
0.00000
0.00613
0.00022
0.00198
-0.00003
-0.00003
-0.00003
-0.00003
-0.00003
-0.00003
0.10294
0.08967
0.07677
0.05192
0.07381
0.02514
-0.00078
-0.00077
-0.00079
-0.00496
-0.00419
-0.00290
0.22799
0.23499
0.27046
0.00000
0.00000
0.00005
0.00015
0.00025
-0.00031
0.00045
0.00058
0.27069
0.10859
0.05890
0.00239
0.00051
0.00003
0.00002
0.00003
0.00001
0.00000
0.00001
0.00000
0.00000
0.00000
0.03607
0.82578
0.22700
0.00149
0.00097
0.00127
0.00000
0.00028
0.00029
0.01777
0.01426
0.01705
0.02788
0.01819
0.02037
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.02495
-0.01364
-0.12928
0.53871
0.70921
0.00178
-0.00021
-0.00017
-0.00030
-0.00022
-0.00013
-0.00013
0.00534
0.02095
0.00030
0.00717
0.06919
0.09408
-0.00106
-0.00079
-0.00124
-0.00486
-0.00247
-0.00455
0.00041
0.00806
0.00032
0.00000
0.00000
0.00000
0.01534
0.02385
0.01861
-0.01254
0.00930
-0.00529
0.00876
0.00003
0.00118
0.03631
0.08857
0.32714
-0.00005
-0.00004
-0.00005
-0.00007
-0.00004
-0.00007
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.34709
0.57974
0.00000
0.00000
0.28795
0.44298
0.00000
0.00000
176.67000
178.65000
564.20000
602.35000
0.00000
0.00000
0.00000
0.00000
181.10000
196.73000
643.71000
870.79000
0.00000
0.00000
0.00000
0.00000
1810.36300
1811.35400
1933.18100
1952.25500
-3586.70000
-3588.70000
-3826.40000
-3864.50000
0.39110
0.60570
165.70000
302.20000
14.65210
27.11460
0.00000
0.00000
208.86330
718.25780
0.00000
0.00000
210.8522,
667.19430
0.00000
0.00000
(Intercept)
p-value
right
p-value
left
p-value
ISSinform
p-value
servint
p-value
exp
p-value
transf
p-value
old
p-value
urb
p-value
density
p-value
eletr
p-value
compu
p-value
emp
p-value
inc
p-value
poverty
p-value
doctor
p-value
transport
p-value
spatial error
p-value
spatial Lag
p-value
LR
p-value
Wald
p-value
Llikel
AIC:
Adj. R
F-statistic:
Moran´s I
p-value
LMerr
p-value
LMlag
p-value
Table 6 - Regression Score Efficiency for Informal Sector Size
Dependent - Input: K , L (inf. )
Dependent output (inf. )
Lin. Reg.
Spatial Lag
Spatial Error
Lin. Reg.
Spatial Lag
Spatial Error
0.38740
0.24107
0.35596
0.05108
-0.00995
0.05400
0.00000
0.00000
0.00000
0.18507
0.78685
0.19931
-0.00519
-0.00734
-0.00966
0.00129
-0.00043
-0.00275
0.42017
0.23358
0.11951
0.81313
0.93444
0.59742
0.00195
-0.00003
0.00115
0.01541
0.01352
0.01465
0.76749
0.99668
0.85720
0.00583
0.01078
0.00637
0.01038
0.01123
0.01347
0.00336
0.00263
0.00473
0.11583
0.06176
0.02811
0.52693
0.60265
0.35881
0.01983
0.01866
0.01738
0.01604
0.01502
0.01308
0.00100
0.00119
0.00267
0.00161
0.00186
0.00710
0.00002
0.00001
0.00001
0.00013
0.00010
0.00011
0.27359
0.62363
0.68304
0.00000
0.00000
0.00000
-0.00013
-0.00012
-0.00012
-0.00010
-0.00008
-0.00008
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00176
0.00121
0.00076
0.00157
0.00115
0.00089
0.00908
0.06172
0.29065
0.00583
0.03366
0.14312
0.00075
0.00069
0.00065
0.00084
0.00069
0.00058
0.00000
0.00001
0.00010
0.00000
0.00000
0.00005
0.00004
0.00003
0.00003
0.00006
0.00004
0.00005
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.00078
-0.00055
-0.00045
-0.00121
-0.00086
-0.00080
0.00731
0.04788
0.18039
0.00000
0.00021
0.00511
0.01529
0.01168
0.01412
0.01272
0.01000
0.01297
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.12590
-0.06723
-0.09098
-0.05865
-0.02540
-0.04309
0.00145
0.07649
0.02975
0.07899
0.42384
0.22357
0.00008
0.00010
0.00007
0.00034
0.00028
0.00026
0.31020
0.17689
0.42927
0.00000
0.00001
0.00022
0.00038
0.00064
0.00027
0.00081
0.00112
0.00045
0.29713
0.06399
0.51004
0.00808
0.00012
0.19466
0.00724
0.01934
0.01179
0.00253
0.01763
0.00890
0.21423
0.00059
0.03881
0.60727
0.00021
0.06355
-0.00006
-0.00004
-0.00006
-0.00004
-0.00003
-0.00004
0.00000
0.00000
0.00000
0.00000
0.00002
0.00001
0.35644
0.38337
0.00000
0.00000
0.30621
0.32843
0.00000
0.00000
217.46000
183.05000
262.37000
210.11000
0.00000
0.00000
0.00000
0.00000
217.39000
210.40000
265.52000
253.87000
0.00000
0.00000
0.00000
0.00000
1850.01300
1832.81200
2432.81500
2406.68800
-3662.00000
-3627.60000
-4827.60000
-4775.40000
0.41970
0.47780
142.80000
191.60000
14.61770
15.49310
0.00000
0.00000
206.37460
232.23870
0.00000
0.00000
266.52700
316.65520
0.00000
0.00000
(Intercept)
p-value
right
p-value
left
p-value
ISSinform
p-value
servint
p-value
exp
p-value
transf
p-value
old
p-value
urb
p-value
density
p-value
eletr
p-value
compu
p-value
emp
p-value
inc
p-value
poverty
p-value
doctor
p-value
transport
p-value
spatial error
p-value
spatial Lag
p-value
LR
p-value
Wald
p-value
Llikel
AIC:
Adj. R
F-statistic:
Moran´s
p-value
LMerr
p-value
LMlag
p-value
Table 7 - Regression Score Efficiency - Average
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
0.40550
0.27674
0.39385
0.38520
0.20402
0.39545
0.39540
0.24914
0.39092
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.00535
-0.00737
-0.00945
-0.00705
-0.00823
-0.01048
-0.00620
-0.00782
-0.01014
0.38692
0.21674
0.11540
0.16902
0.08067
0.02373
0.21180
0.09711
0.03102
-0.00477
-0.00553
-0.00378
-0.00894
-0.00767
-0.00201
-0.00685
-0.00677
-0.00337
0.45197
0.36589
0.54123
0.08923
0.11228
0.67345
0.17850
0.16128
0.48637
0.01107
0.01225
0.01372
0.03211
0.02506
0.02060
0.02159
0.01940
0.01858
0.06651
0.03525
0.02064
0.00000
0.00000
0.00001
0.00001
0.00002
0.00006
0.02140
0.02043
0.01912
0.01695
0.01843
0.01753
0.01917
0.01923
0.01803
0.00021
0.00024
0.00064
0.00040
0.00003
0.00005
0.00004
0.00001
0.00004
-0.00005
-0.00005
-0.00005
0.00003
0.00001
0.00002
-0.00001
-0.00002
-0.00002
0.00307
0.00176
0.00370
0.04113
0.46386
0.21867
0.42930
0.13962
0.25924
-0.00009
-0.00008
-0.00008
-0.00006
-0.00005
-0.00006
-0.00008
-0.00007
-0.00007
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00041
0.00019
-0.00005
-0.00255
-0.00233
-0.00170
-0.00107
-0.00111
-0.00104
0.52258
0.76503
0.94220
0.00000
0.00000
0.00240
0.04050
0.02493
0.06035
0.00046
0.00053
0.00053
0.00013
0.00049
0.00049
0.00029
0.00048
0.00047
0.00252
0.00035
0.00101
0.32181
0.00003
0.00020
0.01670
0.00004
0.00026
0.00003
0.00002
0.00003
0.00003
0.00001
0.00002
0.00003
0.00002
0.00002
0.00000
0.00000
0.00000
0.00000
0.00031
0.00002
0.00000
0.00000
0.00000
-0.00068
-0.00052
-0.00045
0.00048
0.00034
0.00057
-0.00010
-0.00006
0.00009
0.01418
0.05368
0.15348
0.03723
0.10663
0.03655
0.65110
0.76148
0.72490
0.01813
0.01455
0.01721
0.02305
0.01639
0.01876
0.02059
0.01587
0.01835
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
-0.03818
-0.00470
-0.03435
-0.04356
-0.02177
-0.09105
-0.04087
-0.01313
-0.05578
0.31408
0.89807
0.39249
0.16593
0.45217
0.00523
0.17970
0.65048
0.08315
-0.00002
0.00000
-0.00007
-0.00001
0.00001
0.00000
-0.00001
0.00000
-0.00004
0.83725
0.98342
0.38895
0.87696
0.89949
0.98994
0.83540
0.94612
0.51990
-0.00028
0.00003
-0.00046
-0.00293
-0.00141
-0.00303
-0.00160
-0.00081
-0.00178
0.41340
0.92473
0.23423
0.00000
0.00000
0.00000
0.00000
0.00258
0.00000
0.00800
0.01910
0.01226
-0.00907
0.01013
-0.00164
-0.00054
0.01353
0.00493
0.15299
0.00045
0.02596
0.05062
0.01980
0.70296
0.90510
0.00180
0.25538
-0.00006
-0.00004
-0.00006
-0.00006
-0.00004
-0.00006
-0.00006
-0.00004
-0.00006
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
0.32686
0.52219
0.41500
0.00000
0.00000
0.00000
0.27628
0.38409
0.31182
0.00000
0.00000
0.00000
177.38000
151.87000
455.76000
468.65000
274.34000
263.87000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
178.11000
169.43000
492.86000
613.99000
283.49000
313.64000
0.00000
0.00000
0.00000
0.00000
0.00000
0.00000
1965.23900
1952.48400
2730.16300
2736.60700
2745.27400
2740.03600
-3892.50000
-3867.00000
-5422.30000
-5435.20000
-5452.50000 -5442.10000
0.45700
0.65530
0.60020
176.40000
397.10000
313.80000
13.33690
24.01020
17.74260
0.00000
0.00000
0.00000
171.44820
562.37740
305.47400
0.00000
0.00000
0.00000
211.94650
531.47020
322.55320
0.00000
0.00000
0.00000