1
Transfers and marginal cost of public funding:
Empirical evidence for local governments in Brazil
Enlinson Mattos
(EESP-FGV)
Ricardo Politi
(UFABC)
Rafael Cardim
(EESP-FGV)
Abstract
This paper documents empirical evidence on price-effect caused by lumpsum transfers using unconditional and conditional transfers for local
governments in Brazil between 2006 to 2010. Dahlby (2011) demonstrates
theoretically that lump-sum transfers can reduce the cost of public goods
provision (price-effect), in addition to the traditional income effect. Our
constirbutions are threefold. First we calculate the MCF of the local tax imposed
on the supply of services (ISS) for Brazilian municipalities, Next we control for
potential simultaneity between unconditional transfers and local tax revenue in a
two-stage least square approach using instrumental variables (IV-2SLS); and
third we estimate the effects off tax rate changes on tax base. Our price-effect
estimation for ISS tax suggests that a R$ 1 increase in unconditional transfers
reduces the local price effect (MCF) around R$ 0.12.
Keywords: Price effect, income effect, transfers, taxes, and fiscal
federalism.
JEL Codes: H20, H70, R50.
AREA: Applied Microeconomics
2
1. Introduction
Dahlby (2011), following Hamilton (1986), provides a model in which local
governments are financed by distortive taxes. That allows lump-sum
intergovernmental transfers to have both an income effect, which is widely
reported in the literature, and a price effect. The mechanism that such priceeffect works is via a decrease in the marginal cost of public fund (MCF), after
receiving those transfers, because tax rate can be lowered to maintain the
same level of public goods provision.
The main objective of the paper is to estimate the price-effect in lump-sum
transfers using data from 5,260 Brazilian municipalities between 2006 and 2010
(five years sample). In this article we (i) calculate the MCF of the local tax
imposed on the supply of services (ISS) for Brazilian municipalities; (ii) control
for potential simultaneity between unconditional transfers and local tax revenue
in a two-stage least square approach using instrumental variables (IV-2SLS);
and (iii) estimate the effects off tax rate changes on tax base and (iv) estimate
the effect of transfers on calulcated MCF.
The literature concerning fiscal federalism has provided considerable
attention to the intergovernmental transfer systems, which are important tools
for correcting horizontal fiscal imbalances caused by the spatial concentration of
taxation sources and the dispersion of the need to provide public goods.
However a companion effect observed in empirical papers due to those
intergovernmental transfers is associated with larger propensity to public
spending compared to a correspondent income increase, a phenomenon
entitled flypaper effect (Gramlich, 1977, Fisher, 1982, Wycoff, 1991 and Inman,
2008).
Lump-sum transfers seem to stimulate more the local public spending than
increases in local income, reinforcing the expression “money sticks where it
hits”, i.e., the money received by the public sector remains in that sector. Hines
e Thaler (1995) expressed that as an anomaly and there seems to have many
theoretical explanations for that1. More related to our work, Hamilton (1986)
claims that flypaper effect might be due to the distortion caused taxes and the
difference between income and transfers effect captured by empirical evidence
might be due to the deadweight loss of taxation.
This issue is further explored in Dahlby (2011) that argues that these
transfers can reduce the cost of taxation for local governments through changes
in the MCF. In particular, he shows that lump-sum transfers allow the recipient
government to reduce its tax rate, which in turn, reduces the MCF in order to
keep the same level of public service. Whether local MCFs are larger than
national counterpart, transfers could result in welfare enhancing policy.
The author still provides a numerical example using Brazilian data (Dahlby,
2011, pp 309) in order to offer a potential explanation for the flypaper effect. In
1
: See for instance Courant et al. (1979), Oates (1979); Winer (1983), Logan (1986), Romer
and Rosenthal (1979), Wickoff (1988), Dougan and Kenyon (1988), Chemick (1979), Becker
(1996), Bailey and Connoly (1998), Worthington and Dollery (1999). For a detailed survey, see
Inman (2008) Dollery and Worthington (1996), Bailey and Connolly (1998), and Gamkhar and
Shah (2007). For Brazil, Siqueira et al. (2010) found (0.002 to 2.2) using 27 groups of goods.
Lanzer and Porto Júnior (2011) estimated the MCF for the whole Brazilian tax system in
between 1.167 and 1.173. See also Blanco (2006) and Blanco and Carvalho (2000).
3
addition, the Brazilian system of intergovernmental transfers is broad; totaling
12% of gross domestic product (GDP) and it corresponds about 70% of local
revenue streams in 2008. That motivates our choice to use Brazilian data in
order to provide empirical evidence of the price-effect of transfers2.
The MCF measures the marginal loss to society caused by a tax increase.
The imposition of a (non lump-sum) tax distorts the allocation of resources in an
economy, such as the labor supply, consumption, and investment decisions.
The MCF aims to capture that distortion resulting from tax-response. According
to Browning (1987) tax-response can produce very disperse estimates (1%
increase of a particular tax can trigger a marginal loss in well-being between
9.9% and 300%).3 Our results suggests that a R$ 1 increase in unconditional
transfers reduces the local price effect (MCF) around R$ 0.12.
The paper is organized in four sections. Section 2 presents a small
modification in Dahlby (2011) in order to focus on comparing the price effect of
transfers to the price effect of income, describes how the data are constructed
and empirically implemented. Section 3 describes the results, and Section 4
presents the conclusions.
2. Theoretical background
2.1 Marginal Cost od Public Finance (MCF) Model
In this section, we explore Dahlby´s (2011) model of MCF in order to
develop an empirical approach to obtain the price effect of unconditional
transfers. First, suppose that all local governments have a homogeneous, fixed
population that may be represented by a single agent. The local government is
in charge of selecting a tax rate t that affects the tax base, B, and the amount of
public services provided, g, at a constant production cost per capita c. The local
government receives a lump-sum transfer T. Therefore, the government’s
budget constraint is tB + T = cg.
The utility of the representative resident is U = u (x, B) + w (g), where x is
the private consumption of goods (price equal to 1), u() is a quasi-concave
function, w`> 0, and w``< 0. In our case, the tax base (B) depends only on the
local tax, t . Thus, we can write B  B (t ) . Dahlby derives MCF as (his equation
(1) on page 307):
Bi
Bi
1
MCFti 


(1)
[dR / dt i ]
 dBi  1  t i

Bi   t i
dt
i 

,
2
Also, Mattos, Rocha and Arvate (2011) use Brazilian data to provide evidence that transfers
reduce tax collection efficiency which follows from Dahlby’s model.
3 Recently, Auriol and Walters (2011) estimated a mean MCF of 1.21 for 38 African countries
using a general equilibrium model, finding that taxes on production factors have higher MCFs
than do taxes on imports and domestic goods.
4
where
   ln B t  0 is the elasticity of the tax base with respect to the tax
rate (i.e., R is total IPTU tax revenue). It is expected that the elasticity inherent
in the tax base,  , is negative because a higher tax could lead to greater tax
avoidance, which reduces the tax base. Equation (1) indicates that the MCF is a
measurement that reflects the cost of a tax rate increase in terms of tax
revenue. For example, if the tax base is not affected by the increase in taxes
(i.e.,  = 0), then an increase in the tax rate reflects the same measurement in
tax revenue. However, it is expected that the tax base shrinks with a tax
increase (as we expect that < 0), which leads to MCF greater than1.4
The local government maximizes the well-being of the residents, supplying
public goods up to the point at which the marginal benefit equals the effective
price (P), e.g., P
MCF.c, where c is the constant cost of production of the
public good per capita. If this government receives a lump-sum transfer, T from
the central government, the quantity of public goods provided could increase
and the tax rate might decrease. The MCF decreases because tax rate is
lowerhave decreased, which in turn, allows for a reduction in the effective price
of providing the public good. Deriving the effective price in relation to T, keeping

1
g constant ( ti / T   B .MCF ) the price effect relative to an increase in
transfers may be expressed as
P P t
c 1  E


T
t T (1  t )3 B
(2)
where E = (d  i / d t i )( t i / i ), which is the change in the elasticity of the
tax base in relation to the tax rate. If the local government is financed with lumpsum taxes, we have MCF = 1. If E = -1, and there would be no price effect for
lump-sum transfers. Supposing that MCF > 1 and -1< E, a lump-sum transfer
has a greater price effect than that of the lump-sum transfer ratio over the local
tax revenue, as well as that of the local MCF.
To find the expression that reflects the relationship between the two
forms of variation in the price-effect (transfers and income), we first have to
completely differentiate the budget constraint, while keeping g constant, then
one has to derive P with respect to Y and to t. After some algebra, one can find
the price effect of income:
P
c
1 E

.BY .t
3
Y (1  t )
B
4
(3)
Furthermore, the municipalities are assumed to be in the increasing segment of the Laffer
Curve, i.e., 1  ti  0. It can be shown that MCF is increasing with respect to tax rate under some
conditions.
5
Equation (3) represents the change in the price of the public good
resulting from the variation in income5. Comparing the price-effects of income
(equation (3)) to its correspondent of the transfers (equation (2))
P
c
1  E P 1


.
T (1  t ) 3 B
Y tB y
(4)
Equation (4) shows that if 1  tBY  0 , one has
P P

. The intergovernmental
T Y
transfers have a greater ability compared income to decrease the effective price
of the public good.
2.2 Local tax revenues and unconditional transfers in Brazil
Unconditional transfers are the main source of fiscal revenue resource
for local governments in Brazil. Table 1 shows that meanwhile
intergovernmental transfers from the Municipalities Participation Fund (FPM, in
Portuguese) accounts for on average 60% of total revenue resources, local tax
corresponds to only 20% of fiscal resources on average for municipal
jurisdictions. Regarding local tax, the tax imposed on the supply of services
(ISS) is the main source of revenues. The ISS median revenue corresponds to
about 80% of total tax revenue. There are other two (main) sources of
intergovernmental transfers: Federal Support for Education Fund (FUNDEB, in
Portuguese), and Federal Support for Health System (SUS, in Portuguese).
These transfers together account for around 10% of municipalities revenue
resources on average. However, as opposed to FPM transfers, they are
denominated matching grant transfers as they produce different allocative
effects because they impose a counterpart spending for local governments.
TABLE 1 - Average of per capita fiscal variables by populational ranges (in 2010 R$)
Population
Range
up to 5 k (thous.)
> 5 to 10 k
> 10 to 25 k
> 25 to 50 k
> 50 to 250 k
> 250 k
FPM
Total
1,359.6
611.8
457.0
337.7
241.0
136.3
Tax Revenue
ISS
IPTU
110.5
98.9
95.0
141.3
213.2
386.4
53.6
47.9
40.8
64.8
98.3
172.7
12.0
15.4
16.8
33.7
52.7
110.3
State
573.1
382.0
289.0
305.2
360.6
432.7
Transfers
SUS FUNDEB
105.2
98.5
102.6
100.6
132.1
160.5
256.4
255.9
253.9
249.4
221.0
164.1
GDP
13,020
11,378
10,238
12,388
16,121
22,313
Nº
munic.
1,302
1,212
1,761
682
508
99
An important characteristic of FPM transfers is that it follows exogenous
criteria based on populations cutoffs. Excluding the 27 State capitals,
municipalities which follows different distributive rules (based on both population
5
For example, if we consider c=1, E=0 (i.e., there is no variation in the elasticity of the tax
base), B=100, and t=1.01 (IPTU rate of 1%), one has a price effect resulting from the transfers
that is equal to -0.0001 and a price effect of income equal to 0.00001, less (in magnitude)
because the price effect of income is multiplied by the variation in the tax base in relation to
income.
6
and income inverse rules), municipalities with less than 142 thousands
inhabitants receive FPM different amounts based only on populational ranges.
Those ranges are on Table 2. What makes this interesting for our
methodological approach is that it allow us to explore this legal framework in a
instrumental variable approach6 as discussed in further detail in the next
section.
TABLE 2 - Population cutoffs for FPM (transfers) distribution
Population Range
Up to 10.188
From 10.189 to 13.584
From 13.585 to 16.980
From 16.981 to 23.772
From 23.773 to 30.564
From 30.565 to 37.356
From 37.357 to 44.148
From 44.149 to 50.940
From 50.941 to 61.128
From 61.129 to 71.316
From 71.317 to 81.504
From 81.505 to 91.692
From 91.693 to 10.1880
From 101.881 to 115.464
From 115.465 to 129.048
From 129.049 to 142.632
From 142.633 to 156.216
> 156.216
Factor
0,6
0,8
1,0
1,2
1,4
1,6
1,8
2,0
2,2
2,4
2,6
2,8
3,0
3,2
3,4
3,6
3,8
4,0
Source: Law by Decree nº 1.881/1981.
3 Methodology
3.1 Empirical strategy
There are several methodologies to calculate the MCF, as shown by
Dahlby (2008a). The method we follow consists of estimating the sensitivity of
the tax base in relation to the tax rate as in equation (1). Therefore we need
local tax rate and the sensitivity of the tax base.7 The only parameter one has to
estimate is the sensivity of the tax base to tax rates and we approach that as
follows
6
We should note that this fact has being previously explored by and Brollo and
Nancinni, 2011 in a regression discontinuity design (RDD).
7
There is a large literature on the determination of the response of the tax base in relation to a
tax variation. Gruber and Saez (2002) perform this estimation for the USA, and Mintz and Smart
(2004) do so for Canada. Dahlby (2008a) provides a good review of the topic and also shows
results in the context of fiscal federalism.
7
log(𝐵𝑖,𝑡 ) = 𝛼𝑖 + 𝜂𝑡𝑖𝑡 + 𝜋𝑋𝑖𝑡 + 𝑑𝑡𝑖𝑚𝑒𝑡 + 𝑚𝑢𝑛𝑖𝑐𝑖𝑝𝑎𝑙𝑖𝑡𝑖𝑒𝑠 + 𝜉𝑖
(5)
where log(𝐵𝑖𝑡 ) corresponds to the (log) of the tax base corresponding to the
municipality i at time t, t i corresponds to the tax rate, and X i is a vector of
control variables and d are time dummies. Therefore, the coefficient of the tax
rate in this regression is the elasticity of the tax base in relation to the tax rate (
 ).
The next step is to identify whether the increase in transfers and income
acts to reduce the municipal MCF according to the equation below:
𝑀𝐶𝐹𝑖𝑡 = 𝛼𝑖 + 𝛽𝑡𝑟𝑎𝑛𝑠𝑓𝑖𝑡 + 𝛾𝑋𝑌𝑖𝑡 + 𝜌𝑋𝑖𝑡 + 𝑑𝑡𝑖𝑚𝑒𝑡 + 𝑚𝑢𝑛𝑖𝑐𝑖𝑝𝑎𝑙𝑖𝑡𝑖𝑒𝑠 + 𝑢𝑖𝑡
(6)
where 𝑀𝐶𝐹𝑖𝑡 corresponds to MCF of municipality i at time t, 𝑡𝑟𝑎𝑛𝑠𝑓𝑖𝑡 is the
transfers received by the municipalities, Y is income, and X includes the same
vector of control variables used in equation (5). Regarding our empirical
approach, since we have constructed MCF from equation (5) we should
calculate standard errors using bootstrapping technique.
According to public finance literature, we should expect that
municipalities decisions over local tax rate (tax revenue) and
intergovernamentals transfers revenues are simultaneously determined which
might bias the estiation of equation (6). For this reason, Ordinary Least
Squares (OLS) regression would produce inconsistent and biased
coefficient estimates. In order to obtain valid estimates of MCF we explore the
fact that the FPM transfers follow populationonal ranges and build binary
variables based on FPM populational cutoffs. Finally, a fixed effect model
should account for heterogeneity across municipalities.
We should note that this formulation of the MCF relies on the following
hypotheses: (i) changes in ISS rates produce very small effect over IPTU tax
revenues, (ii) each municipality’s ISS tax burden is supported only by the its
own; (iii) there is no interaction between the tax bases; (iv) the elasticity of the
tax base may be the same in all of the local governments.
3.2 Data
We collect data for 5,260 (from a total of 5,565) municipalities in Brazil
from year 2006 to 2010 (five years period). The data concerning municipal
revenues and expenditures are annual and are extracted from fiscal balances
and reports from Brazilian Treasury (Secretaria do Tesouro Nacional – STN).
Group data over the period of analysis is available over population, tax revenue
(over ISS and IPTU) and Gross Domestic Product (GDP) by sector of activity
excluding taxes. We also collect data on number of service firms (which
compound ISS tax base), and their number of employes and firms´s payroll total
costs. This last group of information is available in the Brazilian Bureau of
Geography and Statistics (IBGE) reports on Services Firms Census. All
monetary variables are deflated to year 2010 in Brazilian currency (R$ Reais)
values.
8
One of the main difficulties to estimate policies related to marginal cost of
public funding is the identification of the appropriate tax base for local tax. In our
case, there is not systematic data available for ISS tax rates across
municipalities. Thus, we follow Hayashi and Boadway (2001), and instead of
using tax rates, we build an effective average tax rate (AETR). According to
equation (5) we build two different AETR’s: [AETR1] the ratio between ISS tax
revenues and services valued added GDP (last one excludes government
participation) and [AETR2] the ratio between ISS tax revenues and annual
payroll of firms services under the municipalities in the analysis. The main
advantages to use a AETR approach is that we can use a longer time period of
analyses (and explore fixed effects); and that tax revenue reflects both the
effect of tax rate changes on number of firms and firms revenues. Both
measures should capture tax base changes.
As discussed in Section 2 and following equations (2) and (3), we use all
variables in natural logarithm, since we are interested in the elasticity effect of
tax rate change and tax base .Table 3 brings the descriptive analysis of the
variables used.
4. Results
total service tax revenue
number of firms in service sector
number of employees in service sector
total payroll costs for firms in service sector
share of service sector firms with less than 5 employees
total FPM transfers revenues
GDP excluding taxes and service sector
Value added GDP for service sector (excludes taxes)
Population at municipalities
ISS
Firms
Employees
Payroll
up to 4 firms
FPM
GDP
Service GDP
Population
Note: number of observations is 27,820.
Definition
Variable
14,939
2,065,634
16,123,443
1,182,741
3,561,650
379
1,706
138,823
1
8,643,299
169,461
254,679
200,431
2,784
4,437,176
33,883
90,864,947
0
Std. Dev.
Mean
Max
0
0
0
0
0
804
751
11,244,369
264,362,400
74,141,312
581,801,472
1
142,150,176
1,002,814
184,770
0 7,053,318,144
649
Min
TABLE 3 - Descriptive statistics of observable variables from municipalities (from 2006 to 2010, in R$ year 2010 )
9
10
4.1. Tax base elasticity
We first estimate the tax base elasticity as described in equation (5). We
report three different measures of the tax base as dependent variables. We
consider the number of employees in the services sector, the number of firms in
the service sector and percentage of the firm in the service sector with less than
5 employees as a percentage of total firms in that sector. The first two variables
are in natural logarithnm.
The dependent variables number of employees and number of firms are
expected to have a negative correlation with the ISS tax base ( < 0). Results
on Table 4 show that a ISS tax rate increase reduces number of employees on
those firms as well as the number of firms. In the regressions using AETR1
(ratio of ISS revenue and value added GDP from services) as tax rate, an 1%
change in ISS average tax rate reduces in 1,5% firms workforce. Similarly, an
1% change in ISS average tax rate reduces in 1,3% the number of service
firms. Both coeffcients estimates are significant at the 1% significance level.
The effect on number of firms is smaller but still large. It reflects the fact that
most service firms around municipalities in Brazil are very small with less than
five employess. In fact, although not significant, the coefficient for the tax rate
elasticity over percentage of firms with few employess is positive: the tax rate
rise reduces the size of the firms. Meanwhile, income appears to move in the
opposite direction, increasing the tax base, as predicted in our model BY  0 .
Results are similar when we use AETR2 (ratio of ISS revenue and firms
from service sector total payroll costs). The coeffcients are larger and still
significant at the 1% level. We drop from the controls personal income since it is
correlated to the AETR2 denominator (payroll costs). Since our tax base
elasticity results seems to produce consistent (and expected) results we
estimate equation (6) in the next sub-section.
TABLE 4 - Regression Results for Tax Elasticity
Dep. Variable
AETR
Pop
Income (Payroll)
GDP
Employees
-0.015***
(-5.676)
0.105***
(5.363)
0.084***
(14.380)
-0.009
(-1.274)
AETR1
Firms
-0.013***
(-4.681)
0.103***
(5.115)
0.077***
(12.777)
-0.023***
(-3.224)
Up to 4 firms
0.001
(1.348)
0.002
(0.545)
-0.001
(-0.619)
-0.006***
(-4.479)
Employees
AETR2
Firms
Up to 4 firms
-0.019***
(-7.317)
0.116***
(5.744)
-0.021***
(-8.233)
0.119***
(6.068)
0.000
(0.910)
0.002
(0.515)
-0.012*
(-1.751)
0.003
(0.404)
-0.006***
(-4.629)
Notes: all regressions include fixed effect and time effects; *** p<0.01, ** p<0.05, * p<0.1; in parentheses are t
statistics; all variables are in natural log, number of obs is 26,344 .
4.2. MCF’s Price-effect
In this section we estimate the unconditional transfers price effect on the
MCF using our model described in Section 2. We expect that FPM transfers
11
present a negative effect on the MCF since this should reduce the price of
public provision as in Dahlby (2011). We use baseline model the estimate
elasticity based on number of employers for AETR1 and number of firms for
AETR2. Table 5 brings the results panel fixed effect model with bootstrapping
standard errors (FE - bs) and with two stage least squares instrumental variable
with and without (2SLS-IV) bootstrapping standard errors (2SLS-IV bs).
TABLE 5 - Regression Results for Price Effect
Dependent variable: MCF
AETR1
Model
FE - bs
IV2SLS
IV2SLS - bs
FPM
Pop
Income (Payroll)
GDP
-0.001**
(-2.044)
0.002
(1.594)
-0.001***
(-3.155)
0.002***
(4.924)
F test
Stock-Yogo weak ID (5%)
Sargan test
-0.012**
(-2.348)
0.004***
(2.898)
-0.001***
(-4.860)
0.002***
(6.232)
-0.012*
(-1.748)
0.004**
(2.019)
-0.001***
(-2.798)
0.002***
(4.146)
18.189
21.010
6.691
(0.824)
18.189
21.010
6.691
(1.824)
FE - bs
AETR2
IV2SLS
IV2SLS - bs
-0.001
(-1.563)
0.003**
(2.008)
-0.008
(-1.214)
0.004**
(2.439)
-0.008
(-0.902)
0.004*
(1.913)
-0.001**
(-2.281)
-0.001***
(-3.072)
-0.001***
(-2.691)
18.221
21.010
6.548
(0.834)
18.221
21.010
6.548
(0.834)
Notes: all regressions include fixed effect and time effects; *** p<0.01, ** p<0.05, * p<0.1; in parentheses are t
statistics; all variables are in natural log, number of obs is 26,344 .
In all models the FPM coefficient is negatively associated to MCF (as
expected) and, when we estimate using AETR1, significant (at least at the 10%
significance level). The significance level drops when we use bootstrapping in
the FE model (FE –bs) and in the 2SLS model. As the MCF reflects the
marginal loss in the economy due to the marginal increase in tax rate, the
negative coefficients show that each Brazilian real increase in unconditional
transfers is associated with a reduction in the cost of taxing about 0.1%.
Regarding AETR1, we should note that meanwhile the 2SLS-IV
approach estimates that 1% increase in the FPM transfers causes a 1.2%
reduction in the MCF, in the FE model the coefficient is under estimated. This
occurs probably because by instrumenting FPM we seem to eliminate the main
source of bias that could be the political capacity to obtain federal funds on the
part of the municipality.This unobserved variable which seems to be related
negatively with the amount of resource that would be entitled to receive.
Moreover, this political ability to achieve political resources is probably positively
related to MCF that would lead to underestimation of price-effect.
4.3. Results – Robustness
One main concern in the IV model is that we do not face neither the
problem of weak instruments (due the use of multiple instruments) nor the
instruments are related to other unobserved characteristics of the municipalities.
In this section we bring first stages results for our populational binary variables.
Table 6 brings the coefficents for the first stage regression and it shows that all
12
binary variables are significant. They present negative signal which reflects the
fact that the smaller the population the higher the FPM per capita received.
Similarly for elasticity, ie., smaller the population the higher the estimated
elasticity of price-effect.
To test whether those cutoffs could be associated to other
chacacterisitics we do the same regression with GDP as the dependent
variable. At this time, from the 18 binary variables only two are significant at the
10% significance level. Thus, we believe that our IV approach is solid (does not
suffer from selection bias or weak instruments).
Next we split our sample to municipalities with less than 52 thousand
inhabitants and less than 250 thousand. We note that the coefficient is similar
and significant in both samples. We run this regression in FE approach and
obtain similar results for FPM coeffcients. We sustain that our results (based on
populational cutoffs) describe better small and medium municipalities.
13
TABLE 6 - First Stage (IV) Regression Results
Population cutoffs
(binary variables)
dfxpop1
dfxpop2
dfxpop3
Dep. Variables
FPM
GDP
-0.737***
-0.080
(-11.446)
(-0.649)
-0.642***
-0.090
(-10.102)
(-0.741)
-0.571***
-0.101
(-9.087)
(-0.839)
-0.511***
-0.095
(-8.240)
(-0.801)
-0.470***
-0.119
(-7.670)
(-1.016)
dfxpop6
-0.433***
-0.129
(-7.152)
(-1.109)
dfxpop7
-0.393***
-0.109
(-6.608)
(-0.954)
-0.365***
-0.088
(-6.259)
(-0.789)
-0.327***
-0.130
(-5.740)
(-1.193)
dfxpop10
-0.305***
-0.166
(-5.518)
(-1.563)
dfxpop11
-0.289***
-0.134
(-5.422)
(-1.306)
-0.264***
-0.142
(-5.212)
(-1.456)
-0.234***
-0.165*
(-5.025)
(-1.841)
dfxpop4
dfxpop5
dfxpop8
dfxpop9
dfxpop12
dfxpop13
dfxpop14
dfxpop15
dfxpop16
dfxpop17
Pop
-0.198***
-0.165*
(-4.448)
(-1.918)
-0.182***
-0.124
(-4.552)
(-1.607)
-0.182***
-0.086
(-5.188)
(-1.277)
-0.076***
-0.033
(-3.050)
(-0.663)
0.083***
0.223***
(7.112)
(10.085)
Notes: all regressions include fixed effect and time effects;
*** p<0.01, ** p<0.05, * p<0.1; in parentheses are t
statistics; number of obs is 26,344 .
14
TABLE 7 - IV2SLS Regression Results for Price Effect
Dependent variable: MCF
Samples: small and medium municipalities
Model: IV2SLS
FPM
Pop
Income (Payroll)
GDP
F test
Stock-Yogo weak ID (5%)
Sargan test
Sample I
-0.010*
(-1.858)
0.003**
(2.443)
-0.001***
(-2.995)
0.002***
(4.196)
36.676
19.860
5.977
(0.426)
Sample II
-0.011*
(-1.792)
0.004**
(2.113)
-0.001***
(-2.921)
0.002***
(4.294)
17.983
21.010
6.583
(0.832)
Notes: all regressions include fixed effect and time effects; ***
p<0.01, ** p<0.05, * p<0.1; in parentheses are t statistics; all
variables are in natural log, number of obs is 23,590 .
Finally, we include additional independent variables which could affect
our FPM coefficient as SUS and FUNDEB transfers and IPTU revenues. We
note that our IV results are maintained.
15
TABLE 8 - IV2SLS Regression Results for Price Effect
Dependent variable: MCF
Including other transfers as covariables
Model
FPM
Pop
Income (Payroll)
GDP
SUS
FUNDEB
IPTU
F test
Stock-Yogo weak ID (5%)
Sargan test
IV2SLS - bs
-0.011*
(-1.848)
0.003**
(2.069)
-0.001***
(-3.282)
0.002***
(4.450)
-0.001***
(-2.639)
-0.001***
(-2.795)
-0.000**
(-2.179)
18.189
21.010
6.691
(0.824)
Notes: all regressions include fixed effect and time effects; ***
p<0.01, ** p<0.05, * p<0.1; in parentheses are t statistics; all
variables are in natural log, number of obs is 26,344 .
5. Conclusion
This paper investigates the effect of unconditional transfers, conditional
transfers, and income on the MCF using Brazilian local governments data. In
particular, we attempt to show how these three different methods of increasing
income change the effective price of public goods.
First based on the model proposed in Dahlby (2011), we design a
theoretical extension that shows the difference between the price effect
resulting from transfers and from income.
Using data from Brazilian national Treasury for the years 2006 to 2010
we construct average effective ISS local taxes and unconditional (FPM - the
recipient government has complete freedom to decide on the allocation of the
resources) transfers to estimate such difference
We first estimate the effect of tax rates on a tax basis in order to
construct MCF estimations. That estimated elasticity is used to identify the
MCF, representing the price of the public good for each municipality. Next, we
estimate the effect of transfers and income on the constructed MCF, correcting
the standard error of these estimations. We estimate that for R$ 1 increase in
unconditional transfers reduces the local price effect (MCF) around R$ 0.12.
16
The magnitude of this effects is not different across any of the models. We also
estimate a positive effect of income on tax base.
We are not particularly interested in the flypaper effect although
consequences of results above are straightforward. If lump-sum transfers
encompasses price-effect (substitution effect) and if we estimate the income
effect properly the eventual flypaper effect could be caused by the differences
between the two.
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