Marginal Benefits of the Mining Property Tax on
Mining Counties in Chile∗
Nathaly M. Rivera1†, Dusan Paredes1,2
1
Department of Agricultural, Food and Resource Economics, Michigan State University, USA.
2
Department of Economics, Universidad Católica del Norte, Chile.
September 2015
Draft, Please do not cite
Abstract
Public provision of goods is likely to increase in mining towns given the large amount
of negative externalities derived from this sector, raising also the need for public revenues. In the case of Chile, this need is uniquely encountered with a non-distortionary
property tax on mineral concessions. The existing mineral legislation explicitly defines
this mining property tax as entitled to the local funding of activities aimed to increase
the development of the community. We estimate the marginal benefit of this mining
property tax on local public expenditures on several items in Chile from 2009 to 2013
between mining and non-mining counties. Using pooled and panel regressions, we show
a partial significant positive effect on a reduced set of expenditure items for mining
counties. In particular, we identify a positive impact on mining counties expenditure
level on community services and cultural activities only.
Keywords: Mining, Marginal Benefits, Public Goods Provision.
JEL Classification Numbers:
∗
We thank enormously to the participants of the Graduate Students Symposium at Michigan State
University and of the REAL Seminars at University of Illinois at Urbana-Champaign, whose comments have
contributed to improve this work.
†
Corresponding autor. Email: [email protected]
1
1
Introduction
The literature has vastly documented the external damages from the mining industry
to the community where it is located.1 These externalities vary not only in terms of their
intensity, but also in the extent of their impacts, highlighting indeed the role of local
governments since they must provide a relative wider supply of public goods to compensate
the local communities. Consider for example the case of pollution stemmed from mining
exploitation. As defined by Bovenberg and van der Ploeg (1994), the two major tasks of
local governments in presence of environmental costs only, are the provision of a natural
environment together with undertaking public abatement activities.
The performance
of these two tasks will involve the use of public resources that local governments of
free-of-pollution counties, ceteris paribus will not have to face, increasing the need for public
revenues in mining economies.
Yet in the previous example we should also expect a higher demand not only for a
free-pollution environment (direct shock), but also, for instance, for health services in
communities sited around the mine (indirect shock). This second shock also increases the
pressure for both, private and public local health services, with the latter situation forcing
national and local governments to provide these services for lower-income inhabitants.2 At
the end, both direct and indirect shocks increase the need of local governments for collecting
revenues.
One way of financing these additional public expenditures without increasing the tax
burden of local residents is the use of property taxes as it is a convenient scheme for
local governments (Stinson, 1977).3 These property taxes linked to property rights, ensure
1
See for example Andrews-Speed and Rogers (1999), Gunton (2003), Sinha et al. (2007), Morrice and
Colagiuri (2013).
2
It is also possible that the private supply for health services will absorb this suddenly excess in demand.
However, mining counties with high presence of artisanal and small-scale mining are generally characterized
by precarious income levels, allowing us to assume the public supply for this type of services will still be
needed in those cases of no access to the private sector.
3
Mining property taxes are also referred as mineral patents in the literature. However, along the whole
document we will keep the reference as mining property tax only.
2
not only the private ownership over the land and the minerals in the reservoir, but also
strengthen it by helping to reduce enforcement costs (Gerard, 2001). Moreover, when these
property taxes are based on mine attributes others than the grade of the resource in the
reservoir or the production level of the site (i.e. non ad valorem property taxes), there will
not be incentives for mining companies to shift extraction from future to present and so not
accelerated depletion will take place (Conrad and Hool, 1981).
In this study, we analyze the direct benefits of a non ad valorem mining property tax in
Chile. This is an interesting case because mining is the principal industrial activity in this
country and, despite the economic benefits derived from its activity, still there are some
concerns about their negative effects on social welfare.4 Chile currently leads the world
in the extraction of raw copper, and its production is characterized by a capital-intensive
large-scale production, which is vastly controlled by foreign companies. In order to retain
some of the resource rents, Chile has three types of taxation schemes for this industry. A
general tax of 19% on profits, an ad valorem severance tax on operating income which
range from 0% to 14%, and a non ad valorem property tax on mineral concessions. The
first two taxes have a national collection, while the property tax is currently the unique
taxation scheme that goes in direct benefit of local governments. The mineral legislation
that regulates the specifications of this last scheme also specifies the items in which this tax
must be spent by local governments. In a broad sense, public revenues collected from this
tax are entitled to activities aimed to increase the development of the community, becoming
an excellent empirical instrument for policy evaluation. We use this institutional framework
to analyze the marginal benefits that this last non-ad valorem property tax scheme has for
the local expenditure of Chilean counties.5
The idea of marginal benefits of taxes has been widely documented in the literature6 but
4
See for example Aroca and Atienza (2011) and Aroca (2001) for specific examples of the external social
costs of mining in Chile.
5
The plus of having a non-ad valorem tax is that it allows the estimation of its direct benefits, with no
much concern about the distortionary consequences that an increase in this tax can generate on the excess
burden.
6
Most of the work has been done on marginal cost of public funds. However, a similar logic will apply
to marginal benefits of public funds. See for example Slemrod and Yitzhaki (2001) for an argument of how
3
is during the last two decades where an increasing interest on resource industries motivated
the study of marginal effects of taxes for externalities-generator industries. Although much
of this attention has been on the cost of funds at the margin7 , our focus here is instead in
evaluating the marginal benefits of a policy instrument aimed to increase the revenues of
local governments, specifically those with presence of mining.
We approximate the public expenditure variable by using four different indicators on
expenditures, which are expenditures in community services, and in social, cultural and
recreational activities.8 While the limited information situation is a well-known issue in
developing countries, this decision is not only motivated by information constraints, but also
by the current legislation in Chile and its ambiguity about the destination of the mining
property tax collection. On this item, the law is vague enough in stipulating that revenues
from the mining property tax are for the investment in the community development9 but
there are not specific details that might help in providing a common understanding for
this mandate. In a highly cited paper, Robert et al. (2005) refers to the malleability of
a concept like sustainable development, and how due to its flexibility, the concept opens
up the possibility of enabling participants at multiple levels to redefine and reinterpret
its meaning to fit their own situation. Despite this description, the authors emphasize
three basic ideas for the developing what question: people, economy and society, with
social capital and community ties as the main ideas inside the last group. Given the
institutional characteristics of a highly centralized country as Chile, where all the decisions
concerning people and economys development are left to the central government, we find
reasonable to understand that those activities aimed to increase the development of a
local community can roughly be grouped into the labels of community services, and social,
cultural and recreational activities. At the same time, and given the widespread negative
social consequences that mining imposes on developing countries, it is very likely that the
the marginal benefit of public funds concept is supplementary to the marginal cost of public funds concept.
7
See for example Cordano and Balistreri (2010)for a study of the marginal cost of public funds of taxes
on both mining and the energy industry in Peru.
8
We are aware that a much broader analysis will follow once we consider the marginal benefits of this
particular tax on the communities welfare.
9
The tax is due on March of each year. This means that local governments get to have those revenues
available for their use during the same calendar year of their actual payment.
4
provision of these activities will be in deficit in those counties with presence of mining.
For carrying out the empirical side of this study, we combine a data set using first
financial information obtained from the National System of Municipalities Information
(SINIM) on expenditures, revenues collection and public efficiency for 345 counties in Chile
from 2009 to 2013, and also demographic and socio-economic characteristics at county-level
from both the National Institute of Statistic (INE) and the National Socioeconomic
Characterization Survey (CASEN). From this information, we construct a panel data set
with information for 345 counties and 5 periods.
The identification strategy consists in an official rule for county classification into either a
mining or non-mining county (BCNC, 2015). This dichotomist and exhaustive classification
based on both the region where the county is, and the relative importance of the revenues
from mining property tax in the local budget, will allow us to differentiate the marginal
effects that this tax has on local public expenditures of those counties that, according to
the Chilean government, have an significant presence of mining. This official instruction
is based on the relative importance of the tax collection on the local annual budget, and
so it adds dynamism to the panel data set, as the dichotomist label can change from one
year to another. Additionally, we use this exogenous classification to diminish the potential
endogeneity of the property taxes collection and preferences of local inhabitants by public
goods. We finally estimate these partial effects using both pooled regressions and a fixed
effect panel data estimations.
Our results do not throw enough systematic statistical evidence to argue that mining
property taxes generate a homogeneous and significant impact on our proxies of local
expenditure of public goods aimed to increase the development of communities. Throughout
different econometric exercises, our estimations reveal that mining taxes only generate an
impact on the expenditures associated with community and cultural services, but they do
not show any statistical evidence to support an effect on social and recreational activities.
Here we avoid discussing on which one of these items should be more relevant for the
5
development of mining communities, and we rather highlight the fact that even when mining
counties show higher per capita expenditure, they are not necessarily corresponded with
higher expenditures in public goods once confounder factors are controlled for. This lack
of evidence, according to our understanding, suggests the possibility of policy evaluation
on the magnitude of this tax given the advantage of the non-distortionary characteristic
of the tax, and the evident need for public revenues that mining communities have over
non-mining communities.
The division of this work is as follows: section 2 contains the institutional details of the
mining property tax scheme in Chile. Section 3 provides a brief description of the theoretical
framework from where to understand the calculations of marginal effects of taxes, while
section 4 describes the data and methods. Section 5 gives the main results and section 6
concludes.
2
The Mining Property Taxation Scheme in Chile
The national mining code introduced in 1983 ordered the full concession of property
rights over mineral reservoirs as part of the denationalization process of the copper mining
industry that took part during that decade in the country (Moya and Carlos, 1996).
Currently in effect, this mining law includes two types of concessions in the form of
exploration and exploitation of mineral reservoirs. On the one hand, exploration concessions
or exploration claims- allow the companies the temporal property of the land during the
ore scanning process. On the other hand, exploitation concessions or exploitation claimsadmit the private property of the land, a process needed for the initiation of the extraction
process.
This mining code also defines lower and upper bounds over the amount of surface
intended to each types of claim. That is, between 100 hectares (247 ac) and 5.000 hectares
(12.355 ac) can be designated to exploration concessions, while a maximum of 10.000
hectares (24.710 ac) can be called for an exploitation concession, or full property rights
6
over the land. Exploration concessions have a length of two years extendible to other two
years, while the exploitation allowances have an indefinite duration. Once an exploitation
concession is set, a firm will own all the minerals that could be extracted from the reservoir.
Aside from the establishment of property rights, this law also orders the annual payment
of a mining property tax that works as a final step in the procurement of the mining
claim. This tax is a function of both the number of hectares and the type of claim. In this
way, those mining companies with interest in metallic minerals (e.g. copper) must provide
an annual payment of a tenth of a monthly tax unit (UTM10 ) for each hectare in the
exploitation concession form, and a fiftieth of a UTM for those in the exploration form.11
The subsequent national law 19.143 acting since 1993 defined the spatial distribution of
these payments between regions and counties.12 The distribution rule assigns 50% of each
payment to the regional government where the legal registration of the mining concession
was made, while the other 50% goes directly to the county where the mining concession is
spatially located.13
Currently, there is approximately a 41% of the national territory given away in the
form of mineral concessions according to the National Service of Geology and Mining
(SERNAGEOMIN14 ), of which 54% correspond to concessions for exploration and 46%
for the extraction of the mineral. Once we disaggregate these values at the regional level,
mainly obligated by the lack of disaggregation at county level, we observe these mineral
concessions are highly concentrated in the northern regions, where the geological conditions
have created a good environment for the mining production, especially of a large-scale.
10
Unidad Tributaria Mensual (UTM) is the general currency unit used in Chile for the payment of taxes
and custom duties. During the period 2007-2014 a unit of UTM was equivalent to US$73 in average.
11
A different regime operates for both small and artisanal miners. Given the limited participation of the
small-scale and artisanal mining in the total annual mining production of the country (around 2%), we will
not specify this regime here.
12
Chile is administratively divided into 15 regions and 346 counties (including the Antartica and the
Eastern Island).
13
In the case of two or more counties sharing the geographical location of a mining concession, a mutual
negotiation between the corresponding county governments is performed.
14
http://www.sernageomin.cl/sminera-estadisticasconc.php
7
3
Theoretical Framework
Welfare analysis is the general framework used to analyze partial effects derived from
a tax increase, which allows to understand the economic impacts that a tax policy has
on society’s welfare as a whole. The following explanation is based in the work done by
Slemrod and Yitzhaki (2001), who in their attempt to explain the concept of marginal cost
of funds, establishes a parallel and symmetrical concept referred as the marginal benefit
from public projects.
Consider a small economy setting with neither administrative costs nor evasion. Let the
social welfare function be defined as:
W (V 1 (P G1 , P G2 , ..., P GJ , q1 , ..., qn , y1 )...V H (P G1 , P G2 , ..., P GJ , q1 , ..., qn , yH )),
(1)
where P Gj is the public good (j = 1, ..., J), qi is the price of private good (i = 1, ..., n) that
consumer faces, and yh is lump-sum income of individual h (h = 1, ..., H). Assume that the
producer price vector p is given, so that ti = qi − pi is the commodity tax rate. In this
economy, government actions are constrained by the following budget constraint:
R0 = E − R =
J
X
Pj P Gj −
j=1
n
X
ti Xi ,
(2)
i=1
with E as the amount of total expenditures in the budget, R corresponds to total revenues,
P
Pj is per unit price of producing P Gj , and Xi = h xhi represents the aggregate quantity
of commodity i, with xhi as the quantity of the private good i consumed by individual h.
We can follow the analysis by assuming there is a particular tax scheme that satisfies
equation 2. In this set up, the government considers whether to increase the tax revenue
by $1 in order to finance an increase in the supply of a public good. According to Slemrod
and Yitzhaki (2001), the question of whether it is social-welfare-increasing to expand the
8
production of P Gj by US$1 with a specified financing scheme, should be answered by comparing the increase in social welfare due to an increase in the supply of public goods, and
the reduction in social welfare due to the increase in taxes. This idea is reflected by:
∂W ∂V h
h=1 ∂V h ∂P Gj dP Gj
(Pj − ∂P∂RGj )dP Gj
PH
∂W ∂V h
h=1 ∂V h ∂ti dti
∂R
dti
∂ti
PH
+
(≥<) 0,
(3)
which is derived after rearranging the derivatives of the social welfare function in (1) and
the budget constraint in (2) with respect to both P Gj and ti . While the numerator in
equation (2) represents the direct effects on social welfare of either increasing the amount of
P Gj or increasing ti ; denominators represents similar effects but on the budget constraint
in (2).
Since the budget constraint is given, we can get rid of denominators in (3) as they are
equal to each other. Therefore, equation (3) becomes:
H
H
X
X
∂W ∂V h
∂W ∂V h
dP
G
+
dti (≥<) 0.
j
h ∂P G
h ∂t
∂V
∂V
j
i
h=1
h=1
(4)
The first term in (4) represents the increase in social welfare per dollar spent by the
government on P Gj , while the second term represents the decrease in social welfare per
dollar increase in revenue collected through an increase in ti . The authors use equation (4)
to show the convenience of projects aimed to increase social welfare. We make use of this
specification to illustrate how a marginal change in public goods affects social welfare, and
how this marginal effect is compared with the marginal effect of the tax scheme use to fund
this new public good expenditure.
A most straightforward and general way is to take a total differential from the social
welfare function:
9
n X
H
J X
H
H
X
X
X
∂W ∂V h
∂W ∂V h
∂W ∂V h
dP
G
+
dt
+
dyh .
dW =
j
i
∂V h ∂P Gj
∂V h ∂ti
∂V h ∂yh
i
j
h
h
h
(5)
Assuming movements along the same indifference curve (i.e. dW = 0), and after rearranging the terms in equation (5) we get the following expression:
PH ∂W ∂V h
PH ∂W ∂V h
PJ
dyh
h ∂V h ∂y
h ∂V h ∂ti
j dP Gj
Pn
= − PH ∂W ∂V h + PH ∂W ∂V hh
.
dt
i
i dti
h
h
h ∂V ∂P G
h ∂V ∂P G
j
The term
dyh
dti
(6)
j
at the right hand side of equation (6) represents the marginal effect of a
specific tax on individual’s income. For the case of a non-ad valorem tax, this term will be
equal to zero due to its non-distortionary properties. Therefore:
PH ∂W ∂V h
dP Gj
h ∂V h ∂ti
= − PH ∂W
> 0.
PI
∂V h
i dti
h ∂V h ∂P Gj
PJ
j
Considering that in general the term
∂V h
∂ti
(7)
is less than zero, and that the expression P Gj
represents in this case a good instead of a bad, we expect a positive marginal effect of a
specific tax scheme on the j public good. Hence, along this study we will expect to see a
positive marginal effect of the mining property tax on local public expenditures.
4
Data and Methods
Such as we previously discussed, we build a panel data set that contains annually
reported information on financial statements at county-level for 2009 to 2013.
This
information is obtained from the National System of Municipalities Information (SINIM15 ),
the institution in charge of the condensation of the annual financial information for every
county. From these financial statements, we extract the information regarding counties
revenues, counties expenditures and some indicators on efficiency in local public budget
management.
15
In terms of revenues, the exact information extracted refers to counties
http://www.sinim.gov.cl
10
permanent revenues, the own revenues collection and so the collection of mining property
taxes. In terms of expenditures, the SINIM offers full information on several expenditure
items, although we focus our attention only on those defined in the previous section,
that is, expenditures in either cultural, recreational, and social activities as well as those
related with community services. Ultimately on this, we use two indicators on local public
sector efficiency that help us to control for potential differences in local governments across
counties. On one hand, we use the efficiency rate in collecting what is called a commercial
patent, a concept that refers to specific fees that the commercial sector must annually
pay in Chile in order to operate legally in the local market. This variable will work as a
proxy for the efficiency of the local public sector in collecting the revenues derived from the
mining property tax. On the other hand, we use the execution rate, measured as the ratio
between annual total public expenditures and annual public revenues, which will allow us
to control for differences across local governments in spending annual expenditures budgeted.
The panel data set also includes socio-economic characteristics at county-level to control
for potential confounder factors. For example, information on average years of education
helps us to control by individual preferences of high-educated workers who could demand
higher level of public goods.
These preferences have direct pressure on the agenda of
local policy makers, and so it becomes a clear factor that might show high correlation
with local public expenditures, but is hardly related with the mining property tax. These
variables are obtained from two main sources: the National Institute of Statistic (INE16 )
that constitutes the official source of information for population levels, and the National
Socioeconomic Characterization Survey (CASEN17 ) that collects information every other
year on socioeconomic variables at the household level, and has national representation.
From this last survey, we extract county-averages for education, urbanization level, and
poverty and unemployment rate, for 2009, 2011 and 2013. The county-average is used to fill
in those years in between.
16
17
http://www.ine.cl
http://observatorio.ministeriodesarrollosocial.gob.cl
11
Our identification strategy relies in an exogenous official definition that the national
government has used for several years for the classification of a county as either mining
or a non-mining county (BCNC, 2015). In 2010, the Chilean government set the mining
counties denomination using two guidelines: the importance of mining in the regional GDP
of each countys region, and the role of the mining property tax for local budgets in terms
of their significance relative to the county permanent revenue. In this way, a mining county
is a county located in a region where the mining industry represents more than 2.5% of the
regional GDP, and for which the proportion of the annual mining property tax collection
over the county permanent revenue is equal or higher than 2.5%. We replicate this official
rule to define mining counties as treated counties for every year, that is, counties with a
high presence of mining. This design allows us to add time-dynamism to our posterior panel
data estimations.
The empirical strategy used in this work consists in the estimation of the partial effect of
the mining property tax on the linear conditional expectation of several dependent variables
measuring public expenditures in activities aimed to increase communities development,
as explained previous sections. Our first approach is the use of a pooled ordinary least
square regressions that will give us a consistent estimator of this marginal effect. Later on,
we improve these estimations using a fixed effect panel data estimator, which will give us a
consistent estimator of the marginal effect even in presence of time-constant omitted variables
that can be arbitrarily related to the observables (Wooldridge, 2010). This estimator is
extremely useful in our study as it is superior in those cases where the possibility of having
some county attributes, like for instance annual permanent revenues, affecting both the
mining county label as this attribute is part of its construction- and the level of public
expenditures as it is directly related with the size of the local government - is a real caveat.
Additionally, we exploit panel data setup with the incorporation of time fixed effects, a
critical variable as it allows us to control by any potential structural effect of copper price
on the tax collection. Our final inference will be based on the estimation of marginal effects
of the tax by type of county.
12
5
Estimation Results
Table 1 summarizes the averages for socio-economic variables for both mining and non-
mining counties through the five periods considered in the panel.
[INSERT TABLE 1 AROUND HERE]
From the information displayed in table 1, we observe that the socio-economic characteristics are balanced across groups in terms of years of schooling and urban area rate.
Apparently, there is no significant difference between both groups. The average monthly
income in mining counties seems to be higher at first glance, with US$700 in average
versus the US$609 in non-mining counties. However, the standard deviation is larger for
no mining counties, suggesting a higher inequality within this group. A higher average
income in mining counties could be explained by high cost of living faced by these areas;
such as it is suggested by Paredes (2011).18 In terms of the efficiency of the local public
sector, we observe some differences across groups that are worth to mention. Even when
mining counties have in average a higher efficiency rate than their counterparts, non-mining
counties reveal a higher performance rate in terms of the budget execution. This situation
reveals no a priori expectations on whether the public sector of either mining or non-mining
counties performs statistically different one another in its duties. Instead, it reveals the
relevance of including this proxy of efficiency to control by additional sources.
Table 2 shows additional descriptive about the mining property tax collection as well as
four expenditure variables that we will use as dependent variables. For each year, we see
60 observations labeled as mining counties, for which the mining property tax collection has
been in between $56.36 and $103.90 dollars per capita across time. Its important to note
that the mining property tax collection is not zero for those counties labeled as non-mining
counties as the official rule considers a proportion of the mining property tax collection
on counties permanent revenue as equal or higher than 2.5%. However, this magnitude is
18
See also Paredes and Aroca (2008) and Iturra and Paredes (2014) for additional estimations in this
topic.
13
extremely low in comparison with mining counties confirming that this simple rule effectively
distinguishes between both groups.
[INSERT TABLE 2 AROUND HERE]
For those variables related with expenditure items, we find a pattern of higher expenditures per habitant in mining counties through time when they are compared with non-mining
counties. Although this pattern is uniform across all the four expenditure variables, the difference is more dramatic for expenditures on community services and social activities where
mining counties have more than two times the expenditure for each inhabitant for some
years. Generally speaking, mining counties seem to always spend more money for any of the
four expenditure items for any year. As a first rough approximation, higher expenditure in
mining counties should be consistent with the arguments given at the beginning of this study,
that is, the public sector spend more money in those communities affected by negative externalities. This preliminary conclusion is supported by figure 1, that shows the relationship
between total public expenditures -as the total amount spent on community services, and
social, cultural and recreational activities- and revenues collected from the mining property
tax during 2013. In this boxplot we observe the high variability in the annual distribution
of local expenditures for each decil of revenues financed with this tax. This variability is especially high for counties in the last two decil of mining tax collected, and it is persistent for
the other years with information available. Under the assumption that mining counties will
be those with the higher collection of mining property taxes, then figure 1 would support the
early hypothesis of mining counties spending more on public goods than non-mining counties. In this anticipated conclusion, we have the conviction that local governments do not
have the means for exerting some influence in the amount of hectares requested for mineral
concessions in their counties, as the mineral is initially randomly distributed through the
space, and the tax calculation is uniformly defined for the whole territory. This conviction
rules out the possibility of a bidirectional causality between the level of public expenditures
and the revenues collected from this particular tax in Chile. In this sense, this plot gives us
the first insights that local governments of mining counties could spend more per habitant
than non-mining counties. However, the reader should not be blinded by nave comparisons
14
because differences in the expenditure level of public goods across groups do not imply that
these differences are explained by the mining property tax collection.
[INSERT FIGURE 1 AROUND HERE]
5.1
Pooled OLS Regresions
Our preliminary results from pooled OLS regressions in table 3 using four different response variables: expenditures in community services, social activities, recreational and
cultural activities. For each response variable we estimate three different models reported
in the columns with the corresponding numeration. The first set of model, namely 1, 4, 7
and 10, includes only the explanatory of mining property taxes and some socio-economics
controls. The second set of models, specifically 2, 5, 8 and 11, considers a different intercept
for mining counties. Finally, the third set of models, namely 3, 6, 9 and 12, assumes both a
different intercept and a different partial effect of the tax for mining counties. We represent
this last idea including the interaction between the dummy variable of mining counties and
the continuous explanatory of mining property taxes. Is worth to mention that the estimated coefficient for the interaction will be more valuable for us in the process of calculating
marginal effects rather than in its direct interpretation. In any case, we try to give intuitions
for both, coefficients and margins, as they are complementary to one another.
[INSERT TABLE 3 AROUND HERE]
In the results for models 1, 4, 7 and 10 we see that the mining property tax variable has
not statistically significant effect for none of the expenditure items. The unique significant
covariate is poverty rate for the dependent variable of community services. It shows that
the public sector of poorer counties spend less in this type of expenditures directly related
with the size of the local public sector. The national decree 885 on budget itemizations
commands that expenditures on community services will incorporate all the expenses
directly related with the functioning and maintenance of public goods and public services,
as well as those expenses going in direct support of either public or private entities in the
county. Therefore, our results show that the poorest the county, the smaller the amount of
15
public goods and services offered by the local public sector to its community.
A no statistically significant coefficient for the mining property tax variable can be
explained by the unbalance that exits across groups for this variable. Consequently, models
2, 5, 8 and 11 fill in this requirement by adding the binary variable of groups. After
controlling for this binary variable, we see that mining counties in fact spend less than
non-mining counties in expenditures on community services and in social activities. By the
national official definition (i.e. decree 885), expenditures in social activities correspond to
those expenses related with actions that have the goal of improving both material living
conditions and social welfare of the countys inhabitants. We are aware of the temptation
of interpreting this result as evidence against our early hypothesis of a higher demand
for public goods in counties affected by negative externalities. Although we do observe
evidence of lower public expenditures in social activities for mining counties, this is not
enough evidence to conclude these counties are not demanding more public goods provision.
Instead, the dummy incorporation reveals the importance of controlling for covariates in
order to better understand the picture: mining counties spend less money than non mining
counties when fixed effects, time effects and control variables are incorporated to control in
community and social activities.
In models 3, 6, 9 and 12 we incorporate the interaction between the group classification
and the mining property tax collection. These models confirm the previous pattern of
less public expenditures in community services and social activities for mining counties.
Yet, and despite the smaller level of public expenditures on the community services item
in mining counties, we do find a significant and positive effect of the mining property
tax collection on this expenditure item for mining counties (model 3). That is, the more
revenues the mining counties collect from this mining property tax, the more they spend in
community services. In other words, this result suggests those counties with a significant
presence of mining, a set up that gets translated directly into more mineral concessions for
these counties and so more collection from this tax, are in fact spending more in public
goods and services in direct benefit of the community. The sign in the estimated coefficients
16
for this variable in models 6, 9 and 12 is consistent with this intuition. However, these
results are not statistically different from zero.
5.2
Panel Data Estimations
Results from panel data regressions using a fixed effect estimator are in table 4. The
structure in table 4 is similar to the one in table 3, that is, there are three different specifications for each dependent variable, but now using the time-variant covariates only from the
previous estimations. We opt for estimating the conditional expectation model but using
the variables in level now in order to calculate posterior elasticities of each expenditure item
with respect to the mining property tax.
[INSERT TABLE 4 AROUND HERE]
When looking into the first group of specifications, that is models 13, 16, 19 and 22, we
see the estimated coefficient for the mining property tax variable is statistically significant
and positive for the first dependent variable only. A coefficient of 0.902 for this covariate
indicates that for every extra dollar collected from the mining property tax, 90 cents are
spent in expenditures in community services in the whole country. In other words, the
fixed effects estimator for the coefficient on the mining property tax variable in the first
set of specification is indicating that revenues from this tax are merely designated to the
functioning and maintenance of public goods and services in Chilean counties. The result
is similar after controlling for groups. Results in column 14 are similar to those in column
13. However, the estimated coefficient for the dummy of groups now indicates that mining
counties spend in average US$13 more per inhabitant in community services. In contrast,
these mining counties spend US$4 less per inhabitant in social activities (column 17), a
result that was revealed by results in table 3 before.
Last columns in table 4 reveal the results from the conditional mean specification that
incorporates the interaction between the revenues collected from the property tax and the
distinction between groups. For this last set of specifications, we obtain a significant and
17
positive result for expenditures in community services only. The estimated coefficient for
the interaction in column 15 indicates that mining counties that get more revenues from
this tax spend more in services to the community, a finding that similar to results from
pooled OLS regressions. Results in columns 18, 21 and 24 are consistent with this intuition,
although these are no statistically different from zero.
5.3
Marginal Effects
Results for marginal effects from each type of econometric specification are in table 5.
These marginal effects represent in both cases elasticities of public expenditures with respect
to the revenues collected from the mining property tax by type of county. Nevertheless, these
were calculated using different specifications. For the records only, marginal effects in the
second case are calculated using the intra-groups mean for the covariates.
[INSERT TABLE 5 AROUND HERE]
We find that marginal effects of the mining property tax on several items of public
expenditures are statistically significant only in those expenditures intended to community
services. Estimations from pooled OLS regressions indicates that for 100% of increase in the
revenues collected by this tax, there will be almost a 20% of increase in public expenditure
oriented to community services but in mining counties only. Similarly, results from panel
data estimations reveal that for the same 100% increase in revenues from this tax, we
should expect to see an increase of 40% in community services, and around an 80% in
expenditures intended to cultural activities, again in mining counties only. No significant
marginal effects are found between this mining property tax and public expenditures for
non-mining counties once we control for the interaction between the two key variables.
18
6
Conclusions
In this study we estimate the marginal benefit of the mining property tax on local public
expenditures in Chile. The magnitude of this mining property tax is based on the dimension
of the site given in concession for either mineral exploration or exploitation in each county,
and therefore, it is a non-distortionary tax. We construct a panel dataset with information
on financial statements for 345 counties during the period between 2009-2013, and estimate
pooled OLS regressions and panel data regressions using the official dichotomous labeling
of mining and non-mining counties.
Our preliminary results suggest an interesting pattern.
While in average mining
property taxes help to increase public expenditures oriented to community services of
mining economies only, there is not enough statistically significant evidence to sketch similar
conclusions for the general bundle of public expenditures under analysis here. It seems this
tax is helping to cover a very specific target of a policy that was instead aimed to increase
the development of the community. Based on our estimations, the collection of this tax is
apparently not enough to statistically increase the level of public expenditures oriented to social activities neither that towards recreational activities in counties whit presence of mining.
[in progress...]
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19
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21
Figures and Tables
Figure 1: Boxplot of expenditures and tax collection by counties in 2013.
Note:Excluding outside values. Annual per capita expenditures includes expenditures in community services,
and social, cultural and recreational activities. Median values are highlighted in each box.
22
Table 1: Demographic Characteristics by Type of County. Panel Summary.
Variables
Mining Counties:
Years of Schooling
Monthly Income (US$)
Urban Area (%)
Poverty Rate (%)
Efficiency in Collecting Taxes (%)
Executing Rate (%)
Non-mining Counties:
Years of Schooling
Monthly Income (US$)
Urban Area (%)
Poverty Rate (%)
Efficiency in Collecting Taxes (%)
Executing Rate (%)
Note: ∗ p<0.05
∗∗∗
p<0.01
∗∗∗
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Overall
Between
Within
Mean
Std. Dev
9.234
1.219
1.353
0.341
172.985
157.791
92.048
0.333
0.340
0.053
6.792
6.010
3.575
11.374
10.182
5.248
13.237
10.200
9.091
N
n
T-bar
N
n
T-bar
N
n
T-bar
N
n
T-bar
N
n
T-bar
N
n
T-bar
274
61
4.4918
274
61
4.4918
274
61
4.4918
304
67
4.53731
283
66
4.28788
306
67
4.56716
1.389
1.338
0.371
199.581
186.941
77.799
0.277
0.278
0.052
8.248
7.614
3.167
13.283
10.869
7.556
11.211
8.400
7.474
N
n
T-bar
N
n
T-bar
N
n
T-bar
N
n
T-bar
N
n
T-bar
N
n
T-bar
1356
279
4.86022
1356
279
4.86022
1356
279
4.86022
1411
286
4.93357
1329
287
4.63066
1411
287
4.91638
699.891
0.612
13.110
86.065
83.937
9.263
609.444
0.651
17.243
81.931
86.793
p<0.001 on the null hypothesis of equal means.
23
Observations
Table 2: Summary of principal variables by mining and non-mining regions. 2009-2013
Summary
Mining Counties:
Mining Property Tax Collection
Expenditures in:
Community Services
Social Activities
Recreational Activities
Cultural Activities
2009
N Mean
2010
N
Mean
Year
2011
N
Mean
N
2012
Mean
N
2013
Mean
60
59
61
64
$96.37
62
$103.90
64 $242.68
64 $44.84
64
$7.96
64
$9.98
62
62
62
62
$236.44
$52.83
$3.54
$7.03
283
$1.16
$56.36
60 $87.74
60 $20.20
60 $1.55
60 $2.48
Non-Mining Counties:
Mining Property Tax Collection 280 $0.69
Expenditures in:
Community Services
280 $56.24
Social Activities
280 $11.59
Recreational Activities
280 $1.44
Cultural Activities
280 $1.85
$67.90
59 $162.56
59 $26.94
59
$2.93
59
$5.79
286
$0.98
286
286
286
286
61 $192.59
61 $33.70
61
$6.37
61
$6.00
284
$74.94 284
$15.68 284
$2.02 284
$2.56 284
Note: Variables in US$/per capita.
24
$90.30
$1.20
279
$86.38 278
$18.52 278
$2.81 278
$3.06 278
$1.24
$104.20 283 $122.44
$22.18 283 $24.28
$3.18 283
$3.65
$4.00 283
$4.24
25
-0.0000170
(0.00167)
-0.0371
(0.0389)
-0.0325
(0.135)
Executing Rate (%)
Years of Schooling
% Urban Population
∗
1282
0.179
1282
0.193
4.124∗∗∗
(0.444)
Yes
Yes
-0.0106∗
(0.00453)
0.0000330
(0.000228)
0.0247
(0.130)
-0.0236
(0.0384)
-0.682∗∗∗
(0.185)
-0.682∗∗∗
(0.185)
1260
0.183
3.095∗∗∗
(0.616)
Yes
Yes
-0.000268
(0.00643)
1260
0.187
3.363∗∗∗
(0.613)
Yes
Yes
-0.000611
(0.00640)
-0.000202 -0.0000908
(0.000420) (0.000420)
-0.0798
(0.0631)
-0.00404
(0.00278)
0.00109
(0.00218)
-0.359∗∗
(0.126)
0.0195
(0.0181)
-0.0668
(0.0637)
-0.00381
(0.00278)
0.00123
(0.00219)
0.00180
(0.0164)
1260
0.188
3.095∗∗∗
(0.639)
Yes
Yes
-0.000409
(0.00639)
-0.000169
(0.000423)
-0.650∗∗∗
(0.184)
-0.0687
(0.0636)
-0.00343
(0.00284)
0.00101
(0.00216)
0.105
(0.0616)
-0.578∗∗
(0.181)
0.0154
(0.0183)
Expenditures in Social Activities
(4)
(5)
(6)
1135
0.131
2.142∗
(0.834)
Yes
Yes
-0.000158
(0.00857)
-0.000113
(0.000512)
-0.0817
(0.270)
1135
0.132
2.360∗∗
(0.856)
Yes
Yes
0.0000473
(0.00860)
-0.0000224
(0.000513)
-0.0873
(0.269)
-0.0815
(0.0806)
-0.0128∗∗∗
(0.00372)
-0.0125∗∗∗
(0.00367)
-0.0734
(0.0811)
-0.00384
(0.00328)
-0.308
(0.216)
0.0265
(0.0280)
-0.00391
(0.00327)
0.0117
(0.0265)
1135
0.134
2.813∗∗
(0.928)
Yes
Yes
-0.000539
(0.00859)
0.000114
(0.000513)
-0.125
(0.269)
-0.102
(0.0830)
-0.0137∗∗∗
(0.00373)
-0.00383
(0.00328)
-0.171
(0.112)
0.0803
(0.348)
0.0316
(0.0280)
Expenditures in Recreational Activities
(7)
(8)
(9)
Note: Log-Log model. Robust standard error in parenthesis. Variables are in per capita levels. *p<0.05, **p<0.01, ***p<0.001
N
R-squared (adjusted)
1282
0.177
4.599∗∗∗
(0.432)
4.452∗∗∗
(0.438)
Constant
Yes
Yes
Fixed Effects per Region
Yes
Yes
-0.0113∗
(0.00456)
-0.0113∗
(0.00454)
Poverty Rate
Yearly Dummies
0.000183
(0.000222)
0.000131
(0.000216)
Monthly per capita Income
-0.0328
(0.135)
-0.0441
(0.0385)
-0.000141
(0.00167)
0.000953
(0.00166)
0.00218
(0.00161)
0.00260
(0.00163)
0.00276
(0.00165)
-0.569∗∗∗
(0.137)
-0.174∗
(0.0880)
Efficiency in Collecting Taxes (%)
-0.000162
(0.0132)
0.00764
(0.0132)
0.195∗∗∗
(0.0500)
-0.000722
(0.0120)
Expenditures in Community Services
(1)
(2)
(3)
Mining County x log(Mining Property Tax)
Mining County = 1
log(Mining Property Tax)
Dependent Variable
1125
0.101
2.813∗∗
(1.010)
Yes
Yes
-0.00427
(0.0105)
0.00111
(0.000564)
0.103
(0.285)
-0.157
(0.0895)
-0.00931∗
(0.00444)
-0.00429
(0.00413)
0.0353
(0.0299)
1125
0.103
3.071∗∗
(1.025)
Yes
Yes
1125
0.102
2.858∗∗
(1.078)
Yes
Yes
-0.00399
(0.0105)
0.00115∗
(0.000571)
0.00121∗
(0.000569)
-0.00411
(0.0105)
0.121
(0.290)
-0.158
(0.0892)
-0.00926∗
(0.00459)
-0.00439
(0.00410)
0.0829
(0.120)
-0.536
(0.383)
0.0499
(0.0315)
0.0996
(0.285)
-0.166
(0.0897)
-0.00969∗
(0.00447)
-0.00433
(0.00411)
-0.357
(0.254)
0.0529
(0.0314)
Expenditures in Cultural Activities
(10)
(11)
(12)
Table 3: Results from pooled ordinary least square regressions using four different response variables. 2009-2013
26
46.62
(40.50)
-0.0130
(0.0251)
0.0856
(0.316)
% Urban Population
Monthly per capita Income
Poverty Rate
1525
0.222
62.52
(47.86)
Yes
0.0898
(0.316)
-0.0128
(0.0251)
47.15
(40.57)
-6.917
(5.037)
0.245
(0.215)
1525
0.223
66.76
(48.09)
Yes
0.0814
(0.315)
-0.0133
(0.0251)
45.99
(40.37)
-7.019
(5.048)
1525
0.135
-6.981
(10.09)
Yes
0.0761
(0.139)
-0.0102
(0.00728)
-2.832
(5.898)
1.852
(1.153)
0.111*
(0.0554)
-0.0381
(0.0547)
1525
0.135
-6.220
(10.06)
Yes
0.0747
(0.139)
-0.0103
(0.00728)
-3.013
(5.892)
1525
0.135
-5.384
(10.14)
Yes
0.0730
(0.139)
-0.0104
(0.00728)
-3.241
(5.838)
1.848
(1.150)
0.110∗
(0.0554)
0.112∗
(0.0554)
1.868
(1.153)
-0.0363
(0.0543)
0.436
(0.426)
-5.464∗
(2.226)
-0.0378
(0.0546)
-4.715∗
(2.070)
-0.393
(0.424)
1525
0.132
2.910
(2.021)
Yes
0.00312
(0.0173)
0.00194
(0.00178)
0.484
(0.795)
-0.204
(0.253)
-0.00445
(0.00843)
-0.00736
(0.00669)
-0.0145
(0.0129)
1525
0.135
2.553
(2.097)
Yes
0.00378
(0.0179)
0.00197
(0.00177)
0.569
(0.809)
-0.212
(0.253)
-0.00459
(0.00841)
-0.00751
(0.00667)
2.210
(2.608)
-0.0145
(0.0129)
1525
0.134
2.615
(2.097)
Yes
0.00366
(0.0179)
0.00197
(0.00177)
0.552
(0.809)
-0.213
(0.254)
-0.00474
(0.00842)
-0.00739
(0.00672)
0.0320
(0.0645)
2.155
(2.623)
-0.0464
(0.0636)
Expenditures in Recreational Activities
(19)
(20)
(21)
Note: Level-level model. Robust standard error in parenthesis. Variables are in per capita levels. *p<0.05, **p<0.01, ***p<0.001
1525
0.222
N
R-squared (adjusted)
∗
64.75
(47.86)
Constant
Yes
-6.870
(5.031)
Years of Schooling
Yearly Dummies
0.246
(0.215)
Executing Rate (%)
0.235
(0.216)
0.0208
(0.147)
0.0138
(0.147)
Efficiency in Collecting Taxes (%)
0.0128
(0.147)
2.208∗
(1.096)
10.01
(5.862)
13.81∗∗
(5.305)
0.0415
(0.0368)
0.0415
(0.0368)
-1.300
(1.071)
0.902∗∗
(0.304)
0.902∗∗
(0.304)
Expenditures in Social Activities
(16)
(17)
(18)
Expenditures in Community Services
(13)
(14)
(15)
Mining County x log(Mining Property Tax)
Mining County = 1
Mining Property Tax
Dependent Variable
Table 4: Results from panel data estimations using a fixed effects estimator.
1525
0.084
0.173
(4.492)
Yes
-0.0181
(0.0366)
0.00319
(0.00241)
0.332
(1.212)
0.0919
(0.508)
-0.00333
(0.0163)
-0.0117
(0.0102)
0.0458
(0.0495)
1525
0.085
0.569
(4.495)
Yes
-0.0188
(0.0365)
0.00316
(0.00241)
0.238
(1.215)
0.100
(0.508)
-0.00316
(0.0163)
-0.0115
(0.0102)
-2.457
(1.306)
0.0458
(0.0495)
1525
0.084
0.695
(4.583)
Yes
-0.0191
(0.0367)
0.00314
(0.00240)
0.203
(1.204)
0.0972
(0.511)
-0.00347
(0.0165)
-0.0113
(0.0103)
0.0655
(0.0936)
-2.570∗
(1.306)
-0.0195
(0.0730)
Expenditures in Cultural Activities
(22)
(23)
(24)
27
0.393∗∗∗
(0.0908)
0.426∗∗∗
(0.0943)
Mining County
0.398∗∗∗
(0.0918)
0.139
(0.109)
0.00215
(0.00195)
0.00180
(0.0164)
0.194∗∗∗
(0.0503)
-0.0152
(0.0124)
0.00180
(0.0164)
-0.000162
(0.0132)
0.175
(0.133)
0.00206
(0.00188)
0.0195
(0.0181)
0.0195
(0.0181)
0.182
(0.136)
-0.0195
(0.0209)
0.120
(0.0613)
0.0154
(0.0183)
Expenditures in Social Activities
(1)
(2)
(3)
-0.507
(0.640)
-0.00520
(0.00435)
0.0117
(0.0265)
0.0117
(0.0265)
-0.253
(0.311)
-0.00605
(0.00514)
0.0265
(0.0280)
0.0265
(0.0280)
-0.253
(0.312)
-0.0193
(0.0264)
-0.139
(0.114)
0.0316
(0.0280)
Expenditures in Recreational Activities
(1)
(2)
(3)
0.511
(0.297)
0.0170
(0.0221)
0.0353
(0.0299)
0.0353
(0.0299)
0.793∗
(0.345)
0.0146
(0.0186)
0.0529
(0.0314)
0.0529
(0.0314)
0.803∗
(0.351)
-0.00622
(0.0235)
0.133
(0.120)
0.0499
(0.0315)
Expenditures in Cultural Activities
(1)
(2)
(3)
Note: Marginal effects correspond to dy/dx in pooled log-log OLS regressions, and to ey/ex in the level-level panel data fixed effects regressions. For this second case, variables were evaluated at their
respective in-group means. Robust standard error in parenthesis. Variables are in per capita levels. *p<0.05, **p<0.01, ***p<0.001
∗
0.0106∗∗
(0.00397)
0.00764
(0.0132)
0.00764
(0.0132)
0.0103∗∗
(0.00384)
-0.000722
(0.0120)
-0.000722
(0.0120)
Expenditures in Community Services
(1)
(2)
(3)
Non-mining county
Panel Data Fixed Effects Estimation
Mining County
Non-Mining County
Pooled OLS Regressions
Marginal Effects
Table 5: Results from fixed effects panel data regressions using four different response variables. 2009-2013