Revisiting the link between resource windfalls and subnational crowding out for local mining economies in Chile

Resources Policy 64 (2019) 101523

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Revisiting the link between resource windfalls and subnational crowding out for local mining economies in Chile Mauricio Oyarzo a, *, Dusan Paredes b a b

School of Business Administration, Universidad de Concepci� on Campus Chill� an, Avenida Vicente M�endez 595, Chill� an, Chile Department of Economics, Universidad Cat� olica del Norte, Avenida Angamos 0610, Antofagasta, Chile

A R T I C L E I N F O

A B S T R A C T

JEL classification: H20 H30 H71 Q32 Q33

Literature on the resource curse argues that resource windfalls, such as those resulting from a commodity price boom, crowd out several determinants of long-term fiscal income (Papyrakis and Gerlagh, 2006). Although empirical literature tests this theory at an intercountry context, similar attention has not been paid to that of subnational governments. This different type of spatial scope would reveal how low-tier governments strategi­ cally behave in regard to resource windfalls and covering local costs. Any strategic behavior will directly impact the local community’s well-being due to the role played by subnational governments in providing local public goods. We contribute to this gap in the literature by analyzing how the resource windfalls from mining taxes in Chile crowd out local collected revenue such as a residential and a commercial property tax. Using panel data for 345 Chilean municipalities between 2008 and 2017, we pursue the causal effect derived from a subnational crowding out hypothesis, measured as the cross substitution between an additional monetary unit received from windfalls. We also take advantage of the exogenous allocation rule for the distribution of mining taxes in mining municipalities via the Chilean National Mining Code. Our results are robust and do not reject the hypothesis: mining taxes crowd out property tax collection. Tax laziness is maintained after considering for potential endogeneity and heteroskedasticity imposed by spatial autocorrelation. Our results call for local policies that focus on discouraging undesirable behavior in the collection of local property taxes in mining municipalities.

Keywords: Subnational crowding out Resource windfalls Mining taxes

1. Introduction To what extent does revenue from natural resources influence the subnational crowding out of local mining economies? The seminal contribution of Sachs and Warner (1995, 1999, 2001) and Papyrakis and Gerlagh (2006) on the resource curse along with a recent wave of related literature highlight the effects of crowding out by focusing on the sub­ national level (for example van der Ploeg and Poelhekke, 2017; Masaki, 2018). According to this literature, the existence of natural resource revenues, hereafter referred to as resource windfalls, in localities that host extraction do not necessarily result in higher local budgets. The revenue is instead utilized to substitute local taxes that might have a higher economic or political cost to collect. This is especially true when these resource windfalls are non-matched grants to local municipalities, for example grants that are not proportional to the local public expenditure of a specific public service (Wildasin, 1984) or those that are not intended to redistribute income between regions (Boadway and Wild­ asin, 1984). These windfalls are a recompense for the negative

externalities associated with the production and extraction of resources, principally those associated with the mining industry (Hughes, 1975). In Chile the windfalls collected come from a non-ad valorem property tax on mineral concessions, namely mining patents, correspond to an annual payment for the land used for the extraction and exploration, and by law this levy directly benefits the local government. The local revenue collected depends on the amount of land in the mining concession and therefore it is not significantly influenced by the fluctuations in the price of the mineral (Oyarzo and Paredes, 2018). Although literature regarding the impact of resource windfalls on revenue and tax generation efforts is limited in developing countries, there are articles that highlight the crowding out effect between external grants and local revenue as a particular case of the median-voter model (Mogues and Benin, 2012). Therefore, our hypothesis is labelled as subnational crowding out (SCO) at a local level, meaning that a grant from upper-tier government crowds out other local tax revenue. We adapt the SCO hypothesis to understand to what extent the resource windfalls (non-matched grants) influence local tax behavior in mining

* Corresponding author. E-mail addresses: [email protected] (M. Oyarzo), [email protected] (D. Paredes). https://doi.org/10.1016/j.resourpol.2019.101523 Received 7 May 2019; Received in revised form 16 October 2019; Accepted 17 October 2019 Available online 29 October 2019 0301-4207/© 2019 Elsevier Ltd. All rights reserved.

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Resources Policy 64 (2019) 101523

Fig. 1. Persistence of mining municipalities, 2007–2017. Source: Data from Sinim (2018).

economies. Wildasin (1984) emphasizes that a characteristic of non-matched grants as windfalls is that they are not proportional to public expenditure such as a local public service. Oates (1972) ac­ knowledges that non-matched grants, by design, do not internalize the spatial spillovers of public goods and therefore generate a suboptimal provision of public services, especially in the presence of asymmetric information between government tiers (Lockwood,1999). From a policy perspective, Bucovetsky and Smart (2006) show how federal revenue equalization grants have influence on subnational government behavior based on different assumptions regarding the flexibility of tax bases. Particularly, if tax bases are immobile, equalization grants can induce excessive local tax rates and increase the dead-weight loss from distor­ tionary taxation (Smart, 1998) where these effects are mitigated by this mobility. Literature regarding the fiscal effort framework also highlights the role of the taxation system in the detrimental results for subnational governments because of the potential disincentives for collecting local taxes via higher-tier grants (Zhuravskaya, 2000; Ross, 2004; Moore,

2008; Valle de Souza et al., 2018). Windfalls from resources are intended to compensate localities for the negative externalities attributed to the exploitation of natural re­ sources, but there is no consensus on the impact of the windfalls on local funding for subnational governments. In the case of Chile, Paredes and Rivera (2017) find that for each dollar from mining taxes, the expen­ diture on public services (recreational or cultural activities) increases by only US$0.5. Meanwhile, Oyarzo and Paredes (2018) find that mining municipalities have a worse performance in education than non-mining municipalities. Therefore, it is important to highlight to what extent these non-matched grants from windfalls influence the strategic behavior of local governments in regard to tax collection, especially in developing countries. This strategic behavior not only influences the tax collection effort, it also affects the use of extra revenue within local mining economies and their institutions. For example, Larraín and � (2019) find evidence that resource windfalls in Chile involve Perello local governments behaving in a way that is consistent with political 2

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Resources Policy 64 (2019) 101523

clientelism theory, where the resources are distributed as patronage in the form of public employment (Robinson, Torvik and Verdier, 2006a, 2014b; Robinson and Verdier, 2013). However, crowding out is exceedingly related to the role played by political institutions, especially in the case of developing countries (Dobra and Dobra, 2013; Brollo et al., 2013 Timmons and Garfias, 2015; ~ i et al., 2008). The OECD highlights the low autonomy that local Gon institutions in Chile have to make decisions because of local priorities and territorial particularities. However, the limited fiscal independence that subnational government have in the administration of their budget and resources has resulted in fiscal equalization mechanisms being insufficient. Additionally, the lack of financial independence weakens the local tax collection effort (OECD, 2009), an area in which the pol­ icies designed, and the spatial dimensions considered are directly related to reducing inequality (OECD, 2013). The responsibilities that Chilean municipalities do handle, such as the provision of some local public goods, is complicated by the little technical capacity present in their ranks, especially within less developed localities (OECD, 2009).1 Therefore, if the resource windfalls fail to compensate for negative ex­ ternalities, the subnational government has little or no means to address the situation, which directly affects the quality of life of the local resi­ dents, migrants and commuters that travel to work there. Little has been discussed regarding the effect of resource windfalls at a subnational level or its effect on the strategic behavior of local gov­ ernments in terms of the collection of tax revenue. This paper adapts the crowding out hypothesis at a subnational level and tests whether resource windfalls crowd out local tax collection via greater politicalsocial and administrative costs for the local governments. The mineral law allows us to evaluate to what extent each monetary unit received via mining patents influences the subnational government’s behavior regarding the collection of local taxes. Our research design uses panel data for 345 Chilean municipalities from 2008 until 2017. For the dependent variables we use two indicators as proxies for the local taxes collected: 1) Residential Property Tax (RPT) and 2) Commercial Property Tax (CPT). These proxies correspond to the main local taxes in Chile which account for nearly 50% of the Municipal Permanent Income (Ingresos Propios Permanentes or IPP) (Sinim, 2018). These local resources collected from property taxes are autono­ mously generated in each municipality and the corresponding low-tier government, which has a greater capacity for decision-making, is responsible for their administration. The RPT is calculated via the property’s appraisal value and is earmarked for local municipalities (Servicio de Impuestos Internos, 2018). The CPT corresponds to the permit required for starting any type of commercial activity at a fixed location, and it is granted by the local municipality (Biblioteca del Congreso Nacional de Chile, 2018).2 Therefore, CPT implies a lower socio-political cost for the subnational governments, contrary to the RPT which directly depends on the municipal effort to improve the tax sys­ tem via updating the appraisal values of properties. The exogenous allocation rule for the distribution of mining taxes in mining municipalities rules out problems of endogeneity thanks to the spatial distribution of mining firms and resource windfalls (Dahlberg et al., 2008). However, the endogeneity can persist due to unobservable variables that can affect local public expenditures, and therefore, the collection of local taxes or rather the measurement errors in the tax variables. To address these problems, we take into consideration the

National Mining Code rule for the distribution of mining taxes in mining municipalities: 50% of mining taxes (resource windfalls) are earmarked for the municipality administration, namely the mining municipality in which the mining concession is located (Congreso Nacional de Chile, 1992). The other 50% goes to the corresponding mining region. We use the exogenous rule proposed by the Chilean Congress in 2012 (FON­ DENOR) to categorize mining municipalities (Torche et al., 2012). We test whether resource windfalls crowd out the local collection of prop­ erty taxes and we evaluate if this crowding out effect is maintained via different estimation methods as well as alternative econometric specifications. To provide more details about the case, Fig. 1 depicts the mining concessions in Chile which are spatially clustered in extreme areas of the country such as in the north. This northern cluster has a high level of persistence over the time period (the areas highlighted dark red on the map) given the geological particularities of Chile. This peculiarity is accounted for by spatial autocorrelation, namely, the spatial locations are very similar to each other (for example, the collection of mining taxes). Not considering the spatial autocorrelation could be a potential problem in that it could bias and invalidate the treatment effect used to identify the crowding out (Anselin, 1988). We account for the spatial autocorrelation and the spillover among spatially close neighbors via spatial econometric models that use spatial lags in the response and explanatory variables such as the dynamic component that may influ­ ence local tax decisions via temporal lags over dependent variables. We test the consistency of the results via different spatial weight matrices. We also consider a time lag to capture the contemporaneous and lagged effect of mining taxes on the tax collection effort and subnational gov­ ernment fixed effects in order to control for time-invariant unobservable effects (for example, local leadership characteristics). Unobservable characteristics can affect the collection of local taxes and the mining patents. However, the intra-country analysis and the exogenous allo­ cation of minerals allows us to rule out endogeneity problems because of the inflows of the mining patents and the location of the firms. The panel estimations and the spatial autocorrelation and heteroskedasticity con­ trols allow us find causal results that are validated by alternative spec­ ifications and different methods of estimation. In the case of the RPT, our results do not reject the SCO hypothesis, and they are robust against different specifications. The SCO is main­ tained after considering for potential endogeneity and hetero­ skedasticity imposed by spatial autocorrelation and alternative econometric specifications. On average, for each dollar from resource windfalls within mining municipalities in the present, around US$0.6US$0.9 of the RPT is crowded out in the future. If we consider all the municipalities, the results do not reject the SCO hypothesis and the crowding out effect is between US$0.8 and US$1.2 in the short and long term, respectively. Finally, the results for dynamic spatial models also reveal the substitution effects between the RPT and mining taxes. The spatial impacts considering the spillover effects and spatial interde­ pendence are negative, although they are four times lower than nonspatial estimations: for each dollar collected via mining taxes, US$0.2 to US$0.3 of the RPT is crowded out in the short and long term, respectively. These results suggest that resource windfalls have an un­ desirable effect on the tax collection behavior of subnational govern­ ments, in that the windfalls will only compensate residents of mining municipalities via a reduction the local tax burden. The results for the CPT are not significant, despite being one of the main local taxes in Chile and considering the different method and specifications used. The paper proceeds as follows. Details regarding institutional design and local government revenue generation are reviewed in section 2. The data is described in section 3. Section 4 presents the model, estimation methods and the procedures used in the analysis. The empirical results are discussed in section 5. Section 6 highlights policy implications and concludes.

1

For more information see: Constitutional Law on Municipalities (LOCM). Available here: https://www.leychile.cl/Navegar?idNorma¼30077 (August 2018). 2 60% of the RPT collected via local governments is transferred to the Municipal Common Fund (Fondo Común Municipal or FCM) corresponding to the Chilean grant system. However, a significant portion of the RPT returns to local government via the FCM. For more information visit: http://www.subdere .gov.cl/sites/default/files/documentos/articles-77206_recurso_1.pdf. 3

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Fig. 2. Importance of the RPT and CPT on the IPP, 2008–2017. Source: Data from Sinim (2018). Note: The other local revenues correspond to the mining patents, local fees (for example driver’s licenses and vehicle registration permits) as well as casino taxes, among others.

2. Institutional design and the model

million hectares were under mining concessions, covering around 50% of Chilean territory (Sernageomin, 2017).6 Fig. 1 shows the spatial persistence of the mining municipalities over the 2007–2017 period according to the FONDENOR criterion. This cri­ terion defines a subnational government as a “mining municipality’’ if more than 2.5% of the corresponding region’s revenue comes from resource windfalls (Torche et al., 2012). The persistence (the dark red highlighted areas on the map) over the ten years is spatially concen­ trated in peripheral localities, namely in the north of Chile: XV. Arica �, II. Antofagasta, III. Atacama, and IV and Parinacota, I. Tarapaca Coquimbo. In these localities the mining concessions are the most prevalent in the country, and in some cases more than 90% of the regional territory is part of these concessions (Paredes and Rivera, 2017). This time persistent cluster constitutes a problem that is accounted for by spatial autocorrelation, but which potentially affects the collection of mining taxes and other local taxes. This persistence is also notable in the south of Chile, primarily in certain municipalities of the XI. Ays�en. However, due to its natural and geological conditions, the mining industry there is significantly different from that in the north.

2.1. Resource windfalls from mining in Chilean municipalities The Chilean Mineral Law #19,143 considers the payment of mining patents, which represent the right that firms have to explore and extract the resource. The annual payment corresponds to one tenth of a monthly tax unit (UTM) per hectare for extraction and one fiftieth of a UTM per hectare for exploration.3 50% of the mining patent revenue is trans­ ferred to the regional government (GORE)4 for development and local projects via the National Fund for Regional Development (NFRD).5 The other 50% corresponds to the municipality in which the mining concession is located (Congreso Nacional de Chile, 1992). The concessions are organized via the Organic Constitutional Law on Mining Concessions #18,097, the National Mining Code and current regulations. Exploration claims have a duration of two years and are extendible to four in order to cover the entire scanning process. The extraction claims have an indefinite duration, and they allow mining firms to extract the resources. The mining patent payments correspond to the municipalities where the mining companies operate and do not depend on the level of production but rather on the extension of the mining area in which the concession is located. In 2016 nearly 38

2.2. Local tax efforts by mining municipalities This paper adapts the crowding out hypothesis at a subnational level and analyzes how the resource windfalls from mining taxes crowd out the local revenue collected. Generally speaking, Chilean subnational governments obtain local funding from three sources: The Municipal Permanent Income (IPP), the horizontal grant mechanism called the Municipal Common Fund (FCM) and conditional grants from upper-tier

3 Unidad Tributaria Mensual (UTM) is a currency unit used exclusively in Chile for the payment of taxes, custom duties and fees. Each month it is adjusted for inflation (for more detail see: Paredes and Rivera, 2017; Oyarzo and Par­ edes, 2018). 4 GOREs are public bodies in charge of the management of each region. Chile is divided into 15 administrative regions, 54 provinces and 345 municipalities. In political-administrative terms, only regions and municipalities are considered. 5 For more information visit: http://www.subdere.cl/documentacion/carac ter%C3%ADsticas-del-fondo-nacional-de-desarrollo-regional-fndr.

6 For more information see: Paredes and Rivera (2017); Oyarzo and Paredes (2018).

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government for public health and education.7 While the IPP represents the local fiscal collection, the FCM is composed of transferences made by each municipality via a solidarity system. In fact, the IPP is the income after the municipality has already made the FCM transferences. The IPPs are composed of local taxes including the RPT, the CPT and mining patents, local fees (for example driver’s licenses and vehicle registration permits) as well as gambling tax, among others. The IPP is considered an important proxy for the capacity of municipality self-financing because they have full control of this revenue. However, municipalities do not have full control of the IPP’s components, specifically the parameters that define the tax base and, therefore, the local tax collection. The collection and distribution of the RPT is administrated by the Chilean Internal Tax Service (Servicio de Impuestos Internos or SII). Subnational governments (municipalities) can ensure that real estate appraisals are properly updated. According to the current RPT Law #17,235 this revision happens every four years.8 In the following Fig. 2, we detail the importance of the RPT and the CPT on the IPP, which accounts for, on average, nearly 50% of a locality’s own income: On the other hand, the FCM is a horizontal grant mechanism that redistributes local revenue across municipalities with the aim of off­ setting differences in revenue-raising capacity. The high level of budgetary dependency that some municipalities in Chile have in regard to the FCM is remarkable, with many exceeding 60% of the municipal own incomes. Municipalities also receive conditional grants from uppertier government for public health and education. The grants for public health are funds given directly to municipalities that operate primary healthcare facilities, according to a per capita formula. Grants for public education (via intergovernmental transfers) are also directly allocated to municipalities. Both conditional grants are specifically allocated to these areas and are treated separately from the municipal budget (Bravo, 2013).

Chile the collection and distribution of the RPT are managed by the SII. Municipalities can also update the information according to real estate appraisals of local properties every four years. In the local provision of public goods Y, municipalities have two sources, namely local tax revenue and a windfall tax. In the same sense, this constraint must be bounded above by the total cost of the public good provision: TL þ w � Y. Assuming the local satiation for the municipal behavior, this equation must be equal on both sides. Ac­ cording to Paredes (2019), we must assume that the collection cost for TL must be greater than the cost associated with w. We assume the local taxation as distortionary. To depict this condition, the median consumer has an income expressed by I � X þ gðTL Þ where gðTL Þ represents the shadow cost of the local tax effort in private consumption terms. The intuition behind this is: if the municipality increases the local tax effort then it’s necessary that the median consumer observe a lower income after-tax even if this higher level of control affects other forms of income such as property taxes. According to the formalization proposed by Paredes (2019) and without a loss of generality, we can assume: gð0Þ ¼ 0

(1)

g’ ðTL Þ > 1

(2)

g’’ ðTL Þ > 0 if TL > 0

(3)

By combining these equations, the model specifies that a munici­ pality maximizes the median consumer’s utility UðX; YÞ subject to the subnational government budget constraint: TL þ w and the median consumer’s income constraint I ¼ X þ gðTL Þ. This condition implies that the municipality maximize the equation: maxUðI TL

(4)

gðTL Þ; w þ TL Þ

Assuming the subscripts as partial derivatives, the FOC is:

2.3. Resource windfalls and SCO

(5)

U1 g’ ðTL Þ þ U2 ¼ 0

In this section we introduce a theoretical concern as well as the economic intuition behind the SCO hypothesis developed by Paredes (2019) based on resource windfalls and SCO.9 Consider an economy constituted by a municipality and a representative median consumer with a representative utility function Uð:Þ. This consumer receives utility from two goods, namely private good X and a locally provided public good Y. From a public perspective, there are two sources of revenue for local municipalities: its own local revenue denoted by TL and a windfall tax from resource windfalls w. TL must be understood as local revenue such as a property tax while w has a very low cost for the local gov­ ernment: e.g. the management of transaction costs for receiving the money or a lower economic or political cost. On the other hand, TL in­ volves costs related to compliance and administrative expenses. It is very important to highlight that municipalities in Chile need to invest re­ sources if they want to improve the current collection system. As noted by Cortes and Paredes (2016), a clear example of this situation is the use of municipal tow trucks in some wealthy Chilean municipalities, a sit­ uation not observed in other medium-high income local governments that are criticized for their lack of control regarding illegally parked vehicles. Also, is important to state that regarding collection effort, TL must be understood in the context of a non-federal country such as Chile. According to Aragon (2009), the variation of TL must not be assumed as just modifying the tax rate or tax base, but rather local governments having an effect via improving the collection system. For example, in

From this point, it is noteworthy that g’ðTL Þ is equivalent to the marginal rate of substitution between public and private goods. It is also equivalent to the marginal loss in private consumption from collecting one dollar of local tax revenue. We should also stress that this model has been widely utilized on the well-known flypaper effect, namely as the observed stimulatory effect of unconditional grants on municipal spending that increases community income (Hines and Thaler, 1995). Likewise, it can also be seen as the opposite case of crowding in: a positive effect on the local public expenditure after a resource windfall. In algebraic terms, the flypaper effect can then be estimated by comparing two derivatives: dY U11 g’ ðTL Þ U12 ¼ 2 dI ðU11 g’ ðTL ÞÞ 2U12 g’ ðTL Þ þ U22 dTL ¼ dw ðU11 g’ ðTL ÞÞ2

U12 g’ ðTL Þ

U1 g’’ ðTL Þ

U22

2U12 g’ ðTL Þ þ U22

U1 g’’ ðTL Þ

(6) (7)

According to Paredes (2019) the denominator can be called Δ. Note that Δ must be negative because is represents the second order condition for the objective function of the municipalities which should represent diminishing returns based on the arguments discussed. Thus, the equa­ tion must be negative iff the numerator is positive, a similar statement with a normal public good: dTL dI

7

Local governments do not control transfers from upper-tier government, therefore we do not include this income into our analysis. 8 For more information see: Law #17,235 Residential Property Tax. Available here: http://www.sii.cl/pagina/jurisprudencia/ley17235.htm (August 2018). 9 The theoretical model is developed by Paredes (2019) FONDECYT 1191162 “Do mining tax windfalls crowd out other local revenues? Evidence from Chile”.

dTL ½U11 g’ ðTL Þ ¼ dw

g’ ðTL ÞÞ þ U1 g’’ ðTL Þ

U12 �ð1 Δ

(8)

Such as we assumed at the beginning of the model, we know that ð1 g’ ðTL ÞÞ < 0 and g’’ ðTL Þ > 0. Thus, we can see how the flypaper effect would suggest that an increment of dw could be transferred for tax relief (such as reducing the tax base) and that at some point the consumer would want more public expenditure. However, the main interest of the 5

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factor indicates that the empirical design needs to consider the indi­ vidual preferences of consumers. In our case, the analysis unit is the subnational government, therefore this implies that an appropriate framework is a panel data set where we can control for individual effects in order to capture the potential subnational crowding out of property taxes. The second factor is the role played by economies of scale and the local population in which some public goods could potentially decrease the average cost as the size of the city increases. In the context of Chile, this model is extremely important due to its very asymmetric urban structure in which nearly 50% of the population lives in just 5% of the national territory (Soto and Paredes, 2016). Finally, the theoretical concerns highlight the role played by municipalities in regard to crowding out and the efficient provision of public goods; an empirical framework used to capture the theoretical conditions, albeit outside our research aims.

Table 1 Summary statistics of dependent variables by mining taxes quintiles. 2008–2016. Mining taxes quintiles 1 2 3 4 5

(US$) RPT

(US$) CPT

Mean

Std. Err.

Mean

Std. Err.

29.751 22.965 20.217 32.592 20.923

1.978 1.256 0.981 2.680 1.408

32.519 24.145 23.067 34.507 73.326

1.859 1.242 2.940 3.354 6.744

Note: Dependent variables in per capita terms (US$). Table 2 Summary statistics of explanatory variables. 2008–2017.

Explanatory variables (US$) Mining taxes (US$) Net IPP (US$) FCM (#) Non-farm properties (%) Poverty rate (Km) Distance to Santiago (Km2) Density (%) Efficiency (%) Professionalization (#) Full-time workers (#) Annual contract workers (#) Short-term workers (%) Coastal municipalities N

Mining municipalities

Non-mining municipalities

Mean 89.902 155.009 515.139 12.100 14.936 679.202 10.909 85.309 28.837 3.997 2.013 2.214 0.332 470

Mean 0.251 67.384 153.339 29.155 19.356 410.542 1576.782 81.949 26.881 1.982 0.950 0.620 0.235 1736

Std. Err. 7.192 15.370 53.802 1.129 0.355 26.465 0.704 0.553 0.633 0.305 0.165 0.294 0.022

3. Data

Std. Err. 0.009 1.809 2.750 1.637 0.229 7.637 84.614 0.287 0.195 0.024 0.015 0.031 0.010

Our research design considers a panel with 345 Chilean municipal­ ities from 2008 until 2017. The information is obtained from the Na­ �n tional Municipal Information System (Sistema Nacional de Informacio Municipal, SINIM) and from the Comptroller General of the Republic (CGR).10 We use the dependent variables that correspond to the local taxes, in which municipalities have influence on the level of collection via management, oversight and updating the tax base. This influence can �-vis perverse incentives by be a proxy for local collection efforts vis-a intergovernmental transfers. 3.1. Dependent variables

Note: Explanatory variables in per capita terms (US$). Non-farm properties per 1000/inhab. Full, annual and short-term workers per 1000/inhab. Profession­ alization correspond to percentage of individuals qualified as professionals (college degree). Coastal is dummy variable by coastal municipalities.

The RPT corresponds to the first dependent variable. This variable is a local tax calculated via the appraisal value of the property and it is earmarked entirely for municipalities (Servicio de Impuestos Internos, 2018), compromising one of their principal sources of income and financing (Bravo, 2013). By law, the collection and management of this tax corresponds to the SII. However, municipalities can support the SII by updating information pertaining to property values every four years. The owner or occupant of the property must pay this annual tax in four parts (April, June, September and November of each year). The tax on housing real estate in 2016 was 0.98% for appraisals less than or equal to US$118,000. For real estate valued at more than US$118,000, a rate of 1.143% will be applied.11 The CPT corresponds to the permission necessary to undertake any commercial activity in a fixed location and is granted by the local mu­ nicipality (Biblioteca del Congreso Nacional de Chile, 2018). Munici­ palities handle the collection and management of the tax and the local rates range from 0.25% to 0.5%, depending on the value of the capital assets of the enterprise, and regardless of the type of activity. Given the lack of spatial competition among Chilean municipalities, there is an incentive to maintain the rate at its maximum possible level (Bravo, 2014). Table 1 presents the summary statistics for dependent variables per mining tax quintiles via the exogenous allocation rule for the distribu­ tion of mining taxes in mining municipalities (National Mining Code). The intuition behind this is that if the SCO hypothesis is true then the 1st quintile of mining taxes should have a higher collection while the 5th quintile should have a lower collection.12 On average, the RPT accounts for US$25 per capita. However, there are differences between the

Table 3 Marginal effects of mining taxes for equations (10)–(12). Eq:ð10Þ

Short-run ∂yit ∂ϕit Long-run ∂yit ∂ϕit

Eq:ð11Þ

β1

β1

β1 þ β2

1

β1

τ

Eq:ð12Þ dit ¼ 1

dit ¼ 0

β1 þ β 2

β1

β1 þ β2 1 τ

1

β1

τ

Note: The marginal effects are computed for Eqs. (2) and (3) after system-GMM estimation.

model proposed by Paredes (2019) lies on modeling the crowding out effect generated via resource windfalls. Therefore, the subnational crowding out effect exists iff ðdTL =dw < 0Þ. Finally, according to Cal­ deira and Rota-Graziosi (2014), w discourages the local revenue effort if the marginal utility of the public good is decreasing as shown in the following equation:

∂U2 ¼ U12 g’ ðTL Þ ∂TL

U22 < 0

(9)

According to Paredes (2019), the logic behind Eq. (9) rests on what factors affect the marginal utility of a public good in a real scenario. At least three factors could fit with this theorical concern: 1) the role of individual preferences in public consumption, 2) economies of scale in the provision of public goods and 3) efficiencies of the local adminis­ tration in tax collection. In the context of this paper, the real benefit of this model is that it shows the direct mechanism for considering the local crowding out and SCO hypothesis in the empirical analysis. The first

10 http://datos.sinim.gov.cl. https://www.contraloria.cl/web/cgr/base-de-d atos-municipales. 11 For more information on Law #17,235 see: http://bcn.cl/1v302 (August 2018). 12 The choice for the number of classes is somewhat arbitrary, therefore the results for other numbers of groups or quartiles are available upon request.

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M. Oyarzo and D. Paredes

Table 4 Spatial impacts for Eq. (13). Short-run Direct Indirect Total

Eq. (13) φ ¼ 0

Eq. (13) τ ¼ 0

Eq. (13) unrestricted

T X n 1 X ∂yit nT t¼1 i¼1 ∂ϕit T X n n X 1 X ∂yit nT t¼1 i¼1 j¼1;j6¼i ∂ϕjt T X n X n 1 X ∂yit

T X n 1 X ∂yit nT t¼1 i¼1 ∂ϕit T X n n X 1 X ∂yit nT t¼1 i¼1 j¼1;j6¼i ∂ϕjt T X n X n 1 X ∂yit

T X n 1 X ∂yit nT t¼1 i¼1 ∂ϕit T X n n X 1 X ∂yit nT t¼1 i¼1 j¼1;j6¼i ∂ϕjt T X n X n 1 X ∂yit

nT

Long-run Direct

t¼1 i¼1 j¼1

nT

∂ϕjt

�

�

1 XT Xn ∂yit i¼1 ∂ϕ nT t¼1 it τÞ � Xð1 X 1 T n Xn

Indirect

nT

�

Total

t¼1

i¼1

∂yit j¼1;j6¼i ∂ϕ jt

t¼1 i¼1 j¼1

nT

∂ϕjt

�

�

1 XT Xn ∂yit i¼1 ∂ϕ nT t¼1 it φÞ � Xð1 X 1 T n Xn

�

nT

ð1 τÞ � 1 XT Xn Xn ∂yit t¼1 i¼1 j¼1 nT ∂ϕjt ð1 τÞ

�

t¼1

i¼1

∂ϕjt

t¼1 i¼1 j¼1

�

∂yit j¼1;j6¼i ∂ϕ jt

� 1 XT Xn ∂yit t¼1 i¼1 nT ∂ϕit ð1 τ φÞ � X 1 T Xn Xn

�

nT

ð1 φÞ � 1 XT Xn Xn ∂yit t¼1 i¼1 j¼1 nT ∂ϕjt ð1 φÞ

�

t¼1

i¼1

∂yit j¼1;j6¼i ∂ϕ jt

�

ð1 τ φÞ � 1 XT Xn Xn ∂yit t¼1 i¼1 j¼1 nT ∂ϕjt ð1 τ φÞ

Note: Each column depicts the three spatial model computed according to the proposed specifications.

Table 5 Static and dynamic panel data estimation results for RPT.

ϕit ϕit

1

ϕit � dit yi;t

Table 6 Static and dynamic panel data estimation results for CPT.

FE (Eq (10))

FE (Eq (11))

FE (Eq (12))

FE (Eq. (10))

FE (Eq. (11))

FE (Eq. (12))

(1)

(2)

(3)

(1)

(2)

(3)

All municipalities

All municipalities

Mining municipalities

All municipalities

All municipalities

Mining municipalities

0.073 (0.063) 0.006 (0.030)

0.844* (0.376)

36.025 (43.544)

1

ϕit

0.313* (0.145)

36.662 (43.652) 0.345** (0.100)

β1

0.073

β1

β1 þ β2

Long-Run

Controls Yearly FE AB Test for AR (1) pv AB Test for AR (2) pv Hansen test p-v Difference Hansen test p-v R2 N

(0.063) β1 þ β2

0.844*

yi;t

0.067 (0.058) Y Y –

(0.292) β1 þ β 2 1 τ 0.971þ (0.526) Y Y 0.221

–

0.831

0.561

– –

0.242 0.040

0.042 0.507

0.515

–

–

2848

2739

1857

(0.208) 0.357*

0.108 (0.709)

(0.135)

1

850.357 (0.292)

849.061 0.364

(3918.692) 0.107

(0.325)

(0.977)

β1

β1 þ β 2

Marginal Impacts for ϕit

Short-Run

0.636*

(0.376) β1 1 τ 1.228** (0.464) Y Y 0.196

1

ϕit � dit

Marginal Impacts for ϕit

Short-Run

0.014

ϕit

β1

0.014 (0.208)

Long-Run

Controls Yearly FE AB Test for AR (1) pv AB Test for AR (2) pv Hansen test p-v Difference Hansen test p-v R2 N

Note: All variables in per capita terms. ϕit 1 represents the mining patents collected by a mining municipality i lagged one period. Controls: Net IPP, FCM, non-farm properties per 1000/inhab, professionalization, full, annual and shortterm workers per 1000/inhab, % poverty, density, and yearly dummies. Robust standard errors in parentheses. Dynamic panel-data estimation in columns 2 and 3, two step system GMM. Significance levels: þ p < 0:1; * p < 0:05; ** p < 0:01; * * * p < 0:001.

0.108 (0.709)

0.343 (0.306) Y Y –

0.170* (0.087) Y Y 0.327

β1 þ β2 1 τ 1.171 (4.114) Y Y 0.775

–

0.254

0.788

– –

0.120 0.421

0.107 0.058

0.546

–

–

2848

2739

1857

β1 þ β 2

1

β1

1.296 (3.899)

τ

Note: All variables in per capita terms. ϕit 1 represents the mining patents collected by a mining municipality i lagged one period. Controls: Net IPP, FCM, efficiency, professionalization, full, annual and short-term workers per 1000/ inhab, % poverty, density, and yearly dummies. Robust standard errors in pa­ rentheses. Dynamic panel-data estimation in columns 2 and 3, two step system GMM. Significance levels: þ p < 0:1; * p < 0:05; ** p < 0:01; *** p < 0:001.

extreme quintiles. The last quintile accounts for 40% less than the first quintile. It is the opposite situation for the CPT: on average US$38 per capita is collected, however, mining municipalities collect more than any other and 2.3 times more than the first quintile. Despite the weak productive links that the mining industry represents in localities that host extraction (Aroca, 2001; Arias et al., 2014), it is highly probable

that a portion of the businesses that pay the CPT in these localities are mining suppliers. This partially explains the greater collection of the CPT in the 5th quintile of mining taxes.

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Resources Policy 64 (2019) 101523

Fig. 3. Spatio-temporal Moran’s I for RPT, 2008–2017. Source: Data from Sinim (2018). Note: We consider the inverse distance weight matrix. All Moran’s I estimates are significant at p < 0:001 under 9999 permutations.

remote mining localities far from Santiago.14 We included efficiency indicators to control for potential differences in local budget management. The first indicator corresponds to a SINIM index of efficiency that represents the effectiveness of a municipality in collecting the CPT relative to the total number of commercial patents per year. The second indicator is a professionalization index which depicts the percentage of individuals qualified as professionals (college degree), and full-time workers (planta), annual contract workers (contrata) and short-term (honorario) workers hired by local administrations. Finally, a fixed effect for coastal municipalities is included as a proxy for geographic characteristics which accounts for local amenities or pro­ ductive facilities as harbors. Table 2 displays descriptive statistics for the explanatory variables. As expected, the collection of mining taxes is significantly higher for mining municipalities and corresponds, on average, to US$90 per capita and US$0.3 per capita for non-mining municipalities. As we highlighted in the previous point, the control group (according to the FONDENOR criterion) receives part of the mining taxes via a distribution mechanism (50% of mining taxes correspond to the mining region and are redis­ tributed in equal parts among non-mining municipalities). Regarding the variables for local revenue, net IPP and FCM transfers account for nearly US$155 and US$515 per capita for mining localities. For nonmining municipalities these incomes correspond to US$67 and US $153 per capita. It should be noted that the mining localities have a lower population density so the indicators are, in principle, biased upwards. We can also observe that mining municipalities have almost half of the non-farm properties that non-mining municipalities have (12 versus 29 residential properties per 1000/inhabitants). The indicator for municipal poverty is lower for mining municipalities, which is on average 4% below that of non-mining localities (19%). Mining municipalities are primarily located in the north and extreme south of Chile. Therefore, it is interesting to note that in socioeconomic terms, there is a significant difference between the distance to Santiago and the density by group. For mining localities, the distance

3.2. Explanatory variables The principal explanatory variable for testing the SCO hypothesis is the annual revenue collected by local governments via mining patents. Revenue from resource windfalls can negatively influence municipal­ ities via the collection of other local taxes, especially those in which subnational governments have, by law, some kind of control over their use and collection. In the case of Chile, the limited autonomy that subnational governments have makes this an important issue. Secondly, in this paper we control for other local revenue sources affecting our �-vis the SCO hypothesis, namely net IPP and the response variables vis-a FCM. As noted, the IPP corresponds to the revenue generated autono­ mously in each subnational government via local residential and com­ mercial property taxes (we consider the net IPP), mining patents, local fees (for example driver’s licenses and vehicle registration permits) and a gambling tax among others. The second local revenue source corre­ sponds to the FCM, namely the horizontal grant mechanism that re­ distributes local revenue across municipalities with the aim of offsetting differences in revenue-raising capacity. All of these variables are measured in per capita terms. In order to control for the collection capacity of the RPT we consider the number of tax-paying (taxable) non-farm properties. This variable includes the number of residential properties per thousand inhabitants and constitutes a suitable indicator for the tax collection capacity of subnational governments, whereby a large number of non-farm taxpaying properties will constitute a higher RPT collection.13 Socioeco­ nomic variables such as the average poverty rate are included as a proxy for the median voter’s preferences regarding public goods while de­ mographic variables such as distance to Santiago and density help us to control for the influence of agglomeration economies and political favoritism over the fiscal behavior of the municipalities, especially for

13 According to the information available and the representativeness of the data at the subnational level we use the taxable non-farm properties instead of housing market values via average rental price. This last variable is only available every two years which implies missing data.

14 The density indicator corresponds to the number of people (inhabitants) per Km2 in urban and rural areas.

8

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Resources Policy 64 (2019) 101523

Fig. 4. Lisa Cluster Map RPT, 2008–2017. Source: Data from Sinim (2018). Note: We consider the inverse distance weight matrix. 9999 permutations.

corresponds, on average, to 679 km while in non-mining municipalities this distance is 60% less. Mining communities show a low density (as expected) since most of the mining industry is located in peripheral areas with a low population. Regarding efficiency and the profession­ alization index, there are no significative differences between groups (between 80% and 35%, respectively) while for full-time (planta) workers there is a wide gap, almost double that of non-mining munici­ palities: mining municipalities have 4 per 1000 inhabitants. We observe the same tendencies for annual contracts and short-term workers, although with a greater variability for mining municipalities. Finally, one-third of mining communities are located in coastal areas.

different econometric specifications. We approximate the effect that the mining patents have on the local fiscal collection by estimating the equation: yit ¼ β0 þ β1 ϕit þ β2 ϕi;t

1

þ xit θ þ t þ mi þ εit

(10)

where yit corresponds to the local fiscal collection by the subnational government, namely RPT and CPT in year t; ϕit represents the mining patents collected by a mining municipality i in year t; ϕit 1 is the mining patents collection lagged one period (all in per capita terms). The vector xit includes explanatory variables discussed in the previous section and that affect the tax collection indicators under analysis: net IPP and the FCM (per capita). Additionally, the control variables also include the number of residential properties, the distance to Santiago, socioeco­ nomic controls such as the poverty index and proxies for agglomeration economies, efficiency and professionalization indicators, public workers in local governments and a coastal municipality fixed effect (all continuous variables in per capita terms). t represents a time fixed effect that controls for structural shocks and mi represents the local govern­ ment time-invariant random effect and finally, εit represents the error term.

4. Methodology We take advantage of the Mining Code’s exogenous allocation rule for the distribution of mining taxes in mining municipalities to identify the causal effect of the SCO. A municipality is considered a mining municipality if a mining concession is located within the territorial limits. It then receives the resource windfalls from mining patents. Ac­ cording to the exogenous allocation rule, we adapt the SCO hypothesis at a subnational level and evaluate the crowding out effect against 9

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Resources Policy 64 (2019) 101523

RPT

RPT

RPT

FE (Eq (13) φ ¼ 0)

FE (Eq (13) τ ¼ 0)

FE (Eq (13) unrestricted)

Now, in order to capture the marginal effect of mining taxes on mining municipalities we add (to Eq. (11)) the interaction between the contemporaneous mining tax collection and the treatment indicator dit : a dummy variable equal to 1 if local government i is a mining munici­ pality in year t , 0 otherwise15:

(1)

(2)

(3)

yit ¼ β0 þ β1 ϕit þ β2 ðϕit � dit Þ þ τyit

All municipalities

All municipalities

All municipalities

0.110***

0.100***

0.110***

(0.011) 0.216**

(0.012) 0.313***

(0.011) 0.264***

(0.067) 0.397***

(0.073)

(0.068) 0.410***

The two previous equations (11) and (12) estimated via the BlundellBond system-GMM allow us to obtain the short- and long-term marginal effects of the interest variables. We represent the marginal effects for equations. Eqs. (10)–(12) via the following formulations (see Table 3): The three specifications above do not consider the spatial autocor­ relation in which spatial locations are very similar (such as the collection of local mining taxes). Therefore, the presence of resource windfalls is a strong incentive to substitute those local taxes that might have a higher economic or political cost to collect (van der Ploeg and Poelhekke, 2017). Geographic features relevant to the economic development of the country are also not considered, such as the mining industry being spatially concentrated in the extreme north and south of Chile. This will affect the collection of local taxes and therefore including geographic features in the analysis can be a first step in discarding the absence of spatial autocorrelation in our data. To deal with potential spatial auto­ correlation problems, we use the spatial econometrics techniques in a context of panel data which allows us to exploit the spatial impact and spillover effects of the non-matched grants on the short and long-term local tax collection by estimating the following equation:

Table 7 Dynamic Spatial Panel data estimation results for RPT.

ϕit Wϕit yi;t

1

Wyi;t

(0.014)

0.125

(0.014) 0.372***

0.146

(0.089) 0.131

(0.084) 0.067

(0.130)

(0.138)

(0.130)

0.110***

0.100***

0.111***

(0.010) 0.199**

(0.011) 0.286***

(0.010) 0.261***

(0.065) 0.089

(0.075) 0.186*

(0.068) 0.150*

0.110***

0.100***

0.111***

0.183***

0.100***

0.191***

(0.017) 0.321**

(0.011) 0.310***

(0.018) 0.347***

1

ρ Spatial impacts for ϕit

ShortRun

Direct Impact

Indirect Impact Total Impact LongRun

Direct Impact Indirect Impact

yit ¼ β0 þ β1 ϕit þ τyit

wij yi;t

�

wij xjtk γk þ t þ mi þ εit

1

þρ

n X

K X

wij yjt þ xit θ þ j¼1

k¼1

(13)

j¼1

Total Impact Controls AIC BIC N

(0.105) 0.138

(0.086) 0.210*

(0.074) 0.156*

(0.100) Y 22856.2 23236.2 3078

(0.084) Y 23302.3 23682.3 3078

(0.068) Y 22849.7 23235.8 3078

where φ corresponds to the parameter of the lagged dependent variable for space-time, wij is a spatial weighted matrix, ρ corresponds to the spatial autoregressive term, wij yit 1 ; wij yjt and wij xjtk are the spatial filters of the response and explanatory variables, respectively and γ is the parameter of the k spatially lagged explanatory variables. When considering this Dynamic Spatial Durbin Model, we can control for the inertia hypothesis and the potential bias of omitted variables that exhibit spatial dependence. Additionally, the Spatial Durbin Model provides a general framework that includes the spatial error model (SEM) and the spatial autoregressive model (SAR), which is a powerful reason to use this spatial model (LeSage and Pace, 2009). Although these models allow us control for spatial autocorrelation, the parameters of interest are not directly interpretable as it is necessary to obtain the marginal effects. Spatial econometrics methods allow us to obtain the marginal effects via spatial impacts, namely the direct, in­ direct and total effects of the interest variable in the short and long-run. Finally, the dynamic specification controls the sequential effects of the response variables over their contemporaneous values. The geography of Chile (islands in the south of the country) is highly relevant and so we consider the inverse distance weight matrix in our estimations.16 The spatial impacts, in the short and long term, are depicted in the following table (Table 4): In which the direct impact represents the changes of the ith obser­ vation of the ϕit on yit and could be summarized as the average response of local taxes to mining patent variables. The indirect impacts correspond

Note: All variables in per capita terms. ϕit is the mining patents collection by mining municipality i. Controls: Net IPP, FCM, non-farm properties per 1000/ inhab, professionalization, full, annual and short-term workers per 1000/inhab, % poverty and density. Standard error in parenthesis computed via MC simu­ lation. We consider the inverse distance weight matrix. The missing data have been imputed via nearest neighbor interpolation. Significance levels: þ. p < 0:1; * p < 0:05; ** p < 0:01; * * * p < 0:001

In a political context, subnational governments consider previous decisions regarding the collection of local taxes when making current decisions (Buettner and Wildasin, 2006). However, the aforementioned equation does not consider past collections of the RPT and CPT, both of which might affect the contemporaneous collection of both response variables. If that is true, the interest parameters are inconsistently esti­ mated via OLS because the lags of the explanatory variables are corre­ lated with mi and εit . We consider the dependent variables lagged in one period yit 1 as additional explanatory variables and as an inertia test where the statistical significance on the τ parameter constitutes a test for the inertia hypothesis (Wooldridge, 2010). We estimate Eq (11) via the Blundell-Bond system-GMM approach (Blundell and Bond, 1998): yit ¼ β0 þ β1 ϕit þ τyit

þφ

(12)

þ xit θ þ t þ mi þ εit

n X 1

j¼1 n X

1

1

þ xit θ þ t þ mi þ εit

(11) 15 The control group receives a portion of mining taxes via a distribution mechanism. In particular, 50% of mining taxes are earmarked for the munici­ pality administration and are redistributed in equal parts among non-mining municipalities. 16 The results for other spatial weight matrices can be requested from the authors.

10

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Resources Policy 64 (2019) 101523

Fig. 5. Spatial effects of mining taxes on RPT model 3. Source: Data from Sinim (2018). Note: We consider the inverse distance weight matrix. Explanatory variables: net IPP, FCM, residential properties per 1000/inhab, professionalization, full-time workers (planta), annual contract workers (contrata) and short-term (honorario) workers hired by local administrations per 1000 inhabitants, % poverty and density.

to the changes or feedback of the jth observation of mining taxes on the local collection (j 6¼ iÞ, specifically from the average response of the nearest neighborhood via the spillover effect. Finally, the total impact considers the feedback effects and direct impacts of the mining taxes on property taxes. In the long-term the spatial impacts also consider the autoregressive term which corresponds to the lagged response variable (time, space, and space-time). We compute the marginal effects standard errors using MC simulation (Belotti et al., 2013).

between mining taxes and the mining municipality fixed effect, then the crowding out is maintained, although it decreases by 25% in both cases: for each dollar of mining taxes, US$0.6 and US$0.9 are crowded out in the short and long term, respectively. As we pointed out in the meth­ odology section, the marginal effects in Eq. (12) are calculated accord­ ing to the sum of the coefficients β1 and β2 . The results for the CPT are detailed in Table 6. The three specifications fail to validate the SCO hypothesis in the short term, while the two first estimations (columns 1 and 2) find the crowding out effect only in the long term and at US$0.3 and US$0.2 dollars per capita, respectively. 17 Spatial interrelation amongst subnational governments might have a stronger effect on the dependent and explanatory variables, namely the influence of the nearest neighbors of the municipalities. The results might also be affected because of the geographical particularities of the country, and so the conformation of spatial clusters in the local tax collection is likely. Fig. 3 shows the spatio-temporal Moran’s I that ac­ count for global spatial autocorrelation from 2008 until 2017. A positive coefficient indicates the presence of spatial autocorrelation in the data (Anselin, 1988). The line depicts the Moran’s I coefficients estimate considering an inverse distance weight matrix under random permuta­ tions. According to the Moran’s I coefficients there is a positive and significant correlation between the RPT and the spatially lagged RPT which accounts for the presence of spatial autocorrelation in the local collection, and which is persistent over the ten years.

5. Results Table 5 presents the results for equations (10)-(12) with robust and corrected standard errors in parentheses for the first response variable. We expect that mining taxes will crowd out the future collection of the RPT based on the SCO hypothesis. Column 1 presents the static model while columns 2 and 3 add the temporal lag of the response variable via the Blundell-Bond dynamic system-GMM estimator. Column 1 depicts the static results for Eq. (10) in which the marginal effects for mining taxes in t 1 (hereafter called the long term) have a negative influence on the RPT, although we fail to validate the SCO hypothesis because the parameter is not significant. The second esti­ mation (column 2) validates the FCL hypothesis for both the short and long term. The inertial effect also validates the hypothesis in this equation; the past decisions on local tax collection explain the future RPT collections. As expected, the marginal effects depicted by β1 are negative and significant. For each dollar collected via mining taxes, and when all municipalities are considered, an average of US$0.8 and US $1.2 are crowded out in the short and long term, respectively. If we consider the impact on mining municipalities via the interaction

17 We present only fixed effects estimations that do not include time-invariant factors: distance to Santiago, coastal municipalities and the fixed effect by subnational governments. The results for random effects estimations are available and can be requested from the authors (only for static models).

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In order to account for this local spatial autocorrelation, we show the local Indicator of Spatial Association via a LISA Cluster map (see Fig. 4) that maps the local Moran’s I and which allow us to identify localized map regions where data values are (strongly) positively or negatively associated with one another (Anselin, 1995). As expected, in 2008 (left side of the figure) the mining regions do not show spatial cluster high-high (red color) on the RPT collection. Meanwhile there is a spatial cluster concentrated near Santiago with a higher local collection. The results for 2017 are kept in local cluster terms, which indicates a lasting effect in the local tax capacity over the ten-year period. In line with the previous results, we cannot rule out the presence of spatial autocorrelation in the RPT collection. To test this hypothesis, we use the spatial econometric models that account for spatial autocorre­ lation, and which allow us to report the short- and long-term spatial impacts on the coefficients of interest. Table 7 depicts the result of the dynamic spatial panel according to Eq. (13) which considers the inertia hypothesis and the spatial autocorrelation in the response and explan­ atory variables. The CPT has no significant results and so we show the spatial models and impacts only for the RPT response variable. We consider three specifications: a time lagged dependent variable in the model τyi;t 1 (column 1), a space-time lagged dependent variable φWyi;t 1 (column 2) and both a time lagged and space-time lagged dependent variable τyi;t 1 þ φWyi;t 1 (column 3, unrestricted). The three columns show a significant coefficient for the mining taxes and the lagged response variable, both in space and time. Meanwhile the autoregressive term ρ is positive and less than 1 in all models, but they are not significant. 18 This implies taking the respective precautions in the interpretation of the RPT results. We take into account the information criteria and we present the spatial impact for the unrestricted specification (column 3). The spatial impacts in the short and long term are detailed in Fig. 5. All impacts are statistically significant and robust for both the time lagged and spacetime lagged dependent variables. The test for the short-term direct impact fails to reject the null hypothesis of no SCO, however if we consider the spatial spillover effects, the indirect and total effect are negatives (left side of Fig. 5). Namely, for each dollar collected in nearby neighborhoods via mining taxes, US$0.3 per capita (indirect impact) of the RPT in t þ 1 is crowded out. The impact that considers the feedback and spillover effect (total impact) is negative and significant. Specif­ ically, for each dollar collected via mining taxes, US$0.2 of the RPT is crowded in the short term. Finally, the test for the long-term direct impact again fails to reject the null hypothesis of no SCO (right side of Fig. 5) although the indirect and total impacts are negative and signif­ icant, respectively: the indirect and total effect indicate that for each dollar of mining taxes collected by subnational governments in t, be­ tween US$0.2 and US$0.4 are crowded out in t þ 1.

resource windfalls but that receive a part of the income from mining taxes via a distributive mechanism. We test the SCO via panel estimations. To test the robustness of the results we use different specifications and we add autoregressive terms which account for the inertia hypothesis via a dynamic estimator for panel data. We also consider the interdependence in the behavior of municipal management via spatial econometric models for spatial autocorrelation problems, due to the geographical particularities of the country or in other words, via spatial clusters for the collection of local taxes. These models allow us to obtain the spatial impacts of the pa­ rameters of interest in the short and long term. The results are robust and validate the SCO hypothesis: resource windfalls crowd out the future local tax collection (primarily the RPT). The results also validate the inertia hypothesis when we consider the dynamic models. On average, for each dollar from resource windfalls in mining municipalities in the present, approximately US$0.6 - US$0.9 of the RPT is crowded out in t þ 1. If we consider all subnational govern­ ments, the results do not reject the SCO hypothesis, and the crowding out effect is maintained at US$0.8 - US$1.2 in the short and long run, respectively. Finally, the dynamic spatial model results also reveal the substitution effects between the RPT and mining taxes. The spatial im­ pacts that take into consideration the spillover effects and spatial interdependence are negative, although they are four times lower than non-spatial estimations. For each dollar collected via mining taxes, be­ tween US$0.2 and US$0.3 of RPT, on average, is crowded out in the short and long term. These results imply that resource windfalls generate undesirable incentives for the future collection of other local taxes, particularly the RPT. These results represent non-trivial differences with other authors such as Perez-Sebastian & Raven (2015), specifically, their theoretical concerns related to national decentralization and resource windfalls and James (2015) in which the subnational crowding out is approximately three times below that of our results. However, it should be highlighted that we have only 10 years of municipal level self-reported data to capture the discouragement or laziness of the local tax collection while James (2015) has 51 years of US state-level public finance data. So, caution should be used when interpreting our results. On the other hand, the results for the CPT, despite being one of the principal local taxes in Chile, and regardless of the different methods or specifications used, are not significant. This result could be due to CPT involving a continuous flow of income for the local governments, with a lower socio-political cost for the subnational governments (but the opposite case for RPT). This paper highlights several implications for Chilean subnational governments, specifically in regard to local tax collection behavior. The results for the RPT suggest that resource windfalls have a negative effect on the tax collection behavior of subnational governments in mining municipalities: resource windfalls only result in a reduction of the local tax burden. Additionally, the results suggest that local tax collection decisions are dependent of past decision in which the spatial heteroge­ neity plays an important role in the subnational government tax collection behavior, especially in extreme zones of the country with high costs of living. Future research should pay attention to local manage­ ment indicators that are directly related to the administration of wind­ falls in mining municipalities in order to achieve the objectives established by law regarding non-matched grants.

6. Results discussion and conclusion This paper evaluates to what extent resource windfalls crowd out the future collection of local taxes in mining municipalities. Resource windfalls from the mining industry correspond to non-matched grants for those municipalities with a high presence of non-renewable resource extraction. They also constitute a compensation mechanism for improving the living conditions there. We exploit a panel for the years 2008–2017 and we evaluate the subnational crowding out (SCO) hy­ pothesis over the RPT and CPT. To test this hypothesis, we use an exogenous proposal from FONDENOR to classify mining municipalities (the treated group) and to evaluate the results via different estimation methods. The control group is non-mining municipalities without

Declaration of competing interest None. Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi. org/10.1016/j.resourpol.2019.101523.

18 The results for other spatial structures as Spatial Durbin Model and Spatial Durbin Error Model without a dynamic component are not reliable because the spatial autoregressive terms (ρ and λ) are not within the limits of stability. These results are available and can be requested from the authors.

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Resources Policy 64 (2019) 101523

References

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