Spatial interactions in property tax policies among Italian municipalities

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1 Received: 8 May 2017 Revised: 13 October 2017 Accepted: 19 October 2017 DOI: /pirs FULL ARTICLE Spatial interactions in property tax policies among Italian municipalities Chiara Bocci Claudia Ferretti Patrizia Lattarulo Regional Institute for Economic Planning of Tuscany (IRPET), Via Pietro Dazzi, 1, Firenze, Italy Correspondence Chiara Bocci, Regional Institute for Economic Planning of Tuscany (IRPET),Via Pietro Dazzi, 1, Firenze, Italy. chiara.bocci@irpet.it JEL Classification: C21; H27; H71 Abstract This paper aims to estimate, through the use of a spatial model, the determinants of fiscal policies on property tax adopted by Italian municipalities in 2014, to assess the existence of strategic interactions influencing their revenue decisions and, finally, to investigate the possible sources of such tax mimicking. The analysis evaluates the impact of political and socio economic variables on the local policy decisions and confirms that the choices on property tax are influenced by the neighbouring municipalities behaviour. With regard to the tax mimicking sources, results highlight that the imitative behaviour among municipalities on their tax policy is determined mainly from spillover effects, with a decreasing effect in relation to municipal size. KEYWORDS spatial econometrics, spatial lag model, spillover effects, tax mimicking, yardstick competition 1 INTRODUCTION The study of local policies exercised on property tax is of great interest especially at a time of significant regulatory changes such as is the present in Italy. Recent literature on local fiscal policy making highlights how the decisions related to the level and composition of revenues and expenditures may be determined, on one hand, by political and socio economic features (Inman, 1987) and, on the other, by strategic interactions among local jurisdictions (Brueckner, 2003). Most studies have focused on horizontal tax mimicking and its determinants and all have found empirical evidence of a positive interdependence among neighbouring local governments in many countries, like Belgium (Heyndels & Vuchelen, 1998), Canada (Brett & Pinkse, 2000), Czech Republic (Sedmihradská & Bakoš, 2016), France (Feld, Josselin, & Rocaboy, 2003), Germany (Buettner, 2001), Italy (Bordignon, Cerniglia, & Revelli, 2003), Spain (Solé Ollé, 2003), Switzerland (Feld & Kirchgässner, 2001), United Kingdom (Revelli, 2001) and the United States (Ladd, 1992; Wu & Hendrick, 2009) The Author(s). Papers in Regional Science 2017 RSAI Pap Reg Sci. 2019;98: wileyonlinelibrary.com/journal/pirs 371

2 372 BOCCI ET AL. From an econometric prospective, we talk of tax mimicking in local policy when there is a significant correlation between the tax rates of neighbouring municipalities. Economic theory may help to interpret such correlation by means of three models: the yardstick competition model (Salmon, 1987), the model on fiscal competition (Tiebout, 1956) and the expenditure spillover model (Case, Hines, & Rosen, 1993). The yardstick competition theory focuses on the idea that voters with incomplete information on the costs of public goods and services evaluate the choices of their own local government by comparison with the neighbouring governments tax choices. If neighbouring governments require lower taxes for a similar public good endowment, then local politicians can hardly be re elected. Therefore, the fiscal policies of neighbours become crucial for a politician's future chances and jurisdictions tend to mimic each other (Besley & Case, 1995). The Tiebout model theorizes an alternative citizen response to tax increases (Hendrick, Yonghong, & Benoy, 2007) when jurisdictions have to compete for a mobile tax base; instead of voting out the politician that increased the taxes, firms or households can decide to move away to a jurisdiction with lower tax rates (Brueckner & Luz, 2001). Finally, the spillover approach states that levels of expenditure, and therefore fiscal choices, can result from spillover since the beneficial or detrimental effects of public expenditure spread across the administrative boundaries of one jurisdiction and affect the welfare of the citizens of neighbouring jurisdictions. In other words, a policy change of one jurisdiction produces a strategic incentive for neighbouring jurisdiction to change their own policies. Using a spatial regression model, our study aims to estimate the determinants of the fiscal policies on property tax adopted by Italian municipalities in 2014 with respect to the taxation on both residential and business properties. We also wish to assess the existence of strategic interactions influencing revenue decisions and, finally, to investigate the possible sources behind such tax mimicking. Results confirm that the choices on property tax are determined both by political and socio economic features and by the neighbouring municipalities behaviour. Regarding the tax mimicking sources, the analysis highlights that the imitative behaviours among municipalities are determined mainly from strategic spillover effects. The original contribution of this paper to recent literature is threefold. The first novelty is that, in order to evaluate the local policies, we define an index which indicates the percentage of the municipal total tax revenues due to the fiscal policy on real property. This index measures the actual additional burden on inhabitants and firms produced by the property tax policy. The choice to adopt this index instead of, as in previous literature, the nominal tax rate is due to the unregulated reassessment of the cadastral values experienced in Italy. Because of this irregular reassessment, tax base differences among municipalities are influenced also by the different measurement of the real estate values. Usually, in literature, political choices are measured by the nominal tax rate however, in our opinion, findings based on this indicator could be misleading since they evaluate only the distance between nominal and standard rates without considering possible horizontal disparities in term of tax base. Moreover, the actual tax revenue summarizes all the components of the property tax policy, such as the deductions for main dwellings and the variety of tax rates applied on different type of properties. Second, we test the relation between the imitative behaviour and the municipality size in order to evaluate if the latter influences the spatial interdependence. We believe that this aspect, which has not been addressed in previous empirical literature on tax interaction, is of great relevance for better understanding the characteristics of the main players in the tax mimicking process. Finally, our work contributes to recent literature on tax mimicking across Italian local jurisdictions. Italy represents an interesting case study since it is highly decentralized, with thousands of municipalities managing a relevant percentage of public expenditure and, like other European countries, is actually involved in major reforms of its local government structure (Bibbee, 2007). To date, few papers have focused on the Italian case and only the most recent one considers the totality of Italian municipalities. Bordignon et al. (2003) researched evidence of yardstick competition in business property tax in the 146 municipalities of Milan Province, while Santolini (2008) studied the mimicking behaviour in property tax across the municipalities of the Marche Region. Lastly, Padovano and Petrarca (2014) tested the yardstick competition on property tax during the period, , using all the Italian municipalities, finding a significant spatial correlation of the tax rate, especially among neighbouring non term limited mayors.

3 BOCCI ET AL. 373 The paper is organized as follow: Section 2 introduces the property tax rate revenue in Italy and Section 3 describes the data used for the analysis. Section 4 describes the spatial econometric procedure applied and Section 5 illustrates the results of the estimations. In Section 6 we investigate potential sources of fiscal interdependence. Finally, in Section 7 conclusions are drawn. 2 THE PROPERTY TAX REVENUE IN ITALY The property tax in Italy was introduced for the first time in 1993 and, since then, it has been subjected to many reformations and reconsiderations, the last one in In 2014, the year of our analysis, the local taxation on real property was composed of two parts, the actual municipal property tax IMU (Imposta Municipale Unica) and the tax on indivisible municipal services TASI (Tassa sui Servizi Indivisibili), which are both imposed on the same tax base: domestic and business properties. The value of real estate (the tax base) is calculated by the application of specific multipliers (which are determined by state law and vary according to property category) to the cadastral value, which is assessed by the Italian cadastral office. In general, this assessment is based on indicators which have not been re evaluated in the last 20 years, except for the municipalities that explicitly asked for a new measure on real estate value. Since its introduction, property tax has been the main source of revenue for the Italian municipalities and therefore the decision about the tax rates has become crucial for the local administrators. The base rates of IMU and TASI vary in relation with the property type and are imposed at national level, however, each municipality can determine their own rates, up to a maximum allowed by state law. The IMU base rate is 0.76 per cent for all properties (with a maximum of 1.06%), with the exception of luxury homes (like villas and castles) which are subject to a base rate of 0.4 per cent (with a maximum of 0.6% and a standard deduction of 200 euro) and of standard main dwellings which are exempt. On the other hand, TASI, which contributes to cover the costs of basic municipal services (like street lighting and road maintenance), has a base rate of 0.1 per cent for all properties, but each municipality can choose to reduce or increase the base rate up to a maximum of 0.25 per cent, to differentiate the rate by type of properties and to plan deductions for main dwellings. In addition, administrators must comply with the restriction whereby the sum of TASI and IMU rates for any type of property will not be higher than the maximum allowed by state law for IMU. Therefore, the actual property tax revenue is the result of two components: the tax base and the choices on tax rates and deductions applied by the municipalities. The fiscal policy of a jurisdiction can be measured by the difference between the actual and the standard revenue (extra standard revenue). Looking at property tax revenues in each Italian region (Table 1), we observe that standard revenues are higher in the northern and central regions (more than 400 euro per capita) and lower in the south (less than 200 euro per capita in Calabria and Basilicata). These values show that the property tax base is highly variable in Italy. Regarding extra standard revenues, the municipalities which show a higher difference between actual and standard revenues have exploited more than others the margin of autonomy allowed by law. In particular, as shown in Figure 1, those are the municipalities of the central Italian regions (Liguria, Lazio, Emilia Romagna and Toscana). On the contrary, in the southern regions (Calabria, Basilicata and Campania) the extra standard revenues per capita is much lower than the Italian average. The descriptive results of Table 1 show the average municipal policy behaviour in each Italian region, but obviously every municipality can use its fiscal autonomy differently, in relation to local characteristics and population's needs. As for extra standard property tax revenues for specific demographic groups (Table 2), we observe that, on average, in the smallest municipalities (up to 5,000 inhabitants) the fiscal policy recovers a very small amount: 9 euro per capita for main dwellings and 20 euro for the other properties. On the contrary, the largest municipalities exploit with greater intensity their own autonomy; in fact, in jurisdictions with populations greater than 50,000 inhabitants the tax burden on main dwellings is about 50 euro per capita and is even higher for other types of properties. 1 For a detailed description of Italy's property tax history, see Padovano and Petrarca (2014).

4 374 BOCCI ET AL. TABLE 1 Property tax revenue in the Italian regions in Per capita values in euro and percentage Code Region Standard revenue Actual revenue Extra standard revenue over Total tax revenue (%) North: 1 Piemonte Valle d'aosta Lombardia Trentino Alto Adige Veneto Friuli Venezia Giulia Liguria Emilia Romagna Centre: 9 Toscana Umbria Marche Lazio South: 13 Abruzzo Molise Campania Puglia Basilicata Calabria Sicilia Sardegna Italy As mentioned, the extra standard revenue amount is due both to the tax rate and to the tax base. In theory, a higher tax base could produce a lower tax burden if administrators decide to apply a low tax rate. However, the bigger municipalities, which usually benefit from a richer tax base, often must sustain a higher level of expenditure and therefore they can be induced to increase the tax rate above the standard level. Figure 2 depicts a comparison between the average per capita values of the extra standard revenues and of the current expenditure of the Italian municipalities in each region. It shows that municipalities usually chose between two policy models: (i) in the first model, to a high tax burden level corresponds a high expenditure level, which produces a higher (or more qualified) level of local services (Lazio, Liguria, Toscana, Emilia Romagna, Lombardia, Umbria and Piemonte); and (ii) in the second model, on the contrary, a low level of current expenditure is matched by a low tax burden (Calabria, Basilicata and Campania). Ultimately, the fiscal policy on property tax depends obviously on administrative choices on the provision of public services and the general amount of expenditure. However, it is surely useful to investigate which other factors, related to the local context (political, economic or geographical), could determine tax choices. 3 DATA DESCRIPTION As introduced in the previous section, the object of our analysis are the local choices on property tax and their determinants. The local policies are calculated, for each municipality, as the ratio between the extra standard property tax revenues and the total tax revenues, both as of This variable indicates the percentage of the total tax

5 BOCCI ET AL. 375 FIGURE 1 Extra standard property tax revenues in Per capita values in euro. Regional average TABLE 2 in euro Extra standard property tax revenues in 2014, by municipality size and type of properties. Per capita values Main dwellings Properties other than main dwellings Up to 5000 inhabitants from 5001 to inhabitants from to inhabitants from to inhabitants over inhabitants Italy revenues which is due to the fiscal policy on real property and measures the additional burden on inhabitants and firms produced by the property tax policy. The choice to adopt this index as a measure of local policies instead of the nominal tax rate is due to the following consideration. The tax base differences between neighbouring municipalities is connected not only to the actual

6 376 BOCCI ET AL. Current expenditure Current expenditure Extra-standard revenue Extra-standard revenue 0 0 Lazio Liguria Toscana Emilia-Romagna ITALIA Lombardia Umbria Piemonte Marche Abruzzo Campania Molise Calabria Veneto Puglia Basilicata FIGURE 2 Fiscal policy on property tax and current expenditure of the Italian municipalities in 2014, state law regions. Per capita values in euro differences in real estate market but also to the different measurement of the real estate values, due to the unregulated reassessment of the Italian cadastral values. Obviously, real estate values tend to be higher in the jurisdictions that have undergone a most recent reassessment and this disparity could generate a horizontal inequity between municipalities with the same real estate market characteristics. In this case, the use of the nominal tax rate as an index of political choices could produce misleading findings since it highlights only the distance between the nominal and the standard rate and it does not consider horizontal disparities between jurisdictions in term of tax base. Moreover, the actual tax revenue summarizes all the components of the property tax policy, such as the deductions for main dwellings and the variety of tax rates applied on different types of properties. Figure 3 presents the map of the study variable throughout Italy. It is possible to identify homogeneous areas in which the municipalities adopt similar behaviours, suggesting the existence of a spatial pattern in the data. Concerning the possible determinants of fiscal policies to be included in our analysis, we consider a set of explanatory variables classifiable in four groups: variables regarding the balance sheet, variables about the tax base, variables on territorial, economic and demographical characteristics and variables about the political context. In detail they are: Balance sheet variables: current expenditure in the previous year (2013) per capita; net tax burden in the previous year (2013), defined as municipal own revenues net of the property tax, per capita. This variable measures the sources of income other than the property tax (other taxes, fees or charges); total transfers in the previous year (2013) per capita; 2 poor financial health in the previous year (2013) defined as a dummy variable which assumes value 1 if the jurisdiction checks three or more indicators (over ten mandatory indicators) of poor financial health, contained in Annex B of the annual statement of accounts; 2 In Italy, the municipalities current revenues are composed by three sources: tax revenues (like property tax, additional income tax, tourist tax and waste tax), transfers (grants from regional government, central government and EU) and non tax revenues (like fees and charges). On average in 2014, local tax revenues represent more than 60 per cent of current revenues, while the transfers constitute nearly 15 per cent.

7 BOCCI ET AL. 377 FIGURE 3 Ratio between extra standard property tax revenues and total tax revenues in Percentage values Internal Stability Pact, defined as a dummy which assumes value 1 if the jurisdiction is subject to the Pact and therefore it must comply to the budget constraints imposed by the central government; 3 and high level of tax burden on income in the previous year (2013), defined as a dummy which assumes value 1 if the municipality applies a high additional income tax rate (equal or more than 0.07% with an upper bound of 0.08%). In Italy the income taxation is imposed at national level, however both regional and municipal governments can impose an additional income tax, deciding both the tax rate and the deductions. In particular, municipalities can apply a tax rate between 0 and 0.08 per cent. Tax base variables: property tax base per capita in 2014; average real estate prices for housing market (in euro/m 2 ) from the Observatory for Real Estate Market (OMI) of the Italian Revenue Agency in 2012; 3 The National Stability Pact springs from the need to direct the economies of EU member states towards specific parameters, common to all countries and shared at a European level as part of the Stability and Growth Pact. The net debt of the public administration is the main parameter to be monitored and it is defined as the balance between revenues and expenditures, excluding financial operations. Each year, while preparing and approving the Finance Act, Italy formulates the Internal Stability Pact stating the planning goals in terms of net debt for territorial bodies (local and regional authorities).

8 378 BOCCI ET AL. number of secondary homes per capita, from the 2011 population and housing census; and number of employees in the manufacturing sector, hotel and banking or insurance services per capita. This variable, from the 2011 industry and services census, is used as a proxy for the business tax base. Territorial, economic and demographic variables: municipal size measured as the sum of inhabitants and incoming commuters, from the 2011 population and housing census. This variable measures the demand of public services; population and population density, from the 2011 population and housing census; elderly percentage (+65 years old), from the 2011 population and housing census; number of beds in all accommodation facilities per capita in 2013, which is a proxy for the tourist demand of services; proportion of urbanized land, measured as the ratio between the urbanized area and the total area of the municipality; southern area, defined as a dummy that assumes value 1 if the municipality is in a southern region. This variable considers macro area effects; and per capita income in Political context variables: mandate of the mayor, defined as a dummy which assumes value 1 if the mayor is in his second and last consecutive office term. We recall that in Italy a mayor cannot be re elected for a third time, therefore if the variable takes value 1, the mayor cannot run for office again; election year, defined as a dummy which assumes value 1 if the municipality was in election campaign; and party affiliation of the mayor (centre, centre right, centre left, civil list). The descriptive statistics of all the variables considered for the analysis are reported in Table 3. It is easy to note how the characteristics are highly heterogeneous in the Italian municipalities. With regard to the signs of the explanatory variables coefficients, we expect a positive effect of the current expenditures, of the internal stability pact and of the poor financial health indicator considering that the higher provision of public services, the budget constraints imposed by the central government and the need to restore the municipal accounts can produce a higher tax burden. On the contrary, a higher amount of transfers or revenues from other taxes, fees or charges would reduce the tax burden on real estate. About the variables on the tax base, we expect a positive sign for the number of secondary homes per capita since a municipality with a great amount of secondary homes can place the tax burden on non residents; on the contrary we expect a negative coefficient for all the other variables since a jurisdiction with a higher tax base has less need to increase the property tax revenue above the standard level. Concerning the economic and demographic variables (municipal size, population density, elderly percentage, per capita income and number of beds in tourist accommodations) we expect to find positive coefficients because they are all connected to a higher level of services demand. Moreover, we hypothesize a negative coefficient for the proportion of urbanized land because a city with a greater level of urbanization can reduce its own expenditure. We anticipate a negative sign for the southern regions since, as discussed in Section 2, municipalities of these areas usually show a low level of expenditure. Lastly, about the political context, we expect that a campaigning incumbent is interested in reducing taxes in order to increase his electoral support, whereas a mayor who cannot be re elected does not have this concern. Finally, left wing governments are usually more keen on increasing the level of expenditure and therefore the fiscal policies.

9 BOCCI ET AL. 379 TABLE 3 Descriptive statistics Variables Mean St. deviation Min Max Balance sheet variables: Current expenditures per capita (euro) , Net tax burden per capita (euro) , Total transfers per capita (euro) , Poor financial health (dummy) Internal Stability Pact (dummy) High additional income tax rate (dummy) Tax base variables: Property tax base per capita (euro) 60, , , , Average real estate prices (euro/m 2 ) 1, , Secondary homes per capita Employees per capita Territorial, economic and demographic variables: Municipal size 11, , ,214, Population 7, , ,638, Population density (pop/km 2 ) , Elderly population (%) Bed places per capita , Urbanized land (%) Southern area (dummy) Income per capita (euros) 21, , , , Political context variables: Mandate of the mayor (dummy) Election year (dummy) Party affiliation (categorical): centre right centre left centre civic list In the literature, authors sometimes have chosen to limit their analysis on fiscal decisions to a subset of jurisdictions which are selected by different criteria. For example, guided by a dimensional criterion, Solé Ollé (2003) examines Spanish municipalities with a population greater than 5,000 inhabitants, while Delgado, Lago Peñas, and Mayor (2015a) select Spanish municipalities with more than 1,000 inhabitants. Other authors limit the analysis to a sub national sample, like Bordignon et al. (2003) who choose the Italian municipalities of Milan province, or Santolini (2008), who selects only the Italian jurisdictions in the Marche region. In our opinion, assuming fiscal decisions to be a spatial phenomenon, the selection criterion should be carefully defined since limiting the analysis to a subsample whose borders do not coincide with the limit imposed by institutional differences may undermine the validity of the results (Padovano & Petrarca, 2014). In our analysis we focus on 6,424 municipalities located in the fifteen state law Italian regions, while we exclude the five special statute regions, which are the two main islands (Sardegna and Sicilia) and the three small Alpine regions (Valle d Aosta, Trentino Alto Adige and Friuli Venezia Giulia). Our choice is motivated by the fact that these five home rule regions have special balance rules and are entitled to a greater autonomy in establishing limits and

10 380 BOCCI ET AL. obligations on municipal governments located within their boundaries. Therefore, in these regions, the local fiscal policies and their determinants may differ significantly from the ones in the state law regions. We do not apply an additional dimensional criterion since Italian rules on property tax are not distinguished in relation to the municipal size, as it is the case in other European countries. Finally we use a cross sectional approach since Italian property tax law was fully reorganized in 2012 and the short length of the time series after this structural change greatly limits the use of a panel approach. 4 METHODOLOGICAL FRAMEWORK Since we have observed a spatial interdependence in the study variable, our strategy is based on the estimation of a spatial regression model (Anselin, 1988) which assumes that the decisions of a jurisdiction depend on its own characteristics and on the choices and characteristics of the other jurisdictions. As Manski (1993) asserts, the spatial pattern of economic phenomenon may be due to three different interaction effects: (i) an endogenous interaction effect where the decisions of a spatial unit depend on the decisions of other spatial units; (ii) an exogenous interaction effect where the decisions of a spatial unit depend on independent explanatory variables of the decisions made by other spatial units; and (iii) an interaction among the disturbance terms. The Manski model is the more general model that contains, nested within as special cases, the other simpler models presented in the literature: the Spatial Lag Model, the Spatial Durbin Model and the Spatial Error Model (LeSage & Pace, 2009). Let y be the n 1 vector of the dependent variable, X the n k matrix of explanatory variables and ε the n 1 vector of the independent error term, the Manski model can be formulated as: y ¼ ρwy þ Xβ þ WXθ þ u; u ¼ λwu þ ε; (1) where Wy denotes the endogenous interaction, WX the exogenous interaction and Wu the interaction in the error term. Moreover ρ is the spatial autoregressive coefficient, λ is the spatial autocorrelation coefficient, θ and β are the k 1 vectors of regression coefficients and W represents a n n weight matrix where the generic element w ij indicates the spatial relation between units i and j. Different criteria can be applied to define the spatial matrix W, typically W is defined as a binary contiguity matrix where w ij assumes value 1 if unit i and j share a common boundary, and 0 otherwise. However, many other specifications have been considered in literature, among which are the contiguity matrix of higher order, the k nearest neighbours matrix (with k a positive integer), the distance based neighbours matrix and the inverse distance matrix (with or without a cut off point). 4 Usually, the weight matrices are standardized so that the elements of a row sum to one. This standardisation simplifies the interpretation of the spatial lag variable as an average of neighbouring values. Furthermore, it allows a comparison among the spatial parameters associated to different models. The recent study by Stackhovych and Bijmolt (2009) shows that the spatial matrix selection procedure should be based on a goodness of fit criterion, like the log likelihood function value or the Akaike information criterion (AIC). If a spatial model is estimated on the base of several different spatial weight matrices, one may choose the matrix associated with the highest log likelihood function value or the lower AIC value. If the model estimation is carried out with in a Bayesian framework, LeSage and Pace (2009) and Elhorst (2010) suggest the use of another model selection criterion, namely the Bayesian posterior model probability. As previously mentioned, several spatial models have been considered in the literature. To select the model that better fits the economic phenomenon under study, two specification strategies have been presented in the literature. 4 The neighborhood weights matrix is usually based on geographic proximity. However, it can also rely upon different socio economic characteristics. Delgado, Lago Peñas, and Mayor (2015b), for example, incorporate differences in quality of life as a driver of strategic tax interactions.

11 BOCCI ET AL. 381 The classic specification strategy, the so called specific to general approach, is based on the results of the Lagrange multiplier (LM) test and its robust version (RLM) (Anselin, Bera, Florax, & Yoon, 1996; Florax, Folmer, & Rey, 2003). It consists on starting with a non spatial linear regression model (OLS) and testing whether the model needs to be extended with the inclusion of spatial interaction effects. On the contrary, in the last few years, LeSage and Pace (2009) and Elhorst (2010) stated that it is best to apply a general to specific approach starting with a more general model, like for example the Manski or the Durbin model. Moreover, in order to avoid identification problems, LeSage and Pace (2009) suggest 5 excluding the spatially autocorrelated error term in model (1) and starting the specification strategy from the Spatial Durbin Model (SDM): y ¼ ρwy þ Xβ þ WXθ þ ε: (2) Because of the correlation structure of the dependent variable, it is necessary to base interpretation of the estimated model not on the fitted parameters β but rather on the impact measures illustrated by LeSage and Pace (2009). Let x r be the rth explanatory variable of matrix X; in a linear regression the impact of x r is determined directly from its regression coefficient β r, whereas in a spatial model a change in the value of x r for a single area affects the response of the same area (direct impact) and can potentially affect also the dependent variables in all the other areas through the terms Wy and WX (indirect impact). As a result, the overall impact of each variable will be given by the sum of the two components. The ability of spatial regression models to capture these interactions is one of the most important aspects of this methodology, as pointed out by Behrens and Thisse (2007). LeSage and Pace (2009) formally define the average measures of direct, indirect and total impacts of x r as: Mr ðþ direct ¼ n 1 trðs r ðwþþ; Mr ðþ total ¼ n 1 1 n S rðwþ1 n ; Mr ðþ indirect ¼ Mr ðþ total Mr ðþ direct; where S r (W) =(I n ρw) 1 (I n β r Wθ r ), I n is the n n identity matrix and 1 n is a n 1 vector of ones. In other words, for each variable x r, the average direct impact is represented by the sum of the diagonal elements of the matrix S r (W) divided by n, the average total impact is the sum of all matrix elements divided by n, while the average indirect impact is the difference of the two measures (Bivand, Pebesma, & Gómez Rubio, 2013). The spatial lag model invalidates the use of the classic ordinary least squares (OLS) estimator because of the endogeneity of Wy, an issue well known in spatial econometrics literature (Cliff & Ord, 1973), so it must be estimated by maximum likelihood (ML) methods (Ord, 1975) or instrumental variables (Anselin, 1988) and generalized method of moments (IV/GMM) techniques, like the spatial two stages least square (S2SLS) estimator (Kelejian & Prucha, 1998, 1999). Both estimation methods are extensively discussed in econometric literature and the choice between the two approaches may depend on the empirical analysis at hand (Bivand & Piras, 2015). If the assumption of normality of the residuals is verified then the ML approach could be preferable, otherwise the IV/GMM is a more robust estimation method since they do not require a distribution assumption. The procedure under the S2SLS estimator is to regress Wy on a set of instruments H =[X, WX,, W q X], with typically q 2, and to use the resulting fitted values dwy as instruments for Wy in (2). This procedure, which is widespread in the more recent literature on tax mimicking, has been generalized by Drukker, Egger, and Prucha (2013) to allow the inclusion of other endogenous regressors in addition to the spatial lag Wy. On the contrary, ML estimators of models with a spatial lag and additional endogenous variables would be difficult to derive and for this reason they are less featured in spatial econometrics literature (Elhorst, 2014). 5 If the spatial dependence of the endogenous/exogenous variables is ignored, the estimator for the coefficients is biased and inconsistent. On the other hand, if the spatial autocorrelated error is omitted, the coefficient estimator is less efficient but remains unbiased.

12 382 BOCCI ET AL. Since the second part of our analysis, presented in Section 6, requires the inclusion of additional endogenous regressors in the spatial lag model in order to investigate the possible sources of tax mimicking in the local policies on property tax, we chose to apply the S2SLS approach. 6 The analysis is performed using the sphed package in the R computing environment (Piras, 2010). 5 RESULTS An initial test on the existence of spatial interaction is to compute the Moran's I test, which measures the spatial autocorrelation; its values range from +1, meaning strongly positive spatial autocorrelation to 1, indicating strongly negative spatial autocorrelation, where 0 indicates a random pattern. The Moran I is given as: I ¼ n n n i¼1 j¼1 n n w ij n i¼1 j¼1 i¼1 Þ y j y ðy i yþ 2 ; w ij ðy i y where wij is the general element of the spatial matrix W. We define several W matrices according to the following alternative criteria: the contiguity matrix of first order and of second order, the k nearest neighbours matrix (with k = 4, 6, 8) and the distance based neighbours matrix with a distance of less than 20 km, as in Solé Ollé (2003) and Delgado et al. (2015a). Table 4 shows the Moran I statistics on the local property tax policy measured for all Italian municipalities. The results show a positive and significant spatial autocorrelation structure of the study variable whichever spatial matrix is considered (varying between 0.18 and 0.27 depending on the matrix specification), confirming what was hypothesized by the visual observation of Figure 3 and justifying our empirical strategy. Following the procedure indicated by Elhorst (2010), the most appropriate specification to represent the spatial structure of the data is selected through a combination of the two specification strategies presented in the previous section. At first, the standard OLS model is estimated and the Lagrange multiplier tests are carried out. The test results in Table 5 show that all the LM tests reject the OLS model in favour of a spatial alternative, and that the RLM tests indicate a preference for the spatial lag structure. Second, the SDM is estimated and a likelihood ratio (LR) test is used to test the hypotheses H 0 : θ = 0 and H 0 : θ + ρβ = 0 in order to verify if the SDM can be simplified to either the SLM or the SEM. If both hypotheses are rejected, the SDM best fits the data. On the contrary, as it happens in our results, when the first hypothesis cannot be rejected and the RLM test points to the lag model then the SLM best describes the data. 7 Lastly, we verify how the SLM performs in relation with different specifications of the matrix W. The results summarized intable 6 highlight that all the matrices that we considered produce a similar fitting to the spatial structure of the data, but the contiguity matrix of second order and the nearest neighbours matrix with k = 8 have a slightly better adaptation. Therefore, in the following, the analysis will be presented using the spatial lag model specification: y ¼ ρwy þ Xβ þ ε; (3) with two weight matrices: the contiguity matrix of second order (2ORD) and the nearest neighbours matrix with k = 8 (NN8). 6 The model specification strategy applied in Section 5 requires the use of the ML estimation techniques, therefore both ML and S2SLS were applied in the first part of our analysis. The model estimation results are very similar under both methods and are available on request, but in order to maintain coherency with Section 6, only the S2SLS estimation results are presented in Section 5. 7 For the sake of brevity, only the model specification tests carried out using the contiguity matrix of first order are presented here. However, results are available for all the spatial matrices considered and all point to the SLM specification.

13 BOCCI ET AL. 383 TABLE 4 Moran I statistics on local property tax policies Spatial matrix W Moran I value Moran I std. deviate contiguity of first order *** contiguity of second order *** nearest neighbours k = *** nearest neighbours k = *** nearest neighbours k = *** neighbours within, 20 km *** Note: Level of significance: ***0.001; **0.01; *0.05; 0.1. TABLE 5 Results for the model specification tests Specification tests Test statistic LM Error *** LM Lag *** RLM Error 9.67 ** RLM Lag *** LR H 0 : θ = 0 (SDM vs SLM) LR H 0 : θ + ρβ = 0 (SDM vs SEM) *** Note: Level of significance: ***0.001; **0.01; *0.05; 0.1. TABLE 6 Goodness of fit criteria for the selection of matrix W within SLM specification Spatial matrix W AIC Log likelihood function contiguity of first order contiguity of second order nearest neighbours k = nearest neighbours k = nearest neighbours k = neighbours within 20 km Table 7 shows the IV estimates of the spatial lag model with the 2ORD matrix (column (1)) and the NN8 matrix (column (2)). The results are similar with both matrix specifications and, as expected, when the spatial interdependence is correctly included in the model, the residuals do not show any sign of correlation. The estimated values of spatial parameter ρ indicate the presence of a positive spatial interdependence in the municipal property tax choices and confirm what has been previously verified in the literature in several countries. In particular, the coefficient of spatial autocorrelation is positive and highly significant (equal to 0.46 with the 2ORD matrix and 0.36 with the NN8 matrix), which means that there is a positive horizontal interdependence in the fiscal policies such that an increase of one percentage point in the average extra standard revenue (relative to the total tax revenue) of the neighbours of i can generate, ceteris paribus, an increase of at least 0.36 per cent in the same revenue of municipality i. In other words, municipal property tax policies are defined on the basis of both the explanatory variables (balance, tax base, context) and the property tax choices of adjacent municipalities (tax mimicking). As mentioned in Section 4, in a spatial model a change in the explanatory variable for a single area affects the response of the same area (direct impact) and can potentially affect also the dependent variables in all other areas

14 384 BOCCI ET AL. TABLE 7 Estimation results of the spatial lag model SLM with 2ORD matrix (1) SLM with NN8 matrix (2) Intercept (0.677) ** (0.672) *** Current expenditures per capita ( 1000) (0.438) *** (0.438) *** Net tax burden per capita (( 1000) (0.370) *** (0.370) *** Transfers per capita ( 1000) (0.620) *** (0.619) *** Poor financial health (dummy) (0.258) (0.257) Internal Stability Pact (dummy) (0.259) *** (0.259) *** High additional income tax rate (dummy) (0.167) *** (0.167) *** Property tax base per capita ( 1000) (0.004) ** (0.004) ** Average real estate prices ( 1000) (0.224) *** (0.224) *** Secondary homes per capita (0.179) ** (0.179) * Employees per capita (0.734) (0.733) Municipal size (log) (0.117) *** (0.117) *** Population density (0.157) * (0.157) * Elderly population (%) (0.018) ** (0.018) *** Bed places per capita (log) (0.111) *** (0.111) *** Urbanized land (%) (1.019) * (1.017) * Southern area (dummy) (0.257) *** (0.257) *** Income per capita (log) (0.774) ** (0.774) ** Mandate of the mayor (dummy) (0.157) ** (0.157) ** Election year (dummy) (0.166) (0.166) Party affiliation: right centre (0.654) (0.653) Party affiliation: left centre (0.625) (0.625) Party affiliation: civic list (0.590) (0.590) Spatial parameter, ρ (0.045) *** (0.045) *** Moran I value on residuals n. of observations Notes: Level of significance: ***0.001; **0.01; *0.05; 0.1. Standard errors in parentheses. through the term Wy (indirect impact). As a result, the overall impact of each variable will be given by the sum of the two components. The observation of the total impact (Table 8) shows, first of all, that most of the explanatory variables exhibit the expected signs. In particular, the jurisdictions of the southern regions show a low tax burden on property tax, while the municipalities in central and northern regions exert a higher effort in order to support their higher level of current expenditures. It should be noted that even if the lower extra standard revenues in the southern regions may actually be due to a different choice of policies and to the lower value of the tax base, it can also be attributed to greater tax evasion, which erodes theoretical revenues. Second, the municipalities which show higher amounts of net revenues, transfers or tax bases may apply, in general, a lower tax burden. In other words, an administration that can count on higher economic resources (internal or transferred) is more keen on reducing the tax burden on real estate. However, observing the level of additional income tax rate, it is possible to show that local property tax policies are not complementary to those for income tax, since the municipalities with high additional income tax rates apply also high property tax rates. Focusing on the dummy variable that indicates the municipalities under the Stability Pact, we find that the municipalities subjected to Internal Stability Pact exhibit a higher level of extra standard revenues, since they need to deal with the budget constraints imposed by the central government.

15 BOCCI ET AL. 385 TABLE 8 Impact measures SLM with 2ORD matrix SLM with NN8 matrix Direct Indirect Overall Direct Indirect Overall Current expenditures per capita ( 1000) *** *** Net tax burden per capita ( 1000) *** *** Transfers per capita ( 1000) *** *** Poor financial health (dummy) Internal Stability Pact (dummy) *** *** High additional income tax rate (dummy) *** *** Property tax base per capita ( 1000) ** ** Average real estate prices ( 1000) *** ** Secondary homes per capita ** * Employees per capita Municipal size (log) *** *** Population density * * Elderly population (%) ** *** Bed places per capita (log) *** *** Urbanized land (%) * * Southern area (dummy) *** *** Income per capita (log) ** ** Mandate of the mayor (dummy) ** ** Election year (dummy) Party affiliation: right centre Party affiliation: left centre Party affiliation: civic list Note: Level of significance: ***0.001; **0.01; *0.05; 0.1. The number of secondary homes per capita, as well, has a positive relation with the property tax choices because the municipalities with a high number of second/third homes pour the higher tax burden on non residents. In some cases, the higher tax rate can be explained by the need to ensure a higher level of expenditures; in places with high administrative costs (big cities) and a high demand for services (high rate of elders) and in small jurisdictions with low urbanized land. About the political variables, results show that, as hypothesized, municipalities with ongoing election campaigns reduce their fiscal policies whereas, contrary to what expected, jurisdictions where the mayor was elected twice exploit with lower intensity their autonomy, probably because of better revenue and expenditure policy planning. Finally, there is no finding of a significant impact of the party affiliation. This can be attributed to the fact that, in the last few years, a large and increasing number of Italian mayors are affiliated to civic lists which are not directly ascribable to a leftist or a rightist ideology. 6 THE SOURCES OF SPATIAL INTERDEPENDENCE The results presented in the previous section confirm that the choices on property tax are affected both by political and socio economic features and by the neighbouring municipalities behaviour. The next step is to investigate which of the possible sources of these imitative behaviours is more plausible. 8 8 For the sake of brevity, only the estimations carried out with the 2ORD matrix are presented here. Results are analogous when performing the analysis with the NN8 specification.

16 386 BOCCI ET AL. In advance, the literature offers three explanations for tax mimicking: expenditure spillover (Case et al., 1993), the tax competition model (Tiebout, 1956) and political yardstick competition (Salmon, 1987). According to the spillover approach, several authors found that beneficial or detrimental effects of public expenditure (i.e., with regards to spending on infrastructure and road building, environmental services, recreation and cultural facilities, etc.) spread over the administrative boundary of one jurisdiction and affect the welfare of the residents of neighbouring jurisdictions (Baicker, 2005; Brueckner, 2003; Freret, 2006; Hui & Liang, 2016; Kelejian & Robinson, 1993; Revelli, 2003, 2005; Schaltegger & Zemp, 2003; Solé Ollé, 2006). Within these models, a jurisdiction policy change on public expenditure and/or on tax revenues produces a strategic incentive for neighbouring jurisdiction to change their own policies. The other two theories correspond to the two options that might be open to taxpayers to respond to a possible tax increase: changing one's residence or changing one's vote. The first theory, introduced by Tiebout, is based on the idea that policy makers may mimic the tax policies of their neighbours in order to prevent tax base mobility. If a municipality applies high rates, firms and households may decide to migrate to a different municipality in order to reduce their fiscal burden (Allers & Elhorst, 2005). Note that, assuming that differences on the tax rates are usually not large enough to promote migration, the second theory of yardstick competition could be a more plausible cause of tax mimicking. According to this theory, imperfectly informed voters about costs and suitability of incumbent local fiscal policies infer the quality and reliability of their own politicians comparing other governments performance as benchmark (Salmon, 1987). Clearly, a rational politician will mimic the neighbouring tax policies in order to garner the voters preferences and have a chance to be re elected (Bartolini & Santolini, 2012; Bordignon et al., 2003). About the plausibility of the Tiebout model as an explanation of property tax mimicking, we agree to what was pointed out by Allers and Elhorst (2005), that is, that the property tax rate differences between municipalities (which vary from 0.76 per cent in the cheapest municipality to 1.06 per cent in the most expensive one) are small compared to the stamp duty on the transfer of property ownership (9%) and to the cost of moving. Therefore, if theoretically it could be possible to imagine a taxpayer free to move in order to reduce his level of fiscal burden, in the Italian reality this behaviour is unlikely to be applied to property tax. Because of this evidence, we decided not to further examine this hypothesis. To test for yardstick competition, we define two new spatial lag models by modifying the baseline regression (3) with the inclusion of an interaction term between the average neighbouring extra standard revenue (Wy) and the political indicators mandate of the mayor and election year respectively. If the interaction term is statistically different from zero, then municipalities have a dissimilar imitative behaviour in the two regimes defined by the political indicator; 9 on the contrary, if the interaction term is null, then there is no evidence of yardstick competition. Let M be a diagonal matrix whose elements m ii are equal to 1 if the incumbent of municipality i is in office for two consecutive terms, and 0 otherwise; and let E be a diagonal matrix whose elements e ii are equal to 1 if municipality i is in election campaign, and 0 otherwise. The two models for testing the yardstick completion hypothesis are: y ¼ ρwy þ δ M MWy þ Xβ þ ε; (4) and y ¼ ρwy þ δ E EWy þ Xβ þ ε: (5) As evidence of yardstick competition we expect to find a positive interaction between neighbouring fiscal policies and the dummy variable election year (δ E ) because an incumbent under campaign conditions is even more interested 9 This specification is similar to the two regimes model firstly introduced by Bordignon et al. (2003). They define two regimes identified by a dummy variable D and include two spatial parameters ρ D =0 and ρ D =1 relative to the first and second regimes, whereas, in our model, δ measures the difference in the spatial parameters of the two regimes.