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Tax interactions among Belgian municipalities: Do interregional differences matter?
Regional Science and Urban Economics 40 (2010) 336–342
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Regional Science and Urban Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r e g e c
Tax interactions among Belgian municipalities: Do interregional differences matter? Marcel Gérard a, Hubert Jayet b, Sonia Paty b,c,⁎ a b c
Louvain School of Management, FUCaM, Mons and CESifo, Belgium EQUIPPE (Universities of Lille), France CREM (CNRS and University of Caen), France
a r t i c l e
i n f o
Article history: Received 16 February 2009 Received in revised form 29 March 2010 Accepted 31 March 2010 Available online 9 April 2010 JEL classifications: H24 H31 H71
a b s t r a c t This paper tests the existence of strategic interactions among municipalities, based on a panel of Belgian local tax rates from 1985 to 2004. A special emphasis is put on the role of the interregional differences in Belgium. Our results partly confirm previous findings for Belgium suggesting that municipalities interact over the local surcharge on the (labor) income tax rate but not on the local surcharge on the property tax. Using spatial econometrics tools and an original methodology for specifying weight matrices, we find that municipalities are sensitive to the local income tax rates set by only their closest neighbors. We cannot reject the hypothesis that interregional differences matter for municipalities belonging to the Brussels region: for the local income tax rate, the intensity of interactions is shown to be higher between municipalities belonging to the Brussels region than between municipalities belonging to different regions. © 2010 Elsevier B.V. All rights reserved.
Keywords: Tax interactions Panel data Spatial econometrics Local tax rates Tax competition
1. Introduction Since the end of the 1980s, a large theoretical literature on tax competition has developed following the seminal papers by Zodrow and Mieszkowski (1986), Wilson (1986), Wildasin (1988) — see Wilson (1999) for a survey. The basic argument is that jurisdictions interact with their neighbors to attract a mobile activity or mobile tax base. There is an alternative theoretical explanation for why local authorities are affected by their competitors when setting tax rates. This is based on the idea that citizens evaluate the performance of incumbent leaders by comparing their public decisions with those set by nearby local authorities. This theoretical literature on “yardstick competition” began with Salmon (1987) and was further developed by Besley and Case (1995). Although the theoretical literature on strategic interactions is quite large, empirical studies are fairly recent. Most papers concentrate on interactions over tax, among local jurisdictions within a country or among states within a federal country — see Brueckner (2003) for a survey. These models are usually implemented empirically by means of estimation of a fiscal reaction function, where the optimal tax rate
⁎ Corresponding author. UFR Sciences économiques et gestion, 17 rue Claude Bloch, BP 5186, 14032 Caen Cedex, France. E-mail address: [email protected] (S. Paty). 0166-0462/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.regsciurbeco.2010.03.010
in a jurisdiction depends on the tax rates in nearby jurisdictions (Revelli, 2005). Most papers including those of Ladd (1992), Case (1993), Brett and Pinske (2000), Buettner (2001), Heyndels and Vuchelen (1998), Brueckner (1996), Brueckner and Saavedra (2001), Richard et al. (2002), Feld and Reulier (2005), Feld et al. (2002), Bordignon et al. (2003), Solé-Ollé (2003), Leprince et al. (2007), Charlot and Paty (2007) find empirical evidence of positive interactions among sub-national governments using various data sets. Our paper is in line with this strand of literature in using data on a panel of Belgian local tax rates for 1985 to 2004. Our contribution is twofold. First, we find that Belgian municipalities interact over the local surcharge on income tax but not on the local surcharge on property tax. This result is partly consistent with the results in previous studies, especially Heyndels and Vuchelen (1998), which proposes a cross section analysis, and Richard et al. (2002), which uses a dynamic model where the slope of the reaction function is not directly estimated. Using spatial econometric tools and an original methodology for specifying weight matrices, we find that municipalities are only sensitive to the local income tax rates set by their closest neighbors. Second, we explore the effects of interregional differences on the empirical evidence of tax interdependence, as the three Belgian regions are characterized by political, economic, cultural and geographical differences. Belgium's two largest regions are the Dutch-speaking region of Flanders in the north and the French-
M. Gérard et al. / Regional Science and Urban Economics 40 (2010) 336–342
speaking southern region of Wallonia. The Brussels-Capital Region is a bilingual enclave within the Flemish Region.1 It has been observed that residents' location choices are influenced by the language spoken: Belgian citizens usually prefer to live in a municipality where he/she is familiar with the official language in order to avoid costs related to the language in schools, sport and cultural facilities, or the need to translate official document from another language. Policymakers who are concerned with the mobility of their tax base (the residents) will likely pay more attention to the fiscal choices of nearby municipalities that share the same language. 2 Moreover, an incumbent who does not fear the residents' mobility but wants to be re-elected is likely to take as a yardstick the fiscal choices of nearby municipalities that share the same language because the access to information on fiscal conditions will be supposed to be easier — e.g. Belgian newspapers providing national news tend to focus on their particular language communities. Eventually, we find that the hypothesis that interregional differences matter for interactions among nearby jurisdictions cannot be rejected for the Brussels region. Especially, for the local income tax rate, the intensity of interactions is shown to be higher between two municipalities belonging to the Brussels region than between a municipality from the Brussels region and a Flemish municipality. This observation might be viewed as contributing to the ongoing debate in Belgium about the regionalization or partial decentralization of some taxes. The paper is organized as follows. The next section discusses the empirical design. The results are presented in Section 3 and Section 4 concludes. 2. Empirical design In this section we provide some information on Belgium before discussing the specification of our model and presenting our data set. 2.1. Belgium, municipalities and local taxation Belgium is a multilevel government country,3 a federation consisting of three regions and three communities. The regions – Flanders, Wallonia and Brussels – are basically responsible for territory related aspects, such as economic development, employment policy, infrastructure and the supervision of municipalities. The communities –Dutch, French and German-speaking– are responsible for issues related to individuals, such as education and health. Each region and community has its own government and parliament although Flanders and the Dutch-speaking communities have been merged. Since March 2008, the Walloon Region and the Frenchspeaking Community have the same Minister–President. According to the Belgian Constitution, residual power belongs to the regions and communities. Although not responsible for territorial matters, the authority of the communities corresponds to the territory, that of the Dutch-speaking to Flanders and Brussels, and that of the Frenchspeaking to Wallonia and Brussels; the authority of the small Germanspeaking community applies to a series of municipalities annexed to Belgium after the First World War and located in Wallonia. Corporate income tax and value added tax in present day Belgium are federal taxes; personal income tax is also mainly federal, but regions (and municipalities — see below) are permitted to add positive or negative surcharges; the immovable property tax goes to the regions, which set its base rate although the municipalities may
1
A small German-speaking community exists in eastern Wallonia. Since the linguistic communities coincide with the region, we cannot disentangle the effect due to the language. We can only capture the regional specific effect like institutions, culture (including language), social norms... 3 For details, see e.g. Gérard (2002). 2
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add surcharges. The regions are mainly financed by transfers from federal government and by regional taxes; the communities are essentially financed by transfers. Belgium also consists of 10 provinces and 589 municipalities. In this paper we focus on the municipalities. Their major expenditure categories include culture and education, police, welfare and transport infrastructures, and general administration. They levy numerous local taxes and receive the revenue from the surcharges referred to above: taxation accounts for more than 40% of local revenues. The remainder income comes from transfers from the regional authorities. However, two taxes, local income tax and local property tax, constitute the main sources of revenue for the municipalities (accounting for 80% of municipal tax revenues), in the ratios 1.7 to 1, and 1.2 to 1 depending on the region. Both local taxes are surcharges determined only by the municipalities: - the local income tax is a surcharge on the federal income tax levied on individuals; the rate of the surcharge is between 0 and 10% and, in practice, is a tax on labor income (savings income is taxed separately); and - the local property tax is a surcharge on the regional property tax; its base, also defined at federal level, is an imputed income on immovable property; this tax is levied on all taxpayers – individuals, companies, charities – on the basis of the location of the property. Note that regional property tax in Brussels and the Walloon Region amounts to 1.25% and in the Flemish Region to 2.5% of the imputed income on immovable property; the local surcharge varies between 293.75 and 5750% of the regional tax. It is important to understand that the location of the property determines which place receives the tax payment. For example, an individual resident in municipality A who owns a real property in municipality B will pay local income tax on his labor income in municipality A and local property tax in municipality B. This is the reason why, two municipalities along the North Sea coast whose councils are elected by resident taxpayers, have decided on a zero local income tax rate, putting the burden of local tax on the property tax paid by non-resident owners or users of the houses and flats at the seaside. Each municipality has a council that is elected every six years by residents who vote for the candidates on the lists presented either by political parties –also active at regional and federal level – or by local groups; each list has municipal councillors in proportion to the number of votes obtained. Then a majority contract is passed within the municipal council; on that base the mayor and her deputies are designated by the Region. The mayor and her deputies form the municipal college, which is the municipality executive. Finally, the main mobility factors within Belgium are workplace location, although many Belgian residents commute long distances to their place of work, and price and size of ground and homes; as already explained, individuals' residency choices are also influenced by the language spoken in the municipality. 2.2. Empirical specification 2.2.1. The econometric model Our aim is to test the existence of strategic interactions among municipalities and to find the effect of cultural proximity on this interdependence. The theoretical models of tax and yardstick competition have the same empirical predictions that jurisdiction i's fiscal decisions in year t, τi,t, depend on i's neighbors' fiscal decisions, τj,t, and on i's socio-demographic characteristics Xi,t. Using a linear formulation and adding a time lag of the dependent variable for taking account of slow adjustment in tax rates, we obtain the following model K k τt = α + ∑k = 1 ρk W τt + γτt−1 + Xt β + η + εt
ð1Þ
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where, τt = (τ1,t,…,τN,t)′ is the vector of fiscal decisions by all jurisdictions at date t, Xt is the matrix of socio-demographic characteristics and W1,...,WK are matrices of spatial weights; The specification of W1,...,WK will be examined later in Section 2.2.2). The vector η = (η1,…,ηN)′ is a vector of fixed municipal effects, taking into account the impact of the unknown time-invariant factors influencing the tax rates. Stacking over periods t = 1,…, T, and using Kroneker products, Eq. (1) may also be written as K k τ = α + t IT ⊗ ∑k = 1 ρk W b τ + γ½LT ⊗IN τ + ½eT ⊗IN β + η + ε ð2Þ where IT (resp. IN) is the identity matrix at order T (resp. N), LT is the matrix associated to the time lag operator, and eT is a column (N,1) vector with all its elements equal to 1. This is a spatial panel data model with a temporally lagged and spatially lagged dependent variable.4 We consider that the elements of the vector of municipal effects, η, are fixed coefficients to be estimated jointly with β and the vector of spatial autoregression coefficients, ρ. On theoretical grounds, this strategy may be acceptable. Municipalities are fixed entities, so that a larger sample includes the same municipalities, but has additional periods; therefore, the set of coefficients to be estimated does not vary with the sample size, thus ensuring consistency. Let us note that estimating the whole set of coefficients of this fixed effects model implies estimating 600 coefficients and raises computational problems. Full information maximum likelihood is feasible however, and has been used here. 2.2.2. Specifying the weights matrix: Do interregional differences matter? Usually, a single weights matrix is used, ∑Kk= 1ρkWk = ρW, where W is row normalized, each row summing to unity. Standard choices for W are row normalized contiguity matrices, nearest neighbor matrices, or weights inversely proportional to distance or to the square of the distance. 5 The fact that the weights matrix is arbitrarily chosen is unsatisfactory, as different matrices may lead to different results. Therefore, some authors test several matrices, in order to assess the robustness of the conclusions. But, in most cases, only one matrix is used. But using one matrix only does not allow us to answer the central question of this paper: is the intensity of interactions higher between two municipalities belonging to the same region than between two municipalities belonging to different regions? Therefore, we use a decomposition of the weight matrix which is now a linear combination of partial weights.6 Starting from any standard weight matrix W, we define five partial interaction matrices, WFF for interactions within Flanders, WWW for interactions within Wallonia, WBB for interactions within the Brussels region, WFW for interactions between Flanders and Wallonia, and WFB for interactions between Flanders and Brussels. 7 More precisely, starting from W - The elements of WFF are wFF i,j = wi,j when both municipalities i and j are Flemish, zero otherwise, - The elements of WWW are wWW i,j = wi,j when both municipalities i and j are Walloon, zero otherwise, - The elements of WBB are wBB i,j = wi,j when both municipalities i and j are in the Brussels region, zero otherwise,
Table 1 Summary statistics. Variable
Unit
Local income tax Local property tax Population density Per capita income Unemployment rate
% 6.84 1.07 Centimes 2633.51 689,67 100 inhab/km2 6.71 17.42 1000 BF 350.45 101.92 % 3.60 1.85
Mean
Standard dev. Min
Max
0 10 293.75 5750 0.19 201.96 147 704.86 0 21.51
Notes: Number of observations: 11,780 (T = 20, N = 589). 1000 BF are equivalent to EUR 24,79.
- The elements of WFW are wFW i,j = wi,j when i is Flemish (resp. Walloon) and j is Walloon (resp. Flemish), zero otherwise, and - The elements of WFB are wFB i,j = wi,j when i is Flemish (resp. in the Brussels region) and j is in the Brussels (resp. Flemish), zero otherwise. To sum up, WFF, WWW and WBB, correspond to interactions within the same region, respectively Flanders, Wallonia and Brussels; while WFW and WFB correspond to interactions between Flanders and the other two regions, Wallonia and Brussels respectively.8 Then, we get K
k
∑k = 1 ρk W = ρFF W
FF
+ ρWW W
WW
+ ρBB W
BB
+ ρFW W
FW
FB
+ ρFB W :
ð3Þ
2.3. Data set We estimate Eq. (2) using annual data for the 589 Belgian municipalities over the period 1985–2004. These data were collected by Richard et al. (2002) and by Van Parys and Verbecke (2006). The main data sources are the National Institute of Statistics, the Department of Economics and the Department of Geography of the Université Catholique de Louvain, in Louvain-la-Neuve.9 Table 1 reports summary statistics. As noted above, local tax policies also reflect the impact of differences in economic and demographic factors grouped within the vector X in Eqs. (1) and (2). Following the empirical literature, we include a set of socio-demographic variables, such as unemployment rate, population density and per capita income. These can be considered variables for expenditure needs. We thus expect a positive sign for their respective parameters. We also include three electoral dummies to check the existence of an electoral cycle, i.e. lower tax rates in the years around the election year and higher tax rates in the middle of the legislative period, which lasts for six years — elections occurred in the last quarter of 1988, 1994 and 2000 respectively. We did not take into account the vertical tax externalities because the federal income tax rates and the regional property tax rates are very stable on our period of study. Finally we include a trend variable. Table 1 displays the standard summary statistics for the two main dependent variables and the main explanatory variables. Maps 1 and 2 in appendix show respectively the local income tax rates and the local property tax rates in Belgium for 2004. 3. Results Our estimation strategy is the following. We first estimate the model without spatial effects and run the appropriate spatial tests
4 This type of model is not very well documented in the spatial econometrics literature; however, see Elhorst, 2003 and Anselin et al., 2007. 5 Before normalization, every element wi,j of the contiguity matrix is non-zero if and only if i and j share a common border. For a nearest neighbour matrix, wi,j is non-zero if j is a k nearest neighbour to i, where the threshold k has been appropriately chosen. 6 Arbia et al. (2010) present a similar exercise to go beyond standard specification of the weight matrix for European regions. 7 Brussels being an enclave within Flanders, there is no contiguity with Walloon municipalities.
8 Note that we do not include interactions between the Brussels and Walloon municipalities since Brussels is completely surrounded by Flemish municipalities (see footnote 6). 9 The municipal property tax rates are surcharges to regional property tax rates of 1.25% in Wallonia and Brussels and 2.5% in Flanders. Therefore, following Richard et al. (2002) and Van Parys and Verbecke (2006), we multiplied the property tax centimes charged by Flemish municipalities by two in order to make the centimes comparable across regions.
M. Gérard et al. / Regional Science and Urban Economics 40 (2010) 336–342 Table 2 Lagrange multiplier tests statistics. LM LAG
RLM LAG
Local Property Tax Wcont 264.11 (0.00) 401.67 (0.00) WC1 WD1 387.90 (0.00)
Table 3 Spatial coefficients (Local Income Tax). LM ERR
Local Income Tax Wcont 117.19 (0.00) 15.60 (0.00) WC1 186.06 (0.00) 19.84 (0.00) WD1 173.94 (0.00) 20.35 (0.00)
124.96 (0.00) 239.56 (0.00) 209.29 (0.00)
RLM ERR 23.37 (0.00) 73.34 (0.00) 55.70 (0.00)
LAG & ERR 140.56 (0.00) 259.40 (0.00) 229.64 (0.00)
1.84 (0.17) 512.65 (0.00) 250.37 (0.00) 514.49 (0.00) 1.40 (0.24) 1023.85 (0.00) 623.57 (0.00) 1025.24 (0.00) 1.59 (0.21) 931.45 (0.00) 545.14 (0.00) 933.03 (0.00)
Note: P-values in parentheses. Wcont is the standard contiguity matrix. WC1 is the nearest neighbors matrix for k = 10 neighbors. WD1 is the inverse distance matrix restricted to the k = 10 nearest neighbors. LM LAG and LM ERR are the non-robust Lagrange multiplier tests testing respectively the spatial lag dependence and the spatial error dependence. RLM tests are the equivalent robust tests. Tests have been conducted for WC2, WC3, WC4, WC5 and WD2, WD3, WD4, WD5 and are available upon request.
based on Lagrange multiplier (LM)10 to discriminate between the spatial lag model and the spatial error model using various specifications for the weight matrix, without distinction across regions (see Table 2). These tests confirm the presence of both spatial lag dependence and spatial error dependence for the local income tax rates using every weight matrices. Therefore, the error in Eqs. (1) and (2) takes the following form (Anselin, 1988, p. 182): 2
ε = λWε + μ; μ ∼ Nð0; σ Þ
339
ð4Þ
where λWε is an autoregressive term into the error term ε of the tax rate regression. The parameter λ captures the strength of this autoregressive relation, and the term W is a spatial weight matrix that summarizes the spatial layout of the data. As for the local property tax rates, the LM tests confirm the validity of the spatial error model since the robust LM test for spatial dependence is never significant. Second, in order to investigate the range of interactions for the local income tax11, we estimate the model using ML12 and the different weight matrices (see Table 3). 13 We observe that the interactions measured by the spatial lag parameter are significant using standard contiguity matrix, the WC1 matrix (based on the ten closest neighbors) and the WDk matrices (based on distance decay). When we use WCk, the spatially autoregressive coefficient is decreasing with k and nonsignificant for k N 2. When we use WDk, it is stable and significant for every k. This result suggests that interactions in terms of local income tax only occur between closest neighbors14. Given that matrix WC1 corresponds to the 10 closest neighbors, and that there are 589 municipalities in Belgium, each Belgian municipality interacts with around 2% of the other municipalities.
10 We compute the non-robust and the robust LM test statistics for spatial lag dependence and spatial error dependence (see Anselin et al., 2007). 11 We also estimate the same model for the local property tax. However, as shown by the LM tests, the spatial lag parameter is never significant while the spatial error term is significant using all weight matrices. 12 The general form of the likelihood that has been maximized is LL = −(NT/2)ln 2πσ2 + Tln det(IN − ΣkρkWk) + Tln det(IN − λW) − (1/2σ2)e′e with e = (IT ⊗ (IN − λW)) ((IT ⊗ (IN − ∑ kρkWk))τ − Xb) − (uT ⊗ IN)η. For a similar problem, see Lacombe (2004). 13 We did not report the estimation results for the other explanatory variables for space convenience. They will be discussed in further tables. 14 The matrix WCk gives equal weights to all the 10 k nearest neighbours, while these weights decrease with distance when using WDk. With interactions occurring between closest neighbours only, when k increases, we introduce distant neighbors with which there are weak or even no interactions. Using the matrix WCk, these distant neighbours have the same weight as close neighbours and then drive down the estimated coefficient toward zero. Using the matrix WDk, the distant neighbours have much smaller weight than close neighbours and then have almost no influence on the estimated coefficient.
Wcont WC1 WC2 WC3 WC4 WC5 WD1 WD2 WD3 WD4 WD5
Spatial lag parameter
Spatial error parameter
0.060*** 0.076*** 0.056* 0.042 0.037 0.020 0.076*** 0.070** 0.066** 0.072** 0.077**
0.098*** 0.193*** 0.346*** 0.429*** 0.474*** 0.520*** 0.160*** 0.285*** 0.361*** 0.404*** 0.441***
Notes: *: 10% significant, **: 5% significant, ***: 1% significant. Estimated coefficients for the other explanatory variables have been omitted for space convenience. Wcont is the standard contiguity matrix. WC1, WC2, WC3, WC4, WC5, are nearest neighbors matrices for k = 10, 20, 30, 40 and 50 neighbors respectively. WD1, WD2, WD3, WD4, WD5, are inverse distances matrices restricted to the k = 10, 20, 30, 40 and 50 nearest neighbors respectively.
Third, the estimations of our spatial model in Eq. (2) – without taking into account the interregional differences – were carried out using maximum likelihood (Table 4). Finally, we investigate the impact of interregional differences on local property tax interactions by estimating Eq. (3). 3.1. Without interregional differences Table 4 reports the estimation of our model without interregional differences – a general specification that includes spatially autoregressive effects for both the endogenous variable and the error term – using ML for both taxes. Since interactions – when they exist – were shown to occur between closest neighbors, we only present the estimation results using the contiguity matrix.15 As for local income tax, there are significantly positive interactions, which imply that municipality authorities react to tax changes decided by their neighbors: if neighboring municipality j decreases its tax rate, municipality i also decreases its own rate. However, the presence of spatial error dependence suggests that there may be omitted explanatory variables that are spatially related to each other over space.16 The absence of interactions between municipalities in terms of property tax rates implies that the choice of this tax rate in a municipality is not influenced by the tax levels of its neighbors. Again, as in the case of the local income tax, the presence of spatial error dependence suggests that there may be omitted explanatory variables that are spatially related to each other over space. Let us note that for this specific tax, our result is not consistent with the previous results obtained by Richard et al. (2002) and Heyndels and Vuchelen (1998). We can explain this difference by the presence of a temporal lag in our specification and by the inclusion of the spatial error term. When we estimate a “simple” spatial lag model (without including neither temporal lag nor spatial error term), we get a significant spatial lag coefficient. Moreover, we think that this result is mainly explained by the immobility of the property tax base.17 The temporal lag is highly significant for both tax rates suggesting that tax rates are very persistent over time. We obtain the expected
15 However, estimation results using WC1 and the different WDk are quite similar and are available on request from the authors. 16 Moreover, these omitted variables change over time since fixed effects are included in the specification. 17 Disaggregation of the total variance also suggests that this outcome is not due to a lower spatial heterogeneity with respect to the local income tax rates.
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Table 4 ML estimation results (without interregional differences).
Table 5 ML estimation results with interregional differences (Local Income Tax).
Variable
Local Income Tax
Local Property Tax
Variable
Local Income Tax
Weight matrix Tax rate t − 1 Density Income Unemployment Electoral cycle t − 1 Electoral cycle t Electoral cycle t + 1 Trend Spatial lag parameter Spatial error parameter Log likelihood (initial) Log likelihood (final) Observations
Wcont 0.731*** 0.0016 0.0466*** −0.0102*** −0.0137*** −0.0205*** 0.0037** −0.0006 0.06*** 0.0977*** 22355 22419 11191
Wcont 0.771*** 0.0202 0.0917*** −0.036*** −0.0187*** −0.0309*** −0.0009 0.0007 −0.0164 0.292*** 18737 18945 11191
Weight matrix Tax rate t − 1 Density Income Unemployment Electoral cycle t − 1 Electoral cycle t Electoral cycle t + 1 Trend Spatial lag coefficient (WFF) Spatial lag coefficient (WBB) Spatial lag coefficient (WWW) Spatial lag coefficient (WFB) Spatial lag coefficient (WFW) Spatial error coefficient Log likelihood (initial) Log likelihood (final) Observations
Wcont 0.728*** −0.007 0.038* −0.009 −0.014 −0.022 0.003 0.000 0.012 0.359*** 0.032 0.052 −0.021 0.123*** 22350 22460 11191
Note: *: 10% significant, **: 5% significant, ***: 1% significant. Final log likelihood is estimated with spatial effects. Fixed effects are included.
positive sign for population density but it is not significant. As for unemployment rate, we obtain a negative and significant sign, highlighting that high level of unemployment may encourage local officials to decrease their fiscal pressure. The parameter associated with income per capita is positive for both local tax rates, as in the empirical literature where demand for public services is often positively correlated with income. Dummy variables for the years around the election seem to support the view of an electoral cycle. Finally, trend variables are never significant.
Notes: *: 10% significant, **: 5% significant, ***: 1% significant. Final log likelihood is estimated with spatial effects. Fixed effects are included.
Another important result emerges from the interactions between municipalities located in different regions. The estimation results show that the interactions between one municipality and municipalities located in the other regions are never significant. This is in line with the common-sense argument that interregional barriers (including linguistic ones) matter. 4. Conclusion
3.2. The role of interregional differences In the second stage, we investigate the role of interregional differences across regions on tax interactions (Table 5). More precisely, for the local income tax rates' decisions, which exhibit spatial interactions, we estimate the decomposition given by Eq. (3). Our main result is that there are significant differences between Brussels and the two other regions. For local income tax, interaction of one municipality with its closest neighbors is highly significantly positive in Brussels but not in Wallonia and Flanders. Elasticity of the local income tax rate to the average rate over its closest neighbors is 0.36 for Brussels. For both Wallonia and Flanders, the elasticity of the local income tax to the average rate of the closest neighbors is never significant. For Brussels, this result confirms the impression that residents are more mobile across municipalities than residents of other Belgian municipalities. Substitutability between municipalities is much more important within the Brussels Region, which corresponds to the core of Brussels agglomeration, than within other regions; and between Brussels and those other regions. Two related factors at least might be mentioned which explain that phenomenon. First, unlike other regions, Brussels did not conduct so far a policy of merging municipalities. Therefore, though smaller cities like Antwerp, Ghent or Liège now consist in a single municipality with presumably a high degree of intra-municipality mobility, Brussels still consists in 19 municipalities of relatively small size (in between 20 and 150 thousands inhabitants each for a city of about one million inhabitants); then, what is elsewhere intramunicipality mobility is in Brussels inter-municipality mobility. Second, the existence of a developed network of public transportation including underground makes it easy in Brussels to decide to live in one municipality although working in another one and above all getting her children educated in a third one. Such behavior is much more costly outside Brussels, both in time and money, and makes municipalities less substitutable. The importance of such intra-city inter-municipality mobility is highlighted by Institut Bruxellois de Statistique et d'Analyse (2008).
In this paper, we show first that Belgian municipalities interact over the local surcharge on individual income tax, which is primarily a local tax on labor income, but not on the local surcharge on the property tax. This first result is partly consistent with previous studies, especially Heyndels and Vuchelen (1998), which proposes a cross section analysis, and Richard et al. (2002), which exploits a dynamic model where the slope of the reaction function is not directly estimated. Both studies confirmed the existence of tax interactions for the local property tax rates; however, they did not include temporal lag and did not use robust tests to discriminate between spatial lag dependence and spatial error dependence. Using spatial econometrics tools and an original methodology for specifying the weights matrices, we find that municipalities are sensitive to the tax setting of their closest neighbors. Second, we explore the effects of proximity in terms of regional specificity on the empirical evidence of tax interdependence. We reject the hypothesis that the interregional differences do not matter, especially for interactions among nearby jurisdictions in the Brussels region. In terms of the local income tax rate, the intensity of interactions is shown to be higher between municipalities belonging to the Brussels region. This observation is particularly relevant for present-day Belgium and might be seen as contributing to the ongoing debate on the regionalization of some taxes. In this context, the fact that the difference in terms of interaction occurs for the local income tax, which is levied on a residence principle, rather than for the tax on immovable property, levied on a source base, is per se an interesting result for tax policy debate in Belgium. Acknowledgments This paper has been presented in various conferences and we are indebted to the colleagues who provided us with comments, remarks and suggestions especially Thiess Buettner and Craig Brett. We also thank Hakim Hammadou for his help. We finally thank Magali Verdonck, Stefan Van Parys and Tom Verbecke for the data.
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Appendix A
Map 1. Local income tax rates in Belgium.
Map 2. Local property tax rates in Belgium.
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Brett, C., Pinske, J., 2000. The determinants of municipal tax rates in British Columbia. Canadian Journal of Economics 33, 695–714. Brueckner, J.K., 1996. Testing for strategic interaction among local governments: the case of growth controls. Journal of Urban Economics 44 (3), 438–467. Brueckner, J.K., 2003. Strategic interaction among governments: an overview of empirical studies. International Regional Science Review 26, 175–188. Brueckner, J.K., Saavedra, L., 2001. Do local governments engage in strategic propertytax competition. National Tax Journal 54, 203–229. Buettner, T., 2001. Local business taxation and competition for capital: the choice of the tax rate. Regional Science and Urban Economics 31, 215–245. Case, A.C., 1993. Interstate tax competition after TRA86. Journal of Policy Analysis and Management 12, 136–148. Charlot, S., Paty, S., 2007. Market access effect and local tax setting: evidence from a French panel data. Journal of Economic Geography 7, 247–263. Elhorst, J.P., 2003. Specification and estimation of spatial panel data models. International Regional Science Review 26 (3), 244–268. Feld, L.P., Josselin, J.M., Rocaboy, Y., 2002. Le mimétisme fiscal: une application aux régions françaises. Economie et Prévision 156 (5), 43–49. Feld, L.P., Reulier, E., 2005. Strategic Tax Competition in Switzerland: Evidence from a Panel of the Swiss Cantons. CESifo Working Paper series 1516. Gérard, M., 2002. Fiscal Federalism in Belgium. Commission on fiscal imbalance, a new division of Canada's financial resources report. Supporting document 3. Québec Provincial Government, pp. 175–194. Heyndels, B., Vuchelen, J., 1998. Tax mimicking among Belgian municipalities. National Tax Journal 51, 89–101. Institut Bruxellois de Statistique et d'Analyse, 2008. Indicateurs statistiques de la région de Bruxelles-capitaleIRIS éditions. Ministère de la Région de Bruxelles-Capitale.
Lacombe, D.J., 2004. Does econometric methodology matter? An analysis of public policy using spatial econometric techniques. Geographical Analysis 36, 105–118. Ladd, H.F., 1992. Mimicking of local tax burdens among neighbouring, countries. Public Finance Quarterly 20 (4), 450–467. Leprince, M., Madiès, T., Paty, S., 2007. Business tax interactions among local governments: an empirical analysis of local tax setting in France. Journal of Regional Science 47, 603–621. Revelli, F., 2005. On spatial public finance empirics. International Tax and Public Finance 12 (4), 475–492. Richard, J.-F., Tulkens, H., Verdonck, M., 2002. Dynamique des interactions fiscales entre les communes belges 1984–1997. Economie et Prévision 156, 1–14. Salmon, P., 1987. Decentralization as an incentive scheme. Oxford Review of Economic Policy 3, 24–43. Solé-Ollé, A., 2003. Electoral accountability and tax mimicking: the effects of electoral margins, coalition government, and ideology. European Journal of Political Economy 19, 685–713. Van Parys, S., Verbecke, T., 2006. Tax competition among Belgian Municipalities: a multi-dimensional battle? University of Ghent, mimeo. Wildasin, D., 1988. Nash equilibria in models of fiscal competition. Journal of Public Economics 35 (2), 229–240. Wilson, J.D., 1986. A theory of interregional tax competition. Journal of Urban Economics 19, 296–315. Wilson, J.D., 1999. Theories of tax competition. National Tax Journal 52, 269–304. Zodrow, G., Mieszkowski, P., 1986. Pigou, Tiebout, Poperty Taxation, and the Underprovision of Local Public Goods. Journal of Urban Economics 19, 356.
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Regional Science and Urban Economics j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / r e g e c
Tax interactions among Belgian municipalities: Do interregional differences matter? Marcel Gérard a, Hubert Jayet b, Sonia Paty b,c,⁎ a b c
Louvain School of Management, FUCaM, Mons and CESifo, Belgium EQUIPPE (Universities of Lille), France CREM (CNRS and University of Caen), France
a r t i c l e
i n f o
Article history: Received 16 February 2009 Received in revised form 29 March 2010 Accepted 31 March 2010 Available online 9 April 2010 JEL classifications: H24 H31 H71
a b s t r a c t This paper tests the existence of strategic interactions among municipalities, based on a panel of Belgian local tax rates from 1985 to 2004. A special emphasis is put on the role of the interregional differences in Belgium. Our results partly confirm previous findings for Belgium suggesting that municipalities interact over the local surcharge on the (labor) income tax rate but not on the local surcharge on the property tax. Using spatial econometrics tools and an original methodology for specifying weight matrices, we find that municipalities are sensitive to the local income tax rates set by only their closest neighbors. We cannot reject the hypothesis that interregional differences matter for municipalities belonging to the Brussels region: for the local income tax rate, the intensity of interactions is shown to be higher between municipalities belonging to the Brussels region than between municipalities belonging to different regions. © 2010 Elsevier B.V. All rights reserved.
Keywords: Tax interactions Panel data Spatial econometrics Local tax rates Tax competition
1. Introduction Since the end of the 1980s, a large theoretical literature on tax competition has developed following the seminal papers by Zodrow and Mieszkowski (1986), Wilson (1986), Wildasin (1988) — see Wilson (1999) for a survey. The basic argument is that jurisdictions interact with their neighbors to attract a mobile activity or mobile tax base. There is an alternative theoretical explanation for why local authorities are affected by their competitors when setting tax rates. This is based on the idea that citizens evaluate the performance of incumbent leaders by comparing their public decisions with those set by nearby local authorities. This theoretical literature on “yardstick competition” began with Salmon (1987) and was further developed by Besley and Case (1995). Although the theoretical literature on strategic interactions is quite large, empirical studies are fairly recent. Most papers concentrate on interactions over tax, among local jurisdictions within a country or among states within a federal country — see Brueckner (2003) for a survey. These models are usually implemented empirically by means of estimation of a fiscal reaction function, where the optimal tax rate
⁎ Corresponding author. UFR Sciences économiques et gestion, 17 rue Claude Bloch, BP 5186, 14032 Caen Cedex, France. E-mail address: [email protected] (S. Paty). 0166-0462/$ – see front matter © 2010 Elsevier B.V. All rights reserved. doi:10.1016/j.regsciurbeco.2010.03.010
in a jurisdiction depends on the tax rates in nearby jurisdictions (Revelli, 2005). Most papers including those of Ladd (1992), Case (1993), Brett and Pinske (2000), Buettner (2001), Heyndels and Vuchelen (1998), Brueckner (1996), Brueckner and Saavedra (2001), Richard et al. (2002), Feld and Reulier (2005), Feld et al. (2002), Bordignon et al. (2003), Solé-Ollé (2003), Leprince et al. (2007), Charlot and Paty (2007) find empirical evidence of positive interactions among sub-national governments using various data sets. Our paper is in line with this strand of literature in using data on a panel of Belgian local tax rates for 1985 to 2004. Our contribution is twofold. First, we find that Belgian municipalities interact over the local surcharge on income tax but not on the local surcharge on property tax. This result is partly consistent with the results in previous studies, especially Heyndels and Vuchelen (1998), which proposes a cross section analysis, and Richard et al. (2002), which uses a dynamic model where the slope of the reaction function is not directly estimated. Using spatial econometric tools and an original methodology for specifying weight matrices, we find that municipalities are only sensitive to the local income tax rates set by their closest neighbors. Second, we explore the effects of interregional differences on the empirical evidence of tax interdependence, as the three Belgian regions are characterized by political, economic, cultural and geographical differences. Belgium's two largest regions are the Dutch-speaking region of Flanders in the north and the French-
M. Gérard et al. / Regional Science and Urban Economics 40 (2010) 336–342
speaking southern region of Wallonia. The Brussels-Capital Region is a bilingual enclave within the Flemish Region.1 It has been observed that residents' location choices are influenced by the language spoken: Belgian citizens usually prefer to live in a municipality where he/she is familiar with the official language in order to avoid costs related to the language in schools, sport and cultural facilities, or the need to translate official document from another language. Policymakers who are concerned with the mobility of their tax base (the residents) will likely pay more attention to the fiscal choices of nearby municipalities that share the same language. 2 Moreover, an incumbent who does not fear the residents' mobility but wants to be re-elected is likely to take as a yardstick the fiscal choices of nearby municipalities that share the same language because the access to information on fiscal conditions will be supposed to be easier — e.g. Belgian newspapers providing national news tend to focus on their particular language communities. Eventually, we find that the hypothesis that interregional differences matter for interactions among nearby jurisdictions cannot be rejected for the Brussels region. Especially, for the local income tax rate, the intensity of interactions is shown to be higher between two municipalities belonging to the Brussels region than between a municipality from the Brussels region and a Flemish municipality. This observation might be viewed as contributing to the ongoing debate in Belgium about the regionalization or partial decentralization of some taxes. The paper is organized as follows. The next section discusses the empirical design. The results are presented in Section 3 and Section 4 concludes. 2. Empirical design In this section we provide some information on Belgium before discussing the specification of our model and presenting our data set. 2.1. Belgium, municipalities and local taxation Belgium is a multilevel government country,3 a federation consisting of three regions and three communities. The regions – Flanders, Wallonia and Brussels – are basically responsible for territory related aspects, such as economic development, employment policy, infrastructure and the supervision of municipalities. The communities –Dutch, French and German-speaking– are responsible for issues related to individuals, such as education and health. Each region and community has its own government and parliament although Flanders and the Dutch-speaking communities have been merged. Since March 2008, the Walloon Region and the Frenchspeaking Community have the same Minister–President. According to the Belgian Constitution, residual power belongs to the regions and communities. Although not responsible for territorial matters, the authority of the communities corresponds to the territory, that of the Dutch-speaking to Flanders and Brussels, and that of the Frenchspeaking to Wallonia and Brussels; the authority of the small Germanspeaking community applies to a series of municipalities annexed to Belgium after the First World War and located in Wallonia. Corporate income tax and value added tax in present day Belgium are federal taxes; personal income tax is also mainly federal, but regions (and municipalities — see below) are permitted to add positive or negative surcharges; the immovable property tax goes to the regions, which set its base rate although the municipalities may
1
A small German-speaking community exists in eastern Wallonia. Since the linguistic communities coincide with the region, we cannot disentangle the effect due to the language. We can only capture the regional specific effect like institutions, culture (including language), social norms... 3 For details, see e.g. Gérard (2002). 2
337
add surcharges. The regions are mainly financed by transfers from federal government and by regional taxes; the communities are essentially financed by transfers. Belgium also consists of 10 provinces and 589 municipalities. In this paper we focus on the municipalities. Their major expenditure categories include culture and education, police, welfare and transport infrastructures, and general administration. They levy numerous local taxes and receive the revenue from the surcharges referred to above: taxation accounts for more than 40% of local revenues. The remainder income comes from transfers from the regional authorities. However, two taxes, local income tax and local property tax, constitute the main sources of revenue for the municipalities (accounting for 80% of municipal tax revenues), in the ratios 1.7 to 1, and 1.2 to 1 depending on the region. Both local taxes are surcharges determined only by the municipalities: - the local income tax is a surcharge on the federal income tax levied on individuals; the rate of the surcharge is between 0 and 10% and, in practice, is a tax on labor income (savings income is taxed separately); and - the local property tax is a surcharge on the regional property tax; its base, also defined at federal level, is an imputed income on immovable property; this tax is levied on all taxpayers – individuals, companies, charities – on the basis of the location of the property. Note that regional property tax in Brussels and the Walloon Region amounts to 1.25% and in the Flemish Region to 2.5% of the imputed income on immovable property; the local surcharge varies between 293.75 and 5750% of the regional tax. It is important to understand that the location of the property determines which place receives the tax payment. For example, an individual resident in municipality A who owns a real property in municipality B will pay local income tax on his labor income in municipality A and local property tax in municipality B. This is the reason why, two municipalities along the North Sea coast whose councils are elected by resident taxpayers, have decided on a zero local income tax rate, putting the burden of local tax on the property tax paid by non-resident owners or users of the houses and flats at the seaside. Each municipality has a council that is elected every six years by residents who vote for the candidates on the lists presented either by political parties –also active at regional and federal level – or by local groups; each list has municipal councillors in proportion to the number of votes obtained. Then a majority contract is passed within the municipal council; on that base the mayor and her deputies are designated by the Region. The mayor and her deputies form the municipal college, which is the municipality executive. Finally, the main mobility factors within Belgium are workplace location, although many Belgian residents commute long distances to their place of work, and price and size of ground and homes; as already explained, individuals' residency choices are also influenced by the language spoken in the municipality. 2.2. Empirical specification 2.2.1. The econometric model Our aim is to test the existence of strategic interactions among municipalities and to find the effect of cultural proximity on this interdependence. The theoretical models of tax and yardstick competition have the same empirical predictions that jurisdiction i's fiscal decisions in year t, τi,t, depend on i's neighbors' fiscal decisions, τj,t, and on i's socio-demographic characteristics Xi,t. Using a linear formulation and adding a time lag of the dependent variable for taking account of slow adjustment in tax rates, we obtain the following model K k τt = α + ∑k = 1 ρk W τt + γτt−1 + Xt β + η + εt
ð1Þ
338
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where, τt = (τ1,t,…,τN,t)′ is the vector of fiscal decisions by all jurisdictions at date t, Xt is the matrix of socio-demographic characteristics and W1,...,WK are matrices of spatial weights; The specification of W1,...,WK will be examined later in Section 2.2.2). The vector η = (η1,…,ηN)′ is a vector of fixed municipal effects, taking into account the impact of the unknown time-invariant factors influencing the tax rates. Stacking over periods t = 1,…, T, and using Kroneker products, Eq. (1) may also be written as K k τ = α + t IT ⊗ ∑k = 1 ρk W b τ + γ½LT ⊗IN τ + ½eT ⊗IN β + η + ε ð2Þ where IT (resp. IN) is the identity matrix at order T (resp. N), LT is the matrix associated to the time lag operator, and eT is a column (N,1) vector with all its elements equal to 1. This is a spatial panel data model with a temporally lagged and spatially lagged dependent variable.4 We consider that the elements of the vector of municipal effects, η, are fixed coefficients to be estimated jointly with β and the vector of spatial autoregression coefficients, ρ. On theoretical grounds, this strategy may be acceptable. Municipalities are fixed entities, so that a larger sample includes the same municipalities, but has additional periods; therefore, the set of coefficients to be estimated does not vary with the sample size, thus ensuring consistency. Let us note that estimating the whole set of coefficients of this fixed effects model implies estimating 600 coefficients and raises computational problems. Full information maximum likelihood is feasible however, and has been used here. 2.2.2. Specifying the weights matrix: Do interregional differences matter? Usually, a single weights matrix is used, ∑Kk= 1ρkWk = ρW, where W is row normalized, each row summing to unity. Standard choices for W are row normalized contiguity matrices, nearest neighbor matrices, or weights inversely proportional to distance or to the square of the distance. 5 The fact that the weights matrix is arbitrarily chosen is unsatisfactory, as different matrices may lead to different results. Therefore, some authors test several matrices, in order to assess the robustness of the conclusions. But, in most cases, only one matrix is used. But using one matrix only does not allow us to answer the central question of this paper: is the intensity of interactions higher between two municipalities belonging to the same region than between two municipalities belonging to different regions? Therefore, we use a decomposition of the weight matrix which is now a linear combination of partial weights.6 Starting from any standard weight matrix W, we define five partial interaction matrices, WFF for interactions within Flanders, WWW for interactions within Wallonia, WBB for interactions within the Brussels region, WFW for interactions between Flanders and Wallonia, and WFB for interactions between Flanders and Brussels. 7 More precisely, starting from W - The elements of WFF are wFF i,j = wi,j when both municipalities i and j are Flemish, zero otherwise, - The elements of WWW are wWW i,j = wi,j when both municipalities i and j are Walloon, zero otherwise, - The elements of WBB are wBB i,j = wi,j when both municipalities i and j are in the Brussels region, zero otherwise,
Table 1 Summary statistics. Variable
Unit
Local income tax Local property tax Population density Per capita income Unemployment rate
% 6.84 1.07 Centimes 2633.51 689,67 100 inhab/km2 6.71 17.42 1000 BF 350.45 101.92 % 3.60 1.85
Mean
Standard dev. Min
Max
0 10 293.75 5750 0.19 201.96 147 704.86 0 21.51
Notes: Number of observations: 11,780 (T = 20, N = 589). 1000 BF are equivalent to EUR 24,79.
- The elements of WFW are wFW i,j = wi,j when i is Flemish (resp. Walloon) and j is Walloon (resp. Flemish), zero otherwise, and - The elements of WFB are wFB i,j = wi,j when i is Flemish (resp. in the Brussels region) and j is in the Brussels (resp. Flemish), zero otherwise. To sum up, WFF, WWW and WBB, correspond to interactions within the same region, respectively Flanders, Wallonia and Brussels; while WFW and WFB correspond to interactions between Flanders and the other two regions, Wallonia and Brussels respectively.8 Then, we get K
k
∑k = 1 ρk W = ρFF W
FF
+ ρWW W
WW
+ ρBB W
BB
+ ρFW W
FW
FB
+ ρFB W :
ð3Þ
2.3. Data set We estimate Eq. (2) using annual data for the 589 Belgian municipalities over the period 1985–2004. These data were collected by Richard et al. (2002) and by Van Parys and Verbecke (2006). The main data sources are the National Institute of Statistics, the Department of Economics and the Department of Geography of the Université Catholique de Louvain, in Louvain-la-Neuve.9 Table 1 reports summary statistics. As noted above, local tax policies also reflect the impact of differences in economic and demographic factors grouped within the vector X in Eqs. (1) and (2). Following the empirical literature, we include a set of socio-demographic variables, such as unemployment rate, population density and per capita income. These can be considered variables for expenditure needs. We thus expect a positive sign for their respective parameters. We also include three electoral dummies to check the existence of an electoral cycle, i.e. lower tax rates in the years around the election year and higher tax rates in the middle of the legislative period, which lasts for six years — elections occurred in the last quarter of 1988, 1994 and 2000 respectively. We did not take into account the vertical tax externalities because the federal income tax rates and the regional property tax rates are very stable on our period of study. Finally we include a trend variable. Table 1 displays the standard summary statistics for the two main dependent variables and the main explanatory variables. Maps 1 and 2 in appendix show respectively the local income tax rates and the local property tax rates in Belgium for 2004. 3. Results Our estimation strategy is the following. We first estimate the model without spatial effects and run the appropriate spatial tests
4 This type of model is not very well documented in the spatial econometrics literature; however, see Elhorst, 2003 and Anselin et al., 2007. 5 Before normalization, every element wi,j of the contiguity matrix is non-zero if and only if i and j share a common border. For a nearest neighbour matrix, wi,j is non-zero if j is a k nearest neighbour to i, where the threshold k has been appropriately chosen. 6 Arbia et al. (2010) present a similar exercise to go beyond standard specification of the weight matrix for European regions. 7 Brussels being an enclave within Flanders, there is no contiguity with Walloon municipalities.
8 Note that we do not include interactions between the Brussels and Walloon municipalities since Brussels is completely surrounded by Flemish municipalities (see footnote 6). 9 The municipal property tax rates are surcharges to regional property tax rates of 1.25% in Wallonia and Brussels and 2.5% in Flanders. Therefore, following Richard et al. (2002) and Van Parys and Verbecke (2006), we multiplied the property tax centimes charged by Flemish municipalities by two in order to make the centimes comparable across regions.
M. Gérard et al. / Regional Science and Urban Economics 40 (2010) 336–342 Table 2 Lagrange multiplier tests statistics. LM LAG
RLM LAG
Local Property Tax Wcont 264.11 (0.00) 401.67 (0.00) WC1 WD1 387.90 (0.00)
Table 3 Spatial coefficients (Local Income Tax). LM ERR
Local Income Tax Wcont 117.19 (0.00) 15.60 (0.00) WC1 186.06 (0.00) 19.84 (0.00) WD1 173.94 (0.00) 20.35 (0.00)
124.96 (0.00) 239.56 (0.00) 209.29 (0.00)
RLM ERR 23.37 (0.00) 73.34 (0.00) 55.70 (0.00)
LAG & ERR 140.56 (0.00) 259.40 (0.00) 229.64 (0.00)
1.84 (0.17) 512.65 (0.00) 250.37 (0.00) 514.49 (0.00) 1.40 (0.24) 1023.85 (0.00) 623.57 (0.00) 1025.24 (0.00) 1.59 (0.21) 931.45 (0.00) 545.14 (0.00) 933.03 (0.00)
Note: P-values in parentheses. Wcont is the standard contiguity matrix. WC1 is the nearest neighbors matrix for k = 10 neighbors. WD1 is the inverse distance matrix restricted to the k = 10 nearest neighbors. LM LAG and LM ERR are the non-robust Lagrange multiplier tests testing respectively the spatial lag dependence and the spatial error dependence. RLM tests are the equivalent robust tests. Tests have been conducted for WC2, WC3, WC4, WC5 and WD2, WD3, WD4, WD5 and are available upon request.
based on Lagrange multiplier (LM)10 to discriminate between the spatial lag model and the spatial error model using various specifications for the weight matrix, without distinction across regions (see Table 2). These tests confirm the presence of both spatial lag dependence and spatial error dependence for the local income tax rates using every weight matrices. Therefore, the error in Eqs. (1) and (2) takes the following form (Anselin, 1988, p. 182): 2
ε = λWε + μ; μ ∼ Nð0; σ Þ
339
ð4Þ
where λWε is an autoregressive term into the error term ε of the tax rate regression. The parameter λ captures the strength of this autoregressive relation, and the term W is a spatial weight matrix that summarizes the spatial layout of the data. As for the local property tax rates, the LM tests confirm the validity of the spatial error model since the robust LM test for spatial dependence is never significant. Second, in order to investigate the range of interactions for the local income tax11, we estimate the model using ML12 and the different weight matrices (see Table 3). 13 We observe that the interactions measured by the spatial lag parameter are significant using standard contiguity matrix, the WC1 matrix (based on the ten closest neighbors) and the WDk matrices (based on distance decay). When we use WCk, the spatially autoregressive coefficient is decreasing with k and nonsignificant for k N 2. When we use WDk, it is stable and significant for every k. This result suggests that interactions in terms of local income tax only occur between closest neighbors14. Given that matrix WC1 corresponds to the 10 closest neighbors, and that there are 589 municipalities in Belgium, each Belgian municipality interacts with around 2% of the other municipalities.
10 We compute the non-robust and the robust LM test statistics for spatial lag dependence and spatial error dependence (see Anselin et al., 2007). 11 We also estimate the same model for the local property tax. However, as shown by the LM tests, the spatial lag parameter is never significant while the spatial error term is significant using all weight matrices. 12 The general form of the likelihood that has been maximized is LL = −(NT/2)ln 2πσ2 + Tln det(IN − ΣkρkWk) + Tln det(IN − λW) − (1/2σ2)e′e with e = (IT ⊗ (IN − λW)) ((IT ⊗ (IN − ∑ kρkWk))τ − Xb) − (uT ⊗ IN)η. For a similar problem, see Lacombe (2004). 13 We did not report the estimation results for the other explanatory variables for space convenience. They will be discussed in further tables. 14 The matrix WCk gives equal weights to all the 10 k nearest neighbours, while these weights decrease with distance when using WDk. With interactions occurring between closest neighbours only, when k increases, we introduce distant neighbors with which there are weak or even no interactions. Using the matrix WCk, these distant neighbours have the same weight as close neighbours and then drive down the estimated coefficient toward zero. Using the matrix WDk, the distant neighbours have much smaller weight than close neighbours and then have almost no influence on the estimated coefficient.
Wcont WC1 WC2 WC3 WC4 WC5 WD1 WD2 WD3 WD4 WD5
Spatial lag parameter
Spatial error parameter
0.060*** 0.076*** 0.056* 0.042 0.037 0.020 0.076*** 0.070** 0.066** 0.072** 0.077**
0.098*** 0.193*** 0.346*** 0.429*** 0.474*** 0.520*** 0.160*** 0.285*** 0.361*** 0.404*** 0.441***
Notes: *: 10% significant, **: 5% significant, ***: 1% significant. Estimated coefficients for the other explanatory variables have been omitted for space convenience. Wcont is the standard contiguity matrix. WC1, WC2, WC3, WC4, WC5, are nearest neighbors matrices for k = 10, 20, 30, 40 and 50 neighbors respectively. WD1, WD2, WD3, WD4, WD5, are inverse distances matrices restricted to the k = 10, 20, 30, 40 and 50 nearest neighbors respectively.
Third, the estimations of our spatial model in Eq. (2) – without taking into account the interregional differences – were carried out using maximum likelihood (Table 4). Finally, we investigate the impact of interregional differences on local property tax interactions by estimating Eq. (3). 3.1. Without interregional differences Table 4 reports the estimation of our model without interregional differences – a general specification that includes spatially autoregressive effects for both the endogenous variable and the error term – using ML for both taxes. Since interactions – when they exist – were shown to occur between closest neighbors, we only present the estimation results using the contiguity matrix.15 As for local income tax, there are significantly positive interactions, which imply that municipality authorities react to tax changes decided by their neighbors: if neighboring municipality j decreases its tax rate, municipality i also decreases its own rate. However, the presence of spatial error dependence suggests that there may be omitted explanatory variables that are spatially related to each other over space.16 The absence of interactions between municipalities in terms of property tax rates implies that the choice of this tax rate in a municipality is not influenced by the tax levels of its neighbors. Again, as in the case of the local income tax, the presence of spatial error dependence suggests that there may be omitted explanatory variables that are spatially related to each other over space. Let us note that for this specific tax, our result is not consistent with the previous results obtained by Richard et al. (2002) and Heyndels and Vuchelen (1998). We can explain this difference by the presence of a temporal lag in our specification and by the inclusion of the spatial error term. When we estimate a “simple” spatial lag model (without including neither temporal lag nor spatial error term), we get a significant spatial lag coefficient. Moreover, we think that this result is mainly explained by the immobility of the property tax base.17 The temporal lag is highly significant for both tax rates suggesting that tax rates are very persistent over time. We obtain the expected
15 However, estimation results using WC1 and the different WDk are quite similar and are available on request from the authors. 16 Moreover, these omitted variables change over time since fixed effects are included in the specification. 17 Disaggregation of the total variance also suggests that this outcome is not due to a lower spatial heterogeneity with respect to the local income tax rates.
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Table 4 ML estimation results (without interregional differences).
Table 5 ML estimation results with interregional differences (Local Income Tax).
Variable
Local Income Tax
Local Property Tax
Variable
Local Income Tax
Weight matrix Tax rate t − 1 Density Income Unemployment Electoral cycle t − 1 Electoral cycle t Electoral cycle t + 1 Trend Spatial lag parameter Spatial error parameter Log likelihood (initial) Log likelihood (final) Observations
Wcont 0.731*** 0.0016 0.0466*** −0.0102*** −0.0137*** −0.0205*** 0.0037** −0.0006 0.06*** 0.0977*** 22355 22419 11191
Wcont 0.771*** 0.0202 0.0917*** −0.036*** −0.0187*** −0.0309*** −0.0009 0.0007 −0.0164 0.292*** 18737 18945 11191
Weight matrix Tax rate t − 1 Density Income Unemployment Electoral cycle t − 1 Electoral cycle t Electoral cycle t + 1 Trend Spatial lag coefficient (WFF) Spatial lag coefficient (WBB) Spatial lag coefficient (WWW) Spatial lag coefficient (WFB) Spatial lag coefficient (WFW) Spatial error coefficient Log likelihood (initial) Log likelihood (final) Observations
Wcont 0.728*** −0.007 0.038* −0.009 −0.014 −0.022 0.003 0.000 0.012 0.359*** 0.032 0.052 −0.021 0.123*** 22350 22460 11191
Note: *: 10% significant, **: 5% significant, ***: 1% significant. Final log likelihood is estimated with spatial effects. Fixed effects are included.
positive sign for population density but it is not significant. As for unemployment rate, we obtain a negative and significant sign, highlighting that high level of unemployment may encourage local officials to decrease their fiscal pressure. The parameter associated with income per capita is positive for both local tax rates, as in the empirical literature where demand for public services is often positively correlated with income. Dummy variables for the years around the election seem to support the view of an electoral cycle. Finally, trend variables are never significant.
Notes: *: 10% significant, **: 5% significant, ***: 1% significant. Final log likelihood is estimated with spatial effects. Fixed effects are included.
Another important result emerges from the interactions between municipalities located in different regions. The estimation results show that the interactions between one municipality and municipalities located in the other regions are never significant. This is in line with the common-sense argument that interregional barriers (including linguistic ones) matter. 4. Conclusion
3.2. The role of interregional differences In the second stage, we investigate the role of interregional differences across regions on tax interactions (Table 5). More precisely, for the local income tax rates' decisions, which exhibit spatial interactions, we estimate the decomposition given by Eq. (3). Our main result is that there are significant differences between Brussels and the two other regions. For local income tax, interaction of one municipality with its closest neighbors is highly significantly positive in Brussels but not in Wallonia and Flanders. Elasticity of the local income tax rate to the average rate over its closest neighbors is 0.36 for Brussels. For both Wallonia and Flanders, the elasticity of the local income tax to the average rate of the closest neighbors is never significant. For Brussels, this result confirms the impression that residents are more mobile across municipalities than residents of other Belgian municipalities. Substitutability between municipalities is much more important within the Brussels Region, which corresponds to the core of Brussels agglomeration, than within other regions; and between Brussels and those other regions. Two related factors at least might be mentioned which explain that phenomenon. First, unlike other regions, Brussels did not conduct so far a policy of merging municipalities. Therefore, though smaller cities like Antwerp, Ghent or Liège now consist in a single municipality with presumably a high degree of intra-municipality mobility, Brussels still consists in 19 municipalities of relatively small size (in between 20 and 150 thousands inhabitants each for a city of about one million inhabitants); then, what is elsewhere intramunicipality mobility is in Brussels inter-municipality mobility. Second, the existence of a developed network of public transportation including underground makes it easy in Brussels to decide to live in one municipality although working in another one and above all getting her children educated in a third one. Such behavior is much more costly outside Brussels, both in time and money, and makes municipalities less substitutable. The importance of such intra-city inter-municipality mobility is highlighted by Institut Bruxellois de Statistique et d'Analyse (2008).
In this paper, we show first that Belgian municipalities interact over the local surcharge on individual income tax, which is primarily a local tax on labor income, but not on the local surcharge on the property tax. This first result is partly consistent with previous studies, especially Heyndels and Vuchelen (1998), which proposes a cross section analysis, and Richard et al. (2002), which exploits a dynamic model where the slope of the reaction function is not directly estimated. Both studies confirmed the existence of tax interactions for the local property tax rates; however, they did not include temporal lag and did not use robust tests to discriminate between spatial lag dependence and spatial error dependence. Using spatial econometrics tools and an original methodology for specifying the weights matrices, we find that municipalities are sensitive to the tax setting of their closest neighbors. Second, we explore the effects of proximity in terms of regional specificity on the empirical evidence of tax interdependence. We reject the hypothesis that the interregional differences do not matter, especially for interactions among nearby jurisdictions in the Brussels region. In terms of the local income tax rate, the intensity of interactions is shown to be higher between municipalities belonging to the Brussels region. This observation is particularly relevant for present-day Belgium and might be seen as contributing to the ongoing debate on the regionalization of some taxes. In this context, the fact that the difference in terms of interaction occurs for the local income tax, which is levied on a residence principle, rather than for the tax on immovable property, levied on a source base, is per se an interesting result for tax policy debate in Belgium. Acknowledgments This paper has been presented in various conferences and we are indebted to the colleagues who provided us with comments, remarks and suggestions especially Thiess Buettner and Craig Brett. We also thank Hakim Hammadou for his help. We finally thank Magali Verdonck, Stefan Van Parys and Tom Verbecke for the data.
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Appendix A
Map 1. Local income tax rates in Belgium.
Map 2. Local property tax rates in Belgium.
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