tax mimicking among belgian municipalities - CiteSeerX

National Tax Journal Vol 51 no. 1 (March 1998) pp. 89-101

TAX MIMICKING AMONG BELGIAN MUNICIPALITIES

TAX MIMICKING AMONG BELGIAN MUNICIPALITIES BRUNO HEYNDELS JEF VUCHELEN *

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Abstract - Recent empirical studies show that U.S. states’ and local governments’ tax policy is copied among neighboring jurisdictions. We present empirical evidence on such tax mimicking among Belgian municipalities. Belgian data offer important advantages. The (589) municipalities operate within an institutionally homogeneous setting. Moreover, the data allow us to concentrate on tax rates (instead of tax burdens). Our results support the previous findings, suggesting that mimicking behavior is not an artifact of the U.S. system. We also find that mimicking effects extend beyond municipalities’ immediate neighbors.

both from positive and normative points of view. We present empirical evidence on mimicking of local tax rates among Belgian municipalities. It is—to our knowledge—the first test of the mimicking hypothesis in a context of local governments outside the United States. Applications to different institutional settings may indicate to what extent tax mimicking is typical of the U.S. system rather than being a general characteristic of decision making in a context of decentralized government. Moreover, our application on Belgian municipal data offers a number of methodological advantages over the existing U.S. studies. Our results, therefore, place some of the U.S. findings into perspective. The first section briefly reviews the theory and previous empirical findings on tax mimicking. The next section reports empirical evidence on tax mimicking behavior among Belgian municipalities. A summary and conclusions are in the final section.

INTRODUCTION Recent empirical work has shown that tax policies are copied among U.S. states and local governments (Ladd, 1992; Case, 1993; Besley and Case, 1995). The existence of such tax mimicking has important implications,

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TAX MIMICKING AMONG STATES AND LOCAL GOVERNMENTS That a jurisdiction’s policy may be influenced by other jurisdictions’ policies has been recognized by several authors

CEMS, Vrije Universiteit Brussel, Brussels, Belgium.

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(Hettich and Winer, 1984; Salmon, 1987). Still, it has not reached the status of general acceptance. The conventional approach to modeling taxing and spending decisions consists of explaining the level or composition of revenues and expenditures by economic, political, and sociological characteristics of the jurisdiction itself (for a survey, see Inman, 1988). A casual look at everyday politics suggests, however, that voters and politicians are sensitive to events outside their boundaries. Introducing a tax, for example, may be easier for the official who can refer to similar taxes in comparable jurisdictions. For the same reason, politicians might find it harder to refuse a cutback in tax rates if voters observe such cutbacks elsewhere. In general, “State and local officials tend to be painfully aware of tax rates in other jurisdictions and try to resist getting too far out of line with rates elsewhere” (Oates, 1988a, p. 70). A positive approach to tax policy should take these findings into account. The recent literature on mimicking behavior has the merit of doing so.1 The politicoeconomic starting point of this literature is that citizens’ preferences do not map readily into politicians’ behavior. Especially when reelection constraints are not binding, politicians may follow their own (instead of the citizens’) preferences or ideology. Besley and Case (1995) show how—in a context of decentralized government—mimicking behavior follows naturally from informational problems voters are confronted with. The complexity of real-world government policies leads voters to use simplifying strategies to evaluate the fiscal performance of their government. The mere existence of several jurisdictions at the same level of government allows voters to use other governments’ performance as a yardstick to evaluate their own government (Salmon, 1987). As such, information on the behavior of

politicians in one jurisdiction can affect voters’ opinions and thus election outcomes in another. Thus, it is possible that, for instance, “good” (benevolent, non-rent-seeking) politicians inflict informational externalities on “bad” ones. The externality consists of a reduction of reelection chances (Besley and Case, 1995). When citizens take policies pursued in other jurisdictions into account in their voting decisions, it is rational for policymakers to consider relative performance. Mimicking behavior will result. Mimicking can occur both on the expenditure and the revenue sides of the budget. The existing literature focuses on the revenue (tax) side.2 As such, it fits into the fastly growing positive literature aimed at explaining actual tax choices from a politicoeconomic perspective. Tax systems are thereby seen as the results of constrained maximizing behavior by selfinterested political agents (for a review, see Hettich and Winer, 1997). The mimic model assumes that politicians’ tax setting behavior is dominated by electoral considerations. It is recognized, however, that—especially in the longer run—alternative considerations may interfere. More precisely, tax differentials may induce Tiebout-like migrations, which can become the subject of tax competition. In such a context, individuals (and firms) are expected to choose their residence on the basis of the combined expenditure-revenue pattern of the jurisdictions; the Tiebout model does not necessarily lead to jurisdictions copying each others’ tax policy (Ladd, 1992).3 Although the idea of governments mimicking each other has both intuitive and theoretical appeal, the importance of mimicry for real-world policy outcomes is an empirical matter. Existing 90



evidence on mimicking behavior draws essentially on U.S. data. Only Genser and Weck-Hannemann (1993) consider the mimic idea within a different setting in their study of fuel taxation in European Community (EC) countries.4 They show that fuel tax rates increase more when similar increases occur in all other EC countries. By taking all other EC countries as their jurisdiction of reference, Genser and Weck-Hannemann depart from the “pure” mimic approach, which assumes that information from different jurisdictions is weighted unequally. This brings us to the central methodological question of any analysis of mimicking behavior, namely, the determination of the jurisdictions of reference.5 The ideal strategy to identify these jurisdictions consists of selecting them empirically. Due to a lack of degrees of freedom, one has to opt for an alternative strategy, where reference jurisdictions are selected by a priori reasoning (Case, Hines, and Rosen, 1989). Formally, this procedure implies that a spatial weighting matrix is assumed. This matrix assigns jurisdictions of reference—and their relative weights—to every jurisdiction.

1985. Ladd finds that a $1 rise in its neighbors’ average tax burden (total and property tax) leads to an increase in the county’s own burden from $0.45 to $0.82. Whereas in a context of local governments, use of geographic proximity as a criterion for selecting reference jurisdictions suggests itself, this procedure is far less obvious at a more aggregated level of government. In the case of jurisdictions of sizable geographic dimension (like states), it is not straightforward to identify the selection criterion for the jurisdictions of reference. Still, Case (1993) and Besley and Case (1995), studying changes in tax liabilities at the state level, also select a geographical definition of neighborliness.6 Besley and Case consider changes in income-tax liabilities and in sales, income, and corporate taxes per capita. They show that a given state increases its taxes by about $0.2 in response to a $1 increase in the average neighbor’s tax liability. This reveals rational behavior by governors, as it is shown that chances of reelection are positively affected by neighboring states’ tax changes (while being negatively related to a state’s own tax changes). Interestingly, in states where the governor cannot, by law, run for reelection, Besley and Case find that responsiveness to neighbors’ policy is absent.

The obvious approach is to consider geographic neighbors as jurisdictions of reference and weigh them equally. Ladd’s analysis of tax mimicking at the U.S. county level follows this approach (Ladd, 1992), with the provision that neighbors are defined exclusive of central cities. Using data for 1978, she finds “clear and striking support” for the hypothesis that counties mimic their neighbors when setting the level of the total tax burden, the property tax burden, and the residential property tax burden. For the general sales tax burden and “other” tax burden, mimic effects are not significant. Except for the residential property tax burden, these results are confirmed using data for

These findings have immediate relevance both for theoretical and practical purposes. First, any positive analysis of tax policy neglecting tax mimicking suffers from misspecification. Second, the existence of mimicry has important normative implications, since the implied strategic behavior of elected officials creates welfare losses (Case, 1993). On the other hand, the possibility to observe politicians in neighboring jurisdictions gives citizens an informa91



tion base that improves the quality of their evaluation of government. The informational externalities may help overcome the agency problems and avoid the potential fiscal exploitation by nonbenevolent governments. This informational aspect can thus be seen as an additional argument in favor of a decentralized structure of government.

average, 20 times larger than the (regional) tax on which it is calculated, whereas the former figure indicates that the LIT is only a fraction of the federal income tax on which it is based.7 However, given the large difference between amounts collected through the federal income and the regional property taxes, the strongly diverging LIT and LPT rates correspond to similar sums paid by the average local taxpayer.

MIMICKING OF TAX RATES AMONG BELGIAN MUNICIPALITIES

As discussed, for jurisdictions, geographic neighbors are the straightforward choice of reference at the local level. More generally, mimicking and the implied informational externalities are intuitively more appealing in a context of small scale local governments. From this perspective, our data offer a unique opportunity to analyze tax setting behavior. Belgian municipalities are indeed very small: the country consists of 589 municipalities on an area six times smaller than that of the average U.S. state (the average area of Belgian municipalities is only 19 square miles). Our application to Belgian data also provides additional advantages compared to the existing U.S. studies. First, we have a large data set, consisting of 589 municipalities. A second, and more important, advantage is that our sample is institutionally homogeneous: all Belgian municipalities operate in the same institutional context.8 In this respect, the Belgian situation differs from the U.S. local context, where the division of responsibilities among types of local government such as municipalities, counties, and school districts differs considerably between states. Therefore, we are not confronted with the methodological difficulty of aggregating local data to one (county) level, as in Ladd (1992). A third and related advantage is that we can concentrate on a comparison of tax rates, instead of, for instance, per capita tax burdens, which depend

In this section, we present empirical evidence on tax mimicking among Belgian municipalities. We focus on the choice of tax rates for the local income tax and the local property tax (budgetary year 1991). Belgium consists of 589 municipalities, which have representative democracies with elections every six years. The municipalities are multipurpose governments, their major expenditure categories being education, police, welfare, and transport infrastructure. Taxation accounts for more than 40 percent of local revenues, the remaining 60 percent comes mainly from conditional and unconditional grants. Municipalities levy, on average, around 20 different taxes. Still, only two taxes, namely, the local income tax (LIT) and the local property tax (LPT), can be categorized as major sources of revenue, each accounting for around 40 percent of municipal tax revenues. Both taxes are (single rate) surcharges. More precisely, the LIT is a surcharge on the federal income tax. The LPT is a surcharge on the regional property tax (the base of this regional tax is, however, also defined at the federal level). Municipalities are free to set any tax rate. The average LIT rate is 6.78 percent, while the average LPT rate amounts to 2005 percent (see table in the Appendix). The latter figure implies that the local property tax is, on 92



on tax rates and on the definition of tax bases. As mentioned, LIT and LPT are both surcharges on taxes with federally defined bases. Tax bases are thus identically defined over all municipalities. This is not only an advantage from an empirical point of view,9 but it also facilitates intermunicipal comparisons by individual voters and elected officials. Intermunicipal comparison is also simplified because the local taxes are, in fact, single rate taxes (implying that the municipal tax is proportional to the regional or federal tax on which it is calculated).

A geographical definition of neighborliness is chosen with equal weights attributed to each neighbor. The tREF is thus a vector of average tax rates in neighboring municipalities. Due to potential spatial autocorrelation in the error terms of equation 1, estimation by ordinary least squares is problematic (Case, Hines, and Rosen, 1989). Furthermore, tax rates in municipality i depend on tax rates in j, but these in turn depend on tax rates in i. Therefore, a simultaneous estimation procedure is required. As the process of setting tax rates for LIT and LPT takes place within the same (economic, political, and social) context, regression residuals could be interdependent (see next section also). So both regressions are estimated simultaneously with the 3SLS technique.10 Table 1 summarizes the results. Overall, the model performs better for LPT than for LIT (R2 of 43.87 percent compared to 14.59 percent).

Our empirical analysis starts from a benchmark model, which is in line with the existing studies on U.S. mimicking behavior. From this, we consider four important extensions. Benchmark Estimation The benchmark regression model to test for the mimicking behavior among Belgian municipalities is

Obviously, we are especially interested in the results of the “mimic variables.” These are striking for their similarity to coefficients obtained in U.S. studies. Table 1 indicates that both local tax rates are influenced by the tax rate policy in neighboring municipalities.

1 t = α1 . Z + α2 . t REF + ε where t is a [589 × 1] vector of local tax rates in Belgian municipalities for the budgetary year 1991. Z is a matrix of the internal determinants of the local tax rates. More precisely, Z is a [589 × 5] matrix containing number of inhabitants (INH), per capita income (Y), percentage of people under 20 years (YOUNG), percentage over 60 years (OLD), and the municipality’s area (AREA). Tax rates in any year are fixed in the fall of the previous year. Therefore, the Z matrix contains data for 1990. ε is the error term. Finally, tREF contains information on tax rates in jurisdictions of reference.

TABLE 1 BENCHMARK 3SLS REGRESSIONS

C tREF INH Y YOUNG OLD AREA R2 LMa

LIT

LPT

3.424 (2.31) 0.666 (7.33) 0.007 (4.30) –4.018 (–3.53) –1.574 (–0.55) 1.024 (0.55) 0.002 (0.18)

847.6 (1.72) 0.695 (12.38) 4.127 (7.03) –2911.6 (-6.51) –170.2 (–0.16) 2327.3 (3.20) –5.444 (–1.13)

14.59%

43.87% 9.46

Dependent variables: 1991 LIT and LPT rates; 589 observations; and t-values in parentheses. a LM = Lagrange Multiplier test statistic. 93



Mimic coefficients are highly significant (with t-values up to 12.38). A rise in the average local income tax rate by 1 percentage point leads to a rise in the “own” tax rate of 0.67 percentage points. Changes in property taxes in neighboring municipalities are associated with changes of 0.69 percentage points. The mimic reaction thus turns out to be similar in magnitude for both tax rates.

negative relationship between local tax rates and income indicates that the latter effect (of income on tax base) dominates the effect through the income elasticity of public expenditures. The results in Table 1 should be read with caution. The Lagrange Multiplier (LM) test statistic for seemingly unrelated regressions (Anselin, 1988), has a value of 9.46. With two degrees of freedom (number of constraints), this χ2 statistic differs significantly from 0 at p = 0.05. This implies that the error terms in Table 1 are still spatially correlated,13 indicating the possibility of an incorrect or incomplete specification. In the next section, we present extensions to improve the spatial component of the model.

With regard to the significance of the other explanatory variables, the results are very similar for both tax rates.11 The age structure of the local population affects property tax rates: an older population is associated with higher property tax rates. Population size (INH) has a positive influence on local tax rates. Such a relationship can exist for different reasons. It might indicate that congestion effects outweigh economies of scale in the provision of public services. Alternatively, a positive relation between population size and the level of tax rates might result from Oates’ “Zoo-effect” (Oates, 1988b), reflecting that larger municipalities provide a wider range of services. A third possible explanation for the positive relationship is that nonbenevolent government is less constrained in larger municipalities.12 A higher mean income (Y) is associated with lower tax rates, both for the local income and the local property taxes. These coefficients reflect the net impact of two offsetting effects: the income elasticity of demand for (and consumption of) public services and the income elasticity of tax revenues. When public services are normal goods, we expect the demand—and thus total tax revenue—to rise with income. It is possible, however, that larger revenues can be obtained without enhancing tax rates as a consequence of the growth of the tax base (which can in turn be induced by the growth in income). The

Extensions The benchmark estimation presented in the previous paragraph rests on several specific assumptions about the nature of the spatial interaction and about the underlying economic model. In this paragraph, we deal with the most important limitations as they have been recognized in the existing U.S. studies. First, we address the problem of endogeneity of the non-tax-rate variables. Such endogeneity may result from the individual taxpayer’s reaction to the tax policy. Second, we refine the analysis by extending the geographical dimension over which mimicry takes place. Third, we incorporate the possible interaction between separate taxes more explicitly. Finally, we allow for unequal weighting of neighboring municipalities’ tax rates. Endogenous population and income

As indicated, the existing studies on mimicking behavior assume that electoral voice effects dominate the Tiebout exit effects. It is also recognized 94



that the latter effects may be important, especially in a context of local governments. The econometric implication is that income and population size are endogenous. It should be noted, however, that per capita income may be endogenous even in the absence of migration. Exit and voice are indeed not the only reaction modes for individual taxpayers. In response to higher taxes, they can also choose the more “traditional” incentive reactions (on labor supply) or tax avoidance or evasion. The implication will be that income becomes endogenous even in the absence of exit reactions.

share boundaries. While for large-scale jurisdictions such as the U.S. states this approach seems reasonable, it is far less so in a Belgian context of many small municipalities. There, a jurisdiction’s reference space may extend beyond its immediate neighbors.16 To take this into account, we introduce a second-order lag (Cliff et al., 1975). Thus, we define two separate “reference spaces” for each Belgian municipality i. The first one consists of the municipalities’ immediate neighbors (as in Table 1). Apart from these “First- Order Neighbors” (FON), we consider “Second-Order Neighbors” (SON). These are municipalities that have a boundary in common with the FON municipalities (of course, excluding municipality i itself and the FON municipalities of i). As a result, tREF in expression 1 is now a [589 × 2] matrix with as a first column the vector tFON, the average tax rate in the FON municipalities, and as a second column tSON, the average tax rate in SON municipalities.

To test for this endogeneity, a Hausman–Wu test was performed to test for the exogeneity of income and population size. The results indicate the exogeneity of population size; per capita income, however, is endogenous.14 This supports the assumption of U.S. studies concerning possible Tiebout effects.15 The 3SLS estimation results, with per capita income (and, of course, the neighbors’ average tax rate) treated as an endogenous variable, are reported in Table 2 (column 2.1). Using instrumented income improves the overall explanatory power of our model (in terms of R2); it also lowers the LM test statistic, which, however, remains significant. Regression coefficients and t-values are hardly affected, besides the coefficient of the municipality’s surface (AREA), which now exerts a significant (negative) effect on the local property tax rate.

The geographical dimension of the informational externalities of the mimic model is an empirical matter. In fact, the mimic assumption is an application of Tobler’s first law of geography, which states that “everything is related to everything else, but near things are more related than distant things” (Anselin, 1988, p. 8). As a result, we expect the coefficient of tSON to be smaller than the coefficient of tFON. The regression results in Table 2 (column 2.2) clearly show that including SON municipalities has important effects. The overall explanatory power of our model increases. More important, LM takes a value (3.75) that does not differ significantly from zero, implying that the error terms are no longer correlated in space. The coefficients of tFON in Table 2 indicate that a one percentage point change in FON tax rates is now associ-

Geographical dimension

The existing studies assume a limited geographical dimension of informational externalities. The a priori weighting matrix only considers immediate neighbors, that is, jurisdictions that 95



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ated with a 0.5 percentage change in a municipality’s own tax rate. The earlier results reported in Table 1, therefore, should be interpreted with caution since the coefficients suffer from a misspecification bias. Table 2 also indicates that a municipality’s tax rates are affected significantly by the tax rates of its SON. As expected, on the basis of Tobler’s law, this impact is smaller than that of FON taxes.

the introduction of the left out tax instruments. Although this effect is limited, in general, it may suggest that some of the mimic effects found in the other regressions have an “indirect” nature: it is possible that municipalities copy only one tax and that budget constraints determine the level of the other tax, which therefore will also show “geographical clustering.” Unequal weighting schemes

Interdependent tax policies

A geographical neighborhood concept does not require that all neighboring municipalities (FON and SON) are weighted equally. In the existing studies, Ladd (1992) allows for some unequal weighting by excluding central cities from the set of counties’ neighbors. The underlying assumption is that “individual counties, whether they be counties with central cities or suburban counties, compete with the suburban counties” (Ladd, 1992, p. 459). A similar approach, whereby municipalities consider the tax rates of their neighbors excluding larger municipalities (over 50,000 inhabitants), was tested for the Belgian municipalities. The results, however, did not differ significantly from our results in Table 2: R2 dropped slightly, and the coefficients were hardly affected.

The basic mimic model concentrates on the determination of a single tax rate. Belgian local governments, however, collect revenues through different tax sources, of which local income and local property taxes are by far the most important. The 3SLS estimation procedure allows for the interaction between the setting of these separate taxes through the influence of common (but unspecified) determinants.17 The interdependency of both taxes can be considered explicitly.18 This is done in estimation 2.3 in Table 2. For each equation, the “left out tax instrument” (i.e., LPT for the LIT equation and vice versa) is included as an explanatory variable (because of the implied simultaneity between the two taxes, we include the estimated tax rates—LPTINST and LIT-INST—which were also used to calculate tREF).

Alternative unequal weighting schemes are possible. Municipalities may copy larger municipalities’ tax policies, if only because information on larger municipalities spreads more easily through media coverage or simply because larger cities are typically places with more recreational and professional activities. As a result, informational externalities may be positively related to the number of inhabitants of a jurisdiction of reference. Still another approach may suggest that politicians will copy economically successful municipalities, implying that information will be

The results in Table 2 indicate that municipalities have coherent tax policies in which the level of one tax rate is positively affected by the level of the other. This result is compatible with the Hettich and Winer model, which suggests that political-cost-minimizing politicians will diversify tax revenues among different taxes (Hettich and Winer, 1984, 1988). Observe in Table 2 that all mimic coefficients are somehow lowered by 97



weighted by municipalities’ per capita income. Estimation of our model where neighbors’ tax rates were weighted with population or income did not differ significantly from the results reported in Table 2.

dimension, which extends beyond the immediate neighbors. Our results indicate that municipalities that share borders with these immediate neighbors exert an influence on tax choices in a given jurisdiction. The intensity of this influence diminishes with geographical distance.19 ,20

Conclusions Recent empirical work showed that tax policies are copied among U.S. states and local governments. Analysis of mimicking behavior in a different institutional context allows us to place the U.S. results into perspective. With this in mind, we tested for the existence of tax mimicking behavior among the 589 Belgian municipalities. Our data set offers some important advantages over existing U.S. material. The homogeneous institutional setting in which Belgian local governments operate, municipalities’ small size, and the uniform definition of the tax bases are the most important. The latter allows us to concentrate on tax rates rather than on the more amorphous measure, tax burdens. We considered LIT as well as LPT rates.

ENDNOTES

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The general results are in line with the findings obtained for the United States: tax rates are indeed copied among neighboring municipalities. This suggests that mimicking behavior is not some artifact of the U.S. system, but it is rather a characteristic of decision making within the context of decentralized government.

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We introduce different extensions of the traditional mimic analysis. Our analysis suggests that the average income within municipalities depends on the local tax policy and, therefore, should be treated as endogenous. We also find evidence of interdependency of different tax rates. Finally, mimicking among Belgian municipalities has a geographical

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We thank John Ashworth, Scott Gassler, Victor Ginsburgh, Alain Petit, and Frank Van Driessche for helpful comments. Earlier versions of the paper were presented at the 1995 Annual Meeting of the European Public Choice Society (Saarbrücken, April 19–22) and at the 51st Congress of the International Institute of Public Finance (Lisbon, August 21–24, 1995). The remarks by Kjetil Andersson and Bernd Genser at those occasions are gratefully acknowledged. Three anonymous referees and the editor made valuable comments. The basic idea also underlies the regional diffusion models that explain the adoption of new programs or policies (for applications in the field of taxation, see Hansen, 1983; Berry, 1988; Berry and Berry, 1990; Alm, McKee, and Skidmore, 1993). In the public economics and public choice literature, the existence of interjurisdictional policy influences is prominently present in studies on tax competition and on spillovers (Case, Rosen, and Hines, 1993). Only Case, Hines, and Rosen (1989) and Case, Rosen, and Hines (1993) study mimicking on the expenditure side. The implication is that mimicking behavior may be seen as the net effect of (possibly) compensating Voice and Exit effects. Ashworth and Heyndels (1997) present evidence of “yardstick opinions”: a given tax rate is perceived to be high by politicians if neighboring jurisdictions have lower tax rates; it is perceived to be low if neighboring rates are higher. It is common in the literature to refer to jurisdictions of reference as “neighbors.” These are defined as jurisdictions to which a given jurisdiction compares itself. The neighbor concept does not necessarily refer to geographic proximity. Neighbors may be selected on the basis of similarity in income, racial composition, or any other dimension. We think that use of the term neighbors in such a context is rather confusing. In the present paper, we use the term neighbors for jurisdictions of reference that share boundaries. Only in their analysis of state expenditure policy, Case, Rosen, and Hines (1993) use alternative definitions of the jurisdictions of reference. More precisely they use similarity in income or in the



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proportion of blacks in the population to construct the spatial weighting matrix. The idea is that policies will be influenced by policies in similar states, which need not necessarily be geographical neighbors. The base of the regional property tax is the assessed rental values of immovable property. For a regional tax of 1.25 percent, a local surcharge of 2005 percent thus corresponds with a tax of 25.06 percent (20.05 * 1.25 percent) on assessed rental values. The major institutional reforms that took place in Belgium (during the eighties) did not affect the municipalities in any direct way. The reforms mainly involved important shifts in responsibilities from the federal to the regional level of government. Ladd cites the lack of this kind of homogeneity of the data set as a problem in U.S. studies: “I would prefer to use effective tax rates rather than tax burdens (. . . but statutory tax rates are not available by county and differences in the definitions of tax bases across the country would render the statutory tax rates meaningless” (Ladd, 1992, p. 454). In the first stage, tax rates in neighboring municipalities were estimated, using as instruments the neighboring municipality’s average income, its population size, its percentages of people under 20 and over 60 years, its area, and its average tax rates over the period 1983–90. tREF thus gives the average estimated tax rates for neighboring municipalities. To interpret the coefficients, it should be taken into account that the average level of tax rates differs considerably between both taxes. This may be due to the weaker control that is exercised by a larger electorate (in which individual voters have less incentive to try and influence political decision making and in which interest groups may be more difficult to organize). Estimating the regression while excluding tREF as an explanatory variable leads to a LM value of 88.84. The instruments used to estimate per capita income and population size were per capita income and the population size for 1981, percentages young and old, as well as the municipality’s area and the average local income and local property tax rates over the period 1983–90 (in the latter case, 1983 was a starting point, as in this year the merging operation of local governments, which started in 1977, was finalized). The Hausman–Wu test (Stewart, 1991) was performed first to test for exogeneity of income and population size. The test statistics (χ2 value of 12.63 for local income tax rates and 41.55 for local property tax rates) rejected the assumption of exogeneity. The endogeneity could be attributed solely to the Y variable: Hausman–Wu tests accept

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the exogeneity assumption for the population size, the statistics being 7.81 and 6.40. In what follows, the instruments used are average income for 1981, the other demographic variables for 1990 (population size, percentage young, percentage old), the municipality’s area, and the average LIT rate and LPT rates over the period 1983–1990). Note that these instruments are used not only to obtain estimated values for the municipalities’ income but also to obtain estimated values for the local tax rates of neighboring municipalities (the latter are used to calculate t FON and t SON values as in the benchmark estimation, see note 10). The main reason is that citizens’ and politicians’ “activity space” (which depends on their personal, recreational, and professional activities) will extend beyond the neighboring jurisdictions. This activity space is the major component of people’s “awareness space,” which forms the basis of their judgment (Quigley and Weinberg, 1977). Theoretical work that considers the interdependence of different tax instruments includes Hettich and Winer (1988), Goodspeed (1995), and Nechyba (1997). We thank a referee of this journal for suggesting this extension. Remember that, relative to U.S. standards, geographical distances between Belgian municipalities are very small. The concept of SON may have less appeal at the U.S. state level. At the local level, however, testing for second-order effects is certainly worthwhile. To test whether our results are not an artifact of the statistical procedure in which the neighborhood variable picks up the effect of any random set of municipalities, we reestimated regression 2.2 in Table 2 with an intentionally absurd weighting scheme. As in Ladd (1992) and Case, Rosen, and Hines (1993), we ordered municipalities alphabetically. Municipality i’s FON were then defined as the six municipalities that followed i in the alphabetical ordering. The SON were defined as the 12 municipalities, starting from rank 589 – n, where n is the rank of municipality i. The results are clear: the four neighborhood coefficients all have t-values not larger than 1; and the LM test statistic takes a value of 108.8, indicating important spatial autocorrelation.

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100



APPENDIX A Summary Statistics Mean LIT LPT INH (‘000) Y (‘000,000 BF) YOUNG (%) OLD (%) AREA (km2)

6.78 2005 16.92 0.291 25.54 25.81 51.57

Standard Deviation 1.05 490 28.26 0.043 2.33 3.34 37.37

101

Minimum

Maximum

0.00 520 0.09 0.171 17.90 15.20 1.15

9.50 4600 469.13 0.442 32.80 37.30 216.88
