1. Setting house taxes by Italian municipalities: what the data say
Fabio Padovano
Dipartimento di Istituzioni Politiche
e Scienze Sociali and CREI
Università Roma Tre
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2. Introduction This “research report” has 4 goals
Discussing main hypothesis on the interaction among local governments in setting tax rates;
Reviewing empirical tests based on data about Italian municipalities;
Presenting a new data set on the fiscal variables of Italian municipalities - all municipalities for
Performing some preliminary tests using this data set.
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3. Theories of tax setting
Spillover models (externality)
Resource flow models (transfer of resources)
Problem of observational equivalency
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4. Spillover models
Each jurisdiction i chooses the level of a decision variable zi (tax rate, welfare expenditures)
Jurisdiction is also affected by the z’s chosen elsewhere – externality of some sort
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5. Model structure Objective function FOC Reaction function
Slope of the reaction function depends on nature of preferences – can be positive or negative
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6. Yardstick competition
Information spillovers across jurisdictions
Voters look at public services and taxes in other jurisdictions to evaluate whether their government is wasting resources
Information spillover affects voting behavior
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7. BC&R critique
Politicians aware of voters’ calculations and react strategically
An incompetent government forced to behave more like other governments in order to avoid being detected
More pooling behavior, unless YC features a separating technology
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8. BC&R empirical testing strategy
Term limit is (possibly) a separating technology
Two equations must be estimated:
Standard tax setting equation
Vote popularity function
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9. Resource flow models
Jurisdiction cares about the amount of a particular “resource” that resides within its borders.
Distribution of this resource among jurisdictions depends on the z choices of all jurisdictions.
Jurisdiction i indirectly affected by z-i
Tiebout model
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10. Model structure Objective function Resource function
Reduced form same as in spillover model
Consequence: observational equivalency
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11. Testing for strategic interaction
Estimating equation
Weights define contiguity – flow of information
Coefficient to be estimated
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12. Two problems Endogeneity of the zj Spatial error dependence
Because of strategic interaction, z values in different jurisdictions jointly determined. Linear combination of the zj endogenous and correlated with the error term εi.
Spatial error dependence
Arises when ε includes omitted characteristics of jurisdictions that are themselves spatially dependent
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13. Econometric solutions
Maximum likelihood methods
IV methods
Others?
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14. Italian empirical studies - 1
Bordignon, Cerniglia and Revelli (2002, 2003) and Fedeli and Giannoni (2004)
BC&R theoretically correct but sample size limited (171 municipalities of Milan province)
BC&R basically a cross section
No convergence to steady state – business cycle unaccounted for
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15. Italian empirical studies - 2
F&G theoretically less precise but larger data set (all Italian municipalities )
Ordered probit model: estimates probability that ICI rate fits 1 of 3 cathegories
Significant loss of information - Choice of these classes is arbitrary and unnecessary
Focus on the variability of the ICI rates is at best an indirect evidence of strategic interaction; at worst ARCH
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16. ICI rates: evolution of national averages4
4.5 5
5.5 6
6.5 7
1993
1995
1997
1999
2001
years
mean
Single/Business property rate
Domestic property rate
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17. ICI rates: average per province 1993
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18. ICI rates: average per provinces 2001
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19. ICI per capita revenues
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20. Mean-variance relationship ICI rates
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21. Preliminary estimates - 1
Only tax setting equation
ICI business tax rate depends on:
structural characteristics of the jurisdiction: area, population, and urbanization rate;
socio-demographic characteristics of the resident population: percentage of youngsters, percentage of elderly people, and rate of unemployment;
fiscal variables: grants from central government and disposable income per capita;
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22. Preliminary estimates - 2
Political variables, 3 dummies, 1 %:
ideological differences between right-wing and left-wing governments;
election year (opportunistic behavior of incumbents in election years);
term limit - potential differences in tax setting between first term and second term mayors.
confidence of re-election dummy, share of the votes obtained by the incumbent in the previous election.
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23. Preliminary estimates - 3
Yardstick competition variable:
neighboring governments’ property tax rates
Different model specifications to sort out YC-type of behavior
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24. Model specification Begin with 2 estimating equations:
Mod1: Spatial error
Accounts for endogeneity of the zj
Mod2: spatial error dependence
Accounts for simultaneity bias
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25. Regressions – Mod 1 & Mod 2
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26. Main results -1
No systematic differences between right-wing and left-wing governments
Tax rates lower in election years
Area positive and significant
Population negative and significant - economies of scale in public service provision
Urbanization positive effect
Unemployed, elderly and young negative effect
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27. Main results - 2
Fiscal variables apparently do not impact on the tax rate – conflicting effects
Mod1 yields statistically insignificant estimates of spatial coefficient
Mod2 yields an IV estimate of the spatial coefficient φ = 0.23, statistically significant at the 99% level.
Mod2 more appropriate
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28. Problem Mod2 compatible with:
Yardstick competition
Spatially correlated shocks to tax setting behavior with no behavioral significance
Estimate Mod3: allows for different behavior of mayors, based on term limit.
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29. Mod3 – Term limit - 1 Mod3 specification
Matrix D equals 1 incumbent faces term limit, 0 otherwise.
If mayor faces term limit, his interaction with neighboring jurisdictions’ policies captured by parameter φ1
If mayor runs for reelection, spatial interaction captured by φ2
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30. Mod3 – Term limit - 2 Theoretical prediction: φ1=0 and φ2≠0
If φ1=φ2= φ = Mod2.
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31. Regression results – Mod3 - 1
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32. Regression results – Mod3 – 2
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33. Main results - 1
Little difference in behavior (see constant term): 2 reasons
First term mayors bound by election prospects, second term mayors a selected sample of better than average mayors - do not tend to raise tax rates.
Unexpectedly: both kinds of mayors tend to set lower tax rates in election years – party discipline?
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34. Main results - 2 Only evidence of yardstick competition
Mayor with binding term limit: no evidence of spatial auto-correlation in tax rates.
Parameter φ1 not significantly different from zero.
Mayors that run for re-election: significantly affected by neighbors’ tax rates.
Parameter φ2 = 0.5 at 99% level of confidence
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35. What else should be done?
Econometric improvements (ML techniques alongside IV)
Information about spending and other fiscal instrument levels at the municipal level (such as the income tax surcharge)
Estimation of vote-popularity function alongside tax setting equation
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