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Setting house taxes by Italian municipalities: what the data say. Fabio Padovano Dipartimento di Istituzioni Politiche e Scienze Sociali and CREI Università Roma Tre. Introduction. This “research report” has 4 goals

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## Setting house taxes by Italian municipalities: what the data say

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**Setting house taxes by Italian municipalities: what the data**say Fabio Padovano Dipartimento di Istituzioni Politiche e Scienze Sociali and CREI Università Roma Tre Workshop slides**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 1993-2001 • Performing some preliminary tests using this data set. Workshop slides**Theories of tax setting**• Spillover models (externality) • Resource flow models (transfer of resources) • Problem of observational equivalency Workshop slides**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 Workshop slides**Model structure**• Objective function • FOC • Reaction function • Slope of the reaction function depends on nature of preferences – can be positive or negative Workshop slides**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 Workshop slides**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 Workshop slides**BC&R empirical testing strategy**• Term limit is (possibly) a separating technology • Two equations must be estimated: • Standard tax setting equation • Vote popularity function Workshop slides**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 Workshop slides**Model structure**• Objective function • Resource function • Reduced form same as in spillover model • Consequence: observational equivalency Workshop slides**Testing for strategic interaction**• Estimating equation • Weights define contiguity – flow of information • Coefficient to be estimated Workshop slides**Two problems**• Endogeneity of the zj 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 Workshop slides**Econometric solutions**• Maximum likelihood methods • IV methods • Others? Workshop slides**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 Workshop slides**Italian empirical studies - 2**• F&G theoretically less precise but larger data set (all Italian municipalities 1998-2001) • 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 Workshop slides**7**Single/Business property rate 6.5 mean 6 Domestic property rate 5.5 5 4.5 4 1995 1993 1997 1999 2001 years ICI rates: evolution of national averages Workshop slides**ICI rates: average per province 1993**Workshop slides**ICI rates: average per provinces 2001**Workshop slides**ICI per capita revenues**Workshop slides**Mean-variance relationship ICI rates**Workshop slides**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; Workshop slides**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. Workshop slides**Preliminary estimates - 3**• Yardstick competition variable: • neighboring governments’ property tax rates • Different model specifications to sort out YC-type of behavior Workshop slides**Model specification**• Begin with 2 estimating equations: • Mod1: Spatial error Accounts for endogeneity of the zj • Mod2: spatial error dependence Accounts for simultaneity bias Workshop slides**Regressions – Mod 1 & Mod 2**Workshop slides**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 Workshop slides**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 Workshop slides**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. Workshop slides**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 Workshop slides**Mod3 – Term limit - 2**• Theoretical prediction: φ1=0 and φ2≠0 • If φ1=φ2= φ = Mod2. Workshop slides**Regression results – Mod3 - 1**Workshop slides**Regression results – Mod3 – 2**Workshop slides**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? Workshop slides**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 Workshop slides**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 Workshop slides

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