University of Toronto Working Paper

Constraining the Local Leviathan? The Existence and Effectiveness of

Municipally-Imposed Tax and Expenditure Limits

Leah BrooksDepartment of Economics and

School of Public Policy and GovernanceUniversity of Toronto

Justin PhillipsDepartment of Political Science

Columbia University

July 2010

We wish to thank the Lincoln Institute of Land Policy for providing the funding for thisproject and the hundreds of municipal officials who generously gave of their time by respond-ing to our survey. We are appreciative to the many academics with whom we correspondedwho confirmed that no systematic knowledge about locally-imposed tax and expenditurelimits existed. We also thank Nate Anderson, Chris Cunningham, John Huber, MelissaMarschall, Michael Pagano, Daniel Wilson, and colleagues at the University of Toronto fortheir useful advice. Along the way, our projected was aided by marvelous research assistantsfrom McGill University. Kasia Dworakowski set up the initial Access database and providedtechnical support along the way; Raissa Fabregas-Gil assisted with the final data clean-up.Our most appreciative thanks are to our surveyors, Emily Gaus, Kieran Shah, and MichelleSegal, who almost never took no for an answer. None of these individuals are to blame forany mistakes herein.

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Constraining the Local Leviathan? The Existence and Effectiveness ofMunicipally-Imposed Tax and Expenditure Limits

Voters sometimes choose to restrict the revenue power of government through constitu-tional rules, often a tax and expenditure limitation (TEL). While research has explored TELsadopted by state governments, there is no systematic knowledge of an alternative type ofTEL—those which cities impose upon themselves. In this analysis, we consider the existenceand consequences of such restrictions. We begin by conducting a survey of American cities.Our survey reveals that municipally-imposed TELs are fairly common, existing in one inevery eight cities with a population above 25,000. By combining this survey with municipalfinancial data, we then evaluate whether these limits affect the size of the municipal publicsector. We find that TEL adoption is indeed followed by a meaningful reduction in rev-enue growth. Our results demonstrate that local institutions have large fiscal consequencesand that voters can effectively use constitutional rules to constrain the budgetary choices ofgovernments.

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Taxation enables the government to finance important public goods and services, such

as police and fire protection, environmental monitoring, and welfare programs for the needy.

While it breathes life into the public sector, taxation is coercive, requiring the transfer of

valuable economic resources from individuals and private institutions to the government.

As such, it is a power from which citizens may desire protection. Indeed, scholars have

long argued that governments overuse their revenue powers relative to the preferences of the

median voter (see Peltzman, 1992; Matsusaka, 1995). Elections, though certainly a constraint

on such behavior, may be insufficient to prevent overspending due to common pool problems

(Weingast et al., 1981), interest group pressure (Peltzman, 1976; Becker, 1983), information

asymmetries between bureaucrats and elected officials (Niskanen, 1971), and other “frictions

in the representative process” (Matsusaka, 2004, 86). Thus, the possibility of excessive

taxation remains.

Shortcomings in electoral solutions have led to the search for non-electoral means to

constrain the budgetary choices of governments. One such mechanism is to restrict in advance

the revenue authority of government, typically though some form of constitutional tax and

expenditure limitation (or TEL as they are commonly called). TELs, while varying in

scope and restrictiveness, constrain the freedom of government to make fiscal decisions either

by capping total revenues, capping total outlays, or by restricting the use of a particular

revenue instrument. In the United States, these limits have been widely embraced by small-

government and anti-tax advocates (Smith, 1998; Martin, 2008).

Proponents of these fiscal rules have enjoyed a great deal of success at the state level—

almost every state has imposed some TEL on municipal fiscal behavior and a majority of

states have also limited their own ability to tax or spend (Smith, 1998; Mullins and Wallins,

2004; Waisanen, 2008). State-imposed TELs are consequential, often reducing municipal

spending and service provision as well as altering the distribution of taxation (Shadbegian,

1996; Figlio and Rueben, 2001; Mullins, 2004; Brooks and Phillips, 2010).

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There is, however, no systematic evidence regarding an alternative and plausibly equally

restrictive type of tax and expenditure limit: a limit which a city imposes upon itself. Social

scientists know almost nothing about the existence and frequency of such restrictions, what

form they take, and whether they constrain municipal budgets. As researchers continue to

grapple with the determinants of municipal fiscal policymaking, understanding local TELs

becomes increasing important.

Learning if such rules exist and what their consequences are will enhance our understand-

ing of American fiscal federalism as well as our understanding of the budgetary significance of

municipal institutions. Studies of federalism have long doubted the ability of cities to signif-

icantly shape their own budgetary policies. Under Dillon’s Rule,1 state legislatures exercise

a great deal of control over local governments. States have used this control to restrict the

fiscal autonomy of cities by imposing formal limits on the amount of debt cities may issue,

the tax instruments they can employ, and even the tax rates they may charge (Sokolow,

1998). Many researchers also argue that competition over mobile resources constrains the

ability of cities to tax in a manner that differs from what the market will tolerate (Peter-

son, 1981). Dissatisfied city residents and businesses can restrict Leviathan governments by

credibly threatening to move to other local jurisdictions (Tiebout, 1956).2

The expectation that cities are quite restricted in their ability to raise revenues has given

rise to two related suppositions: (1) locally-imposed budgetary constraints are unnecessary;

and (2) the fiscal consequences of local institutional choice are (in general) either very minor

or nonexistent. The first supposition is manifest in the absence of any systematic effort

(until now) to document or study local TELs. The second has often been supported by

empirical studies of the budgetary effects of municipal form of government (cf. Morgan and

1Dillon’s Rule is a judicial doctrine establishing state preeminence over local governments and was �rstexpressed in Clinton v Cedar Rapids and the Missouri River Railroad, (24 Iowa 455; 1868).

2We use the term Leviathan in the public choice sense (Brennan and Buchanan, 1980) to refer to agovernment that seeks to maximize revenue, rather than a government that follows voter preferences.

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Pelissero, 1980). Finding that locally-adopted TELs are a common and an effective means

of constraining municipal governments would present a strong challenge to both.

More generally, our analysis speaks to the effectiveness of constitutional design as a

constraint on government action. Rawls (1971) and Brennan and Buchanan (1980) define

“constitutional design” as the process of establishing the rules of the game, which is separate

from any outcomes of that game when played. Carefully crafted constitutional rules may

serve as a means of limiting the exercise of governmental power such that future outcomes

fall within acceptable bounds. This belief is probably best expressed by James Madison in

Federalist No. 51 where he argues that governmental threats to liberty can be restrained

through a system of institutional checks and balances. Others have argued, however, that

constitutional rules are often ineffective since they are subject to manipulation and evasion

by government actors and may prove inconsistent with existing cultural values or norms

(Landes, 1999; Wolcott, 1999). Our analysis contributes to this debate by evaluating whether

constitutional budgetary rules “matter.”

The effects of fiscal rules, while of broad theoretical significance, should be particularly

meaningful in local politics. The taxation of property is generally the primary source of

revenue for cities. For individual taxpayers, real property is a very significant “long-term

commitment to a saving-investment profile” (Brennan and Buchanan, 1980, 198), the value of

which is affected by the local property tax (Fischel, 2001; Anderson and Pape, 2010). Given

the financial importance of home ownership, voters have a strong interest in protecting this

asset from Leviathan-like behavior. Doing so exclusively through electoral means, however,

may be particularly difficult given voters’ relative lack of knowledge about local politics and

policy, combined with the difficulty of acquiring such knowledge. A constitutional restriction

on local taxation is a potential non-electoral means of protecting home values.

We begin our investigation by first documenting the existence of locally-enacted fiscal

restrictions. Do municipally-imposed TELs actually exist? If so, how common are they

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and what forms do they take? To answer these questions, we administered a survey to

knowledgable officials from a large sample of American municipalities. The survey asked

officials whether their city has a locally-imposed limit, and if so, to detail its features,

including the year of enactment, the revenue instruments to which it applies, and whether

it is statutory or constitutional in nature. Overall, we find that locally-adopted TELs are

fairly common, existing in at least one of every eight sampled cities. Like their state-imposed

counterparts, municipal TELs apply largely to the property tax and are constitutional.

Second, by combining our survey results with data on municipal finances, we consider

whether voters adopt local TELs in response to high municipal taxation—that is, Leviathan-

like behavior by city officials. If local TELs are enacted primarily in response to higher

taxation, estimates of their effects on fiscal policy may be biased. We test for this relationship

through a time series analysis that compares revenue growth in limit-enacting cities prior to

TEL adoption to revenue growth in non-adopters. We also estimate hazard models of TEL

adoption and undertake more detailed case studies for a subsample of TEL-adopting cities.

Finally, we consider whether local tax and expenditure limits change the path of municipal

revenues. We use data on municipal finances to compare the rate of revenue growth in

cities before and after TEL adoption. To address concerns regarding causality we estimate

alternative specifications in which the year of TEL adoption is randomized and more closely

compare TEL-adopting cities to non-adopters using matching. We find that the adoption of

a local TEL is, on average, associated with a statistically meaningful reduction in revenue

growth. These results are also supported by our survey in which more then half of all

respondents indicated that their city’s tax and expenditure limit has affected municipal

fiscal practices. Our results indicate that local fiscal rules are indeed consequential, even

when they are created and enforced at the local level.

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1 Survey and Supplemental Data

To document the existence and extent of locally-imposed tax and expenditure limitations,

we undertook a survey of large and mid-sized American cities. Our sample consists of all

247 cities with populations of 100,000 or more, and a random sample of 100 cities with

populations between 25,000 and 100,000.3 Our sampled cities account for 26 percent of the

total U.S. population. While the principal purpose of the survey was to identify cities that

have adopted a local TEL, we also use it to collect data on the features of the limits, such

as their date of adoption and override provisions, and the perceptions of local officials as to

the effects of TELs on fiscal policymaking.4

The survey was conducted during 2007 and administered telephonically by research assis-

tants.5 For each city in our sample, we collected contact information from municipal websites

for the City Manager, Budget Director, and Finance Director and attempted to contact each

of the 736 officials for whom we had information.6 In total, we spoke with 412 officials, and

received responses from 320 unique cities, generating a 92 percent response rate. While none

of the questions asked were of a sensitive nature, all participants were assured that their

3We use the Census of Governments 2002 Governments Integrated Directory as our sample frame. Wekept only cities with the following political descriptions: Charter Township, City, City and Borough, Cityand County, City-Parish, Consolidated Government, Municipality, Town, and Village. We refer to all ofthese entities throughout as either cities or municipalities. As expected, summary statistics comparing ourrandomly sampled cities to all non-sampled cities show few di�erences.

4We use the universe of larger cities and a sample of smaller cities for two key reasons. First, by selectinglarger cities with certainty, our �nal data set consists of a meaningful proportion of the U.S. population,making our �ndings particularly policy-relevant. Second, when choosing where in the population distributionto sample, we prefer to sample the \thicker" part of the distribution (smaller cities) rather than the \thinner"part (larger cities). Relative to the thin part of the distribution, the thick part contains cities that are moresimilar, meaning that any sampled city is more likely to be representative of those that are unsampled. Inaddition, the Census of Government Finances uses a population weighted sampling scheme, so that largercities are particularly likely to have �nance data.

5Before conducting the full survey, we made an e�ort to determine whether locally-imposed TELs exist.During the summer of 2006, we selected a random sample of 60 cities and searched their municipal chartersor code for evidence of TELs. We also called local o�cials from each to ask whether their city had adoptedsuch limits. The results of our investigation led us to conclude that a larger survey was warranted. It alsorevealed that reading municipal documents is a poor mechanism for identifying TELs absent interviews.

6Not all municipalities have each of these o�ces; we collected contact information for all available types.

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state government. We did this through our survey interviews and by locating the limit in

either the city’s municipal charter or code.

We combine the results of our survey with a wealth of data on municipalities in order to

estimate the fiscal effects of TELs. To describe cities’ fiscal condition, we use the Census’s

Annual Survey of Government Finances, 1970-2004. This survey collects fiscal information

from all larger cities with certainty, and from a random sample of smaller cities. To de-

scribe the demographic features of cities, including the metropolitan area in which each

city is located, we use the Census’s Summary Tape File 3, combining information from the

decennial censuses of 1970, 1980, 1990, and 2000.9 We linearly interpolate all decennial

census data between survey years. Data on city political structure comes from the 1987

Census of Government Organization and from the Legal Landscape Database which de-

scribes direct democracy provisions in the thousand largest American cities.10 Information

on state-mandated tax and expenditure limits come from Mullins and Wallins (2004) and

Advisory Commission on Intergovernmental Relations (1995). We use the urban consumers

price index to convert all of our fiscal and economic information into 2006 dollars.

2 Basic Survey Results

Our survey demonstrates that locally-imposed TELs are more common than theories of

fiscal federalism suggest, at least among cities with populations greater than 25,000. Forty

of our 320 respondent cities, or 12.5 percent, have at least one local limit. In total, these 40

municipalities have 56 individual limits. Local TELs exist in all census regions, though cities

in the Northeast are least likely to adopt a limit. While cities in the Northeast constitute 17

percent of respondent municipalities, they account for only 6 percent of the TELs identified

9Census municipal codes change from 1980 to 1990, and we construct a cross-walk to merge across years.Full information on all data sources is in the online appendix.

10This dataset is from the Initiative and Referendum Institute at the University of Southern California.

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by our survey. Midwestern and Southern cities, however, constitute 18 and 29 percent of

respondent cities (respectively), but account for 35 and 33 percent of all TELs.

As shown by the top panel of Table 1, locally-imposed TELs overwhelming target the

property tax—historically the largest source of revenue for local governments (Sokolow,

1998). Property tax rate limits and levy limits, at 39 and 16 percent respectively, make

up the majority of the TELs we observe. Rate limits set a maximum ceiling on the city’s

property tax rate while levy limits constrain the total amount of money that can be gen-

erated from the property tax, independent of the overall rate. The third type of property

tax restriction we observe is an assessment limit (7 percent of all local TELs). Assessment

limits are intended to restrict a city’s ability to “automatically” garner increased revenues

from rising property values or through administrative reassessment of value. These limits

are usually expressed as an allowable annual percentage increase in assessed value.

The most comprehensive and restrictive type of local TEL (existing in three cities) is

a general revenue or expenditure limit. Such a limit caps either the total amount of own-

source revenue or total own-source expenditures, and is typically expressed as an annual

allowable percentage increase. The charter of Colorado Springs, Colorado, for instance,

requires revenue raised not to exceed the previous years collection, adjusting for population

growth and inflation. Anchorage, Alaska has adopted a similar restriction.

The remaining tax and expenditure limits either apply to the sales tax or are categorized

as “other.” Sales tax limits, consisting of nearly 11 percent of local TELs, typically either cap

the overall rate that can be charged or restrict the items that can be taxed. TELs that fall

into the “other” category, just over 21 percent, apply to a wide range of municipal revenue

sources, including entertainment, business, and income taxes as well as certain user fees.

Our survey finds that most locally-imposed TELs are not easily circumvented. First,

difficult and politically costly than a similar limit in the municipal code. Second, though

most TELs have an override provision, overrides typically require a majority or supermajority

vote of the electorate. Only 15 percent of limits can be circumvented through city council

action alone, and nearly one-third of these require a council supermajority for override.

Through the survey and subsequent research efforts we have obtained the date of adoption

for over half of the TELs identified by our survey and, in many instances, the method of

adoption as well. Local TELs generally come into existence through one of two mechanisms—

city council action or a ballot measure proposed by a citizen or interest group. The earliest

TEL among our sampled cities was enacted in 1928 (a property tax rate limit in the city of

Eastpointe, Michigan) and the median year of adoption is 1980. As Figure 1 demonstrates,

there is no distinct period of local TEL adoption. Unlike many of the stringent state-imposed

TELs, local limits do not appear to be closely connected to the tax revolt of the late 1970s

and early 1980s. Only a small number were enacted during this period of time, with nearly

as many being adopted in the decades before the tax revolt as in the decades that followed.

Importantly, many of the local limits identified in our survey are either more restrictive than

the TEL imposed by the state government (if one exists), apply to different revenue sources

than the state TEL, or pre-date many of the most rigorous state restrictions.

Table 2 presents basic descriptive statistics by whether a city has ever had a local limit.

We use 2002 data, because this is the most recent Census of Governments year and has the

best coverage of fiscal information for our surveyed cities. The final column in the table

reports a t-test for equality of the sample means. We observe limited fiscal and demographic

disparities across the two types of cities. Regardless of the fiscal measure considered, there

are no meaningful differences in 2002 per capita revenues in cities with and without a local

TEL. Similarly, adopters and non-adopters are similar with respect to population size, the

share of the population that is African American or Latino, and population heterogeneity (in

terms of race and income as measured by a Herfindahl index). The only significant difference

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in demographic characteristics is income, which is lower in TEL-adopting municipalities.

The third panel of Table 2 reveals some differences in institutions between adopters and

non-adopters. First, cities with a TEL are 25 percent more likely to be home rule, that is they

are more likely to have greater powers of self-government. Second, TEL-adopters tend to be

in metropolitan areas with fewer cities—19 on average—compared to non-adopters, which are

in metropolitan areas with an average of 41 cities.11 Third, jurisdictions in which voters have

access to the citizen initiative are more likely to adopt a limit. Interestingly, there appears

to be little direct relationship between the existence of state-imposed municipal TELs and

the adoption of local limits. While 80 percent of TEL-adopting cities also face a potentially-

binding state-mandated limit, 70 percent of non-adopting cities do as well.12 We also find no

evidence that local TEL adoption is related to the strength of the state-mandated limit (i.e.,

whether the state TEL is constitutional). Ultimately, however, nearly all of the differences

between TEL-adopting cities and non-adopters disappear in a multivariate cross-sectional

regression analysis (for results, see Online Appendix Table 3).

3 Are Local TELs Endogenous to Taxation?

We now consider whether voters adopt TELs in response to high municipal taxation—that

is, current Leviathan-like behavior by city officials. The possibility that excessive taxation is

driving the adoption of local limits is justified given the arguments made by TEL proponents.

Indeed, scholars of state-level TELs have often tested this expectation (Danziger, 1980; Alm

and Skidmore, 1999; Cutler et al., 1999; Chernick and Reschovsky, 1982), though they have

11Data concerning the number of municipalities in a metropolitan area come from decennial censuses.For each year in the sample, we count the number of cities with more than 25,000 people per metropolitanstatistical area (MSA). We �nd the maximum and the minimum for each MSA over all years. We report themaximum here; no results are a�ected by using the minimum.

12Potentially binding TELs include revenue or expenditure limits, property tax levy limits, or the com-bination of a property tax rate limit and a limit on assessment increases. For more discussion of whatconstitutes a potentially-binding state-imposed TEL as well as their e�ects on local budgeting see Mullinsand Wallins (2004) and Brooks and Phillips (2010).

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found only limited empirical support. If local TELs are enacted in cities primarily in response

to higher taxation, this could ultimately bias estimates of their effects on fiscal policy.

We assess the role of revenue behavior in three ways: two time series analyses and a set

of more detailed case studies. We begin with our first time series analysis, which estimates

the following model of municipal own-source revenue:

own source rev pcc,t =�0 + �1yeart + �2cityc + �3Xc,t + �4State TELc,t+

�5 trendt � 1fnever local limit citygc+

�6 trendt � 1flocal limit city, pre limitgc+

�7 trendt � 1flocal limit city, post limitgc + �c,t.

(1)

In the equation pc stands for per capita, c denotes city, and t denotes years 1970 through 2004.

Xc,t is a matrix of time-varying city characteristics, including population, log of population,

median family income, and racial composition. StateTELc,t measures the existence of TELs

adopted by the state government.13 Trendt is a linear trend variable, never local limit cityc

is a dummy for cities that have never adopted a TEL, local limit city, pre limitc is a dummy

for TEL-adopting cities coded 1 in years before adoption, and local limit city, post limitc is

a dummy for TEL-adopting cities coded 1 in years after adoption.

We also control for year fixed effects (yeart) in order to account for macroeconomic

and political factors affecting all cities in a given year. City fixed effects are cityc. These

capture unchanging institutional, functional, and cultural characteristics of cities as well

as unchanging state-level laws. With the exception of state-imposed and locally-adopted

TELs, we only observe municipal institutional data at a single point in time.14 This means

13We use two dummies|one for state TELs that apply to municipal governments and one for stateTELs that apply only to state governments. For those that apply to municipalities, we only use limits orcombinations of limits that are considered to be \potentially binding". These are the types of state-imposedlimits that have been shown to a�ect municipal budgets.

14An alternative source for institutional data is the International City/County Management Association(ICMA) which conducts a survey of municipal forms of government every �ve years. The survey’s response

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that Equation 1 cannot separately identify the effects of institutional variables from city

fixed effects. There are two reasons this is unlikely to pose a problem. First, some existing

research shows that municipal institutions are fairly stable (Baqir, 2002). Second, in order

to affect our results, changes in other municipal structures would need to occur at or about

the same time as the adoption of local TELs. Our case studies of the events leading up to

TEL adoption (which we discuss in greater detail later) reveal that TELs are seldom enacted

as part of a broader set of institutional reforms.

The coefficients of interest here are �5 and �6. The coefficient �5 captures average own-

source revenue growth in cities that never adopt a TEL, while �6 measures average revenue

growth in TEL-adopting cities before adoption. If the adoption of local tax and expenditure

limits are driven by high levels of taxation, �6 should be meaningfully larger than �5.15

We report estimations of Equation 1 in Table 3. The first three estimations use all

respondent cities: 270 non-adopting cities, 22 cities that have a local TEL throughout all

of the years included in our data set (1970-2004), and 18 cities that adopt a limit at some

point during this period of time. Model 1 estimates the equation using only year and city

fixed effects plus our interactions terms; the second model adds the full set of demographic

controls Xc,t, and the third adds the dummies for state TELs that are imposed upon cities

and for state TELs that apply to the state government itself. Rows 1 and 2 at the bottom of

the table test for differences between our estimates of �5 and �6, specifically evaluating the

hypotheses that these two coefficients are equal and that �6 is smaller than �5. Rows 3 and

4 test for differences between �6 and �7, and are discussed in Section 4. Surprisingly, we do

not find evidence that revenue growth in TEL-adopting cities (prior to the enactment of a

limit) is meaningfully higher than revenue growth among non-adopters. While �6 is larger

rate, however, is under �fty percent and responses come disproportionately from cities that are professionallymanaged (ICMA, 2006). The Census of Governments remains the most complete source of data.

15The coe�cient �7 reports average revenue growth in TEL-adopting cities after they enact a TEL. Wewill discuss this in greater detail in Section 4.

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than �5, this difference fails to reach statistical significance.

The next three estimations exclude the 22 cities that enacted a TEL prior to 1970, the

first year of our municipal finance data. This empirical strategy may (arguably) represent

a cleaner test since we now use only cities that enacted a limit during the period of time

included in our analysis. In these new models, there is virtually no difference between the

size of �5 and �6. In total, these estimations fail to uncover any systematic evidence that

TEL adopters had higher revenue growth (prior to limit adoption) than non-adopters.

As a second time series analysis, we estimated several Cox proportional hazard models,

which measured the duration of time until “failure.” In this case, a city fails the year in which

it adopts a municipal TEL. The start time for each of our hazard models (the year in which

a city is first at risk) is 1970, the first year for which we have municipal financial data. Since

cities are obviously at risk of TEL adoption prior to 1970, we relied upon the exponential

distribution which does not require proper specification of the start time.16 In order to test

the importance of taxation for TEL adoption, we included own-source revenues per capita

as a covariate. If taxation drives the adoption of municipal TELs, we expect the hazard

rate to be higher for cities with larger revenues. However, we did not find any evidence that

taxation has a significant effect on the hazard rate, at least not in the presence of even a

small number of control variables. Since all of these models produced null results, we do

not present them here (see Online Appendix Table 4). These results and those presented in

Table 3 remain unchanged if we use alternative measures of taxation, including per capita

measures of total revenue, total tax revenue, or simply property tax revenue (see Online

Appendix Table 7).

To supplement these quantitative analyses, we conducted case studies using a sample of

the TELs identified by our survey. For each of the 41 limits in our sample for which we

16The exponential distribution does assume a constant baseline hazard over time. Our results are not,however, sensitive to the choice of distribution.

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observe the year of adoption, we searched local newspaper articles for coverage of the events

surrounding enactment. We found reasonably detailed newspaper coverage on 17 TELs. In

the vast majority of cases, our inability to find rich historical accounts of TEL adoption was

due the enactment of a local TEL prior to the availability of electronically-searchable local

newspapers. Since our case studies are not a random sample, the findings they generate

should be treated with some caution. However, this method does allow us a careful look

inside the process of adoption in a way that is not possible using quantitative analysis and

enables us to further consider the role that may be played by high levels of taxation.

In nine of our cases, newspaper coverage indicates that TEL adoption was driven, not

by high taxation, but by the activities of individual anti-tax political entrepreneurs or by

powerful economic interests. Coverage indicates that individual political entrepreneurs often

promote TELs as part of an effort to further their political careers or out of what appears

to be very intense preferences for constitutional fiscal constraints. In each of the seven cases

where adoption efforts were spearheaded by a political entrepreneur, the city had relatively

low levels of taxation in the years preceding enactment and local officials had not recently

increased tax rates. For instance, Douglas Bruce used the citizen initiative to enact four local

TELs in Colorado Springs, Colorado—one of the lowest-tax cities in Colorado even before

the adoption of the limits. He succeeded in enacting these TELs only after his attempts at a

state-level TEL (named the Tax Payers’ Bill of Rights) failed in 1988 and 1990.17 Capitalizing

on the fame he gained by promoting TELs, he was elected a county commissioner in 2004 and

appointed to a vacant state senate seat in 2007. This finding is consistent with other work

in the social science literature demonstrating that political entrepreneurs can be important

drivers of policy adoption and diffusion (see Mintrom, 1997).

In two cases, local TELs were largely designed and backed by powerful economic interest

groups which played a role similar to that of the political entrepreneur. These groups were

17Bruce did succeed in passing a state-level TEL in 1992.

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spurred into action by what they perceived, rightly or wrongly, as a threat to their financial

interests. In 1997, voters in Anchorage, Alaska approved a ballot measure (Proposition

1) amending the city charter to require 60 percent voter approval of any municipal sales

tax. The measure was primarily backed by the alcohol beverage industry, restaurants, and

retailers following failed attempts by the city (in 1994 and 1995) to adopt a tax on alcohol.

Newspaper coverage never suggested that high current levels of taxation were the driving

force behind TEL adoption—indeed, taxation in Anchorage was (and remains) quite low.

However, our investigation uncovered eight cases where the adoption of a local limit was

linked (at least in part) to status quo tax policy. For instance, In the early 1990s, Baltimore,

Maryland established a 4 percent annual limit on property assessment increases. This was

a period of growing property values in the city and ballooning tax burdens. Voter anger

over taxation gave rise not only to the adoption of a limit in Baltimore, but also by several

surrounding counties and by the state legislature (Sarris, 1992; Pelton, 2004).18 In two cases

where newspaper coverage linked TEL adoption to taxation—Columbus, Ohio, and Pueblo,

Colorado—the TEL was adopted as part of a deal to increase taxes, rather than as a response

to high current levels of taxation. The example of Columbus is particularly instructive. Since

1948, Columbus has relied heavily on a local income tax for revenue. The tax began as a

temporary measure with the rate set at 1/2 percent; occasionally the rate crept upward to

deal with seemingly temporary budget problems. In 1956, to secure a 1 percent rate over a

five year period of time, the city council agreed to place a charter amendment on the ballot,

capping the rate at 1 percent unless the public votes to increase it (Curtin, 2008).19

Overall, the evidence is mixed. Our quantitative analysis finds virtually no evidence that

high taxation leads to the adoption of a municipal TELs, while the case studies indicate that

18The state-imposed TEL requires 23 counties and city of Baltimore to cap the assessment increase onwhich home owners can be taxed at 10 percent annually. This is weaker than the city TEL. Counties alsoadopted more stringent limits, suggesting that locally-imposed limits are not adopted only by cities.

19The state of Ohio eventually adopted an income tax limit of its own. This limit is imposed upon cities,but clearly post dates the Columbus TEL.

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status quo tax policy plays an important role for a small number of limits. Importantly, it

does not appear to be the case that TELs are universally caused by high initial taxation.

4 The Fiscal Effects of Local TELs

Are municipal tax and expenditure limits effective constraints on the revenue powers of local

elected officials? As discussed in the introduction, it is not clear ex ante that municipal TELs

should have meaningful effects. Theories of fiscal federalism suggest that cities are already

so financially constrained (by market competition and state-imposed fiscal restrictions) that

locally-imposed rules are unlikely to have much of an effect. Even beyond these concerns,

implementation of municipal TELs depends on local elected officials who may be hostile to

the goals of the limit. In the case of locally-imposed TELs, which our survey shows usually

target a single tax instrument, elected officials may be able to maintain or even increase the

size of the local public sector by turning to unconstrained revenue sources.

To evaluate the effects of TELs on the growth of municipal revenue, we first rely upon

the estimations of Equation 1 reported in Table 3. Here the coefficients of interest are �6

and �7. The coefficient �6 measures average revenue growth in TEL-adopting cities before

they enact a limit and �7 reports the average revenue growth in TEL-adopting cities after

the limit is enacted. If the adoption of local tax and expenditure limits reduces revenue

collection, �7 should be significantly smaller than �6.

The first three estimations of Equation 1, which include all respondent cities, each indicate

that the growth of own-source municipal revenue is lower in the period after the adoption

of a local TEL. Our point estimates show that revenue growth prior to the adoption of a

limit is, on average, about 1.8 percentage points higher than growth after adoption. We can

reject the hypothesis that revenue growth is actually larger after adoption at either the 5 or

10 percent levels in all models. When we control for demographic variables and state TELs

16

we can additionally reject the hypothesis that �6 and �7 are equivalent.

Again, the next three estimates exclude the 22 cities that enacted a TEL prior to 1970.

In these new models, the magnitude of the difference between revenue growth pre and post

adoption declines, but our findings remain unchanged—the adoption of a local TEL is still

associated with a decline in the growth of own-source municipal revenue. Moreover, we can

now reject the hypothesis that �6 and �7 are equivalent with increased confidence.

These estimations do not distinguish between TELs that target different revenue instru-

ments (i.e., property taxes versus sales taxes) or between limits that target a single revenue

instrument but work through slightly different mechanisms (i.e., a property tax rate limit

versus a property tax levy limit). Unfortunately, our sample is not large enough to distin-

guish between fine-grained categorizations of TELs. If we only include in our analysis the

most rigorous limits—those that restrict the property tax and those that restrict total tax

revenues or expenditures—our results remain unchanged, and the difference between the co-

efficients widens (as one would expect; see Online Appendix Table 6). Our results also remain

unchanged if we use total revenues as our dependent variable instead of total own-source rev-

enues (see estimates in Online Appendix Table 7). This indicates that TEL-adopters do not

compensate for lost local revenue by obtaining increased intergovernmental transfers from

either the state or federal government. Similarly, we do not observe an increase in the state

government share of total revenues following TEL adoption.

While the results of these estimations show a strong correlation between local TELs and

declines in revenue, it is unclear whether this is a causal relationship. Suppose that TEL-

adopting cities have more volatile own-source revenues. In this case our estimate may pick

up an inflection point in the volatility, but adoption could be one of many such points. To

test this possibility we randomly assign, for each TEL-adopting city, a fake year of adoption.

We re-estimate the specification in Column 2 (above) using the false year. We repeat this

process 5,000 times and generate p-value for tests of equivalency between �6 and �7.

17

When the year of adoption is randomized, we no longer find that TELs affect municipal

budgeting. The average p-value on the equivalency test for �6 and �7 is now .49, meaning

that we cannot reject the hypothesis that the rate of revenue growth before placebo TEL

adoption is the same as revenue growth afterwards. Indeed, we also can no longer reject

the hypothesis that �6 is smaller than �7.20 These results suggest that the year of TEL

adoption is meaningful and that our estimations reported in Table 2 were not simply picking

up a volatility in own-source revenues. These placebo tests do not, however, eliminate the

possibility that some other event which occurred at or near the time that the limit was

enacted is the “true” cause of the decline in revenue growth.

As a second robustness check, we employ propensity score matching. This technique

weights non-adopting cities with covariates similar to adopting cities more heavily than the

prior estimations of Equation 1. This empirical strategy helps us gain additional leverage

over whether TELs are driving the fiscal patterns we observe or whether they are due to other

features of the city. We generate propensity scores two ways—using cross-sectional data and

using time-series data. For the cross-section, the propensity score uses all cities in the sample

for which we observe data in 1970. With this sample, we estimate a probit for local TEL

adoption ever as a function of all of the demographic and institutional data employed above.

For the time-series matching, we use a probit model for local TEL adoption in a given year

as a function of the same covariates. The predicted values from these regressions are used

to create weights which are employed in new estimations of Equation 1.21

20The average p-value for the test that revenue growth before TEL adoption is less than revenue growthafter adoption is .247.

21The approach we use here is that of Imbens (2004). The weights are calculated as below, where thepropensity score is e(Xc) (the �tted value from the matching estimation) and TELc is coded one for citiesthat have a locally-adopted tax and expenditure limitation and zero otherwise. When we use time seriesdata in the matching process, the equation has a t subscript in addition to the c subscript.

�c =

√TELc

e(Xc)+

(1� TELc)

(1� e(Xc))

18

The results in Table 4 affirm that, even relative to cities that are more like adopters,

revenue growth declines after TEL adoption. The coefficients on revenue growth pre and

post adoption using matching techniques are quite similar to those generated in our original

estimations (Table 3). Tests of equality show that differences in the magnitude of these

coefficients are statistically significant, at either the 5 or 10 percent level. This is true

regardless of whether we use the cross-sectional or time-series propensity scores and true if

we exclude from our analysis cities that enacted a limit prior to 1970.

While matching techniques and placebo tests cannot fully address concerns regarding

causality, they do provide additional evidence of a TEL effect and strengthen our faith in

the original finding.22 Given the nature of our data collection, however, we do not need to

rely solely upon these analyses. We asked officials from TEL-adopting cities two additional

questions about the effects of these fiscal institutions. Though we are careful not to place

too much importance on their responses, they are consistent with a world in which local

TELs have budgetary consequences. Responses to these questions are reported in Table 5.

The first question asks officials whether their city has reached the limit established by the

TEL. Since this is largely a technical question and because the individuals we interviewed

occupy formal positions within the budgeting process, they should be well positioned to

provide an accurate answer. Based upon the results of our regression analysis, the answers

to this question should frequently be “yes.” Indeed, that is what we find. The vast majority

of respondents indicated that their city’s local TEL is binding or potentially binding on the

targeted revenue instrument. Over 45 percent of all respondents said that their city has hit

the limit, with another 39 percent answering that it is close to doing so. Only officials from

three municipalities said that the mandated limit was not close to being reached.

The second question asks officials how the TEL has affected fiscal policy. This question

22Of course, it is ultimately impossible to determine whether the TEL itself is causing the decline inrevenue growth or whether the reduction is caused by the threat from voters embodied by the TEL.

19

(unlike the first) requires a great deal of speculation from respondents—it may not be rea-

sonable to ask municipal officials how a TEL enacted decades ago has altered policy today.

That being said, respondents were provided with several choices (all options provided in the

survey are listed in Table 5). The modal response was “no clear effect”, which may confirm

the difficulty of the question. However, just over half of all responses indicated some policy

consequences with one-third of those saying that service provision has been curtailed. Many

officials also noted that their city’s TEL has resulted in the use of new revenue sources.

Overall, the weight of evidence presented in this section indicates that the adoption of

a local tax or expenditure limit leads to a reduction in the growth of municipal revenues.

This evidence is robust, coming from statistical analyses of municipal finance data and from

surveys of local budgeting officials. These findings are similar to several studies showing that

TELs imposed upon cities by states affect the size of the local public sector and the quality

of service provision (Shadbegian, 1996; Figlio and Rueben, 2001; Mullins, 2004; Brooks and

Phillips, 2010), but differ from recent work demonstrating that most TELs which apply to

state governments exhibit little or no effect on outcomes (Kousser et al., 2008).

5 Conclusion and Discussion

This paper makes three contributions. First, we document the existence of a new class of

fiscal rules: self-imposed municipal tax and expenditure limits. Our survey results show that

among cities with populations greater than 25,000 at least one in eight has a locally-adopted

TEL. These limits are distinct from those imposed upon cities by state governments and

typically target a major source of municipal revenue—the property tax. Importantly, local

limits appear to be difficult to circumvent since they often appear in city charters and usually

require a majority or super-majority vote of the electorate to override. The existence of these

TELs demonstrates that constitutional limits on government revenues and expenditures are

20

more common than previously thought. Until now, the literature has assumed that TELs

are only enacted at the state level, overlooking the possibility that similar limits may be

adopted by cities and other local governments.

Second, we find that local TELs are generally not adopted in response to high taxation by

city governments. While our case studies provide evidence that taxation levels are critical

in a limited number of cases, systematic analyses using time-series revenue data find no

meaningful evidence that adoption is more likely to occur in cities with higher own-source

revenues or where revenues have recently spiked. Instead, local newspaper coverage indicates

that adoption is often driven by policy entrepreneurs who promote a TEL to further their own

political careers or out of very strong policy preference. The lack of a systematic relationship

between taxation and TEL adoption suggests that the existence of TELs is not prima facie

evidence that city governments are acting like budgetary Leviathans.

Third, we present strong evidence that locally-adopted tax and expenditure limits are

meaningful constraints on the budgetary choices of elected officials. Utilizing a wealth of

data, we find that the adoption of a municipal TEL is associated with a statistically signifi-

cant reduction of 1.8 percentage points in the growth of municipal revenues. Compounded

over decades a reduction of this magnitude should have a meaningful effect on the fiscal ca-

pacity of cities. The finding of a TEL effect is confirmed by tests in which we randomize the

year of TEL adoption and is robust to estimations that rely upon propensity score matching.

Our analysis also indicates that cities are, on average, unable or unwilling to compensate

for revenues lost following TEL adoption by switching to alternative or unconstrained tax

instruments or by obtaining increased inter-governmental transfers. The finding of a TEL

effect is also confirmed by responses to survey questions. Budget officials from nearly all

municipalities with a locally-adopted limit said that their city has either reached or nearly

reached the established cap and many indicate that fiscal practices have changed as a re-

sult. By demonstrating that municipal fiscal rules (even those that are enacted and enforced

21

by localities themselves) are meaningful fiscal constraints, our analysis challenges market-

driven approaches to urban politics which have long questioned the fiscal significance of local

political institutions.

More broadly, our results speak to the effectiveness of constitutional fiscal rules as con-

straints on what many believe to be the natural proclivity of governments to tax and spend

too much. As noted in the introduction and demonstrated by our survey, voters often turn

to these rules as a non-electoral means of limiting government taxing powers. Not only does

our analysis suggest that such rules can be effective, but when combined with the prior

literature, our findings suggests a key pattern: TELs that target localities generally “work,”

regardless of whether they are imposed by the state or adopted by cities themselves, while

those that apply to state governments have little or no effect on fiscal outcomes. Why do

TELs affect only municipal budgets? The best explanation is probably that state govern-

ments have significantly more fiscal authority than cities (even cities with home rule powers)

and are able to use this authority to skirt all but the most stringent requirements and caps.

This raises the possibility that a similar pattern may also hold true for other types of fiscal

restrictions, including balanced budget requirements and debt limitations.

While our paper sheds needed light on the existence and consequences of municipal TELs,

we hope that it is just the opening salvo in the study of locally-adopted fiscal restrictions.

Important questions remain unanswered and beyond the scope of our analysis. What are the

patterns of TEL adoption in other types of local governments, including counties and school

districts? What factors give rise to the emergence and success of the political entrepreneurs

who advocate for local TELs? In addition to TELs, do local governments enact other, equally

as meaningful, fiscal restirctions? The answers to these and similar questions will further

enhance our understanding of subnational budgeting and local institutional choice.

22

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Kousser, Thad, McCubbins, Matthew D., and Moule., Ellen, 2008. “For Whom the TELTolls: Can State Tax and Expenditure Limits Effectively Reduce Spending?” StatePolitics and Policy Quarterly 8(4): 331–362.

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Morgan, David R. and Pelissero, John P., 1980. “Urban Policy: Does Political StructureMatter?” American Political Science Review 74(4): 999–1006.

Mullins, Daniel R., 2004. “Tax and Expenditure Limitations and the Fiscal Response ofLocal Government: Asymmetric Intra-Local Fiscal Effects.” Public Budgeting and Financewinter: 111–147.

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Shadbegian, Ronald J., 1996. “Do Tax and Expenditure Limitations Affect the Size andGrowth of State Government?” Contemporary Economic Policy 14: 22–35.

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25

Figure 1: Adoption of Local Limits Over Time

Source: Authors’ survey. Notes: This chart presents the number of surveyed cities reporting any local limit by year.

26

27

Table 1: Description of Local Limits, by Limit

   (1)  (2)   Number  Share      Number of Cities with Limits  40  12.5      Type of Limit       Assessment Limit  4  7.1 Property Tax Rate Limit  22  39.3 Other  12  21.4 Property Tax Levy Limit  9  16.1 Revenues or Expenditure Limit  3  5.4 Sales Tax Limit  6  10.7 Total  56  100      Where is the Limit adopted?       In municipal charter  38  67.9 In municipal code  13  23.2 No valid response  5  8.9      Is an override possible?       No valid response  5  8.9 No  2  3.6 Yes: Majority vote of the city council  6  10.7 Yes: Majority vote of the electorate  36  64.3 Yes: Super majority of the city council  3  5.4 

Yes: Super majority of the electorate  4  7.1 

Source: Authors’ survey.

Table 2: Comparison of Cities with and without Limits

        

29

   

Table 3: Local TEL Adoption and Own-Source Revenues Per Capita

      (1)  (2)  (3) (4)  (5)  (6) 

   Non‐Adopters, and Adopters, Including 

Adoption Before 1970   Non‐Adopters, and Adopters, Excluding 

Adopters Before 1970 Trend *                     

  1{Never Local Limit City} [A] 0.030***  0.038**  0.037**    0.030***  0.042**  0.042**               (0.002)  (0.013)  (0.012)   

      

      

      

                      

         

   

(0.002)  (0.013)  (0.013) 

  1{Local Limit City, Pre‐Limit} [B] 0.045*  0.051**  0.051** 0.026***  0.042**  0.041**               (0.019)  (0.017)  (0.017) (0.005)  (0.014)  (0.014) 

  1{Local Limit City, Post‐Limit} [C] 0.026***  0.033**  0.033** 0.022***  0.036**  0.036**               (0.005)  (0.013)  (0.013) (0.003)  (0.013)  (0.013) Year Fixed Effects  x  x  x x  x  x City Fixed Effects  x  x  x x  x  x Demographic Controls x  x x  x State Limits, on Cities & States x x R‐squared     0.839  0.855  0.856 0.828  0.843  0.843 Observations  9,348  9,348  9,348 8,972  8,972  8,972 Number of Cities  310  310  310 297  297  297 Relative Magnitude of Coefficients                     1.  p value, test [A] = [B]  0.429  0.266  0.259 0.448  0.918  0.943 2.  p‐value, test [A] > [B]  0.214  0.133  0.130   

   

   

0.224  0.459  0.471 3.  p value, test [B] = [C]  0.179  0.071  0.076 0.199  0.014  0.023 

4.  p‐value, test [B] < [C]  0.090  0.036  0.038 0.099  0.007  0.012 

Notes:*** Significant at the 0.1% level; ** significant at the 1% level; * significant at the 5% level; + significant at the 10% level. Dependent variable is per capita own-source revenue, and standard errors are clustered at the city level. 18 cities with local limits report a year of limit adoption over the period observed (1970-2004). Standard errors are clustered at the city level. Control variables are population, share of persons 25 and over with a college degree, share of persons 25 and over with a high school degree or more, number of families, number of housing units, number of occupied housing units, civilian labor force 16 and over, real median per capita income, persons below the poverty level, share black, share hispanic, share employed in government, share of housing units built since last census, housing units built before 1940, share manufacturing employment, number of occupied housing units with more than 1.01 persons per room, share service employment (by industry), share wholesale/retail employment, unemployment rate, share of persons less than 18, share of persons greater than 65, share of population of foreign origin, number of vacant housing units, log of population. “State Limits, on Cities and States” refers to two variables: one is a dummy equal to one if the state has a potentially binding limit on municipalities (defined as either a property tax levy limit, a general revenue or expenditure limit, or a property tax rate limit combined with an assessment limit) in that state in a given year, and the other is a dummy equal to one if the state has a limit on state spending in that year.

30

    

Table 4: Using Matching to Test Results

      (1)  (2) (3)  (4) 

              

Non‐Adopters, and Adopters, Including Adoption Before 1970

Non‐Adopters, and Adopters, Excluding Adopters Before 1970 

Propensity Score Based on Propensity Score Based on                 Cross‐Section  Time Series    Cross‐Section  Time Series Trend *                  1{Never Local Limit City}  0.034*  0.034*    0.045**  0.044**               (0.015)  (0.013)    (0.016)  (0.014) 

  1{Local Limit City, Pre‐Limit} [A] 0.042*  0.044**    0.047**  0.045**               (0.017)  (0.016)    (0.018)  (0.016) 

  1{Local Limit City, Post‐Limit} [B] 0.029  0.029*    0.042*  0.040*               (0.015)  (0.013)    (0.017)  (0.015) Year Fixed Effects  x  x    x  x 

City Fixed Effects  x  x    x  x 

Demographic Controls  x  x    x  x 

State Limits, on Cities & States  x  x    x  x 

R‐squared     0.837  0.854    0.815  0.833 Observations  7,029  8,030    6,612  7,654 Number of Cities  310  310    297  297 p value, test [A] = [B]  0.068  0.064    0.104  0.063 

p‐value, test [A] < [B]  0.034  0.032     0.052  0.032 

Sources: Authors’ survey & U.S. Census Bureau; see Section 3 in text for complete details. Notes: *** Significant at the 0.1% level; ** significant at the 1% level; * significant at the 5% level; + significant at the 10% level. This table repeats columns the estimation in columns 4 and 8 of the previous table, using propensity score weights. Standard errors are clustered at the city level. For the cross-section, the propensity score estimation uses cities in the sample for which we observe data in 1970. With this sample, we estimate a probit for local limit adoption ever as a function of population, share of persons 25 and over with a college degree, share of persons 25 and over with a high school degree or more, number of families, number of housing units, number of occupied housing units, civilian labor force 16 and over, real median per capita income, persons below the poverty level, real median gross rent, share black, share hispanic, share employed in government, share of housing units built since last census, housing units built before 1940, share manufacturing employment, number of occupied housing units with more than 1.01 persons per room, share service employment (by industry), share wholesale/retail employment, unemployment rate, share of persons less than 18, share of persons greater than 65, share of population of foreign origin, number of vacant housing units, log of population and census division dummy variables (9 divisions). For the time-series matching, we estimate a probit for local tel adoption in a given year as a function of the same covariates.

Table 5: Effects of Local Limits, by City

   (1)  (2)   Number  Share      Has Your City Reached the Legislated Limit?       No valid response  3  7.4 No, but close  3  7.4 No, not close  16  39.3 Yes  19  45.9      Has the Limit Affected Practices in Your City?       Affects long term projects only  1  1.6 No valid response  9  14.1 No clear effect  22  34.4 Other  9  14.1 We have increased borrowing  1  1.6 We have new revenue sources  11  17.2 

We have reduced service provision  11  17.2 

Source: Authors’ survey. Notes: Responses to the question of whether a city had reached its limit came from officials; this table uses the average response of officials by city and thus does not exactly total to 40 cities. We allowed respondents to choose more than one way the local TEL could affect practices in their city, so this question has more answers than there are cities with TELs.

31

Online Appendix Data Sources

This note lists the data sources we combine with our survey information. Demographic Data

• Decennial Censuses o 1970 Census, ICPSR 8109, 8129 o 1980 Census Summary File 3A, ICPSR 8071 o 1990 Census Summary File 3A, ICPSR 9782, save CA which is damaged; used

file from UCLA ATS o 2000 Census Summary File 3, ICPSR 13324-13392

Fiscal Data

• Annual Survey of Government Finances, Census of Government Finances o 1970-2006, municipal data only

• Consumer Price Index o Bureau of Labor Statistics, 1970-2006 o Series is Seasonally Adjusted, All Urban Consumers (CUSR0000SA0)

Institutional Data

• Home Rule status, Mayor-Council status, elected officials, & other municipal institutional characteristics:

o 1987 Census of Governments, Organization File, Municipal Level • Municipal Initiative Information

o Legal Landscape Database, USC Initiative and Referendum Institute • Tax and Expenditure Limits by States on Cities

o Advisory Commission on Intergovernmental Relations, 1995. Tax and Expenditure Limits on Local Government, Washington, DC.

o Mullins, Daniel R. and Wallins, Bruce A., 2004. “Tax and Expenditure Limits: Introduction and Overview.” Public Budgeting and Finance 24(2): 2-15.

• Tax and Expenditure Limits by States on States o Waisanen, Bert. “State Tax and Expenditure Limits – 2008.” National Conference

of State Legislatures, 2008. http://www.ncsl.org/default.aspx?tabid=12633 • Additional information on Home Rule

o Krane, Dale, Platon N. Rigos, and Melvin B. Hill, 2000. Home Rule in America, Washington, D.C.: Congressional Quarterly Press.

Case Study Data We relied on the sources below, supplemented by searches on municipal websites. Please contact us for specific citation on any particular limit.

• www.newsbank.com, with coverage of many local daily newspapers, primarily starting in the late 1990s or early 2000s

• Lexis/Nexis, with coverage of additional local daily newspapers • for Pueblo, Colorado, Researcher Carol Edwards undertook primary source investigation

1

Online Appendix: Survey Instrument

[letterhead omitted for confidentiality of submission]

Hello – I am conducting a [university name omitted] survey on city-level restrictions on taxes and expenditures, and am writing to ask for your help in completing the survey. In particular, our research team is interested in whether your city imposes limits on taxes and expenditures in addition to those limits imposed by state statutory or constitutional law. More information on the team and the survey is available at [website omitted]. To complete the survey, fill in this writeable pdf file. If you have Acrobat Writer, you may save the form and return it to us as an attachment by email [address omitted]. If you don’t have Acrobat Writer or if you prefer to print and mail or fax the form, our fax number is [omitted], and address is [omitted]. We greatly appreciate your completing our survey. If you have questions, please feel free to call me at [omitted]. If you would like a copy of the completed survey, please check here: Many thanks, [name omitted] City Name: State: Position: Name (optional): Question 1. Does your city charter or code contain any of the following tax and expenditure limits? In particular, these are not limits from the state, but limits imposed by your city on itself. Please select all that apply. Yes or No If Yes, When? If Yes, How? A) Ceiling on Property Tax Rates This limitation caps property tax rates. __ No

__ No, but considered __ Yes

Year: _____ __ Don’t Know

__ Limit in municipal code __ Limit in municipal charter __ Other

B) Property Tax Levy Limit This limitation restricts the amount of revenue that can be raised using the property tax. __ No

__ No, but considered __ Yes

Year: _____ __ Don’t Know

__ Limit in municipal code __ Limit in municipal charter __ Other

C) Limit on Assessment or Assessment Increases

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This limitation caps the assessed value of property or restricts the rate at which the government can increase the assessed value of property for tax purposes. __ No

__ No, but considered __ Yes

Year: _____ __ Don’t Know

__ Limit in municipal code __ Limit in municipal charter __ Other

D) Limit on the Overall Sales Tax Rate This limitation caps the local sales tax rates. __ No

__ No, but considered __ Yes

Year: _____ __ Don’t Know

__ Limit in municipal code __ Limit in municipal charter __ Other

E) General Revenue or Expenditure Limitation This limitation caps the amount of total revenues a government can collect or the total expenditures that it can make. __ No

__ No, but considered __ Yes

Year: _____ __ Don’t Know

__ Limit in municipal code __ Limit in municipal charter __ Other

F) Any Other Type of Limitation Any other municipally-imposed limit on taxes or expenditures. __ No

__ No, but considered __ Yes

Year: _____ __ Don’t Know

__ Limit in municipal code __ Limit in municipal charter __ Other

Please describe this other limit:

Question 2. How would you characterize the general attitude of the majority of your city’s voters toward taxation? Answers to this question are completely confidential.

Strongly anti-tax……… Moderately accepting of tax increases..... Moderately anti-tax…… Very accepting of tax increases………… Neutral………………... Don’t know……………………………..

If your answer to Question 1 above was no for all limits, you are finished with the survey. We greatly appreciate your response. If you have answered yes to any of the above, please continue. Question 3. How has your city been affected by these limits?

Has Your City Reached Its

Limits?

Has the Limit Affected Fiscal Practices?

Can Your City’s Limits be Overridden?

A) Ceiling on Property Tax Rates __ Yes

__ No, but close __ No, not close

__ No clear effect __ We have increased borrowing __ We have reduced service provision

__ No __ Yes, Majority vote of the electorate __ Yes, Super majority of the electorate

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__ We have found new revenue sources __ Other – Please describe in notes below

__ Yes, Majority vote of the city council __ Yes, Supermajority vote of council __ Yes, Other – Please describe in notes below

B) Property Tax Levy Limit __ Yes

__ No, but close __ No, not close

__ No clear effect __ We have increased borrowing __ We have reduced service provision __ We have found new revenue sources __ Other – Please describe in notes below

__ No __ Yes, Majority vote of the electorate __ Yes, Super majority of the electorate __ Yes, Majority vote of the city council __ Yes, Supermajority vote of council __ Yes, Other – Please describe in notes below

C) Limit on Assessment or Assessment Increases __ Yes

__ No, but close __ No, not close

__ No clear effect __ We have increased borrowing __ We have reduced service provision __ We have found new revenue sources __ Other – Please describe in notes below

__ No __ Yes, Majority vote of the electorate __ Yes, Super majority of the electorate __ Yes, Majority vote of the city council __ Yes, Supermajority vote of council __ Yes, Other – Please describe in notes below

D) Limit on the Overall Sales Tax Rate __ Yes

__ No, but close __ No, not close

__ No clear effect __ We have increased borrowing __ We have reduced service provision __ We have found new revenue sources __ Other – Please describe in notes below

__ No __ Yes, Majority vote of the electorate __ Yes, Super majority of the electorate __ Yes, Majority vote of the city council __ Yes, Supermajority vote of council __ Yes, Other – Please describe in notes below

E) General Revenue or Expenditure Limitation __ Yes

__ No, but close __ No, not close

__ No clear effect __ We have increased borrowing __ We have reduced service provision __ We have found new revenue sources __ Other – Please describe in notes below

__ No __ Yes, Majority vote of the electorate __ Yes, Super majority of the electorate __ Yes, Majority vote of the city council __ Yes, Supermajority vote of council __ Yes, Other – Please describe in notes below

F) Any Other Limitation, As Identified in Question 1F __ Yes

__ No, but close __ No, not close

__ No clear effect __ We have increased borrowing __ We have reduced service provision __ We have found new revenue sources __ Other – Please describe in notes below

__ No __ Yes, Majority vote of the electorate __ Yes, Super majority of the electorate __ Yes, Majority vote of the city council __ Yes, Supermajority vote of council __ Yes, Other – Please describe in notes below

If you have any additional notes or comments about municipally imposed tax and expenditures limits, we are eager to hear them. Please write in the box below.

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We thank you again for your participation in this survey.

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Online Appendix Table 1: Comparison of Samples          (1)  (2)  (3)  (4) 

       Cities Between 25,000 & 

100,0000 people   

     

Sampled with 

Certainty  In Sample Out of Sample 

t test:  (2) vs (3) 

Fiscal Covariates ($1,000s per capita)               Total Revenues  mean  2.131  1.874  1.608  2.26     sd  1.612  1.076  1.010       count  246  90  908     Own‐Source Revenues    1.609  1.445  1.285  1.79       1.188  0.810  0.835         246  90  908     Property Tax Revenues    0.374  0.522  0.413  1.65       0.364  0.609  0.486         246  90  905   Demographic Covariates                  Population  mean  318,763  47,605  47,851  0.13     sd  625,360  18,168  19,569       count  246  95  963     Real Median Family Income ($1,000s)  58.3  67.1  65.3  0.65       16.4  25.5  22.5         246  95  963     Share African American    0.18  0.11  0.10  0.20       0.18  0.16  0.15         246  95  960   Institutional Covariates                  Home Rule Status  mean  0.62  0.51  0.48  0.38    (1 if yes, 0 otherwise)  sd  0.49  0.50  0.50       count  227  77  790     Mayor‐Council Form of Government  0.39  0.33  0.36  0.65    (1 if yes, 0 otherwise)    0.49  0.47  0.48         246  95  963     Citizen Initiative    0.87  0.82  0.83  0.26    (1 if city has, 0 otherwise)    0.34  0.39  0.37   

         230  67  692    

Notes: This table uses data from the 2002 cross-section, the most complete and recent year of data. Column one includes cities with a population greater than 100,000 in 2002; columns 2 and 3 compare sampled and unsampled cities between 25,000 and 100,000 people.

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Online Appendix Table 2: Share of Cities with Local Limit by Type of State Limit

   (1)  (2)  (3)  (4) (5)   State Limit on Cities     

Local Limit Property  Tax Rate  

Property  Tax Levy   Assessment  

Revenue or Expenditure     

State Limit  on State 

Property Tax Rate   0.91  0.91  0.36  0.18    0.77 Property Tax Levy   0.89  0.89  0.11  0.33   

   

    

0.56 Assessment   0.25  0.25  0.25  0.00 0.25 

Revenue or Expenditure   1.00  1.00  0.00  0.50 0.50 

Sources: Local limit information from authors’ survey. State limit data from ACIR (1995) and Mullins and Wallins (2004). Notes: This table presents the share of cities with a local limit that have that named limit at the state level.

Online Appendix Table 3, continued

    (1)  (2)    (3)  (4)       OLS    Probit Number of Occupied Housing Units (10,000s)    ‐0.145      ‐0.931                 (0.105)      (0.690) Civilian Labor Force, 16 and over (10,000s)    ‐0.061      ‐0.461                 (0.048)      (0.309) Persons Below Poverty Level (10,000s)    ‐0.074+      ‐0.518+                 (0.043)      (0.304) Share Employed in Government    0.505      3.544                 (0.656)      (4.856) 

Share of Housing Units Built Since Last Census    ‐0.851***     ‐

11.773***                (0.240)      (3.278) Housing Units Built Before 1940 (10,000s)    ‐0.01      ‐0.043                 (0.016)      (0.140) Share of Employed in Manufacturing    0.483      8.213*                 (0.527)      (3.916) Number of Occupied Housing Units with     0.026      0.17      ≥ 1.01 persons per room (10,000s)    (0.048)      (0.438) Share Service Employment    0.465      3.648                 (0.521)      (4.282) Share Wholesale/Retail Employment    ‐0.69      ‐0.098                 (1.410)      (9.895) Unemployment Rate    ‐1.795      ‐16.179                 (1.552)      (13.502) Share of Persons Less than 18    0.049      ‐8.491                 (1.605)      (13.694) Share of Persons Greater than or Equal to 65    ‐2.826**      ‐35.526***                 (1.085)      (9.886) Share of Population of Foreign Origin    ‐0.321      ‐2.839       (0.372)      (2.999) R‐squared     0.125  0.243       

Observations  253  253    253  253 

Notes: ** Significant at the 1% level; * significant at the 5% level; + significant at the 10% level. Dependent variable is one if the city has a local TEL and zero otherwise. Cross-section is from 2002; this is the most recent year with the best coverage. Region dummies are the four census regions. Sample is largest number of cities that include all covariates; results do not differ substantively if we use the largest possible sample in each regression. Standard errors are clustered at the city level.

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Online Appendix Table 4: Hazard Models of Local Limit Adoption

         (1)  (2)     (3)  (4) 

                   Cox Proportional Hazard   Exponential Survivor 

Model Real Own‐Source Revenue, per capita    0.135  0.176    0.13  0.162                   (0.157)  (0.218)    (0.161)  (0.222) Herfindahl Index for               Income    34.661  52.916    46.139  74.596                   (35.886)  (32.996)    (49.224)  (51.932)   Race    2.436  2.402    2.314  2.328                   (1.705)  (3.999)    (1.687)  (3.945) 1 if State Has a Binding TEL,     ‐0.416  ‐0.778    ‐0.428  ‐0.777   0 otherwise    (0.575)  (0.620)    (0.569)  (0.611) 1 if Homerule, 0 otherwise    1.570*  1.216    1.562*  1.228                 (0.798)  (0.867)    (0.792)  (0.875) 1 if Mayor‐Council government,     0.541  0.704    0.497  0.708      0 otherwise    (0.538)  (0.541)    (0.548)  (0.537) Number of Cities in MSA    ‐1.834  ‐2.621+    ‐1.824  ‐2.719+                 (1.427)  (1.584)    (1.406)  (1.595) 1 if City Voters Have initiative Power    0.327  0.063    0.293  0.085                 (0.777)  (0.901)    (0.782)  (0.946) Region Dummies (Northeast omitted)               Midwest      ‐0.223      ‐0.184         (1.016)      (1.026)   South      ‐0.270      ‐0.279         (0.795)      (0.820)   West      ‐0.700      ‐0.732                   (0.891)      (0.918) Population      ‐0.295      ‐0.235                   (0.261)      (0.283) Log of Population      0.637+      0.520                   (0.358)      (0.437) Real Median Family Income, $1000s      37.137*      47.018+                   (18.599)      (24.130) Share African‐American      ‐2.934      ‐2.671                   (3.771)      (3.729) Share Hispanic      1.634      1.922                     (1.871)      (1.801) Year Fixed Effects          x  x 

Observations     6,741  6,741     6,741  6,741 

Notes: The dependent variable is time to adoption of a local limit. Standard errors are clustered at the city level.

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Online Appendix Table 5: Is Time Variation in Local Limit Adoption Explained by Variation in Any Revenue

Source?

      (1)  (2)  (3)  (4)     Tax or revenue variable is total per capita 

                Own‐Source Revenue   Revenue  

Tax Revenue  

Property Tax 

Revenue  Tax or Revenue Variable  0.176  0.217  0.493  0.99                 (0.218)  (0.152)  (0.335)  (0.770) Herfindahl Index for           Income  52.916  47.544  44.28  49.642                 (32.996)  (36.162)  (33.560)  (35.940)   Race  2.402  3.124  3.664  2.386                 (3.999)  (4.199)  (4.913)  (3.754) 1 if State Has a Binding TEL,   ‐0.778  ‐0.722  ‐0.699  ‐0.733   0 otherwise  (0.620)  (0.626)  (0.604)  (0.610) 1 if Homerule, 0 otherwise  1.216  1.184  1.103  1.197               (0.867)  (0.866)  (0.890)  (0.883) 1 if Mayor‐Council government,   0.704  0.655  0.697  0.684      0 otherwise  (0.541)  (0.548)  (0.541)  (0.534) Number of Cities in MSA  ‐2.621+  ‐2.738  ‐2.543  ‐2.505+               (1.584)  (1.846)  (1.616)  (1.420) 1 if City Voters Have initiative Power  0.063  ‐0.015  ‐0.002  0.289               (0.901)  (0.945)  (0.995)  (1.039) Region Dummies (Northeast omitted)           Midwest  ‐0.223  0.02  0.098  0.51     (1.016)  (1.055)  (1.088)  (1.458)   South  ‐0.27  ‐0.043  0.16  0.588     (0.795)  (0.822)  (0.852)  (1.353)   West  ‐0.7  ‐0.598  ‐0.483  0.191               (0.891)  (0.902)  (0.897)  (1.399) Population  ‐0.295  ‐0.305  ‐0.308  ‐0.242               (0.261)  (0.258)  (0.259)  (0.270) Log of Population  0.637+  0.618+  0.663+  0.655+               (0.358)  (0.361)  (0.372)  (0.357) Real Median Family Income, $1000s  37.137*  31.350+  23.008  23.359               (18.599)  (18.623)  (22.575)  (22.846) Share African‐American  ‐2.934  ‐4.443  ‐5.666  ‐3.57               (3.771)  (4.306)  (5.853)  (3.340) Share Hispanic  1.634  1.136  0.399  1.13                 (1.871)  (2.024)  (2.503)  (1.922) 

Observations  6,741  6,741  6,738  6,738 

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Notes: The dependent variable is time to adoption of a local limit. We estimate Cox proportional hazard models, and the first column of this table repeats Column 2 from Online Appendix Table 4. Standard errors are clustered at the city level.

Online Appendix Table 6: TEL Adoption and Own-Source Revenues, Strong Limits Only

      (1)  (2)  (3)     (4)  (5)  (6) 

                         

Non‐Adopters, and Adopters, Including Adoption Before 1970   

Non‐Adopters, and Adopters, Excluding Adopters Before 1970 

Trend *

  1{Never Local Limit City} [A] 0.030***  0.038**  0.038**    0.030***  0.042**  0.042**               (0.002)  (0.013)  (0.012)   

      

      

      

                      

         

                         

(0.002)  (0.013)  (0.013) 

  1{Local Limit City, Pre‐Limit} [B] 0.049*  0.055**  0.055** 0.028***  0.044**  0.043**               (0.021)  (0.017)  (0.018) (0.006)  (0.014)  (0.014) 

  1{Local Limit City, Post‐Limit} [C] 0.027***  0.035**  0.035** 0.024***  0.037**  0.037**               (0.006)  (0.013)  (0.013) (0.003)  (0.014)  (0.013) Year Fixed Effects  x  x  x x  x  x City Fixed Effects  x  x  x x  x  x Demographic Controls x  x x  x State Limits, on Cities & States x x R‐squared     0.839  0.855  0.855 0.828  0.843  0.843 Observations  9,176  9,176  9,176 8,867  8,867  8,867 Number of Cities  305  305  305 294  294  294 Relative Magnitude of Coefficients  p value, test [A] = [B]  0.373  0.222  0.216 0.753  0.764  0.735   p‐value, test [A] > [B]  0.186  0.111  0.108   

   

    

0.376  0.382  0.367   p value, test [B] = [C]  0.177  0.067  0.071 0.222  0.012  0.021 

   p‐value, test [B] < [C]  0.089  0.034  0.036 0.111  0.006  0.010 

Notes: This table has fewer unique cities than Table 3 in the paper; we drop cities that adopt local limits, but which do not adopt strong limits.

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Online Appendix Table 7: TEL Effects on Alternative Dependent Variables

      (1)  (2)     (3)  (4) 

          

Sample is Non‐Adopters, and Adopters, Including Adoption Before 1970

Sample is Non‐Adopters, and Adopters, Excluding Adopters Before 1970 

Dependent Variable is     Dependent Variable is  

                     

Total Revenue per capita 

Share of Revenue from State 

Total Revenue per capita 

Share of Revenue from State 

Trend *

  1{Never Local Limit City} [A] 0.047***  0.001    0.053***  0.001               (0.014)  (0.001)   

      

      

                     

                             

   

(0.014)  (0.001) 

  1{Local Limit City, Pre‐Limit} [B] 0.067**  0.002 0.053**  0.002               (0.020)  (0.002) (0.016)  (0.002) 

  1{Local Limit City, Post‐Limit} [C] 0.042**  0.001 0.045**  0.002                 (0.014)  (0.001) (0.014)  (0.001) Year Fixed Effects  x  x x  x City Fixed Effects  x  x x  x Demographic Controls  x  x x  x State Limits, on Cities & States  x  x x  x R‐squared     0.877  0.818 0.862  0.817 Observations  9,348  9,348 8,972  8,972 Number of Cities  310  310 297  297 

Relative Magnitude of Coefficients  p value, test [A] = [B]  0.233  0.270 0.928  0.443   p‐value, test [A] > [B]  0.116  0.135   

   

    

0.464  0.221   p value, test [B] = [C]  0.063  0.373 0.035  0.471 

   p‐value, test [B] < [C]  0.032  0.187 0.018  0.236 

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