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The Choice for Scrutiny in Local Revenue Decisions:

A Case for California Counties 2001~2010

Sanghee Park, Seoul National University (selotus@gmail.com; 82-10-8698-0417; 1-213-505-4769)

Abstract

This study examines what drives local governments to choose to rely more on alternative sources

to increase revenue in the context of California counties between 2001 and 2010. By focusing on

nontax revenues such as fees and charges, fines and forfeits, this paper seeks to understand the

strategic behavior of local governments under financial stress and political constraints. Fixed

effects estimation on the two types of dependent variables—the ratio and the dollar amount—

shows the importance of the economy variable in the county revenue decisions. The ratio and the

amount of fees and charges, fines and forfeitures increases when the county unemployment rate

rises. The ratio of nontax revenues also differs across local demands and characteristics such as

county median income and willingness to pay taxes. The intergovernmental aids and the

percentage of Democratic votes in the gubernatorial elections have significant effects on the share

of nontax revenues, yet signs differ between the ratio and the amount models.

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Introduction

Public sector economics have focused on how to spend money, but the question of how to earn it

becomes much more important in times of economic stress (Stiglitz 2000). Under chronic financial

stress, decreasing state budgets have left local governments few options in revenue decisions—

cutting spending, raising taxes, and finding new sources of revenue (Wu 2009). Reducing the

existing level of expenditure could be a direct response from the localities in the face of shrinkage,

but it is certainly neither particularly viable nor politically popular. Cities and counties are more

inclined to cut spending rather than raise taxes because it causes less resistance from residents

(MacManus 1993). Meanwhile, raising local property tax rates has been more realistic and easier

because such taxes are already a main source of local revenue. However, local revenue decisions are

always constrained by state controls as well as competition among other local governments

(Pagano and Johnston 2000). Moreover, such measures have become more difficult to pass since

the tax and expenditure limitations (TEL) in 1970s, such as California's Proposition 13 in 1978.

For these reasons, neither reducing spending nor raising taxes has been a popular option

(Neimand and Krimm 2009: 22). Now local governments are desperate to exploit new sources of

revenue in order to survive in local competition for economic efficiency and better services. There

has been a marked change in the source of government revenues (Stiglitz 2000: 44). It is time for

local governments to think about how to raise revenue without much adverse effect on the

economy. However, scholarly attention to revenue politics has been relatively thin for several

reasons (Royed and Borelli 1999; Blom-Hansen et al. 2006: 446).

Under fiscal federalism, there is a division of economic responsibilities between the federal,

state, and local governments (Stiglitz 2000: 728). Local governments have more responsibility for

allocating resources in programs, while state governments are required to take responsibility in

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distribution and stabilization (Musgrave 1959: 179-83). Cities and counties may react to fiscal

stress differently, given the different resource bases, functional responsibilities, authorities, and

local needs (MacManus 1993; Neiman and Krimm 2007). Accordingly, revenue decisions also vary

significantly among cities and counties (Pagano and Johnston 2000).

County governments operate not only as administrative arms of the state government but

also as direct providers of local services (Ostrom and Ostrom 1988; Berman 1993; Berman and

Salant 1996; Benton 2002a, b; Chapman 2003; Choi et al. 2010). The role of county governments

has been significantly expanded in recent years (Benton 2002a; Streib et al. 2007), which allows

them a wide range of choices in services or programs as well as in revenue decisions (Morgan and

Kickham 1999; Sokolow and Detwiler 2001; Streib et al. 2007). Demands for more services and

devolution to local governments, coupled with the worsening economy, may accelerate this trend.

Counties are more likely to rely on revenue source-related strategies than cities and school districts

(MacManus 1993: 204); however, the studies on county responses have been limited (e.g.,

MacManus 1993).

This study examines what drives local governments to choose to rely more on alternative

sources to increase revenue. By focusing on nontax revenues such as fees and charges, fines and

forfeits, this paper seeks to understand the strategic behavior of local governments under financial

stress and political constraints. Specifically, we investigate the factors determining dependence on

nontax revenues in the context of California counties between 2001 and 2010. We also consider the

difference between the economic incentives levying user fees and charges and hard measures such

as fines and forfeitures.

We claim that counties facing under economic hardship will be more dependent on nontax

revenues associated with incentive tools, such as fees and charges, fine and forfeitures, in delivering

local services under state oversight. Among the various tools of government, decision-makers in

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local governments, such as county supervisors, may choose to specify monetary charges for certain

activities and/or crack down on fraud as a way of securing the budget, increasing services without

more taxation, and achieving equity and equality. Although the portion of fines and forfeits is small

in local revenue shares, it is an important question as the decision will affect individual and

organizational behavior in the long run.

Local choices for economy and scrutiny will likely vary according to several factors. This

study explores the impact of the economic, political, institutional, financial, and local factors closely

related to local revenue decisions. The hypotheses are tested across 58 counties in California over

10 years between 2001 and 2010. Data will be drawn from various sources such as the Counties

Annual Report, the Bureau of Labor Statistics, RAND California, the California Elections Data

Archive (CEDA), and the Public Policy Institute of California (PPIC). The dependent variable will be

measured by the ratio of the amount of fees and charges, fine and forfeitures to the total revenues.

The model will include variables that explain economic conditions, such as unemployment

rate and county residents’ expectations for 12 months; political factors as ideological preference,

political support for the governor and Democratic vote shares in gubernatorial elections in every

four years; institutional variables such as county home rule and Proposition 1A; financial factors

such as intergovernmental revenues from federal and state government and property tax ratio out

of total own-source revenue; and local demand factors such as county wealth measured by median

income, willingness to pay taxes, and interest in political issues on the part of county residents.

County Revenue Decisions in Times of Fiscal Crisis

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County revenue structure has changed significantly due to the increasing role of counties as

service delivery agents and to institutional changes such as the TELs and Proposition 13. The tax

and expenditure limitations (TELs) and Proposition 13 decrease dependence on local property

taxes while prompting local governments to seek out alternative sources of revenue (Carroll and

Johnson 2010). Nowadays, local governments are “desperately seeking revenue” (Altshuler et al.

2010: 331), but they are in the difficult situation that state governments under budget pressures

are more likely to transfer program responsibilities without funding. As local governments are now

resistant to unfunded state mandates (Berman and Salant 1996), local revenue decisions are

becoming “the fiercest policy battleground of state-local relations.” (Krane et al. 2004: 515).

Property taxes are the main source of local revenue. However, raising property tax rates is

not always possible because of institutional constraints such as tax and expenditure limitations

(TELs). Since 1970s, local autonomy in property taxation has been seriously limited (Solokow

2000; Krane et al. 2004). In California, for example, Proposition 13 in 1978, seeking to protect

homeowners, forbade local officials from setting their own property tax rates. Moreover, elected

officials in the interlocal competitions for residents, employment, and businesses (Tiebout 1956;

Peterson 1981) may be reluctant to raise property tax rates as the unpopular measure creates

political risks (Krane et al. 2004). Politicians are sensitive to public opinions on local expenditures

and revenue (Soroka and Wlezien 2005).

Due to the institutional constraints and the diminishing importance of property taxes as a

local revenue source, local governments seek alternative sources such as nontax revenues from fees,

licenses, charges, fines, forfeiture, and miscellaneous revenues (Krane 2004: 523-24; Carroll and

Stater 2009). In times of fiscal crisis, state governments prioritize federal programs and services

(Sosin et al. 2010), which allows local officials in cities and counties a considerable amount of fiscal

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discretion (Krane et al. 2004). In particular, the State of California permits wide autonomy to local

governments in the structural, programmatic, and fiscal areas (Sokolow and Detwiler 2001: 59).

California is an appealing context for examining local choices on revenue sources. In

addition to the significant diversity among California counties, local service demands and

population keep growing. California counties have a wide variety of responsibilities in local demand

as well as state mandates. During the economic downturn since the early 2000s, California counties

have experienced both an increase in state mandates and local demands and a decrease in taxable

transactions and intergovernmental aids. They spend relatively large amounts of money compared

to counties in other states, yet they are much influenced by the state’s fiscal decisions (Neimand

and Krimm 2009).

The state has long suffered from continuing fiscal constraints. California’s unemployment

rate has been always higher than the national average, even when the state economy was strong

(PPIC 2009). Since the early 2000s, California has suffered from a bubble-burst caused by the stock

market decline (Musso et al. 2006). Since Proposition 13, the state-local fiscal conflicts in California

have intensified, and state-local antipathies become more severe in times of economic strain

(Neiman and Krimm (2009). Even Proposition 1A in 2004 failed to resolve a number of problematic

aspects of the fiscal system (Barbour 2007).

Despite some distinctive features, however, California’s revenue and spending patterns are

not that different from those of other states, which allow us to find implications for local

governments in other states (Cain and Mackenzie 2008). As in other states, California's reliance on

nontax sources such as fees, charges, fines, forfeitures, and other miscellaneous revenues has been

growing significantly (Shadbegian 1999; Cain and Mackenzie 2008). There is reason to expect a

similar pattern to hold in other states.

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Although local revenues are mostly from taxes such as property taxes, sales taxes, and

income taxes, the importance of nontax revenues has become more evident in recent years.

Significant amounts of revenues are from nontax sources such as fees, charges, fines, and forfeitures.

The growing reliance on other sources of revenue leads to revenue diversification, which has been

regarded as a desirable direction for local finance. Since Proposition 13, local governments have

tried to diversify their revenue structures in order to decrease reliance on property taxes and

increase flexibility and stability during economic downturns. Quite a number of studies in local

finance have investigated the effect of revenue diversification, although it is not always clear and

depends on the types of services provided by local governments (Carroll and Johnson 2010;

Mikesell 2007). Some argue that revenue diversification leads to stability in revenue structure

(Carroll and Stater 2009; Carroll 2009; Jordan and Wagner 2008), while others in economics focus

on higher tax burdens for fiscal illusions (Wagner 1976; Hendrick 2002: 53). Hendrick (2002)

shows that revenue diversification lowers tax efforts when controlling for other factors. However,

revenue diversification itself is not the objective that local governments are pursuing; rather, it is

an outcome of the local governments’ efforts to find alternative sources of revenue. Although the

increasing reliance on other sources coincides with decreasing property tax shares, it is more direct

to see local efforts toward alternative sources in the share of the revenue from fees, charges, fines,

and forfeitures.

Hypotheses

This study focuses on the local decisions for increasing reliance on fees and charges, and fines and

forfeitures as a new source of revenue. This study attempts to identify whether economic hardship

prompts strategic behavior to rely more on pay-use systems and more scrutiny in local services.

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Specifically, counties under economic stress are more likely to raise revenues from (1) fees and

charges to earn money without severe resistance; and (2) fines and forfeitures to save money by

improving oversight on waste and fraud (Cain and Mackenzie 2008).

The two broad categories of solving public sector problems are market-based solutions and

direct regulation (Stiglitz 2000: 223). Economists tend to believe that market-like mechanisms

ensure more efficient behavior (Stiglitz 2000: 233). The research that specifically addresses the use

of user charges presents a set of findings that relate to economic efficiency (Downing 1992; Sun and

Jung 2012). Although the reliance on nontax revenue may be driven by the economic incentive of

local governments, we assume that the reliance on fees and charges is based on a different logic

from that of fines and forfeitures.

The potential of user charges as a new source of revenue has been discussed in several

studies. Fees and charges can be useful when it is possible to exclude those who benefit from the

good or service from the non-users. Under the benefit principle “those who benefit bear the costs”,

fees and charges are a relatively easy and equitable way of raising revenues (Stiglitz 2000: 140).

User charges also have revenue potential with economy-enhancing effects on local expenditures,

especially in decentralized institutional arrangements (Bierhanzl and Downing; Oates 1994: 131;

Jung and Bae 2011; Sun and Jung 2012). Several factors affect reliance on user charges in county

governments such as TELs (Shadbegian 1999; Mullins and Joyce 1996), intergovernmental revenue,

functional changes, changes of expenditure mix (Tannenwald 1990), and county government

structure (Benton 2003). However, user charges are limited to the specific services and also comes

with equity issues (Pearson 1981; Sun and Jung 2012). They engage inefficiencies or distortions like

under-consumption of the relevant good or service, especially when government charges more

money than the good or service costs (Stiglitz 2000:130).

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On the other hand, fines and forfeitures have received little attention, mainly due to their

negligible share of local revenues. Yet the amount, not the ratio, of the money has been increasing

continuously and rapidly. Fines and forfeitures fall into the category of regulation and punitive

monetary sanction, which is the essence of government activities. The primary purpose of levying

fines and forfeitures is to reduce social costs associated with negative behaviors, but it also is

associated with economic benefits related the control (for more information, Becker 1968).

Although fines and forfeitures are nontax revenues category, they are equivalent to corrective taxes,

sometimes called Pigouvian taxes (Stiglitz 2000: 277). The choice for more scrutiny may be related

to economic incentives rather than sanction or coercion itself.

The revenue potential of these alternative sources comes from the possibility of inter-fund

transfer. In particular, user fees are to be used for the specific functions in principle, but they can be

transferred to the general fund with the approval of city councils. Although it involves some

transaction costs, the money from user fees offer a great revenue opportunity for local

governments.

We set forth an array of hypotheses that the ratio and the amount of fees and charges, fines

and forfeitures is affected by the economic, political, institutional, financial factors as well as local

demand and characteristics. We are interested in determining the relative impact of the

independent variables in the equation, rather than using the model for predictive purposes.

Economic factors: Unemployment rate, county residents’ expectation of economy in 12

months

The economy is an essential part of local finance. In times of economic hardship, we expect

more entrepreneurial reactions from county governments that increase or at least maintain own-

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source revenue in order to offset the decrease in the state budget. Under such conditions, counties

are more likely to adopt entrepreneurial revenue-raising strategies in accordance with the ones

employed in the private sector. More specifically, they tend to more actively exploit nontax revenue

bases as a new source of county revenue that fits individuals’ economic incentives. This seems to be

so mainly because local officials will try to find ways to cover the losses in property tax shares or

intergovernmental aids with compensating strategies such as bonds, delaying and canceling

payments, encouraging more businesses, and finding new sources of revenue (Cain and Mackenzie

2008; MacManus 1993).

Although we are ready to accept the importance of the economy, it is not clear what aspects

of economy affect local finance in what directions. Based on the theoretical grounds, elected local

officials with a political incentive to satisfy taxpayers will attempt to create more scrutiny as they

expect it to be jibe with public concerns on waste and fraud. Although not without resistance, there

is strong public opinion favoring user charges, especially under fiscal stress (Downing 1992: 525).

Thus, county residents’ expectations on the economy are also likely to affect the political incentives

to increase reliance on nontax revenues.

Hypothesis 1: The ratio and the amount of nontax revenues will increase when the economy

worsens.

Political factors: Democratic votes in the gubernatorial elections, county residents’

support for the governor

According to Stein (1999: 27), politics at the subnational level are not associated with

meaningful changes in municipal public policies. The fiscal federalism theories expect institutional

factors to have a more decisive influence than public preference. Furthermore, politicians,

regardless of party ideology, may not take a positive attitude in revenue policy as it is unpopular

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with voters. This “taxation by inertia” argument and the political costs of taxation are evident in

political science research (Rose 1985). Because local governments are constrained by state

regulation, political preference of local residents may not be a decisive factor and there may be no

room for local governments to implement their revenue policies in accordance with ideological

preference.

Along with institutional changes, nevertheless, various features affect the budgetary process,

both expenditure and revenue, such as “the interactions of past policies, the changing roles of

participants, the influence of theories of public management, political and economic ideologies, and

pragmatic reactions to the societal environment.” (Caiden 2010: 204). Therefore, politics and

political bargaining among parties with different ideologies could be one of the significant factors in

local revenue decisions (Carroll and Stater 2009; Blom-Hansen et al. 2006). Revenue policy can be

categorized in a redistributive policy area, which is “well suited for ideological positioning” (Blom-

Hansen et al. 2006: 445-446; Boyne 1996: 132-135).

This study expects that the political ideological preference of county residents will affect

revenue politics. Supporters of leftist or liberal governments are believed to be more receptive to

revenue-raising strategies through taxation, while advocates for conservative governments are

reluctant to raise tax rates and favorable to tight fiscal policy. Accordingly, we expect that liberal

counties are more willing to increase taxes and less likely to exploit alternative sources of revenue

to make up for the lack. At the same time, counties with more supporters for Democratic governors

are more likely to take a proactive attitude in raising revenue from alternative sources. Counties

with higher support for the governor may be unwilling to raise taxes that negatively affect their

popularity; however, the political costs involved in raising taxes will be lower when county

residents are more liberal and supportive of the current governor. In Hypotheses 2 and 3, we

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expect a positive relationship between reliance on nontax revenue, liberal/Democratic ideological

preference and political support for the governor.

Hypothesis 2: The ratio and the amount of nontax revenues will increase when counties have

liberal/democratic ideological preference.

Hypothesis 3: The ratio and the amount of nontax revenues will increase when counties are

more supportive of the current governor.

Institutional factors: Home-rule status, Proposition 1A in 2004

Although we cannot include important institutional changes such as the TELs or Proposition

13 in 1978, other institutional variables such as home-rule status and Proposition 1A in 2004 will

be included in the model.

City and county governments with charters have more autonomy in structural, functional,

and fiscal areas (Benton 2002b: 473). Although the effect seems more limited in counties (Benton

2002b; Sokolow and Detwiler 2001), it is reasonable to assume that chartered counties are more

responsive to the needs of county residents (Benton 2002b; Chapman 2003; Choi et al. 2010). In

particular, Benton (2003b) suggested that counties with home-rule tend to value administrative

efficiency highly, so that they are more willing to rely on fees and charges that adhere to the

benefit-based principle. Nelson and Svara (2012) also noticed the importance of government forms

in adopting innovative practices in municipal governments.

The passage of Proposition 1A in November 2004 was a result of local governments'

struggle for fiscal autonomy (Barbour 2007). There have been several attempts to modify the state-

local relationship and to limit the state’s fiscal interference (Misczynski and Mejia 2011; Danielson

and Mejia 2011). Proposition 1A was intended to protect local government budgets from being

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transferred to the state government. With a two-thirds vote of both the state senate and state

assembly, local property taxes can be shifted to the state, but the state cannot change local sales tax

rates and or their allocation (Neiman and Krimm 2009).

Hypothesis 4: The ratio and the amount of nontax revenues will be positively associated with

institutional changes granting fiscal autonomy to local governments.

Financial variables: Intergovernmental revenue, dependence on property taxes

The largest share of county revenue comes from intergovernmental aid. Intergovernmental

aid to local governments is an important tool used by the federal and state governments to direct

and coordinate public policy across the nation (Clark and Whitford 2011). With a flypaper effect,

intergovernmental revenue creates positive effects on spending. Intergovernmental revenue also

helps to improve the efficiency, equity, and stability of the subnational government sector (Fisher

2007). A number of previous efforts have been made to identify the effect of shrinking

intergovernmental aid to local economies. For example, Wu (2009) argues in a study on municipal

property taxes that municipal governments respond to decrease in intergovernmental aid by

increasing property tax rates. In addition to raising revenues from property taxes, we expect

counties will try to increase revenues from nontax sources.

The local property tax is the most important single own-source tax under the discretion of

local governments. The property tax ratio out of total own-source revenue is often used to measure

dependence on property tax base (Neiman and Krimm 2009), tax visibility (Carroll 2009), and

revenue diversification (Pagano and Johnston 2000). According to Pagano and Johnston (2000:

163), revenue diversification as measured by the property tax ratio is negatively associated with

the property tax rates because sales and income taxes respond relatively quickly to local demands.

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Since the reliance on local property taxes varies substantially (Wu 2009: 79), we include the

property tax ratio in our model to explain how much counties depend on property taxes in their

revenue structures. We expect that counties that are more dependent on revenues from property

taxes are less likely to exploit alternative revenue sources.

Hypothesis 5: A decrease in intergovernmental revenue will increase the ratio and the amount

of nontax revenues.

Hypothesis 6: A reliance on property tax will negatively affect the use of alternative sources of

revenues.

Local demands and characteristics: County median income, willingness to pay taxes,

interests in political issues

We include county median income as it is associated with the economic incentives of county

residents in revenue decisions. Studies show that localities with higher income are more inclined to

pass revenue-enhancement measures (Rueben and Cerdan 2003; Cain and Mackenzie 2008).

However, Netzer (1992) found that higher personal income is negatively associated with user

charges reliance.

We also include a variable explaining county residents' willingness to pay taxes. We know

from other studies that politicians and other policy makers respond to public opinion in their

decisions as the public adjusts its preferences for policy (e.g., Soroka and Wlezien 2005). Elected

officials or politicians are assumed to be sensitive to local demand from the voters, taxpayers, and

residents because those constituents are the source of political support. When voters are more

willing to pay taxes for more services, the politicians' decision to raise taxes becomes easier. Since

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California requires direct voter approval of taxing and spending activities (Cain and Mackenzie

2008), we expect that constituents' willingness to pay taxes will affect reliance on nontax revenues.

In line with this reasoning, higher percentages of residents with strong political interests

are considered more likely to affect local revenue decisions. When voters are more interested and

actively participating in the political decision-making process, the gap between public perceptions

and the true cost of public goods will be minimized (Feld and Schaltegger 2005). This is directly

related to the fiscal illusion pushing local governments to spend more than the optimal level of

spending. Therefore, we expect that greater resident interest in political issues will increase

reliance on nontax revenue sources.

The Data

We include one-year lagged variables in the model to capture the potential influence of the previous

year on the local revenue decisions of the current year. All the finance data is inflation-adjusted for

the year 2010. More details on the data, measurement, and sources for the variables can be found in

Table 1.

[Table 1 about here]

The dependent variables are measured in two ways: 1) the ratio of user fees, charges, fines,

and forfeitures out of total own-source revenue; 2) the absolute dollar amount of user fees, charges,

fines, and forfeitures. Although the ratio variable is frequently used in the public finance literature

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(Mikesell 1993; Jung and Bae 2011), it may not reflect the dynamic priority changes involved in

revenue decisions in the level of fees and charges, fines and forfeitures. Also the percentage is

sensitive to the changes of other resource categories and can be changed without a significant

increase or decrease in the amount of the concerned nontax revenues. Therefore, we use both the

relative share of fees, charges, fines, and forfeitures out of total own-source revenue and the dollar

amount of these revenue sources as our dependent variables. The share of fees and charges can be

increased by raising rates for existing fees and charges and by imposing new fees and charges. We

do not differentiate between the two cases. Three models with each dependent variable were

developed to explore further local revenue decisions: the integrated model, the scrutiny model, and

the economy model. The scrutiny model focuses on the ratio/amount of fines and forfeitures and

the economy model engages in the ratio/amount of fees and charges.

We attempt to identify the effect of the economy on county revenue decisions by using two

different measures for economy: the previous year’s unemployment rate and the residents’

expectations for the economy in 12 months. The unemployment rate (%) in county level is

provided by the Bureau of Labor Statistics. The residents’ expectation for the economy are

measured by the percentage of people who answers negatively to the PPIC survey question “Do you

think that during the next 12 months we will have good times financially or bad times?”

Although the strength of ideological preference can be measured in various ways, the

political and ideological preference of county residents in this paper is measured by voter approval

for the Democratic governor or gubernatorial candidate in general elections in the California State

Archives (www.sos.ca.gov). It is measured by the share of Democratic votes in the gubernatorial

elections in every four years 1998, 2002, 2006, and 2010. The support for the current governor is

from the PPIC survey data every year with a question that “Overall, do you approve or disapprove

of the way that ______ is handling his job as Governor of California?” while the question is a little

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modified in the year 2010 to “In general, are you satisfied or dissatisfied with the governor's budget

plan?”

The institutional factor of the introduction of Proposition 1A in 2004 is included in the

model as a dummy variable, while the time-invariant county home-rule variable is dropped in the

model.

Intergovernmental revenue in this paper is calculated by the sum of total federal and state

aid, and dependence on property taxes is measured by the ratio of the property tax out of total

own-source revenue of a county. Total own-source revenue includes all tax revenues, such as

property tax, income taxes, sales taxes; and nontax revenues, such as fees, charges, fines, and

forfeitures, miscellaneous revenue, utility revenue, and transfers. This figure excludes

intergovernmental revenue from federal, state, and other local governments (Mikesell 1993).

County median income data is provided by the US Census Bureau. The data on willingness

to pay taxes is from the PPIC survey. We use the percentage of the positive answer to the question

“In general, which of the following statements do you agree with more—I'd rather pay higher taxes

and have a state government that provides more services, or I'd rather pay lower taxes and have a

state government that provides fewer services?” except for the year 2010, when the question was:

“What if the state said it needed more money just to maintain current funding for _______? Would

you be willing to pay higher taxes for _______?” We averaged the answer to the health and human

services and higher education services in the year 2010.

To measure the level of interests in political issues among county residents, we also use

subjective data from the PPIC survey question “Generally speaking, how much interest would you

say you have in politics—a great deal, a fair amount, only a little, or none?” We use the percentage

of respondents who selected the first two categories, a great deal and a fair amount, to represent

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the percentage of the population with political interests. All the subjective data from the PPIC

survey is yearly data as of January at the county level. For the causal inference based on the

temporal precedence condition, all the independent variables except the variables from the PPIC

survey data were lagged one year.

Results and Discussion

Descriptive statistics and pair-wise correlations are presented in Tables 2 and 3. The trend

of dependent variables in counties with a representative shape permits the reader to gain a sense of

the overall data structure. As we could see the trend changes in Figure 1, there is no discernible

pattern in the ratio of the nontax revenue sources across time, while the amount of the sources

shows more increasing patterns.

[Figure 1 and 2]

Table 4 and 5 provides the fixed effects estimation on the two dependent variables—the

ratio and the dollar amount of each revenue category. Fixed effects models are used to test the

additive model explaining the relative impact of the independent variables in the equation. The

fixed effects approach is preferable for this work, based not only on the Hausman test but also on

the theoretical considerations. To test the robustness of the results, the model was run with various

other specifications. The final solution was robust and stable. Adding or removing additional

variables had little effect on the overall fit of the model or the relative predictive power of the

independent variables.

[Table 4 and 5]

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First of all, we see the significant positive impact of the unemployment rate, which is in

accordance with our expectations. This implies that the share of fees and charges, fines and

forfeitures is increasing when county unemployment rates rise. However, the county residents’

expectations for the economy in 12 months failed to reach the significance level, except for the

scrutiny model explaining the amount of fines and forfeitures.

The ratio models in Table 4 show that the Democratic vote shares in gubernatorial elections

are positively associated with reliance on the alternative revenues sources, indicating that counties’

liberal/democratic ideological preference is increasing the ratio of nontax revenues to total own-

source revenue as we predicted in Hypothesis 2. Notice, however, that the coefficient for this term

turns negative in the amount models. This means that the alternative explanation is also supported

by the analysis. Liberal counties may be willing to increase tax revenues with less political cost,

which results in decreasing amount of fees, charges, fines, and forfeitures, while conservative

counties may be less likely to increase taxes and more willing to rely on the hard measures that are

associated with economic incentives. With the current analysis, this study could not ascertain the

differential effect of political/ideological preference on the ratio and the amount of nontax revenues.

However, the ratio and the amount will not always necessarily correlate; the ratio could increase

while the amount shrinks, and vice versa. Meanwhile, the political support for the governor (or his

Democratic challenger in the most recent gubernatorial election) does not appear to be associated

with significantly higher ratio and amount of nontax revenues.

Among local demands and characteristics, residents' willingness to pay taxes of county is

negatively associated with the ratio of fines and forfeitures, suggesting that counties with more

residents who are unwilling to pay taxes are more likely to increase the ratio of fines and

forfeitures as a way of generating revenues. The ratio models also show that county wealth

measured by median income is a negatively associated with the ratio of the nontax categories. We

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expect its coefficient to be positive, such that more affluent counties will consider providing

services based on a benefit principle. However, it seems that punitive tools such as fines and

forfeitures are not frequently used in counties with less income.

Intergovernmental revenues from the federal and state governments were significant but

positive in the ratio model, which also conflicts with the results from the amount models. This

result calls into question the validity of conclusions from the existing studies on the effect of

intergovernmental aid on revenue decisions. For whatever reasons, the institutional change

brought about by the introduction of Proposition 1A drove significant negative changes in the ratio

models, while the sign turned positive in the amount model of fines and forfeitures. This is contrary

to our expectations. One concern with this result is whether the dummy variable can be used

reliably to make inferences about the effect of institutional changes expanding fiscal autonomy. Our

findings, of course, are only tentative and subject to modification by future research.

The amount models in Table 5 show that the dollar amount of fines and forfeitures is

positively related to the economic factors, indicating that counties are willing to increase revenues

from fines and forfeitures when the county unemployment rate goes up and expectations for the

economy over the next 12 months worsen. It appears that the choice for scrutiny is triggered by the

worsening economy, except for the amount models providing no evidence for the effect of economy

variables on reliance on user charges and fees.

Although not consistent with the ratio models, the percentage of Democratic votes in

gubernatorial elections is indeed fairly closely related to the amount of nontax revenues, but this

relationship disappears in the scrutiny models with fines and forfeitures. The negative sign of the

variable indicates that counties with more supporters of Democratic governors and candidates are

less likely to increase revenues from alternative sources, especially from user charges and fees. In

all the amount models, the statistical results reveal that the factors of local demand and local

characteristics had no discernible effect on the amount of nontax revenues.

21

Interestingly, we found strong support for Hypothesis 5, suggesting the impact of shrinking

intergovernmental aid on reliance on alternative revenue sources. In the integrative models, the

amount of fees, charges, fines, and forfeitures increases when counties suffer from decreasing

intergovernmental aid. It is evident that the results are in line with previous research using the

same or similar covariates. Yet the variable representing the property tax ratio is not significantly

related to the ratio/amount of nontax revenues once the other variables are controlled.

Although the models presented in Tables 4 and 5 show some differences, the most

interesting and significant finding in this study concerns the importance of the economy variable.

Not only does the economy variable have an independent impact on the ratio and amount of the

nontax revenues; it also accounts for considerably more of the variance than do political or fiscal

factors.

Conclusion

This paper examines county revenue decisions, focusing on the county responses to fiscal stress

and political constraints. It is an effort to answer the question of whether counties show strategic

behaviors relying more on the economic benefit principle or on scrutiny as a way of increasing

county revenue. We have estimated the scrutiny and economy model separately under the

assumption that the two categories of nontax revenues—fines/forfeitures and user charges/fees —

have different logics and effects on the local revenue structures. However, narrow difference was

found in the models with different revenue categories.

The result of the fixed effects models with two different types of dependent variables

confirms much of what we expected. County revenue decisions vary significantly according to

economic situation, political environment, fiscal conditions, and local demands and characteristics.

The three factors that stood out most consistently were the county unemployment rate, Democratic

22

vote shares in gubernatorial elections, and intergovernmental revenues. We found a constant

positive association between the unemployment rate and alternative revenue sources in both the

ratio and the amount model.

Unfortunately, we cannot reliably predict the direction of the effect of political/ideological

preference and intergovernmental aid on the share of nontax revenues, although the variables

achieve statistical significance in all models. Except for the economy variable, the two factors show

different signs in the ratio and the amount models. Much remains unexplained here. What does

seem clear is that political/ideological preference and intergovernmental aid affects county

revenue decisions even after one controls for other factors. The findings provide clues to the

important role of professional managers such as county supervisors in determining the financial

stability of local revenue structures. Given this interesting finding, further research is necessary to

determine the degree of influence local managers and decision-makers exert on the revenue

structure. The results also shed light on how important control variables such as willingness to pay

taxes and county median income influence the ratio of alternative revenue sources.

Although we expect the findings of this study will have some implications for local

governments, appropriate caution should be exercised in generalizing the results. California is

somewhat distinctive in its revenue structure in that there is little discretion in determining

property taxes because of Proposition 13. As we have used the total amount of nontax revenues, we

could not tell the difference between the increase caused by rising rates and that caused by the

exploitation of new sources. Another caveat of the analysis is that it does not directly tackle the

issue of inter-fund transfer. The institutional factors in the transaction costs involved in inter-fund

transfers may affect the revenue potential of the nontax sources of local governments.

23

Table 1 Data, Measures, and Sources

Variables Data and measures Source Dependent variable The ratio of fees and charges, fines and

forfeitures out of total-own source revenues (%) Total amount of fees and charges, fines and forfeitures ($)(2000-2010)

RAND California County Finance Statistics and Counties Annual Report http://www.ca.rand.org; http://www.sco.ca.gov

Economic hardship variables Unemployment rate (L)

County unemployment rate (%) (2001-2010) Bureau of Labor Statistics http://www.bls.gov

County residents’ expectation on economy in 12 months

% of people who answers negatively to the question that “Do you think that during the next 12 months we will have good times financially or bad times?”

Public Policy Institute of California (PPIC) http://www.ppic.org

Political variables Democratic votes in gubernatorial elections (L)

% of people who voted for the Democratic candidate in the gubernatorial elections in 1998, 2002, 2006, 2010.

California Elections Data Archive (CEDA)http://www.csus.edu/calst/index.html California State Archives http://www.sos.ca.gov

Support for the Governor

% of people who answers positively to the question that “Overall, do you approve or disapprove of the way that ______ is handling his job as Governor of California?” except for the year 2010: “In general, are you satisfied or dissatisfied with the governor's budget plan?”

Public Policy Institute of California (PPIC) http://www.ppic.org

Institutional changes variables County Home-rule Charter status (0, 1) California State

Association of Counties http://www.counties.org

Proposition 1A Proposition 1A (0, 1) before=0, after 2004=1

California State Association of Counties http://www.counties.org

Financial variables Intergovernmental aids (L)

Intergovernmental revenues from Federal and State government

RAND California County Finance Statistics and Counties Annual Report http://www.ca.rand.org; http://www.sco.ca.gov

Property tax ratio (L) % of property tax out of total own-source revenue

RAND California County Finance Statistics and Counties Annual Report http://www.ca.rand.org; http://www.sco.ca.gov

24

Changes in local demands and characteristics County income (L) Median income ($)

(2001-2010) United States Census Bureauhttp://www.census.gov/did/www/saipe/county.html

Willingness to pay taxes

% of people who answers positively to the question that “In general, which of the following statements do you agree with more - I'd rather pay higher taxes and have a state government that provides more services, or I'd rather pay lower taxes and have a state government that provides fewer services?” except for the year 2010 (average of two questions for health and human services and higher education): “What if the state said it needed more money just to maintain current funding for______? Would you be willing to pay higher taxes for _______?”

Public Policy Institute of California (PPIC) http://www.ppic.org

Interests in political issues

% of people who answers to the positive categories (a great deal and a fair amount) to the question that “Generally speaking, how much interest would you say you have in politics - a great deal, a fair amount, only a little, or none?”

Public Policy Institute of California (PPIC) http://www.ppic.org

25

Figure 1 Changes of the proportion of fines and forfeitures, fees and charges to the total own-

source revenues with a representative shape by year

20

25

30

35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Alameda

20

25

30

35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Humboldt

20

30

40

50

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Imperial

25

30

35

40

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Los Angeles

10

15

20

25

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Modoc

20

25

30

35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Monterey

15

20

25

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Sacramento

20

25

30

35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

San Diego2

02

53

03

54

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

San Joaquin

20

25

30

35

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Solano

25

30

35

40

45

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Stanislaus

25

30

35

40

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Ventura

26

Figure 2 Changes of the amount of revenues from fines and forfeitures, fees and charges with a

representative shape by year

240

00

00

00

260

00

00

00

280

00

00

00

300

00

00

00

320

00

00

00

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Alameda

140

00

00

01

60

00

00

01

80

00

00

02

00

00

00

02

20

00

00

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Humboldt

200

00

00

02

50

00

00

03

00

00

00

03

50

00

00

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Imperial

180

00

00

00

02

00

00

00

00

02

20

00

00

00

02

40

00

00

00

0

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Los Angeles

100

00

00

150

00

00

200

00

00

2000 2005 2010

Modoc

700

00

00

07

50

00

00

08

00

00

00

08

50

00

00

09

00

00

00

02000 2005 2010

Monterey

150

00

00

00

200

00

00

00

250

00

00

00

300

00

00

00

2000 2005 2010

Sacramento

350

00

00

00

400

00

00

00

450

00

00

00

500

00

00

00

2000 2005 2010

San Diego

900

00

00

09

50

00

00

01

00

00

00

00

2000 2005 2010

San Joaquin

600

00

00

07

00

00

00

08

00

00

00

09

00

00

00

01

00

00

00

00

2000 2005 2010

Solano

700

00

00

08

00

00

00

09

00

00

00

01

00

00

00

00

2000 2005 2010

Stanislaus

140

00

00

00

160

00

00

00

180

00

00

00

200

00

00

00

2000 2005 2010

Ventura

27

Table 2 Summary statistics

Variable Obs. Mean Std. Dev. Min. Max.

The ratio of fees, charges, fines and

forfeitures (ffr)

626 25.02394 8.440669 6.336404 53.5041

The ratio of fines and forfeitures

(Rfine)

626 4.534661 2.428179 .6639721 17.28196

The ratio of user fees and charges

(Rcharge)

626 20.48928 7.548672 4.875734 47.96727

The amount of fees, charges, fines,

forfeitures (ff)

627 1.17e+08 2.81e+08 457604 2.32e+09

The amount of fines and forfeitures

(fine)

627 1.84e+07 4.82e+07 60090 3.68e+08

The ratio of user fees and charges

(charge)

627 9.84e+07 2.35e+08 352117 1.97e+09

County unemployment rate (L)

(Unemp)

570 7.742632 3.152111 2.8 26.2

Economy expectation on 12 month

(12mon)

390 51.1359 19.36228 9.54 96.32

Support of the governor

(Sgov)

443 51.65025 16.85449 4.96 90.74

Interests in political issues

(polint)

418 68.42514 13.3538 16.57 100

Democratic vote shares in the

gubernatorial elections (L) (demv_g)

570 39.30912 11.60992 17.2 73.5

Willingness to pay taxes of residents

(wpt)

400 46.9005 15.63873 8.74 95.1

County median income (L)

(income)

570 65006.03 16768.46 37350 129235

Intergovernmental revenues (L)

(Igr)

570 4.90e+08 1.17e+09 3638541 9.31e+09

Property tax rates out of total own-

source revenue (L) (Ptr)

569 33.60449 9.655762 8.207121 60.08281

Introduction of Proposition 1A (L)

(Prop)

570 .5 .5004392 0 1

28

Table 3 Pair-wise correlations

Note: Coefficients with asterisks are statistically significant at Bonferroni-adjusted .05 levels.

(1)

ffr

(%)

(2)

Rcharg

e (%)

(3)

Rfine

(%)

(4)

ff

($)

(5)

fine

($)

(6)

charge

($)

(7)

Unemp

(L)

(8)

12mon.

(9)

Sgov

(10)

Polint

(11)

Demv

(L)

(12)

wtp

(13)

incom

(L)

(14)

igr

(L)

(15)

ptr

(L)

(1) 1.000

(2) .9600* 1.000

(3) .4918* .2282* 1.000

(4) .2150* .2372* .0098 1.000

(5) .1853* .1793* .0869 .9791* 1.000

(6) .2199* .2479* -.0061 .9991* .9697* 1.000

(7) -.0103 -.0515 .1211 -.1584* -.1453 -.1603* 1.000

(8) .0683 -.0551 -.0726 .0096 .0098 .0096 .1057 1.000

(9) -.0136 -.0266 .0399 -.0317 -.0278 -.0324 -.0388 -.3339* 1.000

(10) -.0900 -.0770 -.0834 -.0135 -.0218 -.0117 -.2314* .0850 -.0502 1.000

(11) .3634* .4050* .0087 .2349* .1930* .2422* -.3292* -.1834* -.0802 -.0120 1.000

(12) -.1511 -.1179 -.1826* .0313 .0077 .0360 .0960 .0338 -.0683 .0043 .0826 1.000

(13) 0.0383 0.1097 -.2028* .1982* .1830* .2002* -.4634* -.0334 -.0037 .2615* .4196* -.0183 1.000

(14) .1931* .2132* .0108 .9811* .9624* .9799* -.1343 0.0031 -.0354 -.0366 .2433* .0313 .1607* 1.000

(15) -.2505* -.2473* -.1028 .0210 .0163 .0219 -.0396 .1769 -.0059 .0674 -.0098 .0959 .1952* .0191 1.000

Prop

(L)

-.3705* -.3285* -.2654* .0191 .0164 .0196 .1645* .2479* .0297 .0956 -.4670* .3021* .0520 -.0124 .3129*

29

Table 4 Fixed effects estimation on the ratio models

Note 1: *** p<0.01, ** p<0.05, * p<0.1, Standard errors noted in parentheses are adjusted for 44 clusters in id (county). Note 2: (L) refers to variables that lagged one year.

Variables Model (ratio): User

charges and fees,

Fine and forfeitures

Scrutiny model

(ratio): Fine and

forfeitures

Economy model

(ratio): User charges

and fees

Unemployment rates (L) .4313601***

(.0998359)

.1143694***

(.0368489)

.3169907***

(.0834766)

Economy expectation for 12

months

.0133799

(.013562)

.001502

(.0040347)

.0118779

(.011797)

Support for Governor -.0280916

(.021621)

-.005525

(.0064785)

-.0225666

(.0175395)

Interest in political issues -.0000754

(.0207907)

-.0067932

(.0083897)

.0067178

(.0164465)

% of democratic votes in

gubernatorial elections (L)

.2872048***

(.0647807)

.0553569**

(.0243769)

.2318479***

(.0473415)

Willingness to pay taxes for

more services

-.0413371

(.020954)

-.0134884**

(.0066911)

-.0278487*

(.0165119)

County median income (L) -.0001972***

(.0000725)

-.0000745***

(.0000278)

-.0001227**

( .000058)

Intergovernmental revenues

(Federal+State) (L)

2.08e-09**

(8.72e-10)

5.71e-10*

(3.15e-10)

1.51e-09*

(8.30e-10)

Property tax ratio (L) -.002154

(.0897359)

.0116671

(.0236594)

-.0138211

(.0737089)

Proposition 1A (L) -4.022433***

(.7378299)

-.7046198***

(.2006292)

-3.317813***

(.667243)

Constant 28.21044***

(7.319041)

7.148855***

(2.124864)

21.06158***

(6.042604)

sigma_u 6.4217152 1.8875532 5.585995

sigma_e 4.4835016 1.2750092 3.7954404

Rho (fraction of variance due to u_i)

.67229023 .68668271 .68415312

Number of obs 323 323 323

Number of groups 44 44 44

R-sq: within between overall

0.4553

0.0594

0.1967

0.2863

0.0504

0.0974

0.4336

0.0655

0.1939

Corr(u_i, Xb) -0.4118 -0.5815 -0.2663

F(10,43) 31.56 . 27.89

Prob > F 0.0000 . 0.0000

30

Table 5 Fixed effects estimations on the amount models

Note 1: *** p<0.01, ** p<0.05, * p<0.1, Standard errors noted in parentheses are adjusted for 44 clusters in id (county). Note 2: (L) refers to variables that lagged one year.

Variables Model (amount):

User charges and

fees, Fine and

forfeitures

Scrutiny model

(amount): Fine and

forfeitures

Economy model

(amount): User

charges and fees

Unemployment rates (L) 706427.4*

(407031.1)

512606.1*

(267867)

193821.3

(301375.4)

Economy expectation for 12

months

76370.38

(57804.99)

38542.76**

(18228.06)

37827.62

(50240.37)

Support for Governor 76345.1

(67564.76)

8189.733

(17669.07)

68155.36

(58122.94)

Interest in political issues 45758.48

(70300.62)

-20379.5

(20839.75)

66137.98

(65089.16)

% of democratic votes in

gubernatorial elections (L)

-799108.5***

(270090.7)

-72502.29

(88196.59)

-726606.2***

(255829.4)

Willingness to pay taxes for

more services

37155.26

(52570.78)

-32612.48

(23665.28)

69767.74

(53376.95)

County median income (L) 80.91982

(386.5001)

-167.5232

(103.5216)

248.443

(448.2067)

Intergovernmental revenues

(Federal+State) (L)

-.1433594***

(.0251847)

-.0123211*

(.0063899)

-.1310384***

(.0194339)

Property tax ratio (L) -588246.6

(399204.1)

-135410.9

(125317.1)

-452835.7

(313801.2)

Proposition 1A (L) -1368964

(4713387)

1332761

(1243158)

-2701725

(4200381)

Constant 3.24e+08***

(4.04e+07)

5.33e+07***

(9895327)

2.70e+08***

(4.02e+07)

sigma_u 5.120e+08 71365583 4.416e+08

sigma_e 24416387 7449012.9 21351976

Rho (fraction of variance due to u_i)

.9977307 .9892226 .99766726

Number of obs 323 323 323

Number of groups 44 44 44

R-sq: within between overall

0.3330

0.9760

0.9569

0.0906

0.8841

0.8597

0.3432

0.9745

0.9546

Corr(u_i, Xb) -0.9922 -0.9603 -0.9923

F(10,43) 26.47 4.30 34.51

Prob > F 0.0000 0.0003 0.0000

31

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