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TrafficTickets:PublicSafetyConcernsorBudgetBuildingTools

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Traffic Tickets: Public Safety Concerns or Budget Building Tools

Abstract

States and municipalities across the country are struggling to match revenues with

expenditures. Sometimes these governments use traffic fines and fees to help balance the budget

more so at the city level than at the state level. This paper explores the rationale for the issuance

of traffic tickets and provides a state-level analysis on the occurrence of tickets and its relation

with budget or public safety factors. Utilizing a cross-sectional multiple regression with lags it

was found that public safety concerns as evident in fatal crashes data has a significant and larger

positive effect on the issuance of traffic tickets than budget concerns as measured by state credit

ratings, unemployment rates and housing prices.

Introduction

The unpopularity of raising taxes coupled with an erosion of the tax base due to closing

businesses, increase in foreclosed and vacant homes and demographic changes has caused many

state and local governments across the country to face dire fiscal circumstances. These

governments are facing a difficult reality that something has to change and the answer is not in

issuing more debt which might already be at the ceiling. Politicians rarely make difficult

decisions so long as they fear the ballot box.

Given these realities, many local governments have turned to fines and fees to help

balance their budgets. These are politically feasible because they are not across the board yet

generate a substantial amount of revenue (States raise fees, 2011). Fees are more marketable to

the public because they are only incurred when one uses the service. Fines are similarly

acceptable because they only have to be paid when one breaks the law. The rationale goes that

these fines and fees can be avoided if one does not use a service or break the law. This is counter

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to a tax which is across the board such as property taxes and income taxes. The aura of choice

makes fines and fees more acceptable.

Fines and fees can also make people very unhappy especially if they increase in

frequency and cost. A traffic ticket is received relatively infrequently but costs a lot. A parking

ticket may be received relatively frequently but costs little. A balance is maintained between

these two extremities. If this balance is disturbed people could increase in unhappiness causing

them to vote with their hands at the ballot box or with their feet by moving to a more fee/fine

friendly area. This would exacerbate the fiscal woes of many of these governments.

This paper considers the two reasons for the issuance of traffic tickets. As already

mentioned there are budget concerns that prompt public officials to explore alternative means of

generating revenues that are the easiest for them to control. Traffic tickets represent one of these

sources. The other concern is public safety which is less controversial than budgetary causes for

the tickets.

It is hypothesized that public safety concerns, budget concerns or both have a significant

impact on ticket issuance. Further it is hypothesized that one of these concerns are greater than

the other if they are both statistically significant. The relationship is assumed to be positive

indicating that as public safety or budget concerns increase so do the number of tickets.

Traffic Tickets as Revenue or Public Safety Measures

Traffic stops represent a middle ground between investigative stops also known as Terry

stops and custodial arrests. Moran (2000) suggests labeling them under a different police-citizen

encounter, non-custodial arrests. In this case a traffic stop can be considered an actual arrest. A

traffic stop involves probable cause that a motorist has committed a traffic violation not a

suspicion that he/she has committed one. The legal justification for such a stop is to either issue

3

a citation or a warning (Moran, 2000). The citation or traffic ticket is typically a monetary

amount which is not considered a punishment but a monetarization of a civil offense committed

(Sun, 2011).

The objective of the non-custodial arrest from the perspective of the social planner is to

increase the probability of monitoring citizen behavior and minimize the probability of

transgression. This embodies the first concern that warrants the issuing of traffic tickets which is

the enforcement of law and order. Concurrently, fines often represent large portions of the

operating budget for many public safety departments and the level and frequency of the fine are

large factors in the budgeting process of these agencies. This embodies the second concern

which is revenue considerations (Saha & Poole, 2000).

These two concerns represent the two rationales for the use of traffic tickets. The first

rationale is safety concerns. The National Highway Traffic Safety Administration found that

thirty percent of crashes that involved fatalities involved individuals who were speeding (Perin,

2011). The assumption is that by enforcing a speed limit this statistic would drop dramatically.

The goal is to save lives and most people would agree with this goal as worthy of achievement.

Sun (2011) cites the Transportation Research Board and their three principles for

managing speed. The first principle is the externalities imposed on others by the risky behavior

of the speeding driver. As discovered by the National Highway Traffic Safety Administration,

40.4 billion dollars a year are lost due to the economic impact of speeding (Perin, 2011). When

cars crash not only are there private costs to the drivers involved but there are public costs due to

the need for emergency vehicles, clean-up, repair as well as the costs of delayed arrival times to

other drivers.

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The second principle is the limited abilities of drivers to determine the proper speed on

their own without external reminders (Sun, 2011). The illusion created by the comfortable

environment of the automobile may cause drivers to detach from the reality that they are

traveling at speeds not only dangerous to themselves but to others as well. By referencing the

speed limits and having further reminders by the presence of police officers provides enough

reminders to maintain proper speeds.

The third principle is related to the second principle in that people underestimate the

probability of crashing and the severity of crashing (Sun, 2011). For those who have

experienced accidents even at lower speeds, they are shocked that impacts can be so severe even

when traveling at lower speeds. Again, the use of speed limits with police presence is assumed

to abate this.

The use of traffic tickets is one means to address these principles with safety in mind.

The assumption is that once a driver is stopped and cited for speeding requiring him to pay a fine

the driver will refrain from speeding in the future. Tay (2010) found evidence for this in a study

on crashes in Edmonton, Canada. He looked at the hours of operation of speed cameras and the

total number of injuries from crashes from 2002 to 2005. He found that the number of operating

hours per month of the speed cameras and the number of tickets that were issued had a

statistically significant effect in limiting the number of injuries from crashes per month.

Conversely, another study looked at court appearances for traffic-related offenses and the

different verdicts handed down and their associations with later speeding infractions and

accidents in Maryland. The authors found that those drivers who received suspension of the

prosecution and no prosecution and those drivers that received probation before judgment were

less likely to speed again. Those drivers that went to court were more likely to get in a crash

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than those who paid by mail. Further the authors found that drivers that paid fines and received

points were no different than those that were declared not guilty in regards to the potential to

receive a speeding citation in the future. The authors conclude that severe penalties including

fines and points have a limited effectiveness in reducing speeding and crashes (Li, Amr, Braver,

Langenberg, Zhan, Smith and Dischinger, 2011). They add that, “reviews of studies addressing

the effectiveness of countermeasures on traffic violations have concluded that receiving an

occasional fine for speeding is merely an inconvenience rather than an effective deterrent for

some drivers (Li et.al. 2011, p. 645).”

Whatever the effectiveness of traffic citations on citizen behavior the United States still

prides itself on the promotion of personal freedom. Any curtailment of that freedom might

create a certain level of dissatisfaction. The imposition of a speed limit is an example. If the

goal is to prevent injury to others than limiting the personal freedom of the individual to speed is

justified and traffic tickets are considered a legitimate means to impose this limit upon them.

This follows the rationale of John Stewart Mill’s harm principle in which freedom cannot be

limited unless the conduct harms other people (Bogen & Farrell, 1978). Additionally, Ackerson

and Subramanian (2010) found that there was a strong positive relationship between a state’s

personal freedom, defined as a lack of state policies on individual behaviors, and that state’s

unintentional injury mortality rate. Apparently personal freedoms come with a cost and

governments need to determine at what level of that cost they are willing to accept.

The second rationale for the use of traffic tickets is the revenue they generate. This is the

most controversial aspect of traffic tickets. Although most people would agree with the goal of

traffic tickets in increasing safety, most would disagree with their use in generating revenue for

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the city. A motorist ticketed for speeding might first assume that the traffic ticket only

represents revenue for the city even if the real goal is to get the driver to slow down.

Makowsky and Stratmann (2009) investigated the link between traffic tickets and

municipal revenue. They found that drivers who lived outside the municipality were more likely

to receive a ticket and this increases even more for those who live outside the state where the

ticket was given. Further, they found that drivers were ticketed more when property taxes were

limited or the values of properties were lower. The assumption that fueled this study was that

officers were budget-maximizing as agents of the municipality while considering the voting

ability of the driver and the good work evaluation that the officer would receive. In an apparent

crossover article on traffic tickets from budget concerns to safety concerns, Makowsky and

Stratmann (2011) found that municipal fiscal distress does lead to more ticketing but that more

ticketing reduces car crashes and the number of injuries associated with those crashes.

Garrett and Wagner (2009) found in another study that traffic tickets followed changes in

county-wide economic circumstances. Similarly they found that when there were negative

changes in local revenue from one fiscal year to the next there was a statistically significant

increase in the number of tickets issued. They also found that as tourism spending increased so

did the number of traffic tickets.

The studies cited above provide empirical support to the idea that traffic tickets are used

as revenue generators by the municipalities. This reality is no clearer than by the fact that

municipalities anticipate revenues from fines in their budget forecast and preparation (Scandur,

1964). There are a number of examples where these revenues are considered a very viable part

of the budget as noted by Scandur (1964), the, “collection of fines for traffic and parking

violations has become a major item in municipal budgets throughout the country (p. 144).”

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Garrett and Wagner (2009) include that Houston city officials had predicted increases in

traffic tickets as a means to offset revenue loss. Similarly, in 1993 the mayor of Chicago had

suggested that parking fines be used to close a budget gap for the following year (Munk, 1993).

Stoff (2009) found that after looking at 700 police and sheriff’s departments across the state of

Missouri, cities with smaller populations and busier roads and with police focused on traffic

enforcement tended to ticket more. It has been known that smaller towns tend to use traffic

tickets to build their budgets (Scandur, 1964).

Another example of a state relying on the revenue is California which has granted traffic

ticket amnesty for unpaid traffic tickets for the past three years or more to pay during the first six

months of 2012 and get fifty percent off. The state expects that 46 million dollars will be

collected or two percent of overdue fines. The amnesty includes all tickets except those related

to drunken and reckless driving as well as parking tickets (Egelko, 2011).

In Florida, a state that issues more traffic tickets than any other, a traffic ticket can be a

source of revenue for several funds. Bedard (1995) found in the 1990s a traffic ticket came with

a multitude of surcharges that support different funds including the locality, Brain and Spinal

Cord Rehabilitation Trust Fund, Emergency Medical Services Trust Fund, Child Welfare

Training Trust Fund and Juvenile Justice Training Trust Fund to name a few. Further, expectant

court fees are a source of security for bonds. Miami-Dade and Hillsborough Counties in Florida

charged fifteen to twenty dollars more on traffic tickets to pay off debt (Sigo, 2004).

The use of traffic tickets as revenue is rather controversial primarily because they are

typically presented as a means to secure greater safety in the community as noted above.

Additionally it has been found that traffic stops tend to be racially oriented with minorities being

targeted more often. Miller (2008) found that racial profiling does occur even when controlling

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for legal and quasi-legal factors for a traffic stop. Typically the traffic stop is used as a

justification for further investigation of the motorist not just to offer a citation alone.

Another controversy is the reality that many police officers speed undermining the

legitimacy of having a speed limit or creating a perception that the law enforcers are above the

law. In South Florida speeding officers have killed 19 people and caused 320 crashes with only

one going to jail since 2004. The speeding officers that caused these accidents were not

responding to emergencies (Kestin & Maines, 2012).

Additionally many have considered the monetary amounts of the fines to be excessive

(Sun, 2011). Excessive fines are considered under the Excessive Fines Clause of the 8th

Amendment of the United States Constitution which reads, “Excessive bail shall not be required,

nor excessive fines imposed, nor cruel and unusual punishments inflicted (Mount, 2010).”

Bedard (1995) noted that half of the officers surveyed by the Institute of Police Technology and

Management in Florida felt that the fines were too high. Many of the reasons why traffic tickets

are not paid in California for instance is because they are too high and people are unable to pay

them (Egelko, 2011).

Study Design

This is the first study attempting to include the United States as a whole in an assessment

on the factors linked with ticket issuance. Given the scale of the study the units of analysis are

the states. The dependent variable in this analysis is the prevalence of ticketing in the state. The

data on ticketing per state is derived from the National Motorists Association (NMA) for 2010.

The data is based on search queries in Google’s insights for Search which catalogues search

trends across the United States. NMA used search queries such as “speeding ticket” and “traffic

ticket” and totaled the amount of times these were searched for in each state on Google four

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separate times throughout 2010 (National Motorist Association, 2010). The variable used in the

present study is based on the per 10,000 people value of those total searches for each state

hereafter referred to as per 10,000 ticket search queries. At this time the National Motorists

Association is the only organization that has attempted to calculate the number of traffic tickets

across the United States.

More concrete and direct measures of the number of tickets per state are not available.

The two major studies on the frequency of ticketing and budget and public safety concerns,

Makowsky and Stratmann, 2009 and Makowsky and Stratmann, 2011, rely on local ticket data

which are not available across all communities in the United States. Makowsky and Stratmann

(2009) rely on data obtained by the Boston Globe on all traffic tickets written from April 1, 2001

to May 31, 2001 in the Boston area and around that state. The Boston Globe had to request this

data from the Massachusetts Registry of Motor Vehicles. In Makowsky and Stratmann (2011)

they rely on Massachusetts ticketing data again only this time the data was collected by the

Massachusetts legislature which requested the Registry of Motor Vehicles for the data from

April 1, 2001 to January 31, 2003. This required a legislative request and still only represents

one state. A more recent article on traffic ticket data and traffic accidents also used the Boston

Globe data and again only represents the State of Massachusetts (Lee, 2012)

One might also use the amount of revenue raised on fines in state comprehensive annual

financial reports (CAFR). There are two problems with this data. The first problem is that not

all states report fine revenues. For instance, the CAFR for the State of Idaho in 2010 does not

even contain the word ‘fine’ anywhere in the document (State of Idaho CAFR, 2010). The

second problem is that the fine revenue is not separated from other sources of revenue. For

instance, the CAFR for the State of Florida in 2011 reports fine revenue along with forfeits,

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settlements and judgments (State of Florida CAFR, 2011). Besides this revenue data is dispersed

through several funds.

One might also consider adding up all fine revenue collected by municipalities and

counties in all fifty states. Many cities, but not all, provide a separate line item for fine revenue.

The task itself would be daunting and probably impossible and would still not provide the entire

picture since state-level data would be missing. Also many smaller municipalities do not post

their CAFRs online and the level of professional reporting of these data is quite variable across

cities and counties.

One organization attempts to collect the number of traffic violation cases per state, the

National Center for State Courts (NCSC). This organization has a Court Statistics Project and

requests data from states on court related functions (States Average, 2013). The organization

provided data on the number of incoming caseloads for thirteen states on non-criminal traffic

violations (infractions) for 2010 (A. Allred, personal communication, November 11, 2013). This

lack of data on other states indicates that the organization had to request this data from the states

and not all states responded to the request. The reluctance on behalf of states to report these data

should cause alarm.

In an effort to verify the NMA index with the other available data on tickets a Pearson’s

correlation was done between the total number of traffic violation cases in the thirteen states that

responded to the NCSC request in 2010 and the corresponding thirteen states included in the

NMA measure the same year. A fairly high and significant correlation was found in table 1

between the measures which indicate that the measures are collinear.

There are a number of key independent variables of interest for this study. Based on the

literature it is important to know if tickets are issued more because of safety concerns or because

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of budget concerns. The U.S. Department of Transportation National Highway Traffic Safety

Administration collects data on the number of fatalities from vehicle crashes (National Highway

Traffic Safety Administration, 2012). It is assumed that if the number of vehicle fatalities are

high in a state there may be more impetus to issue more traffic tickets to ensure public safety

(Tay, 2010; Makowsky & Stratmann, 2011; Perin, 2011) This is one of the main independent

variables.

Table 1

Pearson’s Correlation of Traffic Violation Cases with

Per 10,000 Traffic Search Queries

Per 10,000 Traffic

Search Queries per

State

Total Traffic Violations per

State 0.547*

Notes. Data on traffic violations per state from that

National Council for State Courts. N = 13 States. Data

on total traffic search queries per capita from the National

Motorists Association. (*p = .05).

Another main concern is related to the second rationale for issuing tickets, budget

concerns. Several independent variables cover budgetary indicators of fiscal strain. Fiscal strain

is when a government is unable to meet its financial and service obligations (Hendrick, 2004).

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The indicators of fiscal strain are many, but Skidmore and Scorsone (2011) recommend that the

indicator should be exogenous to the decisions made by local governments. The fiscal distress

measure that Makowsky and Stratmann (2011) rely on is endogenous since it relies on decisions

of local government officials to expand property tax collections. One recommended measure of

fiscal strain is credit worthiness as measured by the credit bureaus (Wolff, 2008). One of these

credit ratings is Standard and Poor’s U.S. State Ratings. Moody’s and Fitch are other credit

rating institutions, but Standard and Poor’s ratings are used in this paper since there is typically

little difference between the ratings for states amongst the different agencies. The Standard and

Poor’s rating criteria center on the government’s framework, financial management, economy,

budgetary performance and debt/liability profile. The agency rates each state on a scale of 1

(strongest) to 4 (weakest) (Standard & Poor’s, 2011). The variable is measured on an ordinal

scale in this study with 1 being the lowest state credit rating received at A- and 7 being the

highest credit rating received at AAA. The higher the number the better the score (S & P State

Credit Ratings, 2012).

It is assumed in this paper as it is assumed by the Makowsky and Stratmann (2009) and

Makowsky and Stratmann (2011) that fiscal strain leads to the issuance of more traffic tickets.

Since this is a state-level analysis and not a municipal-level analysis it is further assumed that

fiscal strain at the state-level cascades to the municipal-level through lower levels of transfers

and the decentralization of expenditure responsibilities (Jimenez, 2009). Both states and local

governments receive revenues from traffic tickets. A state-level credit rating which could

indicate fiscal distress on the part of the state would stand as a proxy for general fiscal strain in

the municipalities throughout the state causing them to rely on tax and fee decisions in which

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they have control i.e. traffic tickets as one option along with the state itself through its state

police.

Two other indicators of fiscal strain in a state are the state-level unemployment rates and

the state-level housing price index. Unemployment per state is calculated by the Bureau of

Labor Statistics. The data are percents of the labor force that are unemployed (Bureau of Labor

Statistics, 2012). Unemployment data serves as a proxy for income data. It is assumed that high

levels of unemployment in a state and the accompanying lower levels of income depress tax

revenues causing the governments to rely on other sources of revenue. Similarly, housing prices

are also an indicator of whether there are viable taxable resources within the various

communities (Makowsky & Stratmann, 2011; Makowsky & Stratmann, 2009). Lower housing

prices mean lower property taxes. The housing price index developed by the Census is used in

this study to represent housing prices (United States Census, 2012a).

Additionally, two other variables are included as controls in the model based on the

literature on traffic tickets. The first of these involves states that attract many tourists. As noted

by Garrett and Wagner (2009) traffic tickets are given more in tourist destinations where many

travelers are from out of state or out of town. It is assumed that most of these tourist destinations

are in places with warmer climates such as Florida. The average temperatures in each state

between the years 1971 and 2000 were collected from National Oceanic and Atmospheric

Administration National Climatic Data Center (Osborn, 2013). It is assumed that as the average

temperatures improve between states the number of traffic tickets also increases between states.

The other control variable is the percent of the state population that is African American.

As noted by Miller (2008), African Americans have been shown to be the target for traffic stops

potentially due to racial profiling. It is assumed that as the percentage of African Americans in

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each state increase so do the number of traffic tickets issued in those states. Information on the

number of African Americans per state was obtained from the Census (United States Census,

2012b).

Due to concerns with reverse causality between the independent variables and the

dependent variable, the independent variables were lagged by one and two years (2008 & 2009).

A number of studies lag their data to account for reverse causality such as Buch, Koch and

Koetter (2013), Clemens, Radelet, Bahvnani and Bazzi (2012), Stiebale (2011), Hayo, Kutan and

Neuenkirch (2010) and Vergara (2010). The control variables were not lagged because it was

assumed that they do not change by much from one year to the next. It was also assumed that

the lagged years would have a larger impact on the propensity to ticket in 2010 because of delays

in responses to budget shortfalls and the incidence of traffic fatalities in the state.

A cross-sectional, multiple regression was used with per 10,000 ticket search queries per

state as the dependent variable and state credit rating, state fatal crashes, state unemployment and

state housing prices as the independent variables. State average temperatures and state percent

African Americans serve as control variables in the model. Normality, linearity, collinearity,

correlated errors and homoscedasticity assumptions were tested through diagnostics on the data.

Per 10,000 ticket search queries and fatal crashes were logged to meet normality assumptions.

Data

The data on per 10,000 ticket search queries per state ranged from a low value of .012

searches per capita to .503 searches per capita. The state credit ratings ranged from the lowest to

the highest ratings while the unemployment rate ranged from the lowest value of 3.0 percent to

the highest value of 13.70 percent in all lags. The housing price index score ranged from 126.10

to 309.5, which indicates the highest price values across all lags.

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Table 2

Minimum and Maximum Values, Mean and Standard Deviations for All Variables for all

Lags 2008 – 2010

Min

Max

Mean

SD

Per 10,000 Queries by State 2010 .012 .503 .119 .096

State Credit Rankings 2010 1 7 5.52 1.18

State Credit Rankings 2009 2 7 5.54 1.11

State Credit Rankings 2008 3 7 5.54 1.03

Unemployment Rate 2010 3.8 13.7 8.74 2.05

Unemployment Rate 2009 4.1 13.4 8.44 1.96

Unemployment Rate 2008 3.0 8.3 5.28 1.27

Housing Price Index 2010 126.1 288.5 198.25 32.58

Housing Price Index 2009 135.0 305.7 206.29 33.67

Housing Price Index 2008 155.6 309.5 211.24 35.85

Vehicle Fatalities per State 2010 56 2998 657.22 637.36

Vehicle Fatalities per State 2009 64 3104 677.08 674.12

Vehicle Fatalities per State 2008 62 3476 747.78 764.08

Notes. Data on traffic search queries from National Motorists Association. Data on state

credit ratings from Pew Charitable Trusts. Data on unemployment from the Bureau of

Labor Statistics. Data on the housing price index based on single-family homes from the

U.S. Census Bureau. Data on vehicle fatalities per state from the National Highway Traffic

Safety Administration. N = 50.

16

Lastly, the number of fatal crashes ranged from only 56 to 3476 crashes in all lags.

Alaska had the lowest at 56 in 2010 and Texas had the highest at 3476 in 2008. These values

along with the mean and standard deviation are included in table 2.

A graphical representation of the state credit rating data for the fifty states arranged from

greatest to least ticketing states reveal that as the frequency of ticketing decreases in the states

the state credit rating also decreases. Although this appears counter-intuitive the relationship is

explored in the next sections. Figure 1 shows these relationships graphically for 2009 only

because the differences in the years are not substantial. In all years the relationship is the same.

Figure 1. State credit ratings (greatest to least ticketing states) 2009. From Pew Charitable

Trusts. All states are not shown in figure due to space constraints.

The other indicator of fiscal stress, unemployment, also declined as the level of ticketing

decreased. This relationship is clearly expected if budget concerns are the main impetus for the

issuing of traffic tickets. As the number of unemployed decrease the state and localities can rely

on other sources of revenue such as the increased revenues from sales and income taxes that

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follow. Figure 2 shows these relationships graphically. The unemployment rates for 2008 and

2009 are shown. The unemployment rate for 2010 mirrors the unemployment rate for 2009 so it

is not displayed in figure 2.

Figure 2. Unemployment (greatest to least ticketing states) 2008 & 2009. From the Bureau of

Labor Statistics. All states are not shown in figure due to space constraints.

The final indicator of fiscal stress, housing price index, increased as the level of ticketing

decreased. This relationship is also clearly expected if the budget is the main concern in

ticketing. As the housing prices increase, the localities can rely more on property taxes relieving

state responsibilities for increased transfers to these localities since states usually do not receive a

majority of their tax revenue from property taxes. Figure 3 shows these relationships

graphically. Data for 2009 are shown only because the other years are not substantially different

from this year.

Finally, the indicator of public safety concerns, automobile fatalities, has a very steep

decline in the number of fatalities and the decrease in the number of tickets. This relationship is

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expected if public safety concerns are considered in the issuance of tickets. Figure 4 shows these

relationships graphically. Again data for 2009 are shown only because of the similarities in data.

Figure 3. Housing price index (greatest to least ticketing states) 2009. From the U.S. Census

Bureau. All states are not shown in figure due to space constraints.

Figure 4. Automobile fatalities (greatest to least ticketing states) 2009. From the National

Highway Traffic Safety Administration. All states are not shown in figure due to space

constraints.

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Results

On more than one variable the states of Wyoming and Montana were outliers amongst the

other states. For instance they issued very few tickets according to the indicator and had very

high scores on the housing price index in comparison to the other states. The variables were

tested both with and without the two states included in the analysis. The coefficients remained

largely unchanged between the two analyses. The only major change was in significance. When

Montana and Wyoming were excluded from the analysis the significance of the model increased,

but no variables moved from non-significance to significance at the .05 level by excluding the

two cases. Since there is no theoretical reason to exclude these two cases they remain in the

analysis reported here.

Due to normality concerns the dependent variable, per capita ticket queries, and the

independent variable, fatal crashes per state, were logged. The adjusted R-square for the models

2008 – 2010 range from .860 to .840 indicating that 86 to 84 percent of the variance in the

dependent variable is explained by including the variables. The F ratio ranges from 51.214 (6,

43) to 43.999 (6, 43) at a p-value of .0001 indicating that these models are a much better

predictor of the dependent variable than the mean alone. Out of the variables included in the

analysis the Percent African American and the unemployment rate are insignificant at the .05

level through all the laggings of the variables. State credit ratings are significant and positive for

2010. The log of fatal crashes is significant and negative for the years 2008 – 2010. The log of

fatal crashes has a higher standardized beta from 2008 to 2010 indicating that it has a larger

impact on the dependent variable than any other variable. The housing price index is significant

and negative in 2010. Table 3 reports these findings including accompanying statistics.

20

Table 3

Multiple Regression of Budget and Public Safety Variables with Traffic Search Queries

2008 - 2010

Model

Standardized

Standardized

Standardized

Beta (2010)

Beta (2009) Beta (2008)

State Credit Rankings .125* .112 .081

Unemployment Rate -.053 - .040 - .052

Housing Price Index -.156* -.094 -.105

Log of Fatal Crashes per State -1.082*** -1.090*** -1.100***

Percent African American .095 .112 .128

Average Temperatures per State .258* .293** .278**

Note. Dependent Variable: Per 10,000 Search Queries by State. Adjusted R2 for 2008 is

.840. Adjusted R2 for 2009 is .849. Adjusted R2 for 2010 is .860. N = 50 States. Data on

state credit rankings from Pew Charitable Trusts. Data on unemployment from the

Bureau of Labor Statistics. Data on the housing price index based on single-family

homes from the U.S. Census Bureau. Data on vehicle fatalities per state from the

National Highway Traffic Safety Administration. Data on the percentage of African

Americans per state for the year 2010 from the U.S. Census Bureau. Data on the

average temperatures per state from 1971 to 2000 from the National Oceanic and

Atmospheric Administration. *p < .05. **p < .01. ***p < .001

21

As the number of per 10,000 ticket queries increases so does the ranking on the state

credit ratings from Standard & Poor’s U.S. State credit rating for 2010. This means the credit

score improves when the number of tickets issued increases in a state. As indicated earlier, this

relationship seems counter-intuitive but it is hypothesized since traffic tickets form a viable part

of the budget. The case of Miami-Dade and Hillsborough counties cited earlier supports the

notion that these fines/fees can be utilized in securing public debt, one of the major concerns of

the credit rating agencies. The more tickets, the more security and the higher the credit rating

that is given the state. The insignificance for the other years could be the result of credit ratings

being updated in shorter timeframes in which the year rated reflects actual budget conditions in

that year and not lagged based on previous year’s performance. The year 2010 was the peak of

the ‘Great Recession’ when many states were cutting transfers to local governments while those

states were looking for additional revenue. Traffic ticket revenue could have contributed to the

state’s better credit rating in 2010 and not the previous years.

The significant and positive relationship between state credit ratings and traffic tickets in

2010 supports the idea that traffic tickets are also a crucial aspect of the government’s budget.

What is not known from this relationship is if the positive relationship is due to policy

considerations. Are officials utilizing ticket revenue to balance budgets? Do these officials see a

window of opportunity to issue more tickets when there is a very real problem of traffic

incidents? A follow up study to this study would collect data on individual ticket costs to

determine a potential underlying policy orientation towards budget maximization. Higher ticket

costs could indicate a budget maximization orientation.

The unemployment rate remained insignificant while the last indicator for budgetary

concerns, housing prices, is significant in 2010 only. Its effect on the number of ticket inquiries

22

in 2010 is negative which is the expected direction for this variable. The year 2010 was not only

the peak of the ‘Great Recession’, but it was the peak of the housing foreclosures as well. A

culmination of several years of declining property tax revenue peaking in 2010 may have urged

public officials to rely more on other sources of revenue including tickets in that year.

The indicator for public safety concerns, fatal crashes per state, was the most significant

and largest predictor of per capita ticket queries and it maintained its high level of significance in

the preceding two years as well. One of the major concerns with reverse causality involved this

variable in particular. The lagged years reveal consistent direction and significance with the

dependent variable. The relationship between the number of tickets issued and the number of

fatal crashes per state is negative which indicates that as the number of tickets increase the

number of fatal crashes decrease. The per 10,000 population value of the dependent variable

possibly allows policy effectiveness to be observed through the issuance of tickets. This means

that in states that issue less tickets per capita experience more traffic fatalities than those states

that issue more tickets per capita.

The average temperature per state is significant throughout all years. It was hypothesized

that as temperatures increase in a state, tourism increases and the likelihood of receiving a ticket

increases as well. This may be the cause of this positive and significant relationship with traffic

tickets. Additionally, warmer states also tend to have higher levels of in-migration causing these

state populations to increase and traffic tickets could also increase with this in-migration. Lastly,

it is not known what impact weather has on drivers. It would be a stretch to note that warmer

weather causes people to speed more than colder weather. Although it is interesting, it merely

serves as a control in the larger analysis.

23

Discussion

The results of the analysis provide directions for future research on this topic. The most

significant finding from this study is the impact of fatal crashes on the dependent variable over

the other variables in the model in all years. This relationship indicates that public officials truly

believe that traffic tickets are an effective way to combat traffic fatalities since many of those

fatalities are from speeding and the results indicate that it is having a negative impact on traffic

fatalities per state. Some of the literature covered in this article confirms this assumption.

There needs to be more literature on this topic that evaluates the effectiveness of traffic

tickets in preventing traffic-related fatalities on a wider scale. Currently, much of the literature

focuses on cases such as certain localities or cities. It would be much more generalizable to

expand those analyses to include multiple communities across the United States covering the

fifty states. The National Motorists Association ticket index could also serve this purpose at the

state-level for multiple years, but at this stage only two years of the index are available.

The significance of the budget-related variables also adds credence to the assumptions

that tickets are tied to budget outcomes. As discussed earlier, the existence of a problem such as

high traffic-related fatalities in a state/locality could lead to public officials emphasizing

ticketing as a solution while recognizing the increase in revenue that would mean for the

state/local government. The rationale could also be in the reverse. Those same public officials

aim to close a budget gap and see ticketing as one means to do it. The variables chosen in this

analysis do not answer these questions but instead provide information on whether there is a

relationship between indicators of budget health and the incidence of ticketing. This analysis

does show a relationship.

24

The positive relationship between ticketing and state credit ratings is very interesting

because of the security that traffic tickets possibly create for investors in government securities.

One might wonder if a government is hard-pressed to fulfill its debt obligations if traffic ticket

revenue becomes an option to secure those necessary funds. One step in securing this connection

would be to determine what percentage of debt service is secured through fines. This

information is more elusive than determining how many tickets are actually issued in each state.

One main reason for this is the political ramifications.

It also needs to be explored if wealthier states have more resources which allow them to

ticket more which would be indicated in higher state credit ratings. Wealthier states are less

risky investments which mean they have higher credit ratings. It is possible that these states with

higher credit ratings and higher ticketing have larger police forces which allow for more police

that can assign more tickets.

The negative relationship between the housing price index and ticketing is interesting

because of its impact on property tax revenues. The variable was only significant in 2010 which

is the peak year after the beginning of the housing crisis. The spread for housing prices is a little

wider in 2009, but with similar maximum price index values as 2008. In 2010 this spread

lessens significantly with the lowest maximum price index values between 2008 and 2010.

The real find in this article is the larger effect of traffic fatalities on ticketing. The largest

effect on 2010 per 10,000 ticket search queries is the level of reduced fatalities in 2008. In these

findings both traffic fatalities and budget concerns appear to factor into ticketing decisions. In a

way, this is a mutually-beneficial arrangement for generating public revenue. As an example,

Kravitz (2009) notes that ticket revenue has gone up in Loudoun County (Washington, D.C.

area) because of the increase in volume and the large number of requests for more enforcement

25

of speed limits. Communities struggle with speeders and the dangers that accompany them

especially in regards their children and pets. In this case, the citizens demand action by the

police and the result is increased revenue for the county. It is a win-win situation.

The problem with relying on traffic tickets in response to traffic fatalities is that it

appears to not be having a stable impact. The change from 2008 to 2009 and 2009 to 2010 is

hardly noticeable across the United States. States that had high levels of traffic fatalities in 2008

continued to have high levels of traffic fatalities in 2010 despite having higher levels of traffic

tickets. Policymakers may be responding to these fatality levels with higher levels of traffic

tickets, but it may not be effective at curbing these fatalities beyond a certain level. In this case

it may only be effective at raising revenue. This is a future area of study that needs expanded

upon if the goal is to make driving safer.

Research on fiscal strain in states and local governments is the next step in this research

on traffic tickets. This model is cross-sectional with some lagged data, but a longer range panel

model would be the best method to capture the changes in ticketing in response to changes in

fiscal health. The immediate research questions are centered on the change impact of various

indicators of fiscal health on the issuance of tickets. Some literature has focused on this but as

noted earlier there needs to be a much more generalizable level of research not focused on one

locality.

There are some general weaknesses in this study. The dependent variable relies on an

indirect measure of ticket incidence. Many of those searching about traffic tickets in Google

could be doing so for a wide array of reasons not necessarily because they received a ticket.

Additionally, the ticket they received could have been from another state leading to a

superficially high incidence on the ticket index for that state. Although the index is not perfect it

26

is the only one that exists. State budget documents do not itemize revenues received from

fines/fees. Traffic tickets are included amongst other fines and court fees not necessarily related

to traffic tickets. The interest in this paper is on traffic tickets alone and not fines and fees in

general.

The level of analysis is also a weakness since the local level uncovers the complexities of

this process between localities. The ideal analysis would be multiple communities representing

each state representing each region within the state. The database would be immense but create

much more definite results on the relationships between public safety and budgeting factors and

the incidence of ticketing. Many assumptions are utilized in drawing conclusions from this

analysis.

The choice of the variables to represent budget health is always an issue of contention.

Given the literature on fiscal health there are a multitude of factors that could be included in this

analysis each representing fiscal health in some way. It was determined for this model that state

credit ratings, state unemployment and housing prices capture most of these factors. State credit

ratings include budget performance and liabilities in the rating determination while

unemployment captures many of the factors that cause budgetary fiscal strain such as a loss of

tax revenues from income taxes, sales taxes and the accompanying reliance on state entitlements

for the poor and destitute. Lower housing prices indicate lower property tax revenues.

Conclusion

The policy focus of traffic tickets should determine the monetary amount of those tickets.

Tickets that are expensive can either be a deterrent or a budget building tool. Tickets that are too

expensive lead to non-compliance which is detrimental to any budget forecasted to receive those

revenues. An optimal level of ticket penalty needs to be sought that equally dissuades law-

27

breakers and ensures compliance so that revenues can be secured. Saha and Poole (2000) state

that, “if the choice of penalty were to be left in the hands of the monitoring agency, the chosen

penalty level would be lower than that socially desired (p. 196).” As noted earlier many in law

enforcement feel the tickets are too expensive and non-compliance in California is a potential

indicator of the high costs of these tickets. Saha and Poole’s point may be valid, but social

optimality often is counter to individual optimality.

Possibly one solution is to income-adjust ticket penalties for different individuals. Just as

income taxes are based on ability to pay, ticketing can be based on ability to pay. Finland,

Sweden, Denmark and Germany already implement a system of ticketing based on this method.

By increasing the fine level for those more able to pay and lessening it for those less able to pay,

both social and individual optimality can be maintained no matter what level of ticketing is

implemented. The key is to establish some sort of ceiling on the fine so as to avoid exorbitant

fines for those much better off (Bedard, 2005).

As in all sources of revenue for any government, too much reliance on one particular

source could lead to financial hardships for the city. Ticketing revenue adds revenue and may

fill gaps in state/local government budgets, but should never be relied on without considering

other sources of revenue first. The political repercussions of a government relying on ticket

revenue would not be worth it for the government. If ticket revenue is increased to match social

optimality the best approach would be gradual and fair possibly through the ability to pay. Fines

should not be excessive and governments need to be sensitive to the financial hardships of its

residents. As unemployment goes up in a jurisdiction the accompanying increase in tickets to

replace lost revenue would only increase these financial hardships if an unemployed individual

28

receives a traffic ticket. At the end of the day, safety should be its primary goal and revenue its

secondary goal.

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