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 article 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 negative 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 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 article 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. Furthermore, 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) suggested 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 or she has committed one. The legal justification for such a stop is to either issue 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 30% 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) cited 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, US$40.4 billion 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 also public costs due to the need for emergency vehicles, clean-up, repair as well as the costs of delayed arrival times to other drivers.
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 than those who paid by mail. Furthermore, the authors found that drivers who paid fines and received points were no different from those who were declared not guilty in regard 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 et al., 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 on 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). In addition, 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 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. Furthermore, 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 made clearer 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).
Garrett and Wagner (2009) included 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 3 years or more to pay during the first 6 months of 2012 and get 50% off. The state expects that US$46 million will be collected or 2% 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. Furthermore, expectant court fees are a source of security for bonds. Miami-Dade and Hillsborough Counties in Florida charged US$15 to US$30 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. In addition, 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 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 who caused these accidents were not responding to emergencies (Kestin & Maines, 2012).
In addition, 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”. 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 are derived from the National Motorists Association (NMA) for 2010. The data are based on search queries in Google’s insights for Search which catalogs 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 separate times throughout 2010 (NMA, 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 NMA 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 & Stratmann, 2009, and Makowsky & Stratmann, 2011—rely on local ticket data which are not available across all communities in the United States. Makowsky and Stratmann (2009) relied 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 were 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 these 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, settlements and judgments (State of Florida CAFR, 2011). Besides this, revenue data are dispersed through several funds.
One might also consider adding up all fine revenue collected by municipalities and counties in all 50 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 as 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 (Court Statistics Project, 2013). The organization provided data on the number of incoming caseloads for 13 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 13 states that responded to the NCSC request in 2010 and the corresponding 13 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.
Pearson’s Correlation of Traffic Violation Cases With Per 10,000 Traffic Search Queries.
Note. Data on traffic violations per state from the National Council for State Courts. N = 13 States. Data on total traffic search queries per capita from the National Motorists Association.
p = .05.
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 of budget concerns. The U.S. Department of Transportation, National Highway Traffic Safety Administration (2012) collected data on the number of fatalities from vehicle crashes. 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 (Makowsky & Stratmann, 2011; Perin, 2011; Tay, 2010) This is one of the main independent variables.
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). The indicators of fiscal strain are many, but Skidmore and Scorsone (2011) recommended that the indicator should be exogenous to the decisions made by local governments. The fiscal distress measure that Makowsky and Stratmann (2011) relied on is endogenous as 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 article as there is typically little difference between the ratings for states among 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). 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 (The Pew Charitable Trusts, 2012).
It is assumed in this article, similar to Makowsky and Stratmann (2009) and Makowsky and Stratmann (2011), that fiscal strain leads to the issuance of more traffic tickets. As 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 they have control, that is, 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 serve 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, 2009, 2011). Lower housing prices mean lower property taxes. The housing price index developed by the Census is used in this study to represent housing prices (U.S. Census, 2012a).
In addition, 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 each state increases, so does the number of traffic tickets issued in those states. Information on the number of African Americans per state was obtained from the Census (U.S. Census, 2012b).
Due to concerns with reverse causality between the independent variables and the dependent variable, the independent variables were lagged by 1 and 2 years (2008 and 2009). A number of studies lag their data to account for reverse causality such as Buch, Koch, and Koetter (2013); Clemens, Radelet, Bhavnani, 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 1 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% to the highest value of 13.70% in all lags. The housing price index score ranged from 126.10 to 309.5, which indicates the highest price values across all lags.
Finally, the number of fatal crashes ranged from only 56 to 3,476 crashes in all lags. Alaska had the lowest at 56 in 2010 and Texas had the highest at 3,476 in 2008. These values along with the mean and standard deviation are included in Table 2.
Minimum and Maximum Values, Means, and Standard Deviations for All Variables for All Lags 2008-2010.
Note. 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.
A graphical representation of the state credit rating data for the 50 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.
State credit ratings (greatest to least ticketing states) 2009.
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 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.
Unemployment (greatest to least ticketing states) 2008 and 2009.
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 as 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.
Housing price index (greatest to least ticketing states) 2009.
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 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.
Automobile fatalities (greatest to least ticketing states) 2009.
Results
On more than one variable the states of Wyoming and Montana were outliers among 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 with 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. As 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 R2 for the models 2008 to 2010 range from .860 to .840 indicating that 86% to 84% 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 to 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.
Multiple Regression of Budget and Public Safety Variables With Traffic Search Queries 2008-2010.
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.
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 because 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 in the state. The insignificance for the other years could be the result of credit ratings being updated in shorter time frames 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 toward 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 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 2 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. In addition, 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. Finally, 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.
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 as many of those fatalities occur 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 50 states. The NMA ticket index could also serve this purpose at the state level for multiple years, but at this stage only 2 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.
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) noted 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 of speed limits. Communities struggle with speeders and the dangers that accompany them especially in regard 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. Policy makers 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 to be expanded on 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. In addition, 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 is the only one that exists. State budget documents do not itemize revenues received from fines/fees. Traffic tickets are included among other fines and court fees not necessarily related to traffic tickets. The interest in this article is on traffic tickets alone and not fines and fees in general.
The level of analysis is also a weakness as 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-breakers and ensures compliance so that revenues can be secured. Saha and Poole (2000) stated, “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 receives a traffic ticket. At the end of the day, safety should be its primary goal and revenue its secondary goal.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.