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«Contesting the streets Volume 18, number 1 • 2016 U.S. Department of Housing and Urban Development | Office of Policy Development and Research ...»

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The remainder of this article is organized as follows: we present a review of pertinent background information and literature, followed by a discussion of our research methods. Next, we include the estimated model, its subsequent results, and a discussion. We conclude with a summary of pertinent findings, suggestions for street-vending enforcement agencies and policy change, and implications for future research.

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Background and Literature Review In considering street-vending regulation enforcement and fine repayment, we review two high-level issues—vending regulation and officers’ citation behavior—that provide context for the analysis.

Vending Regulation As the population of NYC expanded over the years, immigrant vendors across the city played an important role in employing and provisioning the city’s residents. Today, immigrant and minority vendors comprise the majority of streetcart entrepreneurs (Sluszka and Basinski, 2006).

Although these street vendors have filled an important need in the community, some city stakeholders have frowned on their activities and have continually pushed for stiffer penalties, regulation, and enforcement. Enforcement historically focused on unlicensed street vendors; however, in response to “quality of life” regulations introduced in the 1990s, policing tactics expanded enforcement to licensed vendors. Work by Duneier (1999) discusses how enhanced police enforcement of booksellers led to an increase in ticketing for minor infractions and, in some cases, even the confiscation of goods. As Stoller (2002) later mentioned, the resulting fines from this increased enforcement caused some vendors to lose their licenses and ultimately exit the vending business.

Earlier work by Austin (1994) examined the role and police treatment of Black street vendors in society. She argued that for many poor Black vendors, the only way to survive economically was to break the law. Austin also noted that police officers subjectively enforced street-vending regulations, with many of them using personal discretion when writing citations. In a later study focusing specifically on street vending in NYC, Devlin (2011) concluded that regulation enforcement was ambiguous and, at times, statutes were contradictory. In particular, he found that regulation in Manhattan was inconsistent and influenced by property owners and other business stakeholders who used intimidation to discourage vendors from operating.

In 2004, before Devlin’s study, the NYC Department of Health and the Department of Consumer Affairs increased the penalties on street-vending violations, some of which were raised from $250 to $1,000 per offense (Turetsky, Vega, and O’Brien, 2010). Two earlier research efforts explored street-vending violation data from NYC and the relationship between fine size and the likelihood of fine payment. The first effort by Schwefel (2011) analyzed violation data from 2009 and 2010 and concluded that, as fine size increased, the likelihood of fine payment decreased. Davis and Morales (2012) extended this research to include violations from 2006 through 2010 and likewise concluded that the most expensive violations are paid with less frequency compared with other fine levels. They proposed that NYC restructure its fine scheme so that frequently written tickets would be associated with a less-expensive fine, thus increasing the likelihood of the vendor paying the ticket. Although the city ultimately reduced the fine levels in 2013, it is probable that other factors beyond fine levels influence vendor default in payment.

Officers’ Citation Behavior The second issue is the subjective behavior associated with giving citations. This issue has been most widely studied in the context of traffic citation issuance, and a large body of research has

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examined situational factors influencing traffic tickets. Ingram (2007) looked at traffic citations for a large metropolitan area of the Southwestern United States and concluded that neighborhood characteristics, such as racial demographics, played a role in the issuance of traffic citations. In particular, officers behaved differently depending on the neighborhood in which they were policing traffic, thus influencing the number of citations written.

Earlier studies by Meehan and Ponder (2002) and Petrocelli, Piquero, and Smith (2003) likewise examined the influence of place on the practices of police traffic enforcement and found the place the citation occurred to be a significant factor. Meehan and Ponder found minority drivers were more likely to be racially profiled when the traffic stop occurred in an area with a low minority composition. In areas where the population was mostly White, minority drivers were also more likely to be stopped and monitored by police.

Petrocelli, Piquero, and Smith (2003) similarly concluded that socioeconomic indicators were a factor: Black drivers were more likely to be searched by police because of officers’ perceptions of them. In addition, the higher the crime rate of the neighborhood, the greater the number of total traffic stops performed by police officials. A similar study by Engel and Calnon (2004) concluded that minority drivers (particularly Black and Hispanic drivers) were at higher risk of being issued a violation, holding constant the traffic behavior of all races.

Taking a more economic approach, Makowsky and Stratmann (2009) explored how traffic officers issue citations using a utility maximization framework and the concept of opportunity costs.

They concluded that officers often make ticket-issuing decisions after first taking into account the likelihood of the recipient contesting the violation. They also consider how the ticketing decision will reflect on their overall work performance. Similar to Ingram (2007), Makowsky and Stratmann (2009) imply that officers might behave differently when faced with similar circumstances, depending on the particular situation. We generalize the work of Makowsky and Stratmann, then, to our current context by exploring situational factors that influence the payment of vendor citations.

Such factors would be of importance to city agencies looking to minimize the public costs of vending regulation enforcement.

Methods To explore the effect of vending regulation and officer citation behavior on unpaid vendor citations, we obtained data on street vendor tickets from the City of New York. We propose an empirical framework to examine the influence of violation-specific and situational factors on the probability of default in payment.

Data Procurement and Variable Coding Data for all civil street vendor tickets for 2010 were obtained from the City of New York through the use of a Freedom of Information Law request. Violations included in the data set consisted of more than 100,000 vendor tickets returnable to the Environmental Control Board for the 5-year period of 2006 through 2010, which the researchers entered from paper tickets. The entered

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data consisted of details for each violation, including the relevant section ordinance, fine amount, borough location, date and time of the offense, and whether the respondent defaulted in payment on the imposed ticket.

To test for significant differences across the 5-year period, we tested several variables for differences in their proportions across years by conducting a series of Tukey-type multiple comparison tests on proportions appropriate for unequal sample sizes (Elliott and Reisch, 2006). This approach enabled us to test all possible pairwise differences simultaneously. We tested the following proportional variables: violations in default, muddy violations issued, health-code violations issued, moderate fines imposed, high fines imposed, violations issued in Manhattan, and violations issued on a street corner location. For each variable, testing the multiple comparisons for differences across time periods failed to yield any comparisons that were significant at the 5-percent level; we found no evidence of differences across time periods for the variables examined. Therefore, we focused on the more recent 2010 data, which contained 25,820 violations with complete ticket information.

Using the available ticket information for 2010, we next created dummy variables for a series of five violation-specific attributes and five situational-factor attributes. It is probable that both types of enforcement factors (beyond fine levels only) influence vendor default in payment. If so, vending enforcement and regulation could be further improved and public cost reduced by taking both types of factor attributes into consideration when revisiting policy recommendations and officer regulation procedures.

Violation-Specific Factors Violation-specific factors investigated included the clarity of the specific law (crystal clear or muddy), the type of section ordinance (health code or administrative), whether the section cited was a frequently cited ordinance, whether the officer was a frequent ticket issuer, and the fine level for the ticket.

One of our central claims is that some rules found in the sections of the New York City Administrative Code and of the New York City Health Code are either muddy or crystal clear; that is, either ambiguous or not. We executed a coding process to determine whether a particular rule should be deemed crystal clear or muddy. Two members of the research team each independently coded the rules on the basis of whether the rule was clearly defined and later compared codes. Each coder executed the same process, which was to examine each rule for its degree of clarity. For instance, a relatively clear rule requires the vendor to be a certain distance from a driveway or subway, and a relatively muddy rule requires the vendor to permit regular inspections. The former can be physically measured; the latter is not measurable and subject to interpretation. We then enjoined an external legal expert to independently code the rules. We found 94 percent initial agreement across the three coders. For cases in which a discrepancy existed, all coders jointly reexamined the rule in question and, if necessary, deferred to the more experienced opinion of the external legal expert.

Type of section ordinance was included as a factor because vendors probably perceive these two types of ordinances differently. Before becoming a licensed vendor, applicants must complete an 8-hour, 2-day “food protection” course offered by the city. They must also pass a vending unit

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inspection by the Department of Health. Vendors then may feel better informed of and educated about health-code violations compared with administrative ordinances, and thus view the importance of these two types of ordinances differently.

Of the 127 different sections cited on vending tickets, 10 accounted for more than 64 percent of all violations issued in 2010. Vendors may be more likely to pay these particular violations if they are commonly understood and less likely to pay if they deem them a nuisance. Likewise, 10 officers accounted for 29 percent of all violations issued in 2010. These 10 officers issued more than 475 tickets each; the average issuance for the remaining officers in the sample was less than one-half this number. As previously mentioned, no single city agency is responsible for street-vending regulation and enforcement. It is unclear why these 10 officers are writing so many vending tickets, but it may be that vendors view their individual and independent vigilance as a nuisance, which reduces the likelihood that a vendor would pay an issued ticket.

Following the earlier work of Davis and Morales (2012), the fine level of the ticket was likewise included here as a potential variable. Because of the city’s fine structure, the violation fines issued were only between the ranges of $25 and $100, $200 and $880, and $1,000 and $2,200.

Situational Factors Situational factors coded included the borough in which the infraction occurred, whether it occurred on a street corner location, the day of the week, the time of day, and the season of the year. Devlin (2011) noted that vending regulation in Manhattan was particularly inconsistent. Of vending violations in 2010, 78 percent were written in the borough of Manhattan, and 62 percent were written on a street corner location. Corner locations may be an area frequently patrolled by officers due to their high visibility. As Makowsky and Stratmann (2009) mentioned, officers may make ticketing decisions based on how it reflects on their work performance. These locational factors may influence the likelihood of default in payment if vendors think tickets issued in these areas are particularly unfair.

Additional situational factors may include day-of-the-week effects. Bryson and Forth (2007) found day-of-the-week effects for office workers; office workers were more likely to be productive midweek. It may be that such effects occur for police officers as well. Therefore, variables for a midweek day and a weekend day were coded.

Tickets also appeared to be somewhat clustered around the early afternoon, with more than 35 percent of tickets issued during the lunchtime hours of 12:01 to 3:00 p.m. During this timeframe, officers would likely be highly visible to vending patrons who are on their lunch break and frequenting vending units. Makowsky and Stratmann (2009) suggested that officers may wish to be seen by large volumes of patrons when issuing citations if they think this action reflects well on their job performance. Because the city issues seasonal vending permits in addition to annual permits, it may be that seasonality influences ticket payment, particularly if ticket payment is linked to the profitability of the vendor’s enterprise at the time of issuance.

Descriptive statistics for the 2010 data are presented in exhibit 1. Approximately 64 percent of tickets issued were for the violation of 1 of a set of 10 different ordinances.

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vations for Manhattan Borough, and 25,916 observations for street corner location.

Empirical Framework We propose that both situational and violation-specific factors influence the probability of default in payment for street vendors. The dependent variable of interest was evenly split in the data, with

54.25 percent of tickets defaulted. Therefore, to estimate the influence that factor attributes and attribute interactions have on the probability of ticket default in payment, we employed a binary logit model.

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