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Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor

Street Prostitution Zones and Crime

IZA DP No. 9038

May 2015

Paul Bisschop Stephen Kastoryano Bas van der Klaauw

Street Prostitution Zones and Crime

Paul Bisschop SEO Economic Research

Stephen Kastoryano

University of Mannheim and IZA

Bas van der Klaauw

VU University Amsterdam, IZA and Tinbergen Institute

Discussion Paper No. 9038 May 2015

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IZA Discussion Paper No. 9038 May 2015

ABSTRACT

Street Prostitution Zones and Crime* This paper studies the effects of introducing legal street prostitution zones on both registered and perceived crime. We exploit a unique setting in the Netherlands where legal street prostitution zones were opened in nine cities under different regulation systems. We provide evidence that the opening of these zones was not in response to changes in crime. Our difference-in-difference analysis using data on the largest 25 Dutch cities between 1994 and 2011 shows that opening a legal street prostitution zone decreases registered sexual abuse and rape by about 30% to 40% in the first two years. For cities which opened a legal street prostitution zone with a licensing system we also find significant reductions in drug-related crime and long-term effects on sexual assaults. Perceived drug nuisance increases upon opening but then decreases below pre-opening levels in cities with a licensed prostitution zone. In contrast, we find permanent increases in perceived drug crime in the areas adjacent to the legal prostitution zones. JEL Classification: J16, J47, K14, K23, K42 Keywords: prostitution, registered crime, perceived crime, regulation, difference-in-difference Corresponding author: Stephen Kastoryano Department of Economics University of Mannheim Block L7, 3-5 Room 144 68131 Mannheim Germany E-mail: s.kastoryano@uni-mannheim.de

* Thanks to Sander Flight, Wim Bernasco, Jan van Ours, Hessel Oosterbeek, Erik Plug, Joop Hartog, Michèle Tertilt, Rei Sayag and Thomas Buser for valuable comments. The responsibility for the views and interpretations expressed in this article rests solely on the authors.

1 Introduction

The Netherlands holds a long tradition of regulated tolerance towards prostitution. Be-

sides the well-known window prostitution in red light districts, the Dutch government

also regulates other parts of the sex industry. Prostitution is known to be related to

international trafficking organizations and various other forms of crime. For instance, the

drug use of prostitutes and clients attracts people in drug trade. The illegal status of

sex work also makes prostitutes more vulnerable to sexual violence and abuse (Flight,

Van Heerwaarden and Lugtmeijer, 2003; Oostveen, 2008).1 Despite the ongoing debate

about legalizing prostitution, there exists little empirical evidence about the effects of

government regulation.2 An important reason for this is a lack of suitable data.

In this paper, we analyze empirically how the presence of a tippelzone affects aggregate

crime in Dutch cities. A tippelzone is a designated legal street prostitution zone where

soliciting and purchasing sex is tolerated between strict opening and closing hours at

night.3 The first tippelzone opened in The Hague in 1983 with eight other cities opening

zones during the following three decades. The first objective of tippelzones was to deal

with complaints of residents in the areas frequented by street prostitutes. A second

objective was to improve the health and safety conditions of street prostitutes, who are

often heroin and crack addicts and sometimes illegal immigrants. More recently four

tippelzones closed again, mainly because of the escalation of conflicts between prostitutes.

However, the closings were controversial.

In the empirical analysis we take advantage of the opening and closing of tippelzones

to obtain empirical evidence for the relation between regulation of prostitution and crime.

Since tippelzones did not come as a response to city specific trends in registered crime,

we can exploit the exogenous variation in openings and closings at different time periods

in different cities to obtain causal effects of regulation of street prostitution on crime. We

estimate a difference-in-difference model using data on registered crime from the Min-

istry of Justice covering the period between 1994 and 2011. These data contain several

measures of sexual, drug-related and violent crime in the 25 largest Dutch municipalities.

In a second step we consider the effect of tippelzones on perceived crime obtained from

the Population Police Monitor. This is a large-scale survey containing questions about

feelings of safety and perceived criminal activity in the respondent’s neighborhood cov-

ering the period between 1993 and 2006. We perform several empirical tests to assess

endogenous crime trends around the moment of opening a tippelzone. Our study is one

of the first to provide causal evidence for the connection between the regulation of pros-

titution and crime. Our study relates to a very recent paper by Cunningham and Shah

1Around 4.5 million women, of which one million younger than 18 years, are bought and sold worldwide

into forced sexual exploitation in an industry generating profits of about 99 billion annually with women

trafficked to Western Europe producing the highest per person revenue (ILO, 2012; 2014). 2See the appendix for an overview of prostitution regulation laws in various countries. 3Tippelzone is derived from the word tippelen, which in Dutch means street walking.

1

(2014) showing that an unexpected court order in Rhode Island decriminalizing indoor

prostitution decreased rape offenses by 31%.

We begin with the premise that the market for sex is connected to criminal activity,

such as sexual violence, drug trade, assault and organized crime. A tippelzone may act as a

coordination point for these types of crime. Isolating street prostitutes within a delimited

area attracts individuals prone to sexual violence and drug dealers which in turn attracts

new drug addicts and dealers. However, police monitoring is higher in tippelzones than in

other areas of the city so criminals of all types - sex-traffickers, pimps, drug dealers, violent

clients - must trade off their willingness to operate in the tippelzone with the higher risk

of apprehension. Indeed, upon the closing of tippelzones, supporters of the zones claimed

that neither street prostitution nor it’s surrounding crime would disappear. Both would

simply spread around the city and become less manageable. The intense debate between

supporters and opponents of tippelzones emphasizes the need to supplement theoretical

models on prostitution and crime spillovers with empirical evidence.

Another argument in favor of tippelzones is that criminalization of prostitution forces

the sex industry into the illegal underground market. Lee and Persson (2013) discuss the

connection between legislation of the sex market and the involvement of sex-trafficking

organizations. Two alternatives to criminalization are to fully legalize prostitution or to

legalize it but restrict access to a limited segment of people using licenses. A unique

feature of our analysis is that we can study both systems. Some tippelzones allowed free

entry while others enforced a licensing system immediately or introduced it after some

years.

Our empirical results show that opening a tippelzone reduces sexual abuse and rape.

These results are mainly driven by a 30%-40% reduction in the first two years after opening

the tippelzone.4 For tippelzones with a licensing system we additionally find long-term

decreases in sexual assaults and a 25% decrease in drug-related crime which persists in

the medium to long-run. We do not find evidence for effects on other types of crime such

as violent assaults and possession of illegal weapons.

We compare these effects on registered crime with those on perceived crime. Since

policy decisions are influenced by public perception it is relevant to know whether these are

in line with registered crime. Our results indicate that perceived drug nuisance increases

by approximately 6%-points when a tippelzone is opened. These effects however vary

depending upon the proximity to the tippelzone and whether the tippelzone enforced

licensing from the start. For cities where licensing was introduced immediately, we find

some indication that tippelzones achieved their stated goal to reduce the nuisance created

by drug-addicted prostitutes and their followers overall in the city, but at the expense of

residents living close to tippelzones.

The remainder of the paper proceeds as follows. In the next section, we provide a brief

overview of the history of Dutch regulation of prostitution and a description of tippel-

4These results are very similar to those of Cunningham and Shah (2014).

2

zones. Next, we discuss the theoretical literature on the connection between regulation,

prostitution and crime, and discuss possible mechanisms through which tippelzones can

influence crime. In Section 3 we present the difference-in-difference model. Section 4

describes the data. Section 5 presents the results on registered crime. Section 6 discusses

results on perceived crime. Section 7 concludes.

2 Background and Literature

2.1 Dutch Tolerance and Tippelzones

Historically, the Dutch policy towards prostitution has balanced periods of strict abolition

with pragmatic regulated tolerance. In 1911, a law passed criminalizing anyone running

a brothel or organizing prostitution. Shortly thereafter, a loophole was introduced which

gave public prosecutors power to ignore criminal infringements (Outshoorn, 2004). The

ban on brothels was therefore only enforced if other laws were broken. During the second

half of the 20th century, problems surrounding prostitution became more prominent when

a new wave of entrepreneurial criminals became involved in drug trafficking, protection

rackets, human trafficking and money laundering (Brants, 1998). In response, the gov-

ernment gave power to local authorities to adapt city by-laws. This allowed for certain

areas where sex shops, window prostitution and brothels were tolerated.

The gradual increase in regulated tolerance initiated a law in 2000 stating that prosti-

tutes older than 18 years are allowed to work in legal sex houses or brothels under certain

conditions (Daalder, 2007). In particular, they must be registered as workers, pay taxes

and maintain regular health checks.5 The new law affected prostitutes differently across

the country since enforcement was again left to local municipalities. According to Brants

(1998), the new law only provided a legal stamp to policies which already existed in many

cities.

The policy change of interest in this paper is the re-localization of street prostitutes

to designated tolerance areas, the so-called tippelzones. Several cities created tippelzones

mainly in response to complaints about the noise and squalor created by crack and heroine

addicted street prostitutes, their pimps and drug dealers.6 Tippelzones were also intended

to address the health and safety needs of prostitutes. According to Daalder (p.38, 2007)

“the changes in policy regarding the streetwalking zones are not connected to the lifting

of the brothel ban”.7

5The social position of prostitutes is improved by a labor union and their financial consultancy orga-

nization. 6See for instance newspaper and municipality reports for Arnhem: Digibron (1996), Tegenoffensief

Arnhem in drugsproblematiek ; for Heerlen: Tops. P and W. Gooren (2009), Police academy research, Een

pact van het hart ; for Groningen: Digibron (1995), Groningen krijgt na tien jaar tippelzone; for Utrecht:

Gemeente eindrapport (2009), Evaluatie Utrechts Prostitutiebeleid ; for Amsterdam: Trouw (1995), Am-

sterdam richt maandag tippelzone Theemsweg in. 7See Flight, Hulshof, Van Soomeren and Soorsma (2006) for an evaluation of the lifting of the brothel

3

Figure 1: Layout of Amsterdam tippelzone (from Van Soomeren, 2004)

Tippelzones are equipped with a variety of features.8 Many tippelzones provide resting

quarters with washing amenities, clean needles and local medical assistance, and include

separate servicing areas where prostitutes remain with clients in a safe environment (see

Figure 1 for a map of the tippelzone in Amsterdam). Permanent supervisors or semi-

permanent task forces are assigned to monitor the tippelzone and neighboring areas. The

task forces are either rotating groups of agents from the local police district or new hirings

for cities with larger tippelzones (11 additional officers in Amsterdam). In the early years

of tippelzones, an implicit understanding was that the police did not prioritize arresting

illegal workers, in particular not when they were in the resting quarters.

The first tippelzone appeared in The Hague in 1983 followed by a second group in the

mid-nineties including Rotterdam (1994) and Amsterdam (1996). In total, nine Dutch

cities introduced tippelzones between 1983 and 2004 (see Table 1). The tippelzones were

placed in non-residential industrial areas slightly outside the city center. Since full de-

criminalization of prostitution was not legally enforceable in the 1980s and 1990s, cities

which opened a tippelzone simply applied selective decriminalization. They did so by en-

forcing more strictly the by-laws and ordinances forbidding street prostitution anywhere

other than in a tolerated zone.

After their introduction tippelzones remained controversial. In medium-sized cities

they generally functioned well attracting 20 to 50 prostitutes a night although some tip-

pelzones faced difficulties in reaching the targeted population of drug-addicted prostitutes

(Oostveen, 2008). Tippelzones in larger cities often attracted over 100 prostitutes in a

ban. 8See Van Soomeren, (2004) for a detailed discussion of the Amsterdam tippelzone.

4

Table 1: Opening and closing of tippelzones in the Netherlands

City Opening year (month) Start licensing Closing year (month)

The Hague 1983 never 2006 (Mar.)

Utrecht 1986 2005 (Oct.)

Rotterdam 1994 (Nov.) 2003 (Apr.) 2005 (Sep.)

Amsterdam 1996 (Jan.) 2002 (Jun.)* 2003 (Dec.)

Arnhem 1996 (Jun.) 2003 (Nov.)

Groningen 1998 (Jan.) never

Heerlen 2000 (Jun.) 2000 (Jun.) 2013 (Jan.)

Nijmegen 2000 (Oct.) 2007 (Sep.)

Eindhoven 2004 (Dec.) 2004 (Dec.) 2011 (May)

*Amsterdam did not formally have a licensing system but implemented

strict policing from June 2002 onwards verifying immigration status.

single night (Hulshof and Flight, 2008).9 In the mid 2000s, some tippelzones started

experiencing difficulties due to an increasing number of prostitutes from Eastern Europe

and South America. To limit the number of prostitutes, some tippelzones introduced a

licensing system. Table 1 shows the moment licensing was introduced. In some cities like

Heerlen and Eindhoven the licensing systems were present from the start while others

introduced them later to control the inflow of new workers. Licensing systems favored

known local prostitutes and drug-addicted prostitutes.

The Rotterdam tippelzone was notoriously turbulent mainly because of conflicts be-

tween prostitutes. Similar problems in The Hague and Amsterdam forced the shutdown

of the tippelzones in these three cities (Amsterdam in 2003, Rotterdam in 2005, The

Hague in 2006). The tippelzone in Eindhoven closed in 2011 despite positive assessments,

and in Heerlen in 2013 because too few prostitutes were working there anymore. As of

2014, four tippelzones are still open across the Netherlands. Korf, Van Vliet, Knotter,

and Wouters (2005) suggest that a small share of the prostitutes previously working in

Amsterdam, The Hague and Rotterdam moved to the tippelzone in Utrecht. However,

most were refused due to licensing restrictions from other tippelzones and also barred

from brothels, window soliciting and other legal sex establishments which also required

licensing.

The closing of tippelzones was controversial. Law enforcement agents were the main

supporters for closing tippelzones which they claimed acted as breeding grounds for ille-

gal trafficking of women, blackmail, violence and kidnapping. However, health workers

claimed that neither street prostitutes nor the surrounding crime would disappear upon

closing tippelzones. The problem would simply spread to other areas in the city and go

underground making it more difficult to monitor (Van Soomeren, 2004). Moreover, clos-

ing tippelzones would complicate addressing health needs of prostitutes and would make

them more vulnerable to sexual abuse and violence.

Oostveen (2008) studies how clients in Nijmegen responded to shifts in the illegal

9On average, prostitutes in the Amsterdam charged around e25 for a standard service and earned

e80 a night. Prices, however, varied by the number of prostitutes present in the tippelzone.

5

Figure 2: Supply and demand response to the introduction of licensing for the Nijmegen

tippelzone in September 2007 (figure reproduced from Oostveen, 2008)

clients

total nbr.

women

different women

0 1

0 2

0 3

0 4

0 5

0 6

0 7

0 8

0 A

v e

ra g

e n

u m

b e

r o

f W

o m

e n

/C li e

n ts

p e

r n

ig h

t

Jan. 2007 May 2007 Sep. 2007 Jan. 2008 May 2008 Sep. 2007

segment of prostitutes in tippelzones due to the introduction of a licensing system. Con-

fronted with an increasing inflow of Eastern European prostitutes, the tippelzone in Nij-

megen introduced strict licensing in September 2007 accompanied by intensive police

control during the first two weeks. Figure 2 illustrates the response of prostitutes and

clients. Immediately after the introduction of the licensing system the number of pros-

titutes reduced from over 80 different prostitutes a month to about 35.10 Similarly, the

average number of prostitutes working in the zone decreased from 20 to 10 per night.

This change was accompanied by a 30%-35% reduction in the average number of clients.

Although it is possible that some clients stopped procuring by prostitutes, these parallel

shifts suggest that introducing the licensing system pushed a share of street prostitutes

to the underground market.

2.2 Prostitution, Regulation and Crime

Several theories in crime location choice and crime displacement are relevant to understand

possible crime spillover effects of tippelzones. From a criminology perspective, tippelzones

can be seen as coordination ‘hot spots’ where the prostitution market attracts criminals

who in turn attract other potential criminals. Cohen and Felson (1979) label this conver-

gence the routine activity approach. Brantingham and Brantingham (1995) add to this

that different urban structures and planning can change the pool of criminals by induc-

ing new people into criminal activity or by inhibiting the actions of existing criminals.

10According to case workers this number slightly underestimates the total since it only measures women

entering resting quarters.

6

Assuming the probability of apprehension were to stay constant in all areas of the city,

tippelzones could increase crime by accelerating the process of convergence for drugs and

human trafficking, and by generating new opportunistic criminals.

However, given the higher probability of apprehension near the tippelzone, a rational

criminal (existing or potential) must weigh the expected gains from offending against the

probability of apprehension and the size of the punishment (Becker, 1968). Depending

on the type of crime, criminals may then be incapacitated or they may be forced to work

at the outskirts of the tippelzones with lower expected profit but also lower probability

of apprehension (Deutsch and Epstein, 1998). The presence of a tippelzone can therefore

prevent some types of crime from occurring by disenabling existing criminals and deterring

future ones. For example, a tippelzone should lead to a decrease in sexual violence on

street prostitutes who relocate to the tippelzone, which is important considering the

vulnerability of street prostitutes. According to Venicz and Vanwesenbeeck (2000) almost

half of the women in the sex industry in the Netherlands experience some form of assault,

sexual abuse, or other form of violence. In 47% of the cases the perpetrator was a client

and in 37% of the cases an (ex-)pimp.11

Lee and Persson (2013) provide additional insight in how tippelzone opening and

regulation can influence the involvement of sex trafficking organizations. According to

their theoretical model, a government which judges full elimination of prostitution to be

impractical or unattainable can instead prioritize reducing a certain type of consumption,

namely involuntary prostitution. Their model predicts that full criminalization of selling

and buying sex, which they label the traditional model, will reduce trafficking relative

to full legalization of the sex market only after eliminating voluntary prostitution. This

equilibrium is arguably suboptimal since it forces the entire sex market underground.

An alternative model, which they label the Dutch model, allows prostitutes to sell sex

provided they obtain a license after passing a background check. As long as voluntary

prostitution exists, their model predicts that the Dutch model unambiguously decreases

trafficking relative to the full criminalization model given a fixed probability of arrest for

illegal prostitutes.

According to the model of Lee and Persson (2013), the supply and share of involuntary

prostitutes trafficked by organized criminal groups should be larger in cities with tippel-

zones that do not enforce a licensing system. This involvement may increase other ‘transit’

crimes since organized criminal groups often also engage in drug trafficking, smuggling

illegal immigrants and arms trafficking (Kruisbergen, et al., 2012).12 The introduction of

a licensing system in previously unregulated tippelzones should reduce involuntary pros-

titution in the tippelzone, but it is unclear what spillovers to expect in other areas of the

11Their sample excludes street prostitutes who, Venicz and Vanwesenbeeck (2000) argue, may be even

more vulnerable to violence than prostitutes operating in clubs or windows. 12Gerben, et al. (1997) study criminals arrested for sex trafficking in the Netherlands and find that the

associated criminal organizations range from 2-3 collaborators to very substantial national or international

organized crime networks.

7

city. Ultimately, we need an empirical analysis to evaluate the effect of a tippelzone on

aggregate crime in a city.

To the best of our knowledge, there exists no empirical study evaluating the effects of

regulating street prostitutes on crime.13 The closest study to ours is a paper by Cunning-

ham and Shah (2014) which exploits an unexpected legal change in the state of Rhode

Island that temporarily decriminalized indoor prostitution. They find that decriminal-

ization leads to a 31% decrease in reported rape offenses. In addition, there are some

empirical studies discussing spatial spillovers of crime control (see Hesseling, 1994, and

Guerette and Bowers, 2009, for overviews in criminology). Weisburd et al. (2006) find

that an increase in police surveillance in two high-crime neighborhoods in Jersey City

reduces drug-related crime both within and around the targeted area. They attribute

this to gang affiliation which avoids that drug-related crime transfers easily to new loca-

tions. Di Tella and Schargrodsky (2004) and Draca, Machin, Witt (2011) find no sign

of displacement effects when focusing on exogenous increases in the supply of police in

specific areas in the wake of terrorist attacks. Although both studies find a decrease in

crime in the areas with additional police, neither study find any reduction in crime in

adjacent locations. Machin and Marie (2011) reach the same conclusion when looking at

a street crime initiative allocating extra resources to certain police force areas in England

and Wales.

While spatial relocation receives most attention, displacement of crime can also be

intertemporal or by changing target, offense, tactic, or offender. The evidence for such

crime spillovers is mixed. Adda, McConnell and Rasul (2014) consider an experiment

where cannabis possession in small quantities is depenalized in one London borough. They

find that the depenalization leads to an increase in offences for large quantity cannabis

possession in this area. Half of the increase is attributable to drug tourism from neighbor-

ing boroughs. Jacob, Lefgren and Moretti (2007) focus on weather shocks and show that

criminals who are prevented from committing property offenses in a given week try to

compensate for lost income by engaging in higher levels of criminal activity in subsequent

weeks.

3 Empirical Model

We use a difference-in-difference specification to study the effects of tippelzones and li-

censing on various types of crime. Let Yit denote the observed crime of city i in year t

13Akee, Bedi, Basu and Chau, (2010) and Cho, Dreher and Neumayer, (2013) use national level data to

investigate correlations between prostitution legislation in different countries and constructed variables

for sex-trafficking. Their findings remain inconclusive since the constructed measures for trafficking are

likely endogenous to country legislation and institutions. The Dutch publication Trafficking in Human

Beings: Visible and Invisible. A quantitative report (2007-2011) provides more detailed arguments on

the problems of measuring human trafficking at a national level.

8

which is modeled in our baseline model as

ln(Yit) = αi + δ0 D − it +δ1 Lit +δ2 D

+ it +β Xit +µt + uit (1)

The city fixed effects are captured by αi and the time trend µt is modeled using year fixed

effects. The variables D−it and D + it take value one if in city i in year t there was a tippelzone

which opened without a licensing system (-) or with a licensing system (+), respectively.

The variable Lit denotes the presence of a licensing system which was introduced some time

period after the opening. So, Lit can only take value one if initially there was a tippelzone

without licensing, so if D−it equals one. Finally, Xit describes other time-varying regressors.

In the estimation we use a logarithmic specification for our outcome variable to deal with

the larger variation in crime in larger cities.

The parameters of interest are δ0, δ1 and δ2. Given our log-linear specification, these

parameters represent the proportional effect of a tippelzone and licensing on local crime.

If δ0 = δ2, then enforcing a licensing system upon opening the tippelzone has no additional

effect on crime. And if δ1 = δ2 − δ0, then implementing a licensing system immediately or after some time has the same effect on crime. In order to give a causal interpretation

to δ0, δ1 and δ2 we assume that cities follow a common time trend in crime. We justify

this assumption in subsection 4.1.

It is not unlikely that crime rates within cities are serially correlated. Given that our

data only contain 25 cities, we produce statistical inference based on the Cameron, Gel-

bach and Miller (2008) wild bootstrap approach. The associated wild bootstrap standard

errors turn out only slightly larger than the usual Huber-White cluster robust standard

errors. As additional robustness checks on the parameters and standard errors we also

estimated the model using polynomial time trends and specifying an AR(1) process for

the error terms. Our results are robust to these alternatives so we present only the results

from our main specification which imposes less structure on the model.14

4 Registered Crime Data

Our data are made available by Statistics Netherlands and contain administrative records

of crime reports collected by the Dutch Prosecutor General (PG). We observe the total

annual number of reports for different crime categories rounded to the nearest fifth integer

for the 25 largest municipalities. The balanced panel data cover the period 1994-2011. Our

crime outcome variables are sexual abuse and rape, drug crime (excessive drug possession,

processing or trafficking), assaults and illegal weapon possession.15 The latter two give an

indication about the presence of criminal networks. Our motivation for focusing on these

14The data and STATA programming code for all results are available at skastoryano.com. 15Since the data are based on records from the police administration, there is likely underreporting for

certain types of crime. This problem may be particularly relevant for sexual crime and some types of

violent crime committed on people fearing extradition, incarceration or social stigma from reporting.

9

Table 2: Crime rates for cities with and without tippelzones (standard deviations in

brackets)

Tippelzone No Tippelzone

big cities medium cities

Annual crime reports per 1000 inhabitants

Sexual Abuse & Rape 0.18 [0.05] 0.15 [0.06] 0.14 [0.07]

Sexual Abuse 0.08 [0.02] 0.06 [0.03] 0.07 [0.05]

Rape 0.10 [0.03] 0.09 [0.04] 0.07 [0.04]

Drugs 1.49 [0.53] 1.56 [1.00] 1.28 [1.42]

Assault 2.13 [0.86] 1.87 [0.59] 1.79 [0.58]

Weapons 0.56 [0.14] 0.44 [0.25] 0.42 [0.51]

City characteristics

Population 597,489 [115,163] 172,419 [54,891] 113,114 [35,475]

Density (Population per km2) 4326 [1135] 2298 [505] 1956 [1456]

Males 15-65 210,145 [45,886] 61,702 [20,054] 39,226 [12,739]

Household Income (1000 e) 29.05 [1.34] 28.99 [2.00] 30.50 [1.85]

Higher education (%) 0.30 [0.08] 0.32 [0.09] 0.25 [0.07]

Immigrants (%) 0.11 [0.02] 0.06 [0.02] 0.05 [0.02]

Benefits recipients (%) 0.07 [0.01] 0.08 [0.02] 0.08 [0.01]

Political party of mayor

Socialist (PVDA) 0.48 [0.50] 0.56 [0.50] 0.41 [0.49]

Christian (CDA or CU) 0.26 [0.44] 0.12 [0.33] 0.31 [0.46]

Liberal (VVD or D66) 0.26 [0.44] 0.32 [0.47] 0.28 [0.45]

3 big cities with a tippelzone include Amsterdam, Rotterdam and The Hague. 6 medium cities with a tippelzone include

Utrecht, Eindhoven, Groningen, Nijmegen, Heerlen and Arnhem. 16 cities without tippelzone include Almelo, Breda,

Deventer, Dordrecht, Enschede, Haarlem, Helmond, Hengelo, Leeuwarden, Leiden, Maastricht, Schiedam, Tilburg, Venlo,

Zwolle and ’s-Hertogenbosch.

crime categories is their frequent association with prostitutes and trafficking organizations

as described in subsection 2.2.

The first panel of Table 2 presents the average yearly crime rates during our obser-

vation period. We distinguish between the three largest cities which all had a tippelzone

(Amsterdam, Rotterdam and The Hague), the six medium-sized cities which opened a

tippelzone (Utrecht, Eindhoven, Groningen, Arnhem, Nijmegen and Heerlen), and the

sixteen medium-sized cities which never opened a tippelzone. Larger cities have, on av-

erage, higher crime rates, with the exception of drug-related crime. Average crime rates

in medium-sized cities with and without tippelzones are very similar, but again the ex-

ception is drug-related crime. In general, drug-related crime rates are slightly higher in

cities with tippelzones.

The second panel describes characteristics of the cities. Tippelzones cities have, on

average, more inhabitants and are more densely populated. Other characteristics do not

differ substantially. On average, about 35% of the total population are men between

15 and 65 years old. Individuals in cities with tippelzones are a bit more educated but

have a slightly lower average household income. Medium-size cities and cities without

a tippelzone have a similar amount of immigrants and both have lower amounts than

large cities. There are also no differences in the share of social security benefits recipients

10

between cities. Finally, there is no clear relation between the political party of the mayor

and whether the city has a tippelzone.16 This is not surprising since tippelzones were not

a partisan policy.

4.1 Assessing Crime Trends

As mentioned in section 3, our key identifying assumption is that cities follow a common

trend in crime. This common trend assumption imposes that, withholding any effect of

opening a tippelzone or enforcing a licensing system, tippelzone cities and non-tippelzone

cities would have followed the same trend in aggregate crime. It excludes, for instance,

the possibility that tippelzones are responses to city-specific increases in aggregate crime,

that other crime-targeting policies were introduced at the same time as tippelzones, or

that the introduction of tippelzones produced spillovers in crime to other cities.

To justify the common trend assumption, we exploit the variation between cities in

the moment of opening tippelzones. For each city which opened a tippelzone, we compare

the trend in crime rates prior to the opening with the trend in average crime rate in

cities which never opened a tippelzone for the same time period. Figure 6 presents this

difference in sexual abuse and rape in years leading up to opening a tippelzone for each

opening city separately. The solid line is the average for the five medium-sized cities which

opened a tippelzone during our observation period.

The trends in crime show no systematic increases or decreases in sexual abuse and

rape in the years prior to opening a zone.17 However, all medium-sized cities show a sharp

decrease in sexual abuse and rape immediately following the opening of a tippelzone both

for cities which enforced a licensing system (Eindhoven, Heerlen) and those which did not

(Nijmegen, Arnhem, Groningen). The decrease in the first two years is in the order of

(exp(−0.4) − 1) × 100% ≈ −33%. We only observe one pre-opening period for Rotterdam, two pre-opening periods for

Amsterdam, and none for The Hague. This means we can not use long-term trends in

both cities to justify a common trend for these cities. Also, the three large cities closed

tippelzones in a span of three years which implies that most of the identifying power for

these large cities comes from comparison with medium-sized cities. For this reason we

limit most of the subsequent analysis and discussion to medium-sized cities.

Figures 5 and 6 in the appendix provide more evidence in favor of the common trend

assumption. In particular, these figures show that prior to opening the tippelzones trends

between cities for the different types of crime are very similar and the same holds for the

crime trend prior to introducing a licensing system.

16In the Netherlands mayors are not elected, but appointed. The political power of mayors is therefore

limited. Cities often have mayors from the same political party for a long period. 17In our empirical analysis the level differences in crime will be captured by the city fixed effects αi.

11

Figure 3: Crime rates in cities with a tippelzone compared to cities without a tippelzone.

avg. 5 Tipp.

ROT

AMS

NIJ

ARN EIN

GRO

HER

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2

.5

D if

fe re

n c

e f

ro m

p re

d ic

te d

c o

n tr

o l

g ro

u p

m e

a n

( in

l o

g )

-6 -4 -2 0 2 Year

The no-tippelzone counterfactual for a city i is generated by estimating model (1) on twenty-one cities leaving

out city i and the three largest cities and then averaging the fitted values fixing D−it = 0, Lit = 0 and D + it = 0.

5 Estimation results: registered crime

In this section we first discuss the results from our baseline model on registered crime

data. Next, we look at time varying effects and provide robustness checks accounting for

possible spillover effects between cities.

5.1 Baseline results

Table 3 shows estimation results for our baseline difference-in-difference model. The left

frame presents results using data on the 22 medium-sized cities, the right frame also takes

the three large cities into account.18 The estimation results show that an open tippelzone

significantly reduces citywide sexual abuse and rape by about (exp(−0.19) − 1) × 100% ≈ −17%. This effect is mainly driven by a 37% − 40% reduction in sexual abuse.

The effects of opening a tippelzone are very similar regardless of whether licensing was

imposed from the start. However, we find that introducing licensing later substantially

increases sexual abuse and rape, and the effect is significant for sexual abuse. These

opposite effects of licensing are not contradictory. Cities which immediately imposed

licensing initially distributed licenses to all known street prostitutes but barred future

entrants. Non-licensed tippelzones allowed free entry and therefore attracted a large share

of foreign prostitutes, in particular Eastern European prostitutes after the opening of EU

18In Tables 11 and 12 in the appendix we present additional results without covariates and specifications

which include a two year lead dummy to check for pre-opening shifts in crime.

12

Table 3: Effect of opening a tippelzone and licensing on citywide registered crime

Sex Ab. & Rape Sex Ab. Rape Sex Ab. & Rape Sex Ab. Rape

22 cities with covariates 25 cities with covariates

Open noLic. (δ0) -0.198** -0.358 -0.090 -0.205* -0.304 -0.133

(0.098) (0.217) (0.072) (0.114) (0.201) (0.101)

Intro. Lic. (δ1) 0.286 0.413** 0.220 0.168 0.250** 0.115

(0.258) (0.208) (0.277) (0.134) (0.124) (0.179)

Open Lic. (δ2) -0.184* -0.447*** 0.012 -0.169* -0.411*** 0.004

(0.094) (0.154) (0.139) (0.094) (0.154) (1.456)

Closing 0.023 0.160 -0.045

(0.144) (0.208) (0.398)

N (city x year) 395 395 395 450 450 450

R2 0.63 0.44 0.55 0.82 0.69 0.77

City fixed effects yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes

Covariates yes yes yes yes yes yes

* p < 0.10, ** p < 0.05 , *** p < 0.01. Significance (and standard errors in parenthesis) based on Cameron, Gelbach

and Miller (2008) wild bootstrap approach with 499 replications; Based on yearly data over the period 1994-2011. The

22 cities exclude the large cities, Amsterdam, Rotterdam, The Hague (and Eindhoven in 2011). Covariates are indicators

for political party of mayor, log(population male 15-65), log(pop. density), income (%), immigrants (%), unemployment

insurance recipients (%), higher educated (%).

borders (Flight et. al., 2003). When licensing was later enforced in these non-licensed

zones, a large fraction of prostitutes as well as clients were sent away from tippelzones

to less controlled environments (see again Figure 2). If tippelzones attract criminals then

the criminal environment in a city prior to opening a tippelzone may be different than the

environment in a tippelzone which has been open for several years. We postpone further

discussion of the results to subsection 5.3.

The right panel of Table 3 shows estimation results for the sample including the three

large cities. Since these three cities closed their tippelzone during our observation period

we can also allow for an effect of closing the tippelzone. The effects of closing are never

significant. Furthermore, the other estimated effects are quite robust against including

the three large cities.

5.2 Time-Varying Effects Sexual Abuse and Rape

A tippelzone is introduced at a specific moment in time but the composition of pros-

titutes changes continuously in a city. Delayed market responses from sex-trafficking

organizations or capacity restrictions inside the zones may produce time-varying effects of

tippelzones. We explore this in Table 4 where we split the opening effects of tippelzones

into short-run and medium to long-run effects. In particular, we allow for different effects

in the first two years after opening and afterwards. All effects (except the ex-post licens-

ing) describe proportional shifts from the pre-opening crime levels. As in the baseline

specification, the effect of ex-post licensing represents a proportional shift compared to

13

Table 4: Time-varying effects and robustness checks on citywide sexual abuse and rape

(22 cities).

Dependent variable: Sexual Abuse & Rape Sex Ab. Rape

pre-opening 0.062

(0.069)

1st-2nd year Open noLic. -0.385*** -0.350*** -0.377*** -0.350*** -0.369*** -0.438** -0.325**

(0.133) (0.121) (0.130) (0.121) (0.127) (0.208) (0.164)

3rd+ year Open noLic. -0.131 -0.054 -0.061 -0.024 -0.064 -0.269 0.029

(0.097) (0.106) (0.083) (0.121) (0.109) (0.243) (0.131)

pre-Intro Lic. -0.202

(0.184)

Intro. Lic. 0.252 0.180 0.196 0.163 0.186 0.306 0.144

(0.252) (0.181) (0.231) (0.189) (0.215) (0.223) (0.275)

1st-2nd year Open Lic. -0.518*** -0.499** -0.525*** -0.525*** -0.543*** -0.944*** -0.238

(0.179) (0.206) (0.197) (0.181) (0.188) (0.326) (0.290)

3rd+ year Open Lic. -0.062 -0.046 -0.065 -0.065 -0.051 -0.295** 0.123

(0.056) (0.058) (0.058) (0.056) (0.063) (0.143) (0.141)

Spillover Lic. -0.003 0.030 0.038 0.104 0.029

(0.057) (0.087) (0.089) (0.121) (0.114)

Spillover Closing -0.057 -0.096 -0.109 -0.294 -0.039

(0.155) (0.236) (0.242) (0.281) (0.270)

Brothel ban lift -0.094 -0.072 -0.050 -0.082

(0.169) (0.152) (0.136) (0.251)

Spillover Tipp. yes yes yes

N (city x year) 395 395 395 395 395 395 395

R2 0.64 0.64 0.64 0.63 0.63 0.44 0.55

City fixed effects yes yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes yes

Covariates yes yes yes yes yes yes yes

* p < 0.10, ** p < 0.05 , *** p < 0.01. Significance (and standard errors in parenthesis) based on Cameron, Gelbach

and Miller (2008) wild bootstrap approach with 499 replications; Based on yearly data over the period 1994-2011. The

22 cities exclude the large cities Amsterdam, Rotterdam, The Hague (and Eindhoven in 2011). Covariates are indicators

for political party of mayor, log(population male 15-65), log(pop. density), income (%), immigrants (%), unemployment

insurance recipients (%), higher educated (%).

the presence of a tippelzone without licensing.

The results in the first column show clear differences between short and long-run

effects. The negative effect on sexual abuse and rape observed in the baseline specification

is driven by sharp decreases in crime in the first two years after opening. The average

decrease in sexual abuse in the first two years of opening is 40% for cities with a licensed

tippelzone and 32% for cities with free entry zones, but the difference between the two is

insignificant. Beyond the first two years there is no difference in crime relative to the pre-

opening period. The ex-post introduction of licensing again increases sex-related crime.

The effect is substantial, but lacks sufficient power to be significant.

The second column of the table provides evidence for the common trend assumption by

including indicators for pre-opening and pre-licensing periods. These variables take value

one in the two years before opening a tippelzone or introducing licensing. As expected

from Figure 6 we do not find any sign of pre-opening shifts in crime. Introducing these

14

lead dummies also does not influence our parameter estimates of interest. This supports

the assumption of a common trend in sex-related crime between cities with and without

a tippelzone, and with and without licensing.

To interpret our findings as causal effects we must exclude that crime shifts non-

randomly between cities following the opening of a tippelzone or changes in licensing

policy. As discussed in subsection 2.1, case workers reported some displacement of pros-

titutes from closed tippelzones to zones in other cities. We therefore include in the third

column of Table 4 variables for spillover effects from closed tippelzones (Spillover Closing)

or from zones which introduced a licensing system (Spillover Tippelzone) dispelling illegal

workers. These variables are non-zero only for cities which had an open tippelzone with-

out a licensing system. The spillover variable for these cities increases in increments of

one for every newly closed or newly licensed tippelzone.19 Our results do not indicate any

shifts in sexual abuse or rape due to spillovers. This is not surprising. The movements

of prostitutes were limited since the closing of tippelzones in Rotterdam, Amsterdam and

The Hague occurred simultaneously with the introduction of licensing systems in other

cities which refused new entrants into tippelzones. We also do not find in the fourth

column any effect of the end of the brothel ban on crime in the cities with free entry

zones.20

In the last three columns we account for possible crime spillovers to neighbouring cities.

In this model, we include a set of dummy variables taking value one for cities within 50km

of a city which opened a tippelzone or introduced a licensing system. The parameters of

interest for sexual violence remain unaffected and the spillover effects21 show no reversed

changes in crime in neighbouring cities.22 The final two columns reproduce these same

results for sexual abuse and rape separately. The decreases in sexual abuse are stronger in

cities with licensed tippelzones and reductions in rape are larger in cities with free entry

tippelzones. Furthermore, we find that the reductions in sexual abuse persist beyond the

first year in cities which implemented licensing from the start.

5.3 Discussion of effects on sexual violence

The opening of a tippelzone with or without a licensing system is correlated with a

short-run decrease of 30% − 40% in sexual abuse and rape, and the results are robust to different specifications. A first possibility is that the reductions follow directly from a

decrease in sexual violence on prostitutes. A survey of street prostitutes in the Nijmegen

tippelzone reports that 27% were victim of abuse and 16% were raped in the previous

19We also estimated the models with dummy variables for spillovers. None of these specifications shows

significant spillover effects or relevant changes in our parameters of interest. 20The variable brothel ban lift takes value one in cities with an open tippelzone and no licensing system,

and zero otherwise. 21The results are presented fully in Table 13 of the appendix. 22Spillovers on 1st-2nd year open Lic. and 1st-2nd year open noLic. are close to zero in magnitude

and insignificant.

15

year alone (Oostveen 2008). Despite this high sexual violence, 95% of the interviewed

prostitutes report feeling safer within the tippelzone. The study explains these seemingly

contradictory findings by the fact that most prostitutes also work during the day for

private clients in more insecure settings.23 Tippelzones may therefore directly reduce crime

on street prostitutes by providing a relatively safe and controlled working environment.

However, limiting the explanation to street prostitutes may obscure effects on a wider

group of victimized women in the population. Indeed, street prostitutes are a prominent

example of a group which shies away from reporting crime due to their illegal status

and drug addiction.24 According to Van Soomeren (2004), tippelzones actually provide

support to immigrants whose illegal status and lack of knowledge of their rights prevent

them from seeking help from officials. If this support increases reporting of sexual abuse

and rape, then our estimated effect is an underestimate of the true effect.

A second possibility is that opening a tippelzone leads to a decrease in sexual violence

on women more generally by providing an anonymous, appealing and easily accessible

outlet for sex to otherwise violent individuals.25 Under the theoretical predictions of

subsection 2.2, a tippelzone may attract potential instigators of sexual abuse and rape

but have the effect of diffusing sexual violence elsewhere in the city. If this type of

substitution behavior occurs then the opening effect of tippelzones may reflect reductions

in sexual abuse and rape on all women, not only on prostitutes. Without more precise

data on the victims of sexual violence we can not separate these two hypotheses.

A last possibility is that the decreases in sexual violence are driven by changes in

crime reporting behaviour. Regarding policing behavior, we did not find any strong

arguments or articles describing citywide policing efforts to have changed concurrently

and systematically with the opening of a tippelzone. Another possible concern would be

if potential criminals shift their crime to victims with a different propensity to report crime

than their previous victims. For instance, in the case of sexual crime, we must exclude

that upon the opening of a tippelzone potential criminals of sexual violence switch to

new victims who are less willing to report sexual assaults and rapes than those women

(prostitutes or others) who were previously victimized. We can not test this type of

substitution empirically. However, it is unclear why the change in victimized individuals

would operate in such a way, in particular given the low propensity of street prostitutes

to report crime.

Overall we find only weak evidence of long-run effects for cities with and without

licensing. It is not clear why the initial reductions in crime fade away over time. One

23A larger survey of prostitutes in nine countries reports that 71% had been physically assaulted and

63% had been raped while working as prostitutes (Farley et al., 2003). 24Among the window, escort and club prostitutes interviewed in Venicz and Vanwesenbeeck (2000) 71%

respond to have withheld reporting one or several personal incidents to the police in the previous year.

The main reasons are fear for reprisals by their procurers, and lack of hope and trust in the police. 25Farley et al. (2011) find that 15% of sex buyers revealed that “they would rape a woman if they

could get away with it and if no one knew about it” in comparison with 2% for non-sex buyers.

16

Table 5: Effect of tippelzone on citywide registered crime

Drugs Weapons Assault Drugs Weapons Assault

22 cities with covariates 25 cities with covariates

Open noLic. (δ0) -0.034 0.052 0.139 -0.067 0.002 0.113

(0.364) (0.184) (0.126) (0.155) (0.034) (0.096)

Intro. Lic. (δ1) -0.244 -0.095 -0.053 -0.155 -0.070 0.030

(0.206) (0.200) (0.154) (0.104) (0.129) (0.123)

Open Lic. (δ2) -0.324** -0.210 0.052 -0.284*** -0.168 0.034

(0.128) (0.324) (0.118) (0.107) (0.310) (0.127)

Closing -0.124 0.095 0.207

(0.134) (0.098) (–)

N (city x year) 395 395 395 450 450 450

R2 0.81 0.76 0.89 0.89 0.89 0.95

City fixed effects yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes

Covariates yes yes yes yes yes yes

* p < 0.10, ** p < 0.05 , *** p < 0.01. Significance (and standard errors in parenthesis) based on Cameron, Gelbach

and Miller (2008) wild bootstrap approach with 499 replications; Based on yearly data over the period 1994-2011. Wild

bootstrap procedure fails on closing parameter for Assaults. The 22 cities exclude the large cities Amsterdam, Rotterdam,

The Hague (and Eindhoven in 2011). Covariates are indicators for political party of mayor, log(population male 15-65),

log(pop. density), income (%), immigrants (%), unemployment insurance recipients (%), higher educated (%).

possibility, for tippelzones without a licensing system, is that increased competition over

time in the tippelzones forced some prostitutes to seek opportunities in other less safe

areas thereby falling victim to more sexual abuse and rape.26 This interpretation can

also explain the increases in sexual violence following the ex-post introduction of licensing

systems. For tippelzones with licensing, there were capacity limitations set on the number

of workers within these zones. The constant inflow and turnover of prostitutes means the

later entrants were forced to work in less safe areas outside of the zones. If we consider

the reductions in sexual violence to affect women more generally, then the fading out of

effects in the medium- to long-run may reflect a short-lived thrill effect of tippelzones for

potential offenders.

5.4 Drugs, Illegal Weapons and Assaults

Recall that one of the initial goals of tippelzones was to remove the nuisance created

by drug-addicted prostitutes, their pimps and their clients. Furthermore, when licensing

systems were enforced, they favored drug-addicted prostitutes. For these reasons, we next

focus on crimes related to drugs and violence. We again consider our baseline model.

The first column of Table 5 explores possible effects of tippelzones and their regulation

on drug crime. Our estimation results follow the theoretical predictions, but only when

26It is difficult to verify this re-sprawl in the data by, for example, looking at the number of arrests for

street prostitution. This is because soliciting by prostitutes comes under the crime category of ‘Public

order offenses’ which includes a wide variety of other public disturbances.

17

tippelzones enforce licensing. In those cities, we find a significant decrease of approxi-

mately 25% in drug crime. The results are robust to different specifications and persist

beyond the first two years (presented in Tables 12 and 13 in the appendix). This sug-

gests that in cities which enforced licensed tippelzones from the start local governments

achieved one of their stated goals to address drug problems related to prostitutes.

The theoretical models also predict that cities with non-licensed tippelzones are more

likely to attract prostitutes trafficked from the underground sex industry since they pro-

vide fewer barriers to entry. If trafficking organizations are associated to trafficking of

drugs, weapons or more violent crimes, then opening a free entry tippelzone may produce

unforeseen spillovers on aggregate city crime. Our panel estimation results in the second

and third columns of Table 5 do not give evidence for spillovers on illegal weapons or

violent assaults.

6 Perceived Crime

The second data source is the Population Police Monitor (PPM) which examines perceived

crime and safety.27 This nationwide survey was conducted every other year from 1993 to

2001 and annually from 2001 to 2006. Respondents are contacted by telephone and are

asked questions about victimization, feelings of safety, contact with police, and crime in

their neighborhood. The participation rate in the survey ranges from 46% to 72% with

higher participation in later years.

We focus on two questions concerning the perception of drug crime and violent crime:

“Is drug nuisance common in your neighborhood?” and “Is violent crime common in

your neighborhood?”. The question on drug crime only entered the survey in 1997.

Answers can take four alternatives: (1) Happens regularly, (2) Happens sometimes, (3)

Never happens/Hardly ever happens, and (4) Don’t know/No opinion. This dataset

also includes the four digit postal code of each respondent which allows us to define their

proximity to the tippelzone. In the analysis, we take all postcodes for the 25 largest Dutch

municipalities based on the geographic delimitations defined by Statistics Netherlands.

Table 6 presents the fraction of answers within each perceived crime category. For

medium-sized cities, about 70% of respondents indicate that violent crime and drug crime

are never or hardly ever observed. Approximately 10% respond that drug nuisance occurs

regularly and about 5% claim that violent crime happens regularly. Again there are

differences between the three largest cities and the rest of the sample. A graphical analysis

(not presented) shows that the trends in these larger cities differ from the rest. For these

reasons, we focus the empirical analysis of perceived crime on the 22 medium-sized cities.

Figure 4a shows how perceived crimes change over time. The trends for all response

27This survey (in Dutch: Politie Monitor Bevolking) is conducted by two research bureaus commissioned

by the Dutch Ministry of Security and Justice: B&A Groep Beleidsonderzoek & - Advies BV, and

Intomart BV.

18

Table 6: Crime rates for tippelzone and non-tippelzone cities

Tippelzone No Tippelzone

big cities medium cities

Fraction perceiving Drug Crime

Often 0.15 0.11 0.10

Sometimes 0.20 0.17 0.15

Never 0.62 0.70 0.73

Fraction perceiving Violent Crime

Often 0.09 0.05 0.05

Sometimes 0.27 0.21 0.19

Never 0.59 0.70 0.73

3 big cities with a tippelzone include Amsterdam, Rotterdam and The Hague. 6 medium cities with a tippelzone include

Utrecht, Eindhoven, Groningen, Nijmegen, Heerlen and Arnhem. 16 cities without tippelzone include Almelo, Breda,

Deventer, Dordrecht, Enschede, Haarlem, Helmond, Hengelo, Leeuwarden, Leiden, Maastricht, Schiedam, Tilburg, Venlo,

Zwolle and ’s-Hertogenbosch.

Figure 4: Trends in registered and perceived crime

Violence: Sometimes

Drugs Sometimes

Drugs: Often

Violence: Often

0 .0

5 .1

.1 5

.2 .2

5 .3

F ra

c ti

o n

P e

rc e

iv in

g V

io le

n t

a n

d D

ru g

C ri

m e

1993 1995 1997 1999 2001 2003 2005 2007 2009 Year

(a) Perceived Crime, 22 cities.

Violence

Drugs

Sexual

0 5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

A v

e ra

g e

Y e

a rl

y C

ri m

e R

a te

( p

e r

1 0

,0 0

0 )

1995 2000 2005 2010 2015 Year

(b) Registered Crime, 22 cities.

categories remain relatively constant during the observation period. For comparison we

show in Figure 4b the trends in registered crime. The public perception of violent crime

does not in general follow the trend in reported crime. As for perceived drug crime, drug-

related registered crime shows no trend but the difference in trends for violence is clear.

Perceived violent crime is slightly decreasing over time, whereas registered violent crime

shows a strong increase until 2007 and then a drop until 2010.

6.1 Estimation Results for Perceived Crime

We repeat the difference-in-difference analysis with the perceived crime data. The out-

come variable is an indicator which takes value one if a person responds that she is

experiencing nuisance from drugs or violence often (or sometimes) in her surrounding.

Given the binary outcome, we are estimating a linear probability model. We control for

individual characteristics (gender, age, education, nationality) and the same city charac-

teristics as in the registered crime analysis. We cluster standard errors at the postcode

19

Table 7: Effect of tippelzone on citywide perceived crime in medium-sized cities

Drugs Violence

Often Often/Some. Often Often/Some.

Non-Tipp. Tipp. Non-Tipp. Tipp. Non-Tipp. Tipp. Non-Tipp. Tipp. area area area area area area area area

Open noLic. (δ0) 0.020* -0.025 0.028** 0.010 -0.009 -0.015 -0.002 -0.029

(0.012) (0.034) (0.013) (0.029) (0.005) (0.013) (0.012) (0.022)

Intro. Lic. (δ1) -0.001 -0.003 -0.000 -0.047 -0.001 -0.018 0.008 -0.023

(0.014) (0.018) (0.021) (0.037) (0.009) (0.020) (0.021) (0.035)

Open Lic. (δ2) -0.016* 0.013 0.007 0.025 -0.003 -0.001 0.001 -0.001

(0.010) (0.013) (0.013) (0.025) (0.005) (0.004) (0.008) (0.023)

N (city x year) 176 176 220 220

N (individuals) 83,494 83,494 107,811 107,811

R2 0.094 0.13 0.026 0.077

Postcode fixed effects yes yes yes yes

Year dummies yes yes yes yes

Covariates yes yes yes yes

* p < 0.10, ** p < 0.05. Clustered standard errors in parentheses; Based on data over the period 1993-2006 for violent

crime and over 1997-2006 for drug crime. Fixed effects at postcode level. 22 cities excludes Amsterdam, Rotterdam, The

Hague. Covariates are indicators for political party of mayor, log(population male 15-65), log(pop. density), income (%),

immigrants (%), unemployment insurance recipients (%), higher educated (%), gender, age, education, Dutch nationality.

level since they are more conservative than those when clustering at the city level.

Table 7 presents results from our baseline model for medium-sized cities. Since resi-

dents may react differently depending upon their proximity to the tippelzone, we stratify

the regression by within city locality. We define the tippelzone area as the postcode in

which the tippelzone is located as well as any adjacent postcode. Perceived drug nuisance

due to the presence of a tippelzone is significantly higher in neighborhoods not located

near the tippelzone, but this only holds for non-licensed tippelzones. There are no signifi-

cant effects in the tippelzone area. Furthermore, our baseline specification does not show

significant effects on violence.

Our baseline specification does not account for changes in public perception over time.

Changes in perception may, for example, arise because media attention on tippelzones was

stronger around opening periods and periods of introducing licensing. Table 8 considers

the extended model with time-varying effects in perceived drug nuisance which we specified

earlier for registered crime. The first two columns focus on often perceived drug nuisance

and show opposing effects depending on the licensing system and the proximity to the

tippelzone. In the first two years after opening, perceived drug nuisance in non-tippelzone

areas increases by 5.8%-points in cities which did not open a tippelzone with a licensing

system from the start. After the first two years, perceived drug crime in those areas still

remains higher than in the pre-opening period. These are large impacts since on average

only 10% of residents respond that drug nuisance occurs often.

In contrast, residents in non-tippelzone areas of cities which introduced licensing from

20

Table 8: Within city effects of tippelzone on perceived crime (22 cities with covariates)

Drugs: Often Drugs: Often/Some.

Non-Tipp. Tipp. Non-Tipp. Tipp. area area area area

pre-opening 0.008 0.012 0.032* 0.057**

(0.013) (0.027) (0.017) (0.022)

1st-2nd year Open noLic. 0.058*** 0.000 0.064*** 0.051

(0.022) (0.034) (0.023) (0.032)

3rd+ year Open noLic. 0.047* 0.032 0.079** 0.082*

(0.027) (0.040) (0.032) (0.049)

Introduce Lic. 0.015 0.011 0.025 -0.022

(0.015) (0.021) (0.027) (0.041)

1st-2nd year Open Lic. 0.001 -0.002 0.029** 0.012

(0.010) (0.015) (0.014) (0.028)

3rd+ year Open Lic. -0.053*** 0.042*** -0.016 0.082***

(0.015) (0.009) (0.019) (0.013)

Spillover Lic. 3 3

Spillover Closing. 3 3

Brothel ban lift 3 3

N (city x year) 176 176

N (individuals) 83,494 83,494

R2 0.095 0.13

Postcode fixed effects yes yes

Year dummies yes yes

Covariates yes yes

* p < 0.10, ** p < 0.05. Clustered standard errors in parentheses; Based on data over the period 1993-2006 for violent

crime and over 1997-2006 for drug crime. Fixed effects at postcode level. 22 cities excludes Amsterdam, Rotterdam, The

Hague. Covariates are indicators for political party of mayor, log(population male 15-65), log(pop. density), income (%),

immigrants (%), unemployment insurance recipients (%), higher educated (%), gender, age, education, Dutch nationality.

21

the start do not see an initial increase in crime but perceive a 5.3%-point reduction in

drug nuisance in the medium to long-run. In addition, residents in areas adjacent to

the tippelzone perceive an 4.2%-point increase in drug nuisance in the medium to long-

run. Although these results are identified only on two cities which immediately enforced

a licensing system, they suggest that drug nuisance was successfully re-located to the

tippelzone areas.

The third and fourth column of Table 8 for “often or sometimes” perceived drug crime

mostly follow the patterns observed in the first two columns. However, perceived drug

nuisance also increases for the first two years in non-tippelzone areas of cities which en-

forced licensing immediately upon opening.28 We also notice that perceived drug nuisance

increased already prior to opening a tippelzone. These shifts may be due to media cover-

age of the announcement that a tippelzone would be opened. Again, we do not observe

any change in perceived drug nuisance in response to the introduction of ex-post licensing.

The results for non-tippelzone areas are consistent with reports for tippelzones which

imposed licensing immediately.29 Initially, the tippelzones were met with opposition.30

Later, as city residents became more informed about the purpose of a tippelzone they

also became more accepting of it’s presence. Furthermore, our empirical results indicate

that the tippelzones achieved one of their stated goals which was to reduce the nuisance

created by drug-addicted prostitutes overall in the city. However, the results also indicate

that this relocation of street prostitutes and their following came at the expense of those

living near the tippelzones who became increasingly exposed to the drug dealers and

drug-addicted clients.

Results for time-varying effects on perceived violence do not show any effect of opening

a tippelzone or licensing (presented in Table 14 in the appendix). As such, they are in line

with the results on registered crime in Table 5 for aggregate illegal weapons and assaults.

7 Conclusion

In this paper we study the effects of opening a legal street prostitution zone on citywide

crime. Theories of crime predict that the effect of opening such a tippelzone depends on

the imposed regulation. Becker’s rational choice theory suggests that opening a tippel-

zone with higher police monitoring reduces sexual violence against prostitutes. Theories

28Note that the response categories are exclusive. For this reason, if fewer residents responding “often”

then the share of “sometimes” responses is likely to increase. This can explain some of the changes

in parameter - e.g. −0.053 in the “often” response regression and −0.016 in the “often or sometimes” regression for 3rd+ year Open Lic. in non-tippelzone areas.

29Oostveen (2008) reports for Nijmegen that residents in the adjacent area to the Tippelzone mention

drugs, junkies and dealers as the main reason for feeling unsafe. About 35% of these residents report

feeling unsafe, compared to 22% of the individuals living in the rest of the city. 30Van Soomeren (2004) states that in Amsterdam “the day after the opening, more than a hundred

residents from neighborhoods south of the zone took to the streets in protest, but the tippelzone remained

open.” (p. 6).

22

from criminology add that opening a tippelzone can reduce sexual violence on a wider

population by attracting sexually violent potential criminals. Theoretical models also

predict that regulation through licensing should reduce involuntary prostitution, but the

predicted effect on total crime is ambiguous.

Our empirical results for aggregate registered sexual abuse and rape are in line with

theoretical predictions. We find that the opening of a tippelzone in a city is associated

with a 30% − 40% decrease in sexual abuse and rape in the first two years of opening. These effects do not depend on whether the tippelzone immediately enforced a licensing

system or not. Our data do not allow us to distinguish between victims. In terms of policy

it is highly relevant to know whether the reduction in sexual violence is attributable to

the relocated street prostitutes or to a wider set of victims. We also find some evidence

that introducing a licensing system some years after opening, effectively forcing a large

share of illegal prostitutes to work outside the tippelzone, leads to a positive increase in

citywide sexual abuse.

In addition to effects on sexual violence, theoretical models predict that licensing can

produce different spillover effects of tippelzones on other crimes linked to the prostitution

market. Our results on registered drug crime show that opening a tippelzone is associated

with a 25% decrease in average citywide drug crime and this result persists over time but

only in cities which enforced a licensed tippelzone from the start. We do not however find

any evidence for effects on other crimes linked to trafficking organizations such as illegal

weapons or violent assaults.

Next, we consider the effect of tippelzones on perceived crime. Our results indicate

that residents in a city which opened a tippelzone without a licensing system perceive a

significant increase of 5 − 6%-points in drug nuisance in the first two years after opening. The results in the medium- to long-run are less precise and differ depending on the

residents’ proximity to the tippelzone and whether or not a licensing system was enforced

from the start. In general, the results on registered and perceived drug crime in cities

which enforced licensed tippelzones suggest that local governments successfully achieved

their goal of reducing drug crime overall in the city.

23

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26

Appendix

Table 9: Overview of prostitution laws in selected countries

Country Legal Status

Prostitution Brothel Ownership Pimping

Netherlands

legal legal

legal Germany

Belgium

illegal

Canada buying sex illegal

Sweden

illegal

USA illegal (except Nevada)

Japan limited legality

Spain

UK

legalFrance

Italy

Most countries enforce additional limitations and requirements for soliciting, procuring and sex establishments. Source:

http://prostitution.procon.org/view.resource.php?resourceID=000772

Table 10: Type of licensed sex establishments in the Netherlands.

Type of establishment Approximate total in the Netherlands

Window prostitution 580

Sex club 260

Private home 130

Escort service 90

Erotic massage salon 60

Sex cinema 60

Swingers club 20

Other 70

Approximations from Flight, et al. (2006) based on survey responses from medium and large sized municipalities across the

Netherlands.

27

Table 11: Effect of a tippelzone and licensing on citywide registered crime.

Sex.A. & Rape Sex.A. Rape

(1) (2) (3) (4) (5) (6) (7) (8) (9)

22 cities

pre-opening 0.046 -0.066 0.133

(0.068) (0.136) (0.179)

Open noLic. -0.136 -0.198** -0.170 -0.296 -0.358 -0.397 -0.022 -0.090 -0.011

(0.130) (0.098) (0.125) (0.298) (0.217) (0.249) (0.073) (0.072) (1.419)

Intro. Lic. 0.214 0.286 0.287 0.360** 0.413** 0.413** 0.136 0.220 0.221

(0.195) (0.258) (0.259) (0.171) (0.208) (0.208) (0.191) (0.277) (0.276)

Open Lic. -0.120 -0.184* -0.170 -0.417*** -0.447*** -0.467** 0.110 0.012 0.052

(0.155) (0.094) (0.111) (0.144) (0.154) (0.185) (0.178) (0.139) (0.158)

N (city x year) 395 395 395 395 395 395 395 395 395

R2 0.62 0.63 0.63 0.43 0.44 0.44 0.54 0.55 0.55

25 cities

pre-opening 0.057 -0.103 0.169

(0.093) (0.175) (0.188)

Open noLic. -0.131 -0.205* -0.167 -0.229 -0.304 -0.373 -0.055 -0.133 -0.019

(0.102) (0.114) (0.124) (0.200) (0.201) (0.260) (0.077) (0.101) (0.146)

Intro. Lic. 0.167 0.168 0.169 0.258** 0.250** 0.250** 0.125 0.115 0.115

(0.123) (0.134) (0.134) (0.111) (0.124) (0.124) (0.153) (0.179) (0.183)

Open Lic. -0.108 -0.169* -0.152 -0.385*** -0.411*** -0.442** 0.099 0.004 0.055

(0.112) (0.094) (0.112) (0.133) (0.154) (0.201) (0.173) (1.456) (0.218)

Closing 0.098 0.023 0.021 0.319** 0.160 0.164 -0.044 -0.045 -0.051

(0.122) (0.144) (0.142) (0.155) (0.208) (0.208) (0.156) (0.398) (0.318)

N (city x year) 450 450 450 450 450 450 450 450 450

R2 0.82 0.82 0.82 0.69 0.69 0.69 0.76 0.77 0.77

City fixed effects yes yes yes yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes yes yes yes

Covariates no yes yes no yes yes no yes yes

* p < 0.10, ** p < 0.05 , *** p < 0.01. Significance (and standard errors in parenthesis) based on Cameron, Gelbach

and Miller (2008) wild bootstrap approach with 499 replications; Based on yearly data over the period 1994-2011. The

22 cities exclude the large cities Amsterdam, Rotterdam, The Hague (and Eindhoven in 2011). Covariates are indicators

for political party of mayor, log(population male 15-65), log(pop. density), income (%), immigrants (%), unemployment

insurance recipients (%), higher educated (%).

28

Table 12: Effect of a tippelzone and licensing on citywide registered crime.

Drugs Weapons Assault

(1) (2) (3) (4) (5) (6) (7) (8) (9)

22 cities

pre-opening 0.080 0.065 -0.052

(0.332) (0.153) (0.095)

Open noLic. -0.024 -0.034 0.013 -0.047 0.052 0.090 0.108 0.139 0.108

(0.499) (0.364) (0.214) (0.135) (0.184) (0.184) (0.106) (0.126) (0.117)

Intro. Lic. -0.283 -0.244 -0.244 -0.166 -0.095 -0.095 -0.121 -0.053 -0.053

(0.205) (0.206) (0.205) (0.170) (0.200) (0.206) (0.181) (0.154) (0.158)

Open Lic. -0.306** -0.324** -0.300** -0.163 -0.210 -0.191 0.055 0.052 0.037

(0.127) (0.128) (0.149) (0.222) (0.324) (0.297) (0.088) (0.118) (0.104)

N (city x year) 395 395 395 395 395 395 395 395 395

R2 0.80 0.81 0.81 0.74 0.76 0.76 0.88 0.89 0.89

25 cities

pre-opening 0.099 0.105 -0.056

(0.285) (0.139) (0.082)

Open noLic. -0.080 -0.067 -0.000 -0.090 0.002 0.073 0.074 0.113 0.075

(0.159) (0.155) (0.021) (0.133) (0.034) (0.171) (0.093) (0.096) (0.111)

Intro. Lic. -0.283** -0.155 -0.155 -0.189 -0.070 -0.070 -0.047 0.030 0.029

(0.134) (0.104) (0.105) (0.118) (0.129) (0.130) (0.081) (0.123) (0.122)

Open Lic. -0.281** -0.284*** -0.254* -0.142 -0.168 -0.136 0.043 0.034 0.017

(0.134) (0.107) (0.151) (0.215) (0.310) (0.252) (0.087) (0.127) (0.231)

Closing 0.000 -0.124 -0.127 0.105 0.095 0.091 0.234 0.207 0.209

(0.004) (0.134) (0.136) (0.085) (0.098) (0.098) (–) (–) (–)

N (city x year) 450 450 450 450 450 450 450 450 450

R2 0.88 0.89 0.89 0.89 0.89 0.89 0.95 0.95 0.95

City fixed effects yes yes yes yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes yes yes yes

Covariates no yes yes no yes yes no yes yes

* p < 0.10, ** p < 0.05 , *** p < 0.01. Significance (and standard errors in parenthesis) based on Cameron, Gelbach

and Miller (2008) wild bootstrap approach with 499 replications; Based on yearly data over the period 1994-2011. Wild

bootstrap procedure fails on closing parameter for Assaults. The 22 cities exclude the large cities Amsterdam, Rotterdam,

The Hague (and Eindhoven in 2011). Covariates are indicators for political party of mayor, log(population male 15-65),

log(pop. density), income (%), immigrants (%), unemployment insurance recipients (%), higher educated (%).

29

Table 13: Time varying effects and robustness checks on citywide crime categories (22

cities).

Dependent variable: SexA. & Rape SexA. Rape Drugs Weapons Assault

1st-2nd year Open noLic. -0.369*** -0.438** -0.325** 0.107 0.230 0.029

(0.127) (0.208) (0.164) (0.363) (0.167) (0.097)

3rd+ year Open noLic. -0.064 -0.269 0.029 0.078 0.036 -0.031

(0.109) (0.243) (0.131) (0.382) (0.283) (0.181)

Intro. Lic. 0.186 0.306 0.144 -0.462 -0.066 0.129

(0.215) (0.223) (0.275) (0.354) (0.158) (0.165)

1st-2nd year Open Lic. -0.543*** -0.944*** -0.238 -0.359** -0.355* -0.062

(0.188) (0.326) (0.290) (0.163) (0.185) (0.091)

3rd+ year Open Lic. -0.051 -0.295** 0.123 -0.246*** -0.239 0.114

(0.063) (0.143) (0.141) (0.085) (0.352) (0.132)

Spillover Lic. 0.038 0.104 0.029 -0.007 -0.057 0.016

(0.089) (0.121) (0.114) (0.202) (0.127) (0.024)

Spillover Closing -0.109 -0.294 -0.039 -0.018 0.009 0.015

(0.242) (0.281) (0.270) (0.077) (0.246) (0.039)

Brothel ban lift -0.072 -0.050 -0.082 -0.175 0.104 0.153

(0.152) (0.136) (0.251) (0.164) (0.189) (0.121)

Spill. Opening noLic. -0.016 -0.065 -0.007 0.043 -0.077 0.074

(0.095) (0.163) (0.068) (0.272) (0.162) (0.111)

Spill. Opening Lic. -0.057 -0.043 -0.060 0.163 -0.083 -0.097

(0.126) (0.153) (0.113) (0.168) (0.133) (0.101)

Spill. Intro. Lic. 0.106 0.145 0.109 0.220** -0.094 0.068

(0.068) (0.144) (0.090) (0.097) (0.101) (0.059)

N (city x year) 395 395 395 395 395 395

R2 0.63 0.44 0.55 0.82 0.76 0.89

City fixed effects yes yes yes yes yes yes

Year dummies yes yes yes yes yes yes

Covariates yes yes yes yes yes yes

* p < 0.10, ** p < 0.05 , *** p < 0.01. Significance (and standard errors in parenthesis) based on Cameron, Gelbach

and Miller (2008) wild bootstrap approach with 499 replications; Based on yearly data over the period 1994-2011. The

22 cities exclude the large cities Amsterdam, Rotterdam, The Hague (and Eindhoven in 2011). Covariates are indicators

for political party of mayor, log(population male 15-65), log(pop. density), income (%), immigrants (%), unemployment

insurance recipients (%), higher educated (%).

30

Table 14: Within city effects of tippelzone on perceived crime (22 cities with covariates)

Drugs: Often Drugs: Often/Some.

Non-Tipp. Tipp. Non-Tipp. Tipp. area area area area

pre-opening 0.008 0.012 0.032* 0.057**

(0.013) (0.027) (0.017) (0.022)

1st-2nd year Open noLic. 0.058*** 0.000 0.064*** 0.051

(0.022) (0.034) (0.023) (0.032)

3rd+ year Open noLic. 0.047* 0.032 0.079** 0.082*

(0.027) (0.040) (0.032) (0.049)

Introduce Lic. 0.015 0.011 0.025 -0.022

(0.015) (0.021) (0.027) (0.041)

1st-2nd year Open Lic. 0.001 -0.002 0.029** 0.012

(0.010) (0.015) (0.014) (0.028)

3rd+ year Open Lic. -0.053*** 0.042*** -0.016 0.082***

(0.015) (0.009) (0.019) (0.013)

Spillover Lic. -0.006 -0.015

(0.007) (0.010)

Spillover Closing. 0.003 0.007

(0.011) (0.014)

Brothel ban lift -0.023 -0.007

(0.017) (0.018)

N (city x year) 176 176

N (individuals) 83,494 83,494

R2 0.095 0.13

Violence: Often Violence: Often/Some.

Non-Tipp. Tipp. Non-Tipp. Tipp. area area area area

pre-opening -0.009 0.000 -0.012 0.009

(0.007) (0.017) (0.012) (0.032)

1st-2nd year Open noLic. -0.001 -0.011 0.016 -0.004

(0.012) (0.019) (0.023) (0.031)

3rd+ year Open noLic. -0.017 -0.017 0.020 -0.001

(0.011) (0.019) (0.024) (0.037)

Introduce Lic. 0.002 0.016 0.044* 0.014

(0.010) (0.021) (0.025) (0.037)

1st-2nd year Open Lic. -0.006 0.002 0.001 0.022

(0.006) (0.009) (0.011) (0.024)

3rd+ year Open Lic. -0.004 -0.005 -0.005 -0.023

(0.009) (0.007) (0.013) (0.024)

Spillover Lic. 0.002 -0.007

(0.004) (0.010)

Spillover Closing. -0.004 -0.004

(0.005) (0.014)

Brothel ban lift -0.005 -0.016

(0.010) (0.020)

N (city x year) 220 220

N (individuals) 107,811 107,811

R2 0.026 0.078

Postcode fixed effects yes yes

Year dummies yes yes

Covariates yes yes

* p < 0.10, ** p < 0.05. Clustered standard errors in parentheses; Based on data over the period 1993-2006 for violent

crime and over 1997-2006 for drug crime. Fixed effects at postcode level. 22 cities excludes Amsterdam, Rotterdam, The

Hague. Covariates are indicators for political party of mayor, log(population male 15-65), log(pop. density), income (%),

immigrants (%), unemployment insurance recipients (%), higher educated (%), gender, age, education, Dutch nationality. 31

Figure 5: Trends in Registered Crime Categories

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2 .5

D if

fe re

n c e f

ro m

p re

d ic

te d

c o

n tr

o l g

ro u

p m

e a n

( in

l o

g )

-6 -4 -2 0 2 4 6 Year

License No License Avg. 5 Tipp.

(a) Sexual Abuse

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2 .5

D if

fe re

n c e f

ro m

p re

d ic

te d

c o

n tr

o l g

ro u

p m

e a n

( in

l o

g )

-6 -4 -2 0 2 4 6 Year

License No License Avg. 5 Tipp.

(b) Rape

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2 .5

D if

fe re

n c e f

ro m

p re

d ic

te d

c o

n tr

o l g

ro u

p m

e a n

( in

l o

g )

-6 -4 -2 0 2 4 6 Year

License No License Avg. 5 Tipp.

(c) Sexual Abuse and Rape

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2 .5

D if

fe re

n c e f

ro m

p re

d ic

te d

c o

n tr

o l g

ro u

p m

e a n

( in

l o

g )

-6 -4 -2 0 2 4 6 Year

License No License Avg. 5 Tipp.

(d) Drugs

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2 .5

D if

fe re

n c e f

ro m

p re

d ic

te d

c o

n tr

o l g

ro u

p m

e a n

( in

l o

g )

-6 -4 -2 0 2 4 6 Year

License No License Avg. 5 Tipp.

(e) Weapons

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2 .5

D if

fe re

n c e f

ro m

p re

d ic

te d

c o

n tr

o l g

ro u

p m

e a n

( in

l o

g )

-6 -4 -2 0 2 4 6 Year

License No License Avg. 5 Tipp.

(f) Assault The no-tippelzone counterfactual for a city i is generated by estimating model (1) on twenty-one cities leaving out city i

and the three largest cities and then averaging the fitted values fixing D−it = 0, Lit = 0 and D + it = 0. Also note that in

contrast to the panel estimations, the plotted trends of tippelzone cities do not control for time varying covariates.

32

Figure 6: Licensing introduction

avg. 3 Lic. intro.

UTR

NIJ

ARN

-1 .5

-1 -.

5 0

.5 1

1 .5

2 2

.5 D

if fe

re n

c e

f ro

m p

re d

ic te

d c

o n

tr o

l g

ro u

p m

e a

n (

in l

o g

)

-6 -4 -2 0 2 4 Year

The no-tippelzone or licensing counterfactual for a city i is generated by estimating model (1) on twenty-one

cities leaving out city i and the three largest cities and then averaging the fitted values fixing D−it = 1, Lit = 0 and D+it = 0.

33