Discussion Paper No. 102
The Dutch Answer to Drug Tourism Within
Southern Dutch Municipalities
An assessment of the 2012 revised drug policy
Kirin Pepijn Moonen

October, 2019

 Data Science and Service Research
Discussion Paper

Center for Data Science and Service Research

Graduate School of Economic and Management

Tohoku University27-1 Kawauchi, Aobaku
Sendai 980-8576, JAPANTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIESThe Dutch Answer to Drug Tourism Within Southern Dutch Municipalities
An assessment of the 2012 revised drug policy
Kirin Pepijn Moonen
Tohoku University, Japan

Address for correspondence:
Tohoku University, Graduate School of Economics and Management
Sendai, Aoba Ward, Kawauchi 27-1, 980-8576
Tel: +815055329767
Email: moonenkipe@gmail.com

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Abstract
The Netherlands has since the 1970s focused on decriminalizing to address the issue ofdrugs within society. As a result of implementing this decriminalization policy, commercial
establishments called coffee shops quickly appeared afterwards to meet market demand formarijuana within the Netherlands. The growth of this newly established marijuana industry wastolerated by the Dutch government leading to an eventual regulatory framework of the AHOJ-G
criterion. However, even with the framework in place, the number of tourists seeking toexperience marijuana continued to exponentially increase leading to issues of drug tourism andother associated nuisances. In 2012, the Dutch government selected three provinces in the Southof the Netherlands where marijuana would no longer be sold to non-residents in order to stymie
drug tourism. Qualitative studies on this issue found that there was a significant reduction in drug
tourism as a result of implementing the policy in 2012 but was coincided with a rise in illicit
drug activity due to the legal purchase channel being shut-down for non-residents.
The aim of this study is to analyze the impact of the 2012 sales restriction on coffee shop
municipalities within these three provinces and the subsequent economic impact on local firms.
Propensity score matching was used to create a control group of coffee shop municipalities thatwere similar to the treated coffee shop municipalities on the basis of observed characteristics
with socio-economic data from the public Statline database maintained by the Dutch Statistics
Bureau. Several robustness checks were conducted to ensure the validity of the matchingprocedure. The difference-in-difference model used municipal drug crime data from the Statline
database for the years 2009 to 2014 to analyze the impact on committed drug crimes. Findings
revealed that the policy did not have a significant impact on drug crimes within treated areas
even after controlling for municipal characteristics. Placebo test of the policy presented that thepolicy had a lagged treatment effect which can be attributed to the sharp increase in illicit drug
market activity in 2012 due to policy disrupting the legal purchase channel of marijuana.
Therefore, this study shows that even though prior literature cites that drug tourism wassignificantly reduced, limiting sales to residents did not lead to an increase in either soft- or harddrug crimes indicating that the policy had an overall beneficial impact on reducing illicit drug
market activity
Keywords: Drug tourism, difference-in-difference, propensity score matching
JEL classification: R11, K23

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3Introduction
The continuing trend of changing perceptions and acceptance of drugs within society,especially marijuana, has led to a string of sweeping legislative changes ranging fromdecriminalization to the existence of fully legalized recreational marijuana with the creation ofan multimillion dollar industry to meet popular demand as can be seen in earlier cases such as
Amendment 64 in Colorado and Initiative 502 in Washington. The increase in acceptance ofmarijuana for medicinal and recreational usage around the world gives government institutions
an additional lucrative revenue stream as documented by Henchman (2014) and Henchman andScarboro (2016). However, the benefits of allowing drug tourists are often accompanied with its
numerous documented negatives such as nuisance and crime as has been documented in The
Netherlands (Snippe, Naayer, and Bieleman, 2006; Van Ooyen-Houben, Bieleman, and Korf,
2014; Nabben et al, 2015). Therefore, policy makers within urban areas with the prime potential
of experiencing drug tourism will look for literature and statistical evidence on what policies caneffectively reduce drug tourism and its associated pollution.
The motivation of this study is to analyze the impact of restricting the commercial sales
and usage of marijuana within the Southern Netherlands on drug crimes. There is currently alimited amount of literature on the effectiveness of drug policies. Prior literature on Dutch drug
policies, qualitative in nature and limited in scope, found that there was an increase in illicit drug
activity within specific municipalities and noted a significant decrease in drug tourism after
introduction. Therefore, this study seeks to answer the question on whether the revision andimplementation of the 2012 drug policy within the three Southern provinces of Limburg, NoordBrabant, and Zeeland had a significant impact on the drug crimes within those municipalities.

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES4

The data used for this paper comes from the Dutch Statistics Bureau in the form its public
Statline database which provides the crime data on municipal level with various socio-economic
characteristics of municipalities located within the Netherlands. Using balanced panel data withdata from 2009 to 2014, propensity score matching was used to create a counterfactual group andusing difference-in-differences to assess the impact while controlling for municipality- and time
fixed effects. Findings reveal that the implementation of the policy led to reduction in themagnitude of the reported coefficient for soft- and hard drugs, these results were not reported as
significant. However, implementing a placebo test by delaying the period of implementation by
one year reported that the policy did significantly affect the soft- and hard drug crime rate which
may be due to the fact that during the initial implementation of the policy led to a sharp increasein committed drug crimes as a response due to the legal channel of purchasing marijuana being
disrupted, leading to an expansion of the illicit drug market activity within the border regions of
The Netherlands.
The contribution of this paper is that the empirical evidence indicates that the restricting
the commercial sales to municipal residents in an effort to combat drug tourism does not lead asignificant increase in illicit crime even though there was a short increase in illicit drug crime
during its implementation. In addition, findings reveal that this type of policy, while reducing
drug tourism does not statistically affect local firm revenue. Thus, the policy has had an overall
beneficial effect without significantly increasing illicit drug activity nor affecting the local
economy within Southern coffee shop municipalities. Additionally, this is the first study on this
topic that uses propensity score matching with difference-in-differences, to assess the impact ofaddressing drug tourism within the Southern Netherlands thereby controlling for the possibilityof observed and unobserved heterogeneity.

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The paper is structured as follows. The next section contains a short review on the ongoing debate and literature between drug prohibition and liberalization followed by an overview
of prior and existing situation within the Netherlands regarding drugs, drug policies and theeconomic role of marijuana. The third section presents the methodology used for estimating thepossible impact on drug crimes and the performance on the local economy as a result of
implementing the revised drug policy within coffee shop municipalities. The fourth section
provides an overview of the data sources used for the results and how the data set wasconstructed. The fourth section presents the main results including robustness checks, and thefifth section contains the conclusion.

Background
One of the early pioneers of adopting decriminalization policies on drug usage was the
Netherlands in the early 1970s. However, like many neighboring countries at the time, it
maintained a policy focused on prohibition on the basis of sources that alluded that marijuana
usage would result in physical and psychological issues for users and thus presented a significant
danger for society at large. The continued difficulty of governmental institutions in dealing withdrug usage within society eventually led to the Dutch government in appointed the Hulsman
commission in 1968 and the Baan commission in 1972 to analyze the issue of drugs withinsociety and how to best deal with the increasing problem.
Based on the works of Cohen (1975), the Commissions’ recommendation was to create
and enforce a policy based on decriminalization of drugs and toleration of usage within a set oflimitations referred to in modern literature as the “Dutch model”. The main emphasis of thismodel was on harm reduction and ensuring continuation of public order, health, and safety of theTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

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general public but also drug users. By adopting a harm reduction model in combination withdecriminalizing the purchasing, possession, and cultivation of marijuana for personal usage, law
enforcement would alter its target to combatting large-scale illicit drug dealing and growing
operations within the Netherlands.
With increases in international tourism and the normalization of attitudes towards drugs
and drug usage within society, especially within the Western hemisphere in the early 2000s, thecoffee shop industry provided a lucrative revenue stream for the government. Earlier estimatesby the research institute Intraval in 2013 on the coffee shop industry found that coffee shops had
a revenue approximately between 875 million and 1.25 billion euros based on a sample of 30
coffee shop establishments. An assessment by OWP Research (2008) on the benefit of the coffee
shop industry within the Southern municipality of Maastricht in Limburg found that theexpenditure of coffee shop visitors resulted in a benefit of 78 million euros for the retail industry
and 37 million euros for the hospitality industry and an employment benefit of 1,617 FTEs ofwhich 958 retail industry and 608 in the hospitality industry. Recent studies such as on the
marijuana industry in the United States show similar benefits for municipalities that legalized thepurchase of recreational marijuana such as Henchmann (2014) and Henchmann and Scarboro(2016). The rise of drug tourism, though economically beneficial overall, it came with various
negative externalities within coffee shop municipalities, especially considering the legal,
medical, and safety risks for drug tourists. Earlier studies have linked that drug tourists havesignificantly linked with nuisance (Bieleman, Mennes, and Sijtsma, 2017)
In a report by the Dutch government in 1995, it detailed three aspects that required action
which was the problem of nuisance associated with not only hard drug users but also
increasingly soft drug users and the coffee shops, the increase of large-scale illicit drug activity

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by organized crime within the Netherlands, and the role of drug tourism within and outside theNetherlands. Therefore, while the main focus of the policy would remain unchanged, thelimitations set by policy makers for drug users and coffee shops would be expanded through theaddition of the AHOJ-G criteria on the national level which prohibited drug advertisements (A),the sales of hard drugs as defined by the Opium Act (H), to cause public disorder or nuisanceassociated with drugs (O), that minors could not enter coffee shops nor be sold soft drugs (J),that no more than 5 grams of marijuana per transaction would be sold to customers, and thatcoffee shops would have a limit of a maximum inventory of 500 grams of marijuana (G). Though
the AHOJ-G criteria was a start in further defining the Dutch model in addressing the three key
issues raised by the Baan commission, the lack of a regulatory framework on the back-end
operations of coffee shops facilitated the continued operations of illicit marijuana cultivation byorganized crime to meet market demand.
Though the impact on public order and local residents is well understood at this point,
few studies have been conducted on the negative impact of drug tourism on local economies,
prior studies such as Snippe, Naayer, and Bieleman (2006) and van der Torre, Cachet, and Dijk,(2008) provide an indication as both studies focus this exact relationship by examining firms
located within coffee shop municipalities within border regions attributed with high degrees ofdrug tourism. Snippe, Naayer, and Bieleman (2006) focused on the border town of Venlo in theprovince of Limburg adjacent to Germany and experiences a large degree of drug tourists. In
their research, they compared the financial performance in qualitative survey between 1999 and2003 and found that firms located within a high degree of drug tourism reported lower revenue(57 percentage points to 33 percentage points) in comparison to those in no or low degree areas(34 percentage points to 25 percentage points). Van der Torre et al. (2008) focused on theTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

8municipalities of Roosendaal and Bergen op Zoom in the province of Noord-Brabant adjacent toBelgium and found similar evidence of reduced retail revenue of firms located with a highconcentration of drug tourism when compared to firms in no or low concentration districts.
In 2009, the government appointed Van de Donk commission presented its findings onwhat measures should be taken on addressing the issue of the illicit domestic and international
drug production and trade, the lack of a regulatory framework on the back-end of coffee shops,
and the ever-increasing presence of drug tourism and its associated negative aspects.
The recommendation of the Van de Donk commission on reducing drug tourism was tocreate a closed membership system for coffee shops to limit drug tourists from purchasing
marijuana within the Netherlands (van de Donk et al., 2009). However, the report noted that sucha system would result in a sharp increase in illegal street dealing of drugs and local nuisancerespectively. Regardless of the stated issues of implementing the closed membership system, thethen Justice minister, Ivo Opstelten, announced that the government was seeking to implement
two additional criteria in 2012 to the existing AHOG-J criterion, namely the closed membershipsystem (B) and local municipal sales restriction (I) for coffee shops located within the Southern
provinces of Limburg, North-Brabant, and Zeeland as a trial to assess the effectiveness of thepolicy and, if successful, expand the policy nationwide (TK, 2011)
The initial response from various stakeholders was anything but positive as the proposal
was rejected by parliament and led to the creation of an alternative proposal which sought toimplement the policy of restricting sales to non-residents as a trial but limited in scope by
seeking a trial area to assess the feasibility and impact of the policy and dropping therequirement for a closed-membership system for coffee shop municipalities.. The trial would
start 2012, the AHOJ-G criterion would be expanded in the three Southern provinces by adding

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the restriction that coffee shops could not sell marijuana to non-municipal residents or risk fines,
a temporary suspension of their commercial license, or a permanent closure of the coffee shop.
Since then, several studies were conducted on the initial effect of the policy in treated areas.
Van Ooyen-Houben, Bieleman, and Korf (2014) conducted a qualitative survey within
five coffee shop municipalities of which three were located within the three Southern provinces.
The findings revealed that the policy implementation was quickly followed by an increase innuisance, drug activity, and reduction in feeling safe in their neighborhood. Nabben, Wouters,
Benschop, and Korf (2015) followed up on this research by expanding the sample size to 31
coffee shop municipalities across 11 provinces and in addition examining the impact on themunicipal crime rate. As a result of restricting sales to non-residents, Nabben et al. (2015) foundthat there was an overall increase in illicit drug activity, especially in coffee shop municipalities
located within border regions in comparison.
Review of existing studies on the topic of drug tourism and the illicit black markets
portrays the following knowledge gap. Prior studies are qualitative in nature and do notencompass all coffee shop municipalities that implemented the I criterion in 2012. While theresearch results from these studies capture the effect of the policy in specific municipalities thatexperience a high degree of drug tourism, these studies do not account for heterogeneity, do notcontrol for differences between coffee shop municipalities, and have more limited sample set.
This study seeks to add to the existing body of literature by analyzing the policy impact on all
coffee shop municipalities at the time of implementation but also two years after implementation.

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Methodology
The aim of this paper is to estimate the impact of the implementing the 2012 revised drug
policy on the registered municipal drug crime rate and the revenue performance of firms locatedwithin Southern coffee shop located in the Netherlands.
I used propensity score matching (abbrev. PSM) in constructing a control group that is
not statistically different from the treatment group on the basis of their observed characteristics,
in combination with a linear difference-in-differences model (abbrev. DD) with a binary dummy
variable for drug policies with firm-, municipal and year fixed effects to assess the change in thenational drug policy on the municipal crime rate and firm performance using the following
standard DD specification:

=

where

+

×

+

+

+

+

(1)

,

is the output of interest of coffee shop municipality or firm i in year t,

is theconstant, D denotes the treatment variable which is equal to 1 when a coffee shop municipality iscovered by the 2012 revised drug policy and 0 if the municipality was not mandated to
implement it in 2012, d is the interaction term for the time period which is equal to 1 for year
2012 and afterwards and 0 for years prior to 2012,

andcapture the municipality- and time

fixed effect which controls for municipality- and time-variant and invariant characteristics forcoffee shop municipalities,
andintroduces linear time trend to control for macro-economic effects,

is the error term. The parameter of interest for the DD model in equation 1 iswhich

captures the average impact of the intervention on the outcome of interest of the treatment group.

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For the DD model, an important component that validates the results of the DD model isthe parallel trend assumption which states the trend of the dependable variable of the controland treatment group is statistically significant different as this would lead to a biased estimator.
In order to verify the parallel trend assumption, I follow the recommendation of Autor (2003)andestimate the parallel trend assumption through the following specification:

=

where

×

×

+

×

+

+

+

+

,

(2)

represents the interaction terms capturing the variable of interest ofcoffee shop municipalities in the pre-treatment period (2009-2011),

×

represents theinteraction dummies of the outcome of interest in the post-treatment period (2012-2014), andplus

capture the municipality and time-fixed effects, andintroduces a linear time trend as

explained in equation 1. The importance of equation 2 is two-fold. First, it provides a year by
year change of the coefficient of the dependent variable and to assess whether the initial impactof the intervention, which in this case is the revision to the drug policy, and the increase or
decrease in the magnitude of the coefficient over time. Second, in order to verify that the DD
model satisfies the necessary assumptions of a parallel trend between the control- and treatmentgroup, the trend of the dependable variable of both groups in the pre-treatment period can becompared. If the outcome variable in the pre-treatment period, represented by the lead dummies,
is reported as statistically significant, then this would indicate that the pre-treatment trend isdifferent between the treatment- and control group and thus the reported outcome in the posttreatment period would be considered biased. As a robustness check for the DD model, the main

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analysis was repeated with a lead and lagged treatment indicator where the revision to the drug
policy was implemented two years earlier (2010) and one year later (2013) as a placebo test. In
the case of the lead treatment indicator being reported as statistically significant, this would
provide an indication that the reaction to the drug policy was predated and if the lagged treatment
indicator is reported as significant, it is possible that the policy had a delayed effect on theoutcome of interest.
In order to control for the possibility of statistical difference between the observed
differences between the treatment- and control group in the DD model, I use propensity scorematching in the pre-treatment period to create a control group that consists out of coffee shop
municipalities that is similar to coffee shop municipalities in the treatment group on the basis of
their observable characteristics. Rosenbaum and Rubin (1983) propose a statistical matching
technique referred to as propensity score matching (henceforth abbreviated as PSM) to overcome
the issue associated with a high dimensional vector. The PSM technique uses a single index
named the propensity score which 47 captures the probability of an observation to be selected fortreatment based on their observed characteristics. Let the ( ) be the propensity score which isdefined as ( ) =(| ). The validity of the PSM technique relies on two assumptions.

The first assumption is the conditional independence assumption which is expressed as,∐|where ∐ indicates that the treatment assignment is independent of the outcome

variable of interest and solely based on the set of observed characteristics. Fulfilling theconditional independence assumption eliminates the bias attributed due to differences in theobservable characteristics between treated- and control coffee shop municipalities. The second
assumption is the common overlap which states that coffee shop municipalities with a similar X
values have an equal probability of being both treated and control coffee shop municipalities

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which is necessary to form valid counterfactuals for treated observations. The propensity score isestimated with either a probit- or logit model using a set of observed characteristics before thestart of the treatment and then matches treated- and control coffee shop municipalities on theproximity of their propensity score.
The main matching method used for creating the control group is the nearest-neighbormatching (NNM) algorithm. The NNM method compares treatment observations with control
firms with the nearest propensity score. The NNM method always varying degree of options ofaltering the matching procedure. In the case of this study, the number of neighbors were set to
the nearest two, four, and six and included a caliper that consisted of 25% of the standard
deviation of the logit model as advised by Austin (2011). The caliper will limit the range of thepropensity score and thus prevent matches that beyond a specified range and improve thematching quality. Lastly, the NMM matching model can also be used with the option of common
support between treatment- and control observations. Should common support not be used withina matching specification, it will lead to matches whose propensity score are distant from one
another and may not prove to be a viable match. To improve matching quality, this study usedcommon support with nearest-neighbor and kernel matching.
There are several methods to determine the quality of the matching model afterwards.
First is to check the bias of the chosen variables between the treatment- and control group in the
pre- and post-policy period. Literature on this topic such as Rosenbaum and Rubin (1983),
Sianesi (2004) and Caliendo and Kopeinig (2008) argue that the bias should be reduced to or
below 5% under a strong ignorability assumption and 10% for weak ignorability.
The second method is to verify whether a variable is significantly different whencompared between the treatment- and control group as the matching procedure should in theory

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have balanced both groups and eliminate significant differences between the two. Secondary
check would be to verify if a similar matching result can be replicated using an alternative
matching algorithm.
The third method follows the recommendation of Sianesi (2004) by checking the joint
significance and Pseudo-R2 before and after the matching procedure. The joint significant test
should be reported as insignificant and the Psuedo-R2 should report a lower value after matching
due to significant differences. Fourth is to compare the common overlap between the treatmentand control group by checking the mean difference in the outcome variable and the density of thepropensity scores given to observations within each propensity block.
The last method is to apply the Rosenbaum Bounds by Rosenbaum et al. (2002) whichassesses the sensitivity of a matching specification. According to Rosenbaum et al. (2002),
Gangl, (2004) and Caliendo and Kopeinig (2008), it is important to check whether the
probability of the treatment assignment is vulnerable and can be altered by unobservable factors.
Letbe the treatment assignment,

be the set of observed characteristics, Γ is the effect ofunobserved characteristics on the treatment assignment probability, and let

be the unobserved

component in the following specification:= Pr (= 1|= (+ +Γ )(3)

which states that the probability of treatment assignment is determined by observed andunobserved characteristics. Parameter Γ captures the effect of the unobserved characteristics on
the probability that coffee shop municipalities are selected as either treated- or control
observations. If there is no unobservable bias influencing the probability, then Γ would be zero
and

=

(

= 1| ) is solely determined by the observed characteristics. ExistingTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

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literature often cited on the issues is Duvendack and Palmer-Jones (2012) who state that in thecase of the social sciences, a Γ value of 2.0 would indicate strong insensitivity to unobserved
characteristics influencing the probability of treatment assignment.

Data
The empirical analysis is based on the combination of two separate data sources
containing a wealth of information collected and maintained by the Dutch Statistics Bureau. The
first data source, Statline, is a publicly accessible database and gathers information on the
Netherlands through government institutions and other stakeholders and thus the databank
contains a range of national, provincial, and municipal statistics. The dependable variables ofinterest chosen for this study are the number of registered soft and hard drugs withinmunicipalities and a range of variables such as population density, employment- andunemployment rate, number of registered students, mean household income, mean real-estatevalue, industry- and coffee shop density within a municipality. These set of characteristics areused during propensity matching to construct a counterfactual group of coffee shop
municipalities that closely resemble the coffee shop municipalities required to implement thepolicy in 2012. Two unique filters were used to limit the control- and treatment groupobservations. First, a dummy variable was created that would identify municipalities that had acoffee shop establishment within their domain by using the coffee shop industry information
provided by Bieleman et al. (2017) where a coffee shop municipality would be equal to one andzero otherwise. Municipalities that were categorized as “Non-coffee shop municipalities” were
dropped from the sample set. Second, the creation of another dummy variable with the purposeof identifying which of these coffee shop municipalities had a coffee shop before theTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

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implementation of the policy of 2012 and had remained coffee shop municipalities after 2012
again based on information provided in the annual coffee shop report by Bieleman et al. (2017).
Coffee shop municipalities that had closed coffee shops within their domain before 2012 andremained closed were dropped from the sample set. Finally, coffee shop municipalities withmissing crime or socio-economic data were dropped from the sample resulting in balanced data
set with 20 coffee shop municipalities in the treatment group and 83 number in the control groupwith a total of 618 municipal observations over six-year period from 2009 to 2014.
Table 1 presents the descriptive statistics of treated- and control coffee shop
municipalities in the pre-treatment period. Column 1 and 2 present the mean values of a set of
dependent and independable variables divided in crime and municipal socio-economic
characteristics. Column 3 reports the mean difference of coffee shop municipalities in thetreatment- and control group. Examining the reported values in Column 3, an important note isthat the number of committed soft drug crimes is more numerous in the treatment group and thedifference between the two is reported as statistically significant. This provides evidence on therationality on why governmental institutes implemented the trial policy within the three borderregions as the descriptive statistics show that there is a larger presence of drug tourism whencompared to other regions.
One possible explanation is the geographical proximity of the coffee shop municipalities
in the three border regions to neighboring countries such as Belgium and Germany as can beseen in Figure 1. The addition of high-quality road and train infrastructure between neighboring
countries allows drug tourists to easily visit these border regions in comparison to those further
North. However, the same is not reported when examining the hard drug crime rate.

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Comparing the municipal socio-economic characteristics, the difference in the meanvalue of coffee shop municipalities in the treatment- and control group is negligible and notreported as statistically significant which indicates that municipalities that allow for thecommercial sales of marijuana are quite similar. However, it is important to indicate that eventhough there are more committed drug crimes in treated coffee shop municipalities, the densityof coffee shops is higher in the control group even though the difference is not reported as being
statistically different.

Results
Based on the descriptive statistics presented in the previous section, there is a significant
mean difference between the treated- and control coffee shop municipalities when looking at theoutcome of interest, namely the soft drug crime rate. To verify whether PSM is required for thisstudy is to check the results of the DD model without prior matching between the treatment- andcontrol group which are presented in Table 2 and Table 3 for the yearly change in the DD
coefficient. Table 2 shows the DD model as specified in equation 1 and shows that the policy had
a statistically significant impact on the soft drug crimes but not on hard drugs when controllingfor municipality- and time fixed effects.
To verify whether the DD model without prior matching can be considered robust,
equation 2 to show the yearly change in the coefficient is examined in Table 3. The interaction
terms for the pre-treatment years of 2009 and 2010 are both reported as being statisticallysignificant indicating that results of the DD model are biased as there is no parallel trend between
the treatment- and control group. One method in controlling for this issue to create a control
group that more specifically matched the treatment group on a select set of covariates.

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The control for the main analysis was created using the nearest-neighbor matchingalgorithm with up to four neighbors and a caliper of 25% of the standard deviation.1 The set ofcovariates chosen for the propensity score specification is the degree of poverty, medianhousehold income, average real-estate value, density of café and other establishments within a 5kilometer radius, number of employed residents, the number of university graduates, the coffee
shop density and the percentage change in the number of arrests made on municipal level.
Table 4, Table 5, and Table 6 provide information on the success of the matchingprocedure of balancing the covariates between the treatment- and control group. Table 4 reports
an overview of the treatment- and control group before and after matching. Columns 1 and 2 list
the mean values of the treatment and control group respectively. The difference between bothgroups differs on the chosen covariate. Column 3 shows the percentage difference between bothgroups where the percentage difference for most covariates is above 10% such as poverty,
household income, café density, university graduates, coffee shop density, and the change inarrested suspects. However, when examining the percentage difference after matching in column
6, there is a large reduction in the mean values of the treatment- and control with the majority ofcovariates being under 10% and a few under 5% indicating that the matching procedure resulted
in better fit at the expense of losing two treatment observations due to being “off support” due tothe caliper being included.
The remainder of the statistics are presented in Table 5. Following the recommendationof Sianesi (2004) as detailed prior, the Pseudo R2 in column 1 has decreased significantly after
matching (0.198 to 0.011) indicating that the matching procedure resulted in observations in both1

Propensity score matching procedure was completed using the Stata module -psmatch2- by M.
Leuven and Sianesi (2003)

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groups to have similar characteristics. Column 2 the test of joint significance and is reported as
insignificant when compared before matching (0.013) and after matching (1.000) further
validating the quality of the control group. Columns 3 presents the absolute bias which according
to literature should be lower than 25 and this match resulted in an absolute bias of 24.4 which
can be seen as relatively high. Table 6 presents the results of using kernel matching and nearestneighbor matching with a different degree of neighbors. Comparing the before (U) and after (M)matched statistics, it is evident that the matching specification can be considered robust as all
statistics are within specified parameters.
Another test of robustness is to check the sensitivity of the matching specification
through the Rosenbaum Bounds. Table 7 presents the Rosenbaum bounds for NNM up to fourneighbors matching specification.2 Column 1 shows the unit increase of Γ starting at 1 up to 2.
The sensitivity of the matching specification can be assessed by checking at which unit of Γ thesignificance level, shown in column 2, becomes insignificant using the Wilcoxon signed-rank
test. The unit increase did not lead to a significance of 0.05 prior to 2 indicating that theunobserved variable of Γ would have to increase with more than 200% before the matchingspecification would become sensitive to a form of hidden bias. On the basis of this, it can beargued that the matching specification is insensitive to the possibility of hidden bias.
The last test is to verify whether there is evidence of common support between thetreatment- and control group using visual representation. Figure 1 shows the propensity score
density on the y-axis and propensity score on the x-axis before matching (left) and after
matching (right). On the basis of Figure 1, there is a significant increase in the overlap between

2

Stata module -rbounds- by Gangl (2004)was used for assessing the Rosenbaum bounds for thepropensity score matching specification.

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the treatment- and control after matching. While the control group lacks a certain amount ofoverlap at specific propensity score blocks (.04 - .06), the visual representation indicates that
there is significant overlap which further validates the matching procedure. Having completed
the matching specification, the main analysis will first present the policy’s impact on drug crimes
followed by examining the impact on firms. The robustness checks have established that theconstruction of the control group passes various requirements as seen in existing literature.
Table 8 presents the impact on hard- and soft drugs using the DD model on municipal
level while controlling for municipality-, time-, and adding a linear time trend. Columns 1 and 2
present the impact on soft drugs and the interaction term of the DD model reports no statisticalsignificance indicating that the implementation of the policy in 2012 and continued enforcement
up to 2014. Similar result is reported in Columns 3 and 4 when examining the impact on harddrugs. These two results would indicate that the coffee shop municipalities in the treatment group
did not see a statistical effect in comparison to control coffee shop municipalities. Though priorliterature mentions that illicit drug activity did increase in the beginning but states that thisphenomenon was not wide-spread or large enough to cause statistical difference.
Table 9 presents the yearly change in the DD estimator to assess not only the validity of
the DD model as done previously but also to check whether any specific year during or after the
implementation of the policy is reported as significant. Columns 1 and 2 present the soft drug
crime and report no significance for the years 2009 up to 2014. However, when comparing the
DD coefficient for 2011 and 2013 in column 1, there is a strong decrease in the reported
magnitude from -.17.08 to -5.43 verifying the mention of rising illicit drug activity referenced inexisting literature but returning back to pre-policy levels in 2014 most likely due to a set offactors such as increased police presence and / or enforcement or the lack of market demand due

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

21

to the reduction in drug tourism. Column 2 presents the soft drug crimes with the addition of
introducing a linear time trend. The coefficient in column 2 present a similar pattern as seen incolumn 1 in the pre-policy period for 2010 and 2011, the magnitude turns positive in the year of
implementation (-9.22 to 6.35) indicating that there may have been an initial increase in illicit
drug activity. However, the coefficient in 2012 is reported as statistically significant withnegative magnitude (-7.35) possibly indicating that the policy has a lagged treatment effect.
The results of the PSM-DD model present the fact that the policy did not lead to aincrease reported as statistically significant when examining either hard- or soft drug crimes after
controlling for municipal- and time fixed effects. However, when examining the reduction inmagnitude by checking the lead and lag indicators as seen in Table X, there is the possibility thatthe policy has a delayed effect after implementation in treated coffee shop municipalities.
As presented in the methodology of this paper, two alternative placebo tests are used todetermine whether or not there is a form of precipitation before the policy is implemented or adelay of the policy having an effect. Table 10 shows the lead- and lag placebo and reveals that
there is statistical significance when delaying treatment by one year indicating that the policy hada lagged effect after implementation when comparing it to estimations presented in Table 8
which were reported as statistically insignificant.
An alternative explanation for the discrepancy in significance between Table 8 and Table
10 can be that the short-term increase in the number of committed drug crimes, as seen in Figure
3 which portrays the yearly committed soft drug crimes, shows a sharp increase between 2011
and 2012 for coffee shop municipalities in the three Southern provinces of Limburg, NoordBrabant, and Zeeland.

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES22

The main results of this study thus show that on an overall level, the I criterion, which
was introduced into the drug policy for Dutch coffee shop municipalities in three border regions
resulted in a decrease in committed drug crimes when compared to a control group constructedusing propensity score matching on the basis of a set of chosen covariates similar to thetreatment group.
Conclusion
The purpose of this study was to analyze the impact of the changes in 2012 to the Dutch
drug policy on crime within Dutch coffee shop municipalities and whether firms located withinthese coffee shop municipalities were significantly affected after a significant reduction in drug
tourism based on prior qualitative studies. Using data from the Statline and Microdata databanks
for the period between 2009 to 2014 while employing propensity score matching with differencein-differences to estimate the impact on variables of interest, namely drug crimes and firm
revenue. The matching procedure balanced the coffee shop municipalities in the treatment- andcontrol group on a various set of municipal characteristics and, in the case of the firm data, on aset of financial ratios. Nearest-neighbor and Kernel matching were used to create the control
group and matching quality was considered to be sufficient based on recommendations fromexisting literature.
The findings of the difference-in-difference model revealed that the policy did not cause astatistically significant impact on hard- and soft drug crimes of treated coffee shop municipalities
after controlling for municipal- and time fixed effects with and without a linear time trend.
However, considering the negative magnitude of the DD coefficient, the occurrence of drug
crimes is still higher within matched coffee shop municipalities. Examining the yearly change byusing year dummies proved that the DD model was robust as the DD coefficients in the pre-THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

23

treatment were not reported as significant and using alternative starting years for treatment as aplacebo further revealed that there is the possibility of a lagged treatment effect.
Therefore, the evidence presented in this paper seems to conclude that the introduction of
the I criteria in the AHOJ-G criterion for coffee shop municipalities in the border provinces ofLimburg, Noord-Brabant, and Zeeland did not cause a discernable increase in the number ofregistered drug crimes and led to a reduction in drug tourists visiting coffee shops located inmunicipalities that implemented the new criterion in 2012.
There are some limitations of this study that should be addressed. The first limitation is
the limited range of years analyzed in this study from 2009 to 2014. While data before 2009 isavailable, several municipalities prior to 2009 held corroborative police projects that were
designed in reducing the negative issues of drug tourism through increased police presence, andtemporary closure of coffee shops within their municipal domain. Second, while the national law
mandated that municipalities within the chosen three provinces had to implement and enforce
this policy in 2012, municipalities were given autonomy in enforcing the policy. Several of thesemunicipalities chose not to continue enforcement activities and others re-aligned enforcement toa lower priority. However, no exact data is available to control for this issue which led to thestudy merely focusing from 2009 to 2014. The second limitation of note is the narrow scope of
this research project as it focuses primarily on committed drug crimes within coffee shop
municipalities.
Future research on this topic is promising with the recent change in the perspective ofusing marijuana for medicinal purposes or for recreational usage in society, especially within the
United States of America that has since the early 1950s been the driving force behind drug
prohibition policies around the world. The most important gap within existing literature with

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

24

regard to this policy is the lack of a more comprehensive research study that examines the impactof the policy on the local economy, especially on firms that benefit greatly from tourism such as
the retail-, hospitality-, and accommodation industries. Prior studies have primarily qualitative innature and focused on crime and the change in attitude of residents that live in close proximity orin an area with a high degree of tourism. With the rapid legalization of full recreational
marijuana in Washington, Colorado, and Illinois, these areas will undoubtably experience thepositives and negatives of drug tourism. Policy makers within these local governmental
institutions would be able to reference this and future studies on what policy to implement withthe purpose of addressing drug tourism.

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

25

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Gangl, M. (2004). RBOUNDS: Stata module to perform Rosenbaum sensitivity analysis foraverage treatment effects on the treated [English]. Retrieved fromhttps://EconPapers.repec.org/RePEc:boc:bocode:s438301
Henchmann, J. B. (2014). Taxing marijuana: The Washington and Colorado experience (Final
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Henchmann, J. B., & Scarboro, M. (2016). Marijuana legalization and taxes lessons for otherstates from colorado and washington (Final Report No. 231; pp. 1–16). Washington: Tax
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Testing. Version 3.0.0.
Nabben, T., Wouters, M., Benschop, A., & Korf, D. (2015). Coffee shops, toerisme, overlast, andthe illegale verkoop van soft drugs (No. 28; pp. 1–83). Amsterdam: Bonger Instituut voor
Criminologie.
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Snippe, J., Naayer, H., & Bieleman, B. (2006). Hektor in Venlo, evaluatie drugs overlast in Venlo(pp. 1–68). Groningen-Rotterdam: Intraval.
TK. (2011). Drugbeleid: Brief ministers over het drugsbeleid (No. 259). Den Hague: SDU
Uitgevers.

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van de Donk, W., Boekhoud, P., Van den Brink, W., Fijnaut, C., van de Mheen, D., Rigter, H., …
Horstink von Meyenfeldt, L. (2009). Geen Deuren maar daden. Nieuwe accenten in het
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van der Torre, E. J., Cachet, E. D., & Dijk, V. (2008). Enquêtes op drie hotspots; Volgens
bewoners, passanten en middenstanders; Vervolgmeting. Den Haag: COT Instituut voor
Veiligheids- en Crisismanagement.
van Ooyen-Houben, M., Bieleman, B., & Korf, D. (2014). Coffee shops, toeristen en lokale
markt Evaluatie van het Besloten club- en het Ingezetenencriterium voor coffeeshops(Final report No. 12; pp. 1–231). Den Hague: WODC, Intraval, Universiteit van
Amsterdam - Bonger Instituut voor Criminologie.

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES28

AppendixDefinition of variables
Statline Databank

Variables Definition
GEMCODE Municipality code given by the Dutch Statistics Bureau
POLICY Dummy variable to indicate whether the municipality implemented
the 2012 I criteria

TIME Dummy variable to indicate the pre- and post-treatment period
EMPLOYMENT The number of residents within a municipality that at a minimum has
UNEMPLOYMENTPOVERTY
REALESTATESOFTDRUGSHARDDRUGSNUISANCE
SUSPECTS
HHINCOMECOFFEESHOPHOSPITALITYDENSITY
STUDENTSGRADUATES

a part-time or full-time employment
The number of residents within a municipality that receive
unemployment benefits from the Dutch UWVThe number of residents within a municipality whose income isbelow or near poverty level and receive a welfare subsidy from theUWVThe average value of housing real-estate located within amunicipality
The number of registered crimes that fall under the soft drug category
as framed by the Opium Act of 1970
The number of registered crimes that fall under the hard drug
category as framed by the Opium Act of 1970
The number of crimes that are classified as nuisance under criminal
code 50 of the Dutch justice system
The number of criminal suspects arrested within a municipality
The median income of households within a municipality
The number of coffee shops located within a municipality based on
the industry reports published by the Intraval institute
The density of cafes, restaurants, and hotels within a five-kilometerradius from residents that are located within a municipality
The number of students that are registered as in either belonging to a
vocational or university based on their residential address
The number of students that graduated from vocational or university
according to their residential address

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES
Table 1

Descriptive statistics of treated- and control coffee shop municipalities pre-treatment period
(1)

(2)

(3)

(4)

Mean differenceStandard error

Coffee shop municipalities
TreatedControl

Variable
Crime characteristics
Soft drugs

96.13

50.54

-45.58*

20.15

Hard drugs

56.88

62.71

5.84

39.63

Suspects

2775.502981.20205.701264.72

Municipality characteristics
Poverty2560.25

2981.20550.491264.72

Householdincome

27.09

27.96

0.87

0.76

Real-estate value

8773.759182.64408.893117.42

Café density
(5km)

26.49

35.30

8.82

14.85

Employment

52.77

51.05

-1.72

18.51

University
graduates

218.43292.8574.43

183.27

Coffee shop
density

4.40

6.99

2.59

5.68

Observations
20
83
Note: ∗p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
Household income and real-estate value are reported in €1,000

29THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES
Table 2The impact of the policy on soft- and hard drug crimes of coffee shop municipalities before
propensity score matching
Policy ≥ 2012

(1)
(2)
Soft drug crimes
Coffee shop municipalities -26.14*-6.17(8.40)(6.81)× Policy2009_2014
Municipal fixed effects
Yes
Yes
Time fixed effects
Yes
Yes
Linear fixed effects
No
Yes
Observations
618
618
R2
0.077
0.501
Note: ∗p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
DD coefficient is captured by (

×

(3)
(4)
Hard drug crimes
-6.01
-0.74
(5.68)(6.99)
Ye
Yes
Yes
Yes
No
Yes
618
618
0.028
0.385

) in equation 1

Clustered standard errors are grouped on municipal level and reported inparenthesis.
Source: Statline databank30

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES
Table 3The impact of the policy on soft- and hard drug crimes of coffee shop municipalities before
propensity score matching
Policy ≥ 2012

(1)
(2)
Soft drug crimes
Policy Implementation-12.63*-5.27(-4.97)(4.85)– 2 Years
Policy Implementation-30.37**-15.66*(9.73)(7.78)– 1 Years-17.23
4.83
Policy Implementation(10.07)(6.79)
Policy Implementation-38.34**-9.43*(10.84)(3.78)+ 1 Year
Policy Implementation-43.37**-6.61(12.82)(3.95)+ 2 Years
Municipality fixed effects
Yes
Yes
Time fixed effects
Yes
Yes
Linear time trendNo
Yes
Observations
618
618
R2
0.107
0.524
Note: ∗p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
DD coefficient is captured by (

×

(3)
(4)
Hard drug crimes
0.96
2.46
(7.42)(7.34)-5.76-2.77(9.37)(8.55)
0.80
5.30(7.52)(6.41)-7.36-1.38(10.68)(6.18)-9.80-2.31(9.49)(2.83)
Yes
Yes
Yes
Yes
No
Yes
618
618
0.030
0.388

) in equation 2

Clustered standard errors are grouped on municipal level and reported inparenthesis.
Source: Statline databank31292.85
6.988

27.0888773.827.10852.768218.434.4-.08207

20

Poverty

HouseholdincomeReal-estate value

Café density
5km

Employment

University
graduates
Coffee shop
density

Registeredsuspect change

Observations

-83.3

-14.3

-11.9

2.7

-16.2

-3.9

-30.5

-11.0

(3)
Bias

17

-.072134.6667237.4754.31727.85

9166.127.0612527.8After matching(4)
Treated coffee
shopmunicipalities

81-.07374

4.125

203.8

50.13728.8188376

27.1782217.1(5)
Control coffee
shopmunicipalities

1.8

3.0

5.4

6.6

-1.9

7.5

-4.1

6.2

(6)
Bias

97.8

79.1

54.8

-143.1

88.2

-93.2

86.5

43.6

(7)
Bias
reduction(%)

32of the standard deviation

Note: Covariate balancing was achieved using nearest-neighbor matching with up to 4 neighbors with a caliper 25 %83-.007751.04835.3019182.627.9593110.72560.3

Variables(2)
Control coffee
shopmunicipalities(mean)(1)
Treated coffee shops
municipalities (mean)

Before matching

Covariate balancing of treated- and control coffee shop municipalities before and after matching in the pre-policy period

Table 4

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIESTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES
Table 5

Propensity score statistics before and after matching in the pre-policy period

Unmatched
MatchedPseudo R2
0.198
0.011

LRꭓ2
19.29
0.56

p > ꭓ20.013
1.000

Rubin’s B
62.3*
24.4

33

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

34

Table 6

Propensity score statistics of alternative matching algorithms and specifications before and after
matching in the pre-policy period
Unmatched versus Matched statistics
(1)
(2)
(3)
(4)
Kernel matching
NearestNearest-neighbor
Nearestneighbor (2NN)(4NN)
neighbor (6NN)
U
M
U
M
U
M
U
M
2
Pseudo R
0.198
0.003 0.198
0.068 0.198
0.011
0.198
0.007
LRꭓ2
19.95
0.14
19.29
3.41
19.29
0.56
19.29
0.34
p > ꭓ21.000
0.011 0.013
0.906 0.013
1.000
0.013
1.000
Mean bias
21.9
2.5
21.7
8.2
21.7
4.6
21.7
2.3
Median bias
13.1
2.1
13.1
8.9
13.1
4.7
13.1
1.6
Rubin’s B
62.7*
12.2
62.3*
59.0* 62.3*
24.4
62.3*
18.9*
Note: Nearest-neighbor and kernel matching is based on a logit model
Caliper used in the PSM specification is 25% of the standard deviation
Source: Statline databankTHE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

35

Table 7

Rosenbaum bounds to check for sensitivity in the propensity score matching specification in the
pre-policy period(1)Gamma (Γ) Significancelevels
1
1.1
1.2
1.3
1.4
1.5
1.6
1.7
1.8
1.9
2.0

Sig.001146.002017.003242.004857.006884.009332.012200.015477.019146.023187.027577(2)
HodgesLehmannSig+.001146.000618.000334.000181.000099.000054.000029.000016
8.8e-06
4.8e-06
2.7e-06

T-hat54.375
45
42.5
42.5
41.25
40
38.75
38.125
37.5
37.5
36.25

T-hat+
54.375
57.5
63.75
72.5
75
77.5
88.12591.25
93.75
101.25106.25(3)
95%
Confidence
intervals
CI22.521.25
19.37516.875
11.25
8.75
5
3.75
2.5
1.875
-1.25

CI+
129.375
132.5
135
140
150
158.75171.875
177.5
190
190
192.5

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

Table 8The impact of the policy on soft- and hard drug crimes after propensity score matching(1)(2)
Soft drug crimes
Coffee shop municipalities -13.80
0.39(9.18)(5.99)× Policy2009_2014
Municipal fixed effects
Yes
Yes
Time fixed effects
Yes
Yes
Linear time trendNo
Yes
Observations
312
312
R2
0.164
0.620
Note: ∗p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
DD coefficient is captured by (

×

(3)
(4)
Hard drug crimes
-8.85
2.67
(4.50)(5.48)
Ye
Yes
Yes
Yes
No
Yes
312
312
0.084
0.402

) in equation 1

Clustered standard errors are grouped on municipal level and reported inparenthesis.
Source: Statline databank36THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

Table 9The yearly change in the DD estimator after propensity score matching
Policy ≥ 2012

(1)
(2)
Soft drug crimes
Policy Implementation
-8.10
-4.17
(4.74)(4.68)– 2 Years
Policy Implementation-17.08-9.22(11.09)(5.67)– 1 Years
-5.43
6.35
Policy Implementation(11.09)(7.34)
Policy Implementation-23.06-7.35*(11.81)(3.07)+ 1 Year
Policy Implementation-25.85-6.21(13.90)(4.18)+ 2 Years
Municipality fixed effects
Yes
Yes
Time fixed effects
Yes
Yes
Linear time trendNo
Yes
Observations
312
312
R2
0.194
0.644
Note: ∗p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
DD coefficient is captured by (

×

(3)
(4)
Hard drug crimes
-0.39
2.41
(3.86)(4.29)-5.19
0.41(5.09)(5.05)-2.12
6.29(5.09)(4.96)-13.56*-2.36(5.64)(3.10)-15.33*-1.32(7.42)(2.47)
Yes
Yes
Yes
Yes
No
Yes
312
312
0.118
0.420

) in equation 2

Clustered standard errors are grouped on municipal level and reported inparenthesis.
Source: Statline databank37THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

Table 10The impact of the drug policy firm revenues when using a placebo policy in 2010 and 2013
Policy ≥ 2010
Coffee shop municipalities
× Policy2009_2014
Municipal fixed effects
Time fixed effects
Linear time trend
Observations
R2

(1)
(2)
Soft drug crimes-15.90-3.46(8.86)(4.74)
Yes
Yes
Yes
Yes
No
Yes
312
312
0.160
0.623

Policy ≥ 2013
(5)
(6)
Coffee shop municipalities -16.80*-9.15(8.10)(6.79)× Policy2009-2014
Municipal fixed effects
Yes
Yes
Time fixed effects
Yes
Yes
Linear time trendNo
Yes
Observations
312
312
R2
0.177
0.623
Note: ∗p < 0.1, ∗ ∗ p < 0.05, ∗ ∗ ∗p < 0.01
DD coefficient is captured by (

×

(3)
(4)
Hard drug crimes
-7.32
2.67
(4.44)
4.77
Ye
Yes
Yes
Yes
No
Yes
312
312
0.068
0.403
(7)
-12.52*(4.60)
Yes
Yes
No
312
0.113

(8)
-9.00
(5.52)
Yes
Yes
Yes
312
0.408

) in equation 1

Clustered standard errors are grouped on municipal level and reported inparenthesis.
Source: Statline databank38THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIESFigure 1: Coffee shop municipalities in the Netherlands

39

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

40

Figure 2: Common overlap between treatment- and control observations before and after matching

Note:

Matching result based on the 1st propensity score specification using nearest-neighbor logit model with up to four neighbors with a caliper of 25% of the standard deviation(0.045)
Matching leads to three treated coffee shop municipalities being labeled as “offsupport” indicating that no viable control coffee shop municipality was available within thecaliper radius

THE DUTCH ANSWER TO DRUG TOURISM WITHIN DUTCH MUNICIPALITIES

41

Figure 3: Soft drug crime trend from 2005 to 2016

Note: Green line signifies the start of policy implementation and enforcement in theSouthern provinces of Limburg, Noord-Brabant, and Zeeland.
The large decrease in 2009 is due to two coffee shop municipalities, Bergen op
Zoom in Noord-Brabant, and Roosendaal in Zeeland closing their coffee shops due to the issues
associated with drug tourism