# RECORD 4a6c4412ae803b09046980401041b08141e48e51
# TITLE: 'No Drugs in My Back Yard:' the Ambivalent Reception of Cannabis Retailers
# YEAR: 2019.0  VENUE: Social Science Research Network  DOI: 10.2139/ssrn.3461820
# AUTHORS: L. M. Bruijn; R. Ribas
# PAGES: 20  CANDIDATE TABLE PAGES (1-based): [9, 12, 13, 14, 15, 16]
# ABSTRACT: Can individuals' aversion to drug markets curb the benefits of decriminalization? We investigate the effect of two policies on housing demand in the Netherlands: the distance-to-school criterion, which closed some cannabis shops in a few cities; and the zero-tolerance policy, which banned shops within municipal jurisdictions. While a small increase in the distance to retailers raised house prices by 1-5%, a substantial increase reduced them by 1-6%. Both policies reduced property crime, but the zero-tolerance was also related to fewer jobs. Our findings reveal that cities benefit from having cannabis shops, but households' aversion to related nuisances depreciates surrounding areas.


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University of Groningen
“No drugs in my back yard”
Bruijn, Larissa Michelle; Ribas , Rafael P.
Published in:
Journal of Economic Behavior & Organization
DOI:
10.1016/j.jebo.2022.05.005
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Publication date:
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Citation for published version (APA):
Bruijn, L. M., & Ribas , R. P. (2022). “No drugs in my back yard”: The ambivalent reception of cannabis
retailers. Journal of Economic Behavior & Organization, 199, 103-121.
https://doi.org/10.1016/j.jebo.2022.05.005
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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Table 1 
Cross-Sectional Relationship between House Prices and Distance to Retailer. 
Period 
1999–2001 
2012–2016 
(1) 
(2) 
(3) 
(4) 
(5) 
(6) 
Distance in 1999 (miles) < 0 . 2 
0.044 ∗∗∗
−0.036 
0.056 ∗∗∗
−0.030 
(0.014) 
(0.035) 
(0.016) 
(0.035) 
0.2–0.4 
0.024 ∗∗
−0.039 
0.048 ∗∗∗
−0.032 
(0.011) 
(0.030) 
(0.011) 
(0.030) 
0.4–1.0 
0.015 ∗
−0.039 ∗∗
0.032 ∗∗∗
−0.035 ∗
(0.008) 
(0.019) 
(0.009) 
(0.020) 
Distance in 2016 (miles) 
< 0 . 2 
0.057 ∗∗∗
0.090 ∗∗
0.067 ∗∗∗
0.094 ∗∗
(0.017) 
(0.041) 
(0.019) 
(0.042) 
0.2–0.4 
0.035 ∗∗∗
0.071 ∗∗
0.060 ∗∗∗
0.088 ∗∗
(0.013) 
(0.035) 
(0.015) 
(0.038) 
0.4–1.0 
0.027 ∗∗∗
0.063 ∗∗∗
0.046 ∗∗∗
0.076 ∗∗∗
(0.009) 
(0.021) 
(0.010) 
(0.023) 
House characteristics 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Year dummies 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Town fixed effects 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Number of observations 
171,804 
171,804 
171,804 
300,967 
300,967 
300,967 
Number of towns 
201 
201 
201 
201 
201 
201 
This table presents the linear regression of log price per square meter of traded houses on their shortest distance to a cannabis retailer. This distance is 
represented by three dummies: less than 0.2 miles, between 0.2 and 0.4 miles, and between 0.4 and one mile; the reference is the group of houses farther 
than one mile. Columns (1) to (3) show estimates for transaction prices from 1999 to 2001 as a function of distance in 1999, distance in 2016, and distances 
in both 1999 and 2016, respectively. Columns (4) to (6) show the same regressions but from 2012 to 2016. All regressions include house characteristics, as 
in Table A1, year dummies, and town fixed effects. The sam ple includes properties located up to 3.1 miles from the nearest retailer in towns that have ever 
had a retailer within one mile. Clustered robust standard errors are in parentheses. ∗∗∗, ∗∗, ∗represent statistical significance at the 1%, 5%, and 10% levels, 
respectively. 
We also estimate the relationship between demographic characteristics and the distance to a retailer. Table A3 of the 
Online Appendix displays the estimates. In summary, areas near coffeeshops concentrate more men, people between 15 and 
39 years old, single-person households, and Western immigrants. In contrast, these areas present relatively fewer children 
and adults over 40. The closures were more often in areas with a lower share of adults under 40, higher shares of adults 
over 40 and non-Western immigrants, and more elderly people in the vicinity. Over time, those areas have experienced an 
outflow of adults under 40, compensated by an inflow of adults over 40 and Western immigrants. 
These initial findings stress the importance of controlling for local characteristics, as well as using a comparison group 
that is equally distant to a retailer, when estimating the effect of closures. Presumably, the postcode fixed effects should 
control for the selection of areas with closed retailers, and the comparison group should control for the overall appreciation 
of areas with a coffeeshop. If specific variations in property values triggered the closure policies, we should observe this 
variation before treatment (see Fig. 3 below). 
5. Empirical strategy 
This section describes our empirical models. First, it presents the reduced-form difference-in-differences (DiD), which 
compares houses whose nearest retailer was closed with houses that were equally distant from a retailer. For this compari- 
son, we consider only closures forced by the distance-to-school criterion (DTSC) and the zero-tolerance policy (ZTP). Homes 
whose nearest shop was closed for another reason are excluded from the analysis. 
The closures made by those policies result in two instrumental variables (IVs): nearest shop closed by the DTSC and 
nearest shop closed by the ZTP. While the former instrument is used to identify the effect of a small but significance change 
in the distance to retailers, the latter is used to identify the effect of a much larger increase in distance. Accordingly, we 
apply an IV strategy, described in the second part of this section, to estimate the nonlinear effect of distance to cannabis 
outlets on house prices. Other empirical models used in this study are described in the Online Appendix. 
5.1. Reduced form 
Closing a coffeeshop should, if anything, increase the shortest distance from homes to retailers. Thus, we first estimate 
the effect that closures have on house prices. Let y ilt be the log price per square meter of house i in location l and year t, c l 
be the indicator whether the nearest retailer is closed, and q l be the year of closure. The average effect is identified by the 
following DiD estimator: 
τ = E(y ilt | c l = 1 , t ≥q l ) −E(y ilt | c l = 0 , t ≥q l ) 
−[ E(y ilt | c l = 1 , t < q l ) −E(y ilt | c l = 0 , t < q l )] 
(1) 
110 


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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Fig. 3. Estimated Differences in House Prices around the Year of Closure. This figure presents the lead and lagged difference-in-differences (DiD) on the 
log price per square meter of traded houses. Panel (a) shows the changes before and after cannabis retailers are closed by the distance-to-school criterion 
(DTSC). Panel (b) shows the changes before and after introduction of the zero-tolerance policy. Each graph uses a different sam ple, defined by the initial 
distance to a retailer. All regressions include house characteristics, as in Table A1, year dummies, year dummies times a dummy for municipalities that 
implemented the DTSC, and six-digit postcode fixed effects. The sample excludes houses in which the nearest retailer was closed for another reason. 
Robust standard errors are clustered by postcode. Vertical lines around the point estimates represent 90% and 95% confidence intervals, respectively. 
where c l is one if the nearest retailer is closed and zero otherwise. Location refers to the centroid of a six-digit postcode. 
The DiD parameter, τ, is estimated using a fixed-effect model and varies with the initial distance to a cannabis shop: 
y ilt = 
⎧ 
⎪ 
⎪ 
⎪ 
⎨ 
⎪ 
⎪ 
⎪ 
⎩ 
τ 0 c l · I(t ≥q l ) + β0 x i + θ 0 gdp lt + μ0 
t + μl + ε ilt 
if d l0 ∈ 
0 , D 0 
τ 1 c l · I(t ≥q l ) + β1 x i + θ 1 gdp lt + μ1 
t + μl + ε ilt 
if d l0 ∈ 
D 0 , D 1 
. . . 
τ n c l · I(t ≥q l ) + βn x i + θ n gdp lt + μn 
t + μl + ε ilt 
if d l0 ∈ 
D n −1 , D n 
, 
(2) 
where x i is a vector of house characteristics, gdp lt is the regional log GDP, { μ j 
t } n 
j=0 are year-specific effects, μl is the postcode 
fixed effect, ε ilt is the deal-specific error term, d l0 is the initial distance from location l to the nearest shop, and { D j } n 
j=0 is 
a set of distance thresholds — say 0.2, 0.3, 0.4, 0.6, and 0.8 miles. Standard errors are clustered by postcode to account for 
serial error correlation ( Bertrand et al., 2004 ). 
Since the regulation of sales and the demand for cannabis have evolved, the (dis)amenity created by retailers has also 
varied. Even if the distance of a home to retailers remains the same, its price may respond to changes in nuisances. To 
control for time-varying unobservables, we let the year-specific effects, { μ j 
t } n 
j=0 , vary across location ranges so that they 
capture distinct improvements happening near and far away from cannabis outlets. This type of DiD is different from those 
applied by, for instance, Linden and Rockoff (2008) and Conklin et al. (2017) , who instead compare near and distant areas 
over time. By breaking the effect into several location ranges, we can also verify the magnitude of general equilibrium effects 
on further areas. 
111 


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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Table 2 
Estimated Effect of Closures on Distance to Retailer and House Price. 
Initial distance to retailer (miles) 
0–0.2 
0.2–0.3 
0.3–0.4 
0.4–0.6 
Distance to retailer (miles) 
Distance-to-school 
0.147 ∗∗∗
0.118 ∗∗∗
0.127 ∗∗∗
0.162 ∗∗∗
(0.010) 
(0.011) 
(0.020) 
(0.026) 
Zero-tolerance 
3.562 ∗∗∗
3.070 ∗∗∗
3.172 ∗∗∗
3.416 ∗∗∗
(0.161) 
(0.221) 
(0.173) 
(0.117) 
Log price per m 2 
Distance-to-school 
0.015 
0.041 ∗∗
0.053 ∗∗
−0.001 
(0.017) 
(0.020) 
(0.021) 
(0.020) 
Zero-tolerance 
−0.024 
−0.059 ∗∗∗
−0.009 
−0.007 
(0.015) 
(0.021) 
(0.020) 
(0.012) 
House characteristics 
Yes 
Yes 
Yes 
Yes 
Regional GDP 
Yes 
Yes 
Yes 
Yes 
Year dummies 
Yes 
Yes 
Yes 
Yes 
Year dummies × DTSC 
Yes 
Yes 
Yes 
Postcode fixed effects 
Yes 
Yes 
Yes 
Yes 
Number of observations 
73,937 
50,368 
45,659 
94,497 
Number of postcodes 
8561 
5445 
5044 
10,504 
with distance-to-school 
479 
210 
98 
83 
with zero-tolerance 
284 
210 
228 
484 
comparison 
7798 
5025 
4718 
9937 
This table presents the difference-in-differences (DiD) estimates, as in Eq. (2) , for two outcomes: shortest distance to a cannabis retailer (top panel) and 
log price per square meter of traded houses (bottom panel). Each regression contains two DiDs: one for closures forced by the distance-to-school criterion 
(DTSC), and another for closures forced by the zero-tolerance policy. Each column shows a regression using a different sample, defined by the initial 
distance to a retailer. All regressions include house characteristics, as in Table A1, year dummies, year dummies times a dummy for municipalities that 
implemented the DTSC, and six-digit postcode fixed effects. The sample excludes houses in which the nearest retailer was closed for another reason. 
Clustered robust standard errors are in parentheses. ∗∗∗, ∗∗, ∗represent statistical significance at the 1%, 5%, and 10% levels, respectively. 
Conklin et al. (2017) , who show that prices increase by 8% after medical dispensers are converted into retailers, and with 
Cheng et al. (2018) , who show a rise of 6% in municipalities in Colorado that legalized marijuana retail. 
Nevertheless, if there are many retailers in the city, closing some of them reshuffles the housing demand, raising prices in 
adjacent neighborhoods and reducing prices further away. Although the city benefits from having cannabis retailers, house- 
holds seem to pay to be just far enough from them. The latter is in line with Adda et al. ’s (2014) evidence that the de- 
criminalization of cannabis possession decreases house prices in hot spots and increases them in areas where it is still a 
crime. 
As regards the consistency of our findings, one may argue that they result from differences in the previous trends 
between places where shops were closed and where they were not — i.e., a violation of the parallel trend assumption 
( Abadie, 2005 ). Fig. 3 shows the difference in log prices between treated and comparison groups for each year before and 
after the closure of the nearest shop. Before, the difference fluctuates around zero and does not exhibit a distinct trend for 
houses affected by the DTSC and by the ZTP. 
After the nearest coffeeshop closes due to the DTSC, prices immediately increase between 0.3 and 0.4 miles away and 
persist at least until the third year. The positive effect on homes between 0.2 and 0.3 miles appears as significant only four 
years later. 15 Therefore, it takes a few years for the housing demand to adjust to the new policy fully. 
Similarly, the effect of a ZTP seems gradual. For homes within 0.3 miles, the effect appears at least a year after the last 
coffeeshop was closed in the municipality. For homes between 0.4 and 1 miles away, we observe a short-term effect that 
does not persist after three years. Overall, the ZTP shifts housing demand in different areas and with different timings, but 
the net impact on the local economy ends up negative after three years. 
6.2. The estimated distance function 
6.2.1. House prices 
The findings in the previous part suggest that the relationship between house prices and distance to cannabis shops is 
non-monotonic. That is, a small increase in distance is followed by an increase in prices, while a large increase in distance 
depreciates prices. Table 3 presents the estimated distance function, as in Eq. (3) , using two estimators. The first one takes 
into account changes in distance provoked by any closure. Then we restrict the sample to homes whose distance to the 
nearest and second-nearest shop has not changed, and those whose distance was affected by either policy. The closures 
15 The higher significance of the effect for this range in Table 2 is due to the aggregation of values in the post-treatment period, which produces narrower 
confidence intervals. 
113 


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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Table 3 
Estimated Effect of Distance to Retailer on House Price. 
Final 
Initial distance to retailer (miles) 
distance (miles) 
< 0 . 3 
< 0 . 4 
< 0 . 5 
< 0 . 6 
Least square 
< 0 . 3 
−0.016 ∗∗∗
−0.013 ∗∗∗
−0.015 ∗∗∗
−0.016 ∗∗∗
(0.004) 
(0.004) 
(0.004) 
(0.004) 
> 2 . 0 
−0.056 ∗∗∗
−0.034 ∗∗∗
−0.028 ∗∗∗
−0.024 ∗∗∗
(0.009) 
(0.008) 
(0.006) 
(0.006) 
House characteristics 
Yes 
Yes 
Yes 
Yes 
Regional GDP 
Yes 
Yes 
Yes 
Yes 
Year dummies 
Yes 
Yes 
Yes 
Yes 
Year dummies × DTSC 
Yes 
Yes 
Yes 
Yes 
Postcode fixed effects 
Yes 
Yes 
Yes 
Yes 
Number of observations 
199,172 
274,769 
350,269 
422,630 
Number of postcodes 
22,087 
30,268 
38,518 
46,630 
Instrumental variable 
< 0 . 3 
−0.019 
−0.046 ∗∗
−0.064 ∗∗∗
−0.067 ∗∗∗
(0.021) 
(0.021) 
(0.022) 
(0.023) 
> 2 . 0 
−0.068 ∗∗
−0.060 ∗∗∗
−0.058 ∗∗∗
−0.047 ∗∗∗
(0.029) 
(0.021) 
(0.016) 
(0.013) 
House characteristics 
Yes 
Yes 
Yes 
Yes 
Regional GDP 
Yes 
Yes 
Yes 
Yes 
Year dummies 
Yes 
Yes 
Yes 
Yes 
Year dummies × DTSC 
Yes 
Yes 
Yes 
Yes 
Postcode fixed effects 
Yes 
Yes 
Yes 
Yes 
LM stat. for under-identification 
155.1 
148.8 
145.5 
143.6 
Wald stat. for weak instruments 
98.9 
92.9 
87.7 
86.3 
Number of observations 
122,605 
167,757 
214,215 
261,261 
Number of postcodes 
12,306 
16,843 
21,530 
26,354 
This table presents the relationship between changes in the distance to the nearest cannabis retailer and in the log of house price per square meter, as in 
Eq. (3) . The distance is represented by two dummies: less than 0.3 miles, and more than two miles; the reference is the group of houses between 0.3 and 
two miles. All regressions include house characteristics, as in Table A1, year dummies, year dummies times a dummy for municipalities that implemented 
the distance-to-school criterion (DTSC), and postcode fixed effects. The columns show the results from different samples, based on their initial distance 
to a retailer. In the top panel, regressions exploit any variation in the distance. In the bottom panel, variations in the distance are instrumented by the 
closures forced by the DTSC and the zero-tolerance policy. The sample in the bottom panel excludes houses in which the nearest retailer was closed for 
another reason. Clustered robust standard errors are in parentheses. ∗∗∗, ∗∗, ∗represent statistical significance at the 1%, 5%, and 10% levels, respectively. 
made by the two policies are used as IVs for the distance function. In both models, we control for house characteristics, 
postcode fixed effects, regional GDP, and year dummies. 
Since both house prices and the operation of coffeeshops can jointly respond to other local events, the variation in 
distance can be endogenous in the first estimated model (in the top panel of Table 3 ). With the IVs, though, changes in 
distance are triggered by exogenous events, which are not preceded by variations in house prices (see Fig. 3 ). Indeed, the 
least-square estimates point to the same conclusion as the IV estimates, but the magnitudes are at most half as large. Fig. A2 
of the Online Appendix confirms that closures brought about for procedural reasons also increase the price of properties 
farther than 0.2 miles. However, it also indicates that prices farther than 0.4 miles were increasing before these closures. 
The IV estimates in Table 3 reveal that houses are 2–7% less valuable if within 0.3 miles of retailers, but they are also 5–
7% cheaper if located farther than two miles. In other words, house prices peak at a distance at which cannabis shops are far 
enough to avoid any disturbance, but they decrease moving farther. This result is robust to different distance categorizations; 
see Table A6 of the Appendix. If we expand the definition of “near” to within 0.4 miles, the magnitudes of IV estimates 
become much larger. However, the IVs get weaker. Thus, we choose to present more conservative estimates as our main 
results. Section 7 examines some reasons for the non-monotonicity in housing prices. 
We also estimate the distance function with a restricted cubic spline. Fig. A3 of the Online Appendix suggests that prices 
are 15–25% higher about two miles from the retailers, and decline moving farther away. This pattern is similar to the one 
Giambona and Ribas (2018) report for prostitution in the Netherlands. Their evidence is that households pay an increasing 
premium to live as far as two kilometers from areas with prostitution. 
6.2.2. Demographic composition 
A critical concern is that the non-monotonic pattern resulted from the sorting of distinct demographic groups after the 
closures. That is, the households driving values down around retailers were different from those affecting values far away. To 
verify this issue, we estimate the share of demographic groups using the same IV model as in Table 3 . For most groups, the 
estimates are not significant. In Table 4 , we present those that are. Table A7 of the Online Appendix presents the remaining 
results. 
114 


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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Table 4 
Estimated Effect of Distance to Retailer on Demographic Composition. 
Final 
Initial distance to retailer (miles) 
distance (miles) 
< 0 . 3 
< 0 . 4 
< 0 . 5 
< 0 . 6 
Log share of 25–39 years old 
< 0 . 4 
−0.515 ∗
−0.466 ∗∗
−0.527 ∗
−0.545 ∗∗
(0.273) 
(0.236) 
(0.275) 
(0.277) 
> 2 . 0 
−0.462 ∗∗
−0.347 ∗∗
−0.394 ∗
−0.361 ∗
(0.222) 
(0.169) 
(0.211) 
(0.201) 
Log share of 60 + years old 
< 0 . 4 
0.514 ∗
0.465 ∗
0.567 ∗
0.590 ∗∗
(0.306) 
(0.251) 
(0.290) 
(0.293) 
> 2 . 0 
0.502 ∗
0.395 ∗∗
0.454 ∗∗
0.411 ∗
(0.260) 
(0.181) 
(0.223) 
(0.214) 
Log share of single-person households 
< 0 . 4 
0.204 ∗∗
0.209 ∗∗
0.288 ∗∗
0.327 ∗∗
(0.095) 
(0.101) 
(0.140) 
(0.155) 
> 2 . 0 
0.165 ∗∗
0.159 ∗∗
0.230 ∗∗
0.236 ∗∗
(0.077) 
(0.070) 
(0.105) 
(0.109) 
Log share of couples with children 
< 0 . 4 
−0.323 ∗
−0.286 ∗
−0.331 ∗∗
−0.341 ∗∗
(0.176) 
(0.150) 
(0.167) 
(0.166) 
> 2 . 0 
−0.288 ∗∗
−0.248 ∗∗
−0.270 ∗∗
−0.250 ∗∗
(0.143) 
(0.105) 
(0.124) 
(0.118) 
Year dummies 
Yes 
Yes 
Yes 
Yes 
Neighborhood fixed effects 
Yes 
Yes 
Yes 
Yes 
Number of observations 
2182 
3226 
4285 
5264 
Number of neighborhood 
151 
211 
280 
338 
This table presents the relationship between changes in the distance to the nearest cannabis retailer and in the log share of certain demographic groups 
in the neighborhood. Results for other groups are presented in Table A7 of the Online Appendix. A neighborhood is defined by its four-digit postcode. 
The distance is represented by two dummies: less than 0.4 miles, and more than two miles; the reference is the group of houses between 0.4 and two 
miles. All regressions include year dummies and neighborhood fixed effects, and the dummies for distance are instrumented by the closures caused by the 
distance-to-school criterion and the zero-tolerance policy. The columns show the results from different samples, based on their initial distance to a retailer. 
The sample excludes neighborhoods in which the nearest retailer was closed for another reason. Clustered robust standard errors are in parentheses. ∗∗∗, 
∗∗, ∗represent statistical significance at the 1%, 5%, and 10% levels, respectively. 
As regards the demographics, adults under 40 and couples with children are the least willing to live either near or far 
away from shops. In contrast, elderly and single people are the most willing to live in these areas. Despite these differences, 
these results indicate that the same demographic groups explain the results in Table 3 . 
7. Additional results 
In this section, we present additional results to help us understand the ambivalent effect of closure policies on house 
prices. First, we show that these policies affect the whole distribution of prices, yet the effect is higher in the lower tail. 
Then we verify whether our main results are explained by changes in housing supply or other amenities, such as bars and 
schools. Finally, we investigate what also happened in terms of employment and crime rates after closures. 
7.1. Estimated effects on the distribution of prices 
From the previous results, one can make inferences on the average capitalization effect of closing retailers ( Kuminoff and 
Pope, 2014 ). This average should lie between former residents’ and future residents’ willingness to pay for coffeeshops (after 
closures), as long as the comparison group is not affected by those policies ( Banzhaf, 2018 ). To shed some light on the 
distribution of tastes, we also estimate the effect on conditional quantiles of house prices, which reveals the house’s risk of 
under- and overvaluation. Details are in Section 11 of the Online Appendix. 
Fig. 4 presents the estimated quantile effects of closures. The left-hand graph shows the effect caused by the DTSC, 
for homes between 0.2 and 0.4 miles. The right-hand graph shows the effect of a ZTP, for homes within 0.3 miles. These 
distance ranges are defined based on the results from Table 2 . In both cases, the effect is higher in the lower tail. 
The DTSC is found to shift the entire distribution of prices, raising all quantiles. Overall, those households agree that 
being 0.13 miles more distant is beneficial. Besides, the higher effect at the lower tail means that a house is less likely to 
be underpriced. That is, the DTSC made it easier to find a buyer. The effect of the ZTP has the opposite pattern. Households 
living within 0.3 miles agree that the loss of coffeeshops is detrimental. Moreover, those houses are more likely to be 
underpriced — i.e., it became harder to find a buyer. 
To complement this analysis, we also estimate the effects on the number of days a house stays on the market. Table A5 
of the Online Appendix displays these estimates. Indeed, they confirm that after the DTSC, houses are sold more quickly. 
The ZTP presents similar effects, but they are not found to be significant. 
115 


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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Fig. 4. Quantile Effects of Closings on House Prices. This figure presents the quantile regression coefficients on the residual of a hedonic model, which 
includes house characteristics, as in Table A1, regional GDP, year dummies, year dummies times a dummy for municipalities that implemented the distance- 
to-school criterion (DTSC), and six-digit postcode fixed effects. See equation (K.1) in the Online Appendix. In the left-hand graph, the sample includes 
properties located between 0.2 and 0.4 miles from the nearest retailer. In the right-hand graph, the sample includes properties located within 0.3 miles 
of the nearest retailer. Both samples exclude houses in which the nearest retailer was closed for another reason. The graphs come from separate quantile 
regressions, which have a simple difference-in-differences specification. The grey area represents the 95% confidence interval of the coefficients. 
Table 5 
Estimated Effect of Closing Policies on Housing Stock. 
Initial distance to retailer (miles) 
< 0 . 3 
< 0 . 4 
< 0 . 6 
< 0 . 8 
< 1 . 0 
< 3 . 0 
Distance-to-school 
−0.011 
−0.016 
−0.012 
−0.012 
−0.011 
−0.001 
(0.014) 
(0.014) 
(0.012) 
(0.012) 
(0.011) 
(0.009) 
Number of observations 
1547 
1781 
2332 
2813 
3313 
5438 
Number of cells 
119 
137 
181 
218 
258 
424 
Zero-tolerance 
−0.007 
0.001 
0.001 
−0.001 
0.000 
−0.006 
(0.011) 
(0.011) 
(0.010) 
(0.008) 
(0.008) 
(0.006) 
Number of observations 
15,525 
20,065 
31,459 
43,711 
56,720 
211,362 
Number of cells 
1196 
1546 
2426 
3373 
4377 
16,383 
Year dummies 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Cell fixed effects 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
This table presents the difference-in-differences (DiD) estimates, as in Eq. (2) , for the log number of homes in a 500m-by-500m cell. Two separate DiDs 
are run using Poisson regressions: one for closures forced by the distance-to-school criterion, and another for closures forced by the zero-tolerance policy. 
Each column uses a different sample, defined by the initial distance of the cell to a retailer. All regressions include year dummies and cell fixed effects. The 
sample excludes cells in which the nearest retailer was closed for another reason. The top panel includes cells only in Rotterdam and Amersfoort. Clustered 
robust standard errors are in parentheses. ∗∗∗, ∗∗, ∗represent statistical significance at the 1%, 5%, and 10% levels, respectively. 
7.2. Housing supply and other amenities 
Other than the effect of the closure policies on housing demand, our main findings could also be explained by changes in 
housing supply and other amenities. Regarding the housing supply, Table 5 shows that the number of houses near cannabis 
shops is not significantly affected. Given the inelastic supply, our results on house prices should be interpreted as shifts in 
housing demand. 
As regards the DTSC, the concern is that the relationship between house prices and distance to schools has changed over 
time. Table A8 of the Online Appendix shows the estimated effect controlling for this time-varying relationship. Indeed, the 
demand for houses closer to primary schools has increased in Rotterdam and Amersfoort. Nevertheless, the estimated effect 
of the closure policy remains nearly the same after controlling for this process. 
Another concern is whether our results were explained by other businesses that tend to cluster around coffeeshops, such 
as bars and nightclubs. Table A9 of the Appendix confirms that the ZTP slightly increased the distance of homes to these 
establishments. This evidence suggests that bars seem to cluster around cannabis retailers. Notwithstanding, the estimated 
effects on house prices do not change after we control for the time-varying distance to bars. 
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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Table 6 
Estimated Effect of Closures on Employment and Crime. 
jobs/ 
soft drug 
hard drug 
property 
inhabitant 
unemployment 
offenses 
offenses 
violence 
crimes 
Distance-to-school 
−0.659 
0.017 
0.048 
−0.238 ∗∗∗
(0.459) 
(0.208) 
(0.073) 
(0.059) 
Zero-tolerance 
−0.039 ∗∗∗
0.001 
−0.054 
0.238 ∗∗∗
−0.097 
−0.125 ∗∗
(0.013) 
(0.001) 
(0.080) 
(0.069) 
(0.140) 
(0.063) 
Regional GDP 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Municipal jobs and unemployment 
No 
No 
Yes 
Yes 
Yes 
Yes 
Year dummies × province 
Yes 
Yes 
Yes 
Yes 
Yes 
Yes 
Year dummies × Rotterdam 
No 
No 
Yes 
Yes 
Yes 
Yes 
Municipality fixed effects 
Yes 
Yes 
No 
No 
No 
No 
Neighborhood fixed effects 
No 
No 
Yes 
Yes 
Yes 
Yes 
No. of observations 
730 
876 
1090 
1150 
1150 
1150 
No. of municipalities/neighborhoods 
73 
73 
109 
115 
115 
115 
with distance-to-school 
0 
0 
32 
32 
32 
32 
with zero-tolerance 
3 
3 
4 
4 
4 
4 
comparison 
69 
69 
80 
80 
80 
80 
This table presents the difference-in-differences (DiD) estimates for labor outcomes and crime. Each regression contains two DiDs: one for closures forced 
by the distance-to-school criterion, and another for closures forced by the zero-tolerance policy. The first two columns show linear regressions using a 
sample of municipalities. The last four columns show Poisson regressions using a sample that combines municipalities and neighborhoods of Rotterdam. 
The samples exclude municipalities that have never had cannabis retailers or in which retailers were closed for another reason. Clustered robust standard 
errors are in parentheses. ∗∗∗, ∗∗, ∗represent statistical significance at the 1%, 5%, and 10% levels, respectively. 
7.3. Change in employment and crimes 
To understand the changes in housing demand, we finally look at other objective outcomes related to employment and 
nuisances. Table 6 presents the average DiD estimates for these outcomes. Fig. 5 shows the estimates per year before and 
after the closures. We use this figure to verify the timing of the effects and whether previous trends explain our results. 
The effects on employment outcomes are at the municipal level, so we only report the effects of the ZTP. The effect 
on crime rates, however, is estimated at the neighborhood level in Rotterdam. Thus, at least for Rotterdam, we compare 
areas where the DTSC closed shops with areas that still have coffeeshops. For other municipalities, crime rates are at the 
municipal level. 
7.3.1. Estimated effects on employment 
In terms of labor market outcomes, the ZTP had no effect on the unemployment rate. This null evidence is consistent 
with other studies that investigate the relationship between access to cannabis and labor outcomes (e.g., van Ours, 2006; 
Sabia and Nguyen, 2018 ). 16 
Although the employment of municipal residents does not change, the number of workers employed in the city gradually 
declines after the ban — see Fig. 5 . Banning cannabis retailers from the jurisdiction reduces by 4 percentage points (or 9%) 
the number of jobs per inhabitant. One explanation for this economic impact is that tight quantity constraints on any 
business should make the economy less efficient ( Buchanan and Tullock, 1975; Gruber, 2011 ). However, we cannot verify 
whether the economic loss is either the cause or the consequence (or both) of a depreciated housing market. 
7.3.2. Estimated effects on drug-related crimes 
Regarding drug consumption, we find that both policies reduced the number of crimes related to soft drugs. This effect, 
though, is not persistent and becomes insignificant four years later. On the other hand, the ZTP has another side effect. After 
closing the last cannabis outlet, crimes related to hard drugs soared by 24%. This effect is also increasing over time, reaching 
over 50% four years after introduction of the policy. The result suggests that legal access to soft drugs is not a gateway, but 
a substitute for hard drugs ( Model, 1993; van Ours, 2003; Chu, 2015 ). 17 Once again, we cannot tell whether the depreciated 
housing market and the declining number of jobs either cause or result from the rise in illicit drug offenses. 
7.3.3. Estimated effects on violence and property crimes 
Neither policy significantly affects violence, but both are found to reduce the number of thefts by at least 12%. Unlike the 
evidence from Chang and Jacobson (2017) , Hunt et al. (2018) , and Brinkman and Mok-Lamme (2019) , this result contradicts 
the “eyes on the street” role of cannabis retailers. Both studies find that marijuana dispensers reduced property crimes in 
16 DeSimone (2002) is one of a few who shows that more access leads to a lower employment rate. See also van Ours and Williams (2015) for a 
comprehensive review. 
17 DiNardo and Lemieux (2001) and Conlin et al. (2005) present a similar pattern regarding alcohol and illicit drugs, and Bradford and Brad- 
ford (2018) present evidence for the substitution between cannabis and prescription drugs. In contrast, Melberg et al. (2010) and Kelly and Ra- 
sul (2014) present evidence for the gateway effect. 
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L.M. Bruijn and R.P. Ribas 
Journal of Economic Behavior and Organization 199 (2022) 103–121 
Fig. 5. Estimated Differences in Employment and Crimes around the Year of Closure. This figure presents the lead and lagged difference-in-differences 
(DiD) on labor outcomes and crime rates. Panel (a) shows the changes before and after cannabis retailers are closed by the distance-to-school criterion 
(DTSC). Panel (b) shows the changes before and after the zero-tolerance policy. The labor outcomes (the first two graphs in each panel) are estimated by 
a linear regression using a sample of municipalities. Crime rates are estimated by a Poisson regression using a sample that combines municipalities and 
neighborhoods of Rotterdam. The samples exclude municipalities that have never had cannabis retailers or in which retailers were closed for another reason. 
Robust standard errors are clustered by municipality. Vertical lines around the point estimates represent 90% and 95% confidence intervals, respectively. 
118