‘High’ Achievers?
Cannabis Access and Academic Performance
Olivier Marie
Ulf Zölitz
Maastricht University
IZA and Maastricht University
RAND
Motivation – Wind of Change in Drug Policies
 Public opinion has reached a tipping point
Cannabis Access and Academic Performance
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Cannabis Access and Academic Performance
Motivation – Wind of Change in Drug Policies
Public policies regarding marijuana/cannabis are changing now
 US: radical recent change in public opinion and policy:
 Colorado & Washington have legalized in 2014
 Alaska, Oregon and Washington, D.C voted in favour of legalization
 California might legalize in 2016
 Uruguay is the first country that fully legalizes marijuana in 2014
 Europe: decriminalisation in Czech Republic, Portugal, Norway…
Cannabis Access and Academic Performance
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Motivation – Wind of Change in Drug Policies
Cannabis Access and Academic Performance
5
Research Question
Does drug policy affect student achievement
via a change in consumption behavior?
Cannabis Access and Academic Performance
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Motivation – The Arguments
Pros:
+ Legalization cuts the link to illegal markets
+ The war on drugs is a loosing battle:
Legalization will reduce crime & cut costs of the legal system
Cons:
− Easy availability will increase demand
− Marijuana is a gateway drug
− Negative externalities for society
 Does legal access affect consumption?
 What are the (unintended) consequences of legalization?
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Medical Evidence: Cannabis Use and Cognitive Functioning
Bossong et al. (2013) experiment with THC admission in fMRIs:
 Subjects had to recall whether they had seen information before
Conclusion: “THC impairs performance on high-level cognitive
functions essential for goal-directed behavior”
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Medical Evidence: Cannabis Use and Cognitive Functioning
 Gilman et al. (2014) look at young, non-dependent marijuana users
and find that recreational use is associated with brain abnormalities:
 abnormalities in gray matter density and volume
 abnormalities in the shape of the nucleus accumbens and amygdala
 Ranganathan & da Souza (2006)
 Review of medical evidence on the effects of THC:
“THC…impairs immediate and delayed free recall of information
presented after, but not before, drug administration”
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Medical Evidence: Cannabis Use and Cognitive Functioning
Crean et al. (2011)
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Related Economics Literature
Pacula et al. (2003)
 10th grade marijuana use is associated with a 15% reduction in std. math test
performance in 12th grade
Chatterji (2006)
 Adolescent marijuana consumption is related to 0.2 years less schooling
Cobb-Clark et al. (2013)
 Early marijuana use (age 14) predicts 8% lower high school completion rates
and lower university entrance scores
Van Ours (2011 & 2014)
 Individuals who grow up closer to cannabis-shops start smoking earlier
 Using cannabis increases the likelihood of mental health problems (onset ages)
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The Causality Problem
The main problem is to identify the causal direction in the
relationship between drug use and outcomes:
 Individual consumption decision driven by unobserved factors
that also affect outcomes
 Policy changes are a result of general societal change
(Pacula & Sevigny; 2014)
 Truthful reporting of consumption is also affected by legal status
and changes
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Contribution
 We study a unique policy experiment that suddenly restricted
legal cannabis access based on nationality
 We and observe student performance before and after
 We have clean identification using a difference in differences
approach
 We estimate the causal impact of a change in soft drug access
on an educational outcome
 We can provide some additional evidence on the underlying
mechanisms and how soft drug use affects performance
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A Reduced Form Approach
 We have a reduced form approach as we do not directly
observe smoking behavior of students but only their test scores
 Reduced form is informative since policy changes also affect
the probability to admit consumption in questionnaire studies
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Drug Policy in the Netherlands
 Basis of Dutch ‘tolerance’ policy is the Opium Law (1976)
introduced to “minimize harm done to users and their
environment”
 Possession and retail of small quantities (< 5g) of cannabis are
legal but large scale cultivation or wholesale remains illegal
 Legal access exclusively via licensed ‘coffie-shops’ which
must follow number of strict rules: no sales to ages <18;
no advertising; etc.
 25% of the Dutch municipalities have decided to allow
licensed cannabis-shops
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Germany
Coffee-shop density
Belgium
Cannabis Access and Academic Performance
The Maastricht Policy Change
 Proximity to borders attracted a lot of ‘drug tourists’
 The City was very concerned about the drug tourism, street
dealing and negative externalities for the city residents
 To respond to city concerns, the coffie-shop union (VOCM)
proposed to only allow Dutch, German, and Belgian (DGB)
passport holders
 As of October 1st, 2011 the ‘neighborhood country criterion’
was introduced in all establishments selling cannabis in
Maastricht
 Volume of sales before: € 100 million per year (with 40% city taxes)
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Cannabis Access and Academic Performance
Potential Effects of the Policy?
 Intended effect: stop drug tourism and reduce street dealing
 Indirect effect: increase in access costs for non-DGB residents
and reduces consumption of marginal consumer
 Although not targeted by the policy many of the 16,000
Maastricht University students were affected.
 We do not directly observe smoking behavior of students but we have very
good panel data on potentially affected educational outcomes:
grades, course passing, course dropout
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Data: Course Grades and Student Course Evaluations
 All students taking Bachelor courses in the School of Business
and Economics over 3 academic years: 2009/10 to 2011/12
 Observe over 58,000 course grades for 4,800 students with
53% German, 33% Dutch, 4% Belgian and 10% Non-DGB
 Teaching structure in all years is composed of 6 blocks:
4 regular blocks (2 months) + 2 skills blocks (2 weeks)
 Additionally we make use of student course evaluation surveys
to look at the underlying mechanisms
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Month - Year
September-2009
October-09
November-09
December-09
January-10
February-10
March-10
April-10
May-10
June-10
July-10
August-10
September-10
October-10
November-10
December-10
January-11
February-11
March-11
April-11
May-11
June-11
July-11
August-11
September-11
October-11
November-11
December-11
January-12
February-12
March-12
April-12
May-12
June-12
July-12
August-12
Cannabis
Access
Academic
Year
2009/
2010
All
Access
2010/
2011
DGB
Only
All
Restricted
2011/
2012
Cannabis Access and Academic Performance
Academic
Period
Total t
1
1
2
2
3
3
4
4
5
5
6
6
1
7
2
8
3
9
4
10
5
11
6
12
1
13
2
14
3
15
4
16
5
17
6
18
Grade Distribution before and after the Policy
.2
.1
0
Non-DGB Grade Distribution
.2
.1
0
0
.1
.2
.3
Grade Distribution After Access Restriction
.3
0
Density
.1
.2
.3
DGB Grade Distribution
.3
Grade Distribution Before Access Restriction
0
1
2
3
4
5
6
7
8
9
10
Course Grades
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0
1
2
3
4
5
6
7
8
9
10
Course Grade
22
Empirical Strategy: Basic Diff-in-Diff
 Adopt simple difference in differences approach to identify the
causal effect of restricting cannabis access on performance
𝐶𝑜𝑛𝑡𝑟𝑜𝑙
Have access
𝑇𝑟𝑒𝑎𝑡
Have access
Have access
Other Nationalities:
Non-DGB
T=0
all access time
No access
T=1
𝑝𝑟𝑜ℎ𝑖𝑏𝑖𝑡𝑖𝑜𝑛 𝑡𝑖𝑚𝑒
 Let’s start by looking at this graphically. Common trends?
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6.6
6
Graphical Analysis
5.4
6.1
6.2
Have access
5.6
6.3
6.4
5.8
6.5
No access
1
2
3
4
5
6
7 8 9 10 11 12 13 14 15 16 17
Academic Period
DGB (Left Axis)
Cannabis Access and Academic Performance
All Other (Right Axis)
24
Empirical Strategy: Enhanced Diff-in-Diff
Econometrically, the basic diff-in-diff coefficient is β:
𝑌𝑖𝑡 = α + 𝛽(𝑁𝑜𝑛𝐷𝐺𝐵𝑖 ∗ 𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑖𝑜𝑛𝑡 ) + 𝛾𝑁𝑜𝑛𝐷𝐺𝐵𝑖 + 𝛿𝑅𝑒𝑠𝑡𝑟𝑖𝑐𝑡𝑖𝑜𝑛𝑡 + 𝜀𝑖𝑡
 Add observable individual characteristics:
age, gender
 For heterogeneity in course choice:
# courses and course FE
 Major problem of individual heterogeneity:
individual FE (!)
 Finally control for temporal cyclicality:
period FE & time trends
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Impact of Restricted Cannabis Access on Student Grades
No-access nationality * Restriction time periods
No-access nationality
Restriction time periods
Observations
Number of students
R-squared
Individual Controls
Course Number & Course FE
Student FE
Period Dummies and Time Trend
(1)
Std. Grade
(2)
Std. Grade
(3)
Std. Grade
(4)
Std. Grade
(5)
Std. Grade
0.0595*
(0.025)
-0.2666*
(0.090)
0.0260
(0.013)
0.0580*
(0.024)
-0.2713**
(0.088)
0.0270*
(0.013)
0.0709*
(0.029)
-0.2597*
(0.100)
0.0423**
(0.014)
0.1044**
(0.018)
0.0926**
(0.016)
-0.0108
(0.007)
0.0161
(0.019)
52,424
4,314
0.004
No
No
No
No
52,424
4,314
0.008
Yes
No
No
No
52,424
4,314
0.157
Yes
Yes
No
No
52,424
4,314
0.545
Yes
Yes
Yes
No
52,424
4,314
0.546
Yes
Yes
Yes
Yes
Note: Robust standard errors clustered at the nationality level reported in parenthesis.
*, and ** indicate significance at the 5 and 1 percent level respectively.
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Restricted Cannabis Access: Other outcomes
No-access nationality * Restriction time periods
Restriction time periods
Mean of Outcome
Effect size
Observations
R-squared
(1)
Std. grade
(2)
Passed
(3)
Dropout
(4)
# Courses
0.0926**
(0.016)
0.0161
(0.019)
0.0400**
(0.008)
0.0131**
(0.003)
-0.0109
(0.010)
-0.0123**
(0.004)
0.0463*
(0.020)
-0.0129
(0.008)
NA
NA
52,424
0.546
0,746
0,054
52,424
0.373
0,142
-0,077
57,816
0.366
2,033
0,023
57,816
0.616
Note: Additional controls are age gender, number of courses enrolled in, Course FE, Student FE, Teaching period dummies
and time trends. Robust standard errors clustered at the nationality level reported in parenthesis.
* and ** indicate significance at the 5 and 1 percent level respectively.
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Main Results - Interpretation
 The cannabis prohibition raised grades by 0.09 std. deviations
 The treatment effect on the treated depends on the fraction of
treated consumers in the underlying population
 To get some idea on baseline consumption rates we ran a survey
‒ Non-DGBs who smoked marijuana in past 7 or 30 days: 26 %
‒ Treatment effect on smokers: 0.09 / 0.26 = .35 SD in grades
 These are large effects! Teachers 0.13-0.19 | Peers 0.01-0.19
 Other reduced form:
Alcohol: 0.03-0.13 | Marijuana: 0.09
 Do students comply with the law / report honestly?
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Further Results
 Are effects heterogeneous across subgroups?
Differences in consumption propensity and policy compliance?
 What are the mechanisms?
How exactly does consumption affect university performance?
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Results by Sub-Groups
Subgroup:
Female
Male
Younger students
Older students
Lower Performers
Higher Performers
Average
passing rate
Number of
observations
0.0457**
(0.012)
0.0356**
(0.009)
81.69
20,380
77.35
37,436
0.1160**
(0.028)
0.0240
(0.031)
0.0571**
(0.014)
0.0050
(0.015)
77.93
28,941
79.92
28,875
0.0890**
(0.032)
0.0555**
(0.017)
0.0472*
(0.017)
-0.0096
(0.009)
62.20
27,001
94.53
26,985
Coefficient on Grade
Coefficient on Passing
0.1257**
(0.031)
0.0692**
(0.014)
Note: Robust standard errors clustered at the nationality level reported in parenthesis.
*, and ** indicate significance at the 5 and 1 percent level respectively.
Cannabis Access and Academic Performance
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Robustness Analysis: Placebo Treatments
1) Placebo in time: Is there a similar effect one year before the actual policy?
2) Placebo in nationality: Effects if we pretend Belgians instead of Non-DGB treated?
(1)
(2)
Placebo policy - 1 year earlier
Std. Grade
Passed course
(3)
(4)
Placebo nationality - Belgians are treated
Std. Grade
Passed course
Placebo Policy Effect
-0.0129
(0.030)
-0.0004
(0.013)
0.0103
(0.048)
0.0284
(0.022)
Observations
R-squared
Same Controls and FE
34,325
0.567
Yes
34,325
0.393
Yes
48,762
0.542
Yes
48,762
0.366
Yes
Note: Robust standard errors clustered at the nationality level reported in parenthesis.
*, and ** indicate significance at the 5 and 1 percent level respectively.
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Evidence on the Underlying Channels
 The evidence from the medical literature suggests that in
particular numerical problem solving skills are affected.
 Can we confirm that?
 What are the effects stronger for more mathematical / theoretical courses?
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Channels – Numerical vs. Non-numerical Skills
Medical literature shows that marijuana consumption harms numerical skills
 Are our results consistent with these findings?
No-access nationality * Restriction
time periods
Restriction time periods
Mean of Outcome
Effect size
All Controls and FE
Observations
R-squared
(1)
Grades NonNumerical
(2)
Pass NonNumerical
(3)
(4)
Grades Numerical
Pass Numerical
0.0426**
(0.016)
0.0329
(0.022)
0.0231**
(0.007)
0.0024
(0.006)
0.2284**
(0.028)
-0.1828**
(0.029)
0.0733**
(0.010)
-0.0554**
(0.020)
NA
NA
Yes
34,347
0.533
0.794
0.029
Yes
34,347
0.375
NA
NA
Yes
18,077
0.672
0.663
0.110
Yes
18,077
0.505
Note: Robust standard errors clustered at the nationality level reported in parenthesis.
*, and ** indicate significance at the 5 and 1 percent level respectively.
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Channels – Changes in Student Course Evaluations
Mechanism Category
Non-DGB
*Restriction
Survey Question(s)
in Course Evaluation
Hours
Worked
[N = 15,987]
-0.244
(0.376)
How many hours per week on average did you spend on self-study?
Feel
Stimulated
[N = 15,937]
0.087
(0.059)
‘The learning materials stimulated me to start and keep on studying’ &
‘…stimulated discussion with my fellow students.’
Functions
Well
[N = 15,997]
0.032
(0.064)
‘overall functioning of your tutor…’ &‘My tutorial group has functioned well.’
Understand
Better
[N = 13,520]
0.122*
(0.064)
‘The lectures contributed to a better understanding…’ & ‘Working in tutorial
groups helped me to better understand the subject matters of this course’
Quality
Improved
[N = 15,897]
0.017
(0.061)
‘The tutor sufficiently mastered the course content of this course’ & ‘give
overall grade for the quality of this course´
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Are there spill overs in the classroom?
 Given the large effect, can we identify classroom spillovers?
 Does being in class with a high share of treated increase
grades?
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Peer effects? – Effect of Fraction Treated in Section
No-access nationality * Restriction time periods * Share no-access nationality
No-access nationality * Restriction time periods
Restriction time periods
Restriction time periods * Share no-access nationality
Share of no-access nationality in class
Observations
R-squared
Individual Controls
Course Number & Course FE
Student FE
Period Dummies and Time Trend
(1)
Std. grade
(2)
Pass
(3)
Dropout
0.1670
(0.133)
0.0772**
(0.020)
0.0144
(0.029)
0.0370
(0.125)
-0.0017
(0.068)
0.2109*
(0.080)
0.0214*
(0.010)
0.0159**
(0.002)
-0.0153
(0.030)
-0.0066
(0.027)
0.0128
(0.084)
-0.0122
(0.009)
-0.0073
(0.006)
-0.0574
(0.032)
0.0080
(0.015)
52,395
0.546
Yes
Yes
Yes
Yes
52,395
0.373
Yes
Yes
Yes
Yes
57,782
0.366
Yes
Yes
Yes
Yes
Note: Robust standard errors clustered at the nationality level reported in parenthesis.
*, and ** indicate significance at the 5 and 1 percent level respectively.
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Conclusion
 To our knowledge first causal evidence on how drug policy
affects performance
 Results are only a part of what has to be considered in societal
cost-benefit analysis of drug policies
 Effects are perhaps not symmetric for prohibition and
legalization
 We provide solid evidence that restricting legal access to
cannabis increases university performance
 In line with clinical evidence the channels point to improved
understanding and improved numerical skills
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End of slideshow, click to exit.
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Motivation – Wind of Change in Drug Policies
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39
6.6
6
Graphical Analysis
5.4
6.1
6.2
Have access
5.6
6.3
6.4
5.8
6.5
No access
1
2
3
4
5
6
7 8 9 10 11 12 13 14 15 16 17
Academic Period
DGB (Left Axis)
Cannabis Access and Academic Performance
All Other (Right Axis)
40
Differences in observables between DGB and non-DGB students
Student nationality
All
DGB
Non-DGB
Difference
Min
Max
Female
.3526
.3480
.4077
.0446***
0
1
Age
20.27
20.29
20.21
.1002***
16.24
39.73
Final GPA
6.5656
6.6142
5.9770
-.5288***
1
9.75
Courses enrolled
1.9927
1.9876
2.0556
-.06794***
1
5
4419
4083
336
All
DGB
Non-DGB
Difference
Min
Max
6.5355
.7892
.0935
57903
6.5688
.7959
.0906
53622
6.0996
.7010
.1296
4281
-.4693***
-.0949***
.0390***
1
0
0
10
1
1
Observations
Student nationality
Grade
Passed course
Course dropout
Observations
Cannabis Access and Academic Performance
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