‘High’ Achievers? Cannabis Access and Academic Performance Olivier Marie Ulf Zölitz Maastricht University IZA and Maastricht University RAND

‘High’ Achievers?Cannabis Access and Academic Performance

Olivier Marie Ulf Zölitz Maastricht University IZA and Maastricht University

RAND

Cannabis Access and Academic Performance 2

Motivation – Wind of Change in Drug Policies

Public opinion has reached a tipping point

Cannabis Access and Academic Performance



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…




Research Question

Does drug policy affect student achievement via a change in consumption behavior?


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?


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”



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”



Crean et al. (2011)

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3037578/table/T2/


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)


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


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


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


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


Belgium

Germany

Coffee-shop density


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)



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


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


Month - YearCannabis

AccessAcademic

YearAcademic

Period Total t

September-2009

All Access

2009/2010

1 1October-09November-09

2 2December-09January-10

3 3February-10March-10

4 4April-10May-10

5 5June-10July-10

6 6August-10September-10

2010/2011

1 7October-10November-10

2 8December-10January-11


4 10April-11May-11

5 11June-11July-11

6 12August-11September-11

2011/2012

1 13October-11

DGBOnly

November-112 14

December-11January-12


4 16April-12May-12

AllRestricted

5 17June-12July-12

6 18August-12


Grade Distribution before and after the Policy0

.1.2

.30

.1.2

.3

0 1 2 3 4 5 6 7 8 9 10

Grade Distribution Before Access Restriction

Grade Distribution After Access Restriction

Dens

ity

Course Grades

0.1

.2.3

0.1

.2.3

0 1 2 3 4 5 6 7 8 9 10

DGB Grade Distribution

Non-DGB Grade Distribution

Dens

ity

Course Grade


Empirical Strategy: Basic Diff-in-Diff

Adopt simple difference in differences approach to identify the causal effect of restricting cannabis access on performance

𝐶𝑜𝑛𝑡𝑟𝑜𝑙

𝑇𝑟𝑒𝑎𝑡

all access time

Let’s start by looking at this graphically. Common trends?

Other Nationalities: Non-DGB

No access

Have accessHave access

Have access


5.4

5.6

5.8

6

6.1

6.2

6.3

6.4

6.5

6.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Academic Period

DGB (Left Axis) All Other (Right Axis)

Graphical Analysis

No access

Have access


Empirical Strategy: Enhanced Diff-in-Diff

Econometrically, the basic diff-in-diff coefficient is β:

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


Impact of Restricted Cannabis Access on Student Grades

(1) (2) (3) (4) (5)

Std. Grade Std. Grade Std. Grade Std. Grade Std. Grade

No-access nationality * Restriction time periods 0.0595* 0.0580* 0.0709* 0.1044** 0.0926**

(0.025) (0.024) (0.029) (0.018) (0.016)

No-access nationality -0.2666* -0.2713** -0.2597*

(0.090) (0.088) (0.100)

Restriction time periods 0.0260 0.0270* 0.0423** -0.0108 0.0161

(0.013) (0.013) (0.014) (0.007) (0.019)

Observations 52,424 52,424 52,424 52,424 52,424 Number of students 4,314 4,314 4,314 4,314 4,314

R-squared 0.004 0.008 0.157 0.545 0.546 Individual Controls No Yes Yes Yes Yes Course Number & Course FE No No Yes Yes Yes Student FE No No No Yes Yes Period Dummies and Time Trend No No No No Yes

Note: Robust standard errors clustered at the nationality level reported in parenthesis.

*, and ** indicate significance at the 5 and 1 percent level respectively.


Restricted Cannabis Access: Other outcomes

(1) (2) (3) (4)

Std. grade Passed Dropout # Courses

No-access nationality * Restriction time periods 0.0926** 0.0400** -0.0109 0.0463*

(0.016) (0.008) (0.010) (0.020)

Restriction time periods 0.0161 0.0131** -0.0123** -0.0129

(0.019) (0.003) (0.004) (0.008)

Mean of Outcome NA 0,746 0,142 2,033 Effect size NA 0,054 -0,077 0,023 Observations 52,424 52,424 57,816 57,816 R-squared 0.546 0.373 0.366 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.


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?


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?


Results by Sub-Groups

Subgroup: Coefficient on Grade Coefficient on Passing

Average passing rate

Number of observations

Female 0.1257** 0.0457** 81.69 20,380

(0.031) (0.012)

Male 0.0692** 0.0356** 77.35 37,436

(0.014) (0.009)

Younger students 0.1160** 0.0571** 77.93 28,941

(0.028) (0.014)

Older students 0.0240 0.0050 79.92 28,875

(0.031) (0.015)

Lower Performers 0.0890** 0.0472* 62.20 27,001

(0.032) (0.017)

Higher Performers 0.0555** -0.0096 94.53 26,985 (0.017) (0.009)




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) (3) (4)

Placebo policy - 1 year earlier

Placebo nationality - Belgians are treated

Std. Grade Passed course Std. Grade Passed course

Placebo Policy Effect -0.0129 -0.0004

0.0103 0.0284

(0.030) (0.013)

(0.048) (0.022)

Observations 34,325 34,325

48,762 48,762 R-squared 0.567 0.393

0.542 0.366

Same Controls and FE 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.


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?


Channels – Numerical vs. Non-numerical Skills

Medical literature shows that marijuana consumption harms numerical skills Are our results consistent with these findings?

(1) (2) (3) (4) Grades -

Non-Numerical

Pass - Non-

Numerical Grades -

Numerical Pass -

Numerical

No-access nationality * Restriction time periods 0.0426** 0.0231** 0.2284** 0.0733**

(0.016) (0.007) (0.028) (0.010)

Restriction time periods 0.0329 0.0024 -0.1828** -0.0554**

(0.022) (0.006) (0.029) (0.020)

Mean of Outcome NA 0.794 NA 0.663 Effect size NA 0.029 NA 0.110 All Controls and FE Yes Yes Yes Yes Observations 34,347 34,347 18,077 18,077 R-squared 0.533 0.375 0.672 0.505




Channels – Changes in Student Course Evaluations

Mechanism CategoryNon-DGB

*RestrictionSurvey 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?

FeelStimulated[N = 15,937]

0.087(0.059)

‘The learning materials stimulated me to start and keep on studying’ & ‘…stimulated discussion with my fellow students.’

FunctionsWell[N = 15,997]

0.032(0.064)

‘overall functioning of your tutor…’ &‘My tutorial group has functioned well.’

UnderstandBetter[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’

QualityImproved[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´


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?


Peer effects? – Effect of Fraction Treated in Section

(1) (2) (3)

Std. grade Pass Dropout

No-access nationality * Restriction time periods * Share no-access nationality 0.1670 0.2109* 0.0128

(0.133) (0.080) (0.084)

No-access nationality * Restriction time periods 0.0772** 0.0214* -0.0122

(0.020) (0.010) (0.009)

Restriction time periods 0.0144 0.0159** -0.0073

(0.029) (0.002) (0.006)

Restriction time periods * Share no-access nationality 0.0370 -0.0153 -0.0574

(0.125) (0.030) (0.032)

Share of no-access nationality in class -0.0017 -0.0066 0.0080

(0.068) (0.027) (0.015)

Observations 52,395 52,395 57,782

R-squared 0.546 0.373 0.366

Individual Controls Yes Yes Yes

Course Number & Course FE Yes Yes Yes

Student FE Yes Yes Yes

Period Dummies and Time Trend 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.


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.




5.4

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6

6.1

6.2

6.3

6.4

6.5

6.6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17Academic Period

DGB (Left Axis) All Other (Right Axis)

Graphical Analysis

No access

Have access


Differences in observables between DGB and non-DGB students

Student nationality All DGB Non-DGB Difference Min Max

Grade 6.5355 6.5688 6.0996 -.4693*** 1 10Passed course .7892 .7959 .7010 -.0949*** 0 1Course dropout .0935 .0906 .1296 .0390*** 0 1Observations 57903 53622 4281

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

Observations 4419 4083 336

Documents

‘High’ Achievers? Cannabis Access and Academic Performance Olivier Marie Ulf Zölitz Maastricht University IZA and Maastricht University RAND