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Local Access to Recreational Marijuana and Youth Substance Use

Christopher A. Ambrose*

Abstract: I examine whether substance use outcomes for Washington youth react to changes in

local access to marijuana retailers after the implementation of retail sales to adults 21 and older

under Initiative 502. Using Healthy Youth Survey data on 8th, 10th, and 12th graders from 2014-

2018, I do not find evidence that proximity to marijuana retailers affects Washington youth as a

whole. However, higher-risk students, such as those who reported binge drinking in the past two

weeks, experience an increase in past-month marijuana use as retailers get closer to their schools.

JEL classifications: I12, I18, I10

Keywords: recreational marijuana legalization, accessibility, substance use

* Ph.D. candidate, School of Economic Sciences, Washington State University, Pullman, WA, 99164-6210. Email:

christopher.ambrose@wsu.edu. Phone: 509-335-5556.

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1. INTRODUCTION

One of the core concerns about marijuana legalization is not just how it may affect those who are

allowed to access and use the drug, but those who are not yet legally eligible to do so, namely,

adolescents. Since 2012, eleven states and the District of Columbia have legalized marijuana for

adults, with most places allowing for retail sales, as well as growing plants at home. In this

paper, I examine youth substance use outcomes for one of the first states to legalize marijuana

for recreational use with retail sales provisions—Washington State. Washington passed its

recreational marijuana law (RML), Initiative 502 (I-502) in November 2012 and retail sales

began in July 2014.

Since adults over 21 are allowed to possess, purchase, and use marijuana under

Washington’s RML, there may be spillovers to youth. On one hand, adults’ greater access to

marijuana via retail stores may make it easier for minors to obtain marijuana from adults, while

on the other hand, the development of the legal market could effectively price drug dealers out of

the market. Since marijuana retailers verify a customer’s age, the local presence of retail sales

could actually diminish accessibility for youth and consequently, youth marijuana use may

decline.

Previous research indicates that adult marijuana use in Washington increases as the

distance to active marijuana retailers decreases (Ambrose, Cowan, & Rosenman, 2020; Everson,

Dilley, Maher, & Mack, 2019), while youth marijuana use and perceived ease of access in

Colorado is not affected by proximity to recreational marijuana shops (Harpin, Brooks-Russell,

Ma, James, & Levinson, 2018). However, Harpin et al. (2018) find that the perceived ease of

access to marijuana increased for youth throughout the period of the Colorado retail market’s

implementation. In the Netherlands, Palali and van Ours (2015) find that growing up closer to

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marijuana retailers reduces the age of onset of marijuana use. While Harpin et al. (2018)

examine the effect of distance from schools to marijuana retailers on youth in Colorado, there are

several issues with their approach. First, they do not control for the heterogeneity between

schools as well as grades within schools (nor demographics), which may bias their results.1

Second, they only account for approximately one year after retail sales began in Colorado, which

does not allow much time for youth to adapt to the presence of marijuana retailers. Third, they

do not indicate that they use network distance to make their explanatory variable of interest

(whether a marijuana retailer is within two miles of school); thus, it is likely that they use

straight-line distance, which is less realistic because travel does not occur in a straight line.

Beyond recreational marijuana retailers, studies examining the impact of distance to

medical marijuana dispensaries on adolescents find no effect on past-month marijuana use (Shi,

2016; Shi, Cummins, & Zhu, 2018). However, I believe that RMLs and medical marijuana laws

(MMLs) are sufficiently different policies to warrant examining them separately (to obtain

marijuana under MML, patients must acquire a medical marijuana authorization, which requires

that patients have a qualifying condition and a healthcare practitioner’s approval).2 Although

previous papers have examined distance to marijuana retailers on youth, I am unaware of any

prior studies examining the effects of recreational marijuana retailer proximity on adolescents in

1 They do not account for the fact that schools in areas that are more permissive of marijuana are likely to be

different (in terms of use and attitudes toward marijuana) than schools in more conservative areas. Also, different

grade levels tend to have different rates of use and attitudes toward the drug (see e.g., Miech et al., 2019). While

Harpin et al. (2018) control for school type (middle school vs. high school), this does not completely account for the

differences between each grade level. 2 In Washington, medical and recreational marijuana dispensaries were separate until the consolidation of the two

markets in July 2016; prior to this, medical dispensaries were largely unregulated (Dilley et al., 2017). In other

states, such as Colorado (Harpin et al., 2018), only medical dispensaries acquired licenses to sell recreational

marijuana for a period before the state licensed recreational retailers. Also, see

(https://www.doh.wa.gov/YouandYourFamily/Marijuana/MedicalMarijuana/AuthorizationDatabase/DataandStatisti

cs). The webpage shows that the number of medical marijuana patients under 18 is a small fraction of Washington

medical marijuana patients (approximately 1.1% of all recognition cards as of October 12, 2020). However, since

the patient registry did not exist in Washington prior to July 2016, I do not know how many minors had medical

marijuana authorizations prior to then.

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Washington State.3 Since each state has its own policies regulating recreational marijuana

retailers (and jurisdictions within states may have their own regulations), it is important to

examine states individually to account for heterogeneity of RMLs.

Several recent studies have examined marijuana use for Washington youth after retail

sales began (Dilley et al., 2018; Fleming, Guttmannova, Cambron, Rhew, & Oesterle, 2016;

Johnson et al., 2019), for working Washington youth (Graves et al., 2019), and for misreporting

of use (Murphy & Rosenman, 2019); similarly, a study on marijuana use of Colorado youth was

performed (Brooks-Russell et al., 2019), but these studies do not directly account for changes in

distance to marijuana retailers in their analyses.4 I contribute to the literature by examining the

effect of distance to recreational marijuana retailers on substance use outcomes for Washington

adolescents during the first four years of retail sales. My distance measure is in the form of

network travel time—the driving time that results from following the road network. In order to

account for regional heterogeneity of marijuana use and whether it is accepted in a community, I

examine within-school variation in distance over time.

From the 2014, 2016, and 2018 Healthy Youth Survey (HYS; Washington State

Department of Health et al., 2019), I create geographic information systems (GIS) measures of

the travel time from school to the nearest operating marijuana retailer, as well as measures of

local retail density to examine if marijuana, alcohol, and tobacco outcomes for 8th, 10th, and 12th

3 While preparing this manuscript, I became aware of a paper focusing on youths’ attitudes toward marijuana in

relation to distance to recreational marijuana retailers, the number of retailers nearby, and marijuana advertising

exposure in Washington (Hust, Willoughby, Li, & Couto, 2020). That paper mainly focuses on intentions to use

marijuana in the future (with no specific timeline), while I primarily focus on actual marijuana use. Also, that paper

uses perceived distance to retailers for 350 adolescents in June 2018, while I use changes in measured distance over

time with a much larger survey. In addition, Hust et al. (2020) uses the number of retailers within a respondent’s

ZIP code to account for retail density, while I use the number of retailers within various thresholds of distance

(which are not confined to specific jurisdictions). 4 Brooks-Russell et al. (2019) include a measure for whether a city or county allowed marijuana retailers, however

this is a much coarser measure than the distance to the nearest active retailer.

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graders are affected. For most of my chosen outcomes, I do not find evidence that proximity to

marijuana sellers has an effect on Washington youth; however, I observe a small reduction in

binge drinking among 12th graders and electronic cigarette use among 8th graders and all grades

combined as stores get closer to schools. Nevertheless, it is possible that these results are

manifestations of a type I error: given an alpha value of 0.05 and 36 specifications, I would

expect approximately two false positives.

I also find limited evidence of a small effect of the number of proximate marijuana

retailers on my selected outcomes. These effects are mostly concentrated amongst 8th graders,

who are the least likely to report consuming substances. In addition to my main sample results, I

examine marijuana use outcomes for students who reported binge drinking in the past two

weeks, following Kerr, Bae, Phibbs, and Kern (2017). The authors find that binge-drinking

college students in Oregon report increases in marijuana use following the implementation of

Oregon’s RML. I find a negative and significant association (at the five percent level or better)

for all three grades combined, as well as 10th and 12th graders alone. For example, a 33%

reduction in travel time leads to a 0.87 percentage point increase in marijuana use in the past

month for binge-drinking students in all grades (an increase of 1.3% relative to the mean). I also

stratify the data using a risk index. Once again, I find that higher-risk 12th graders use marijuana

more as retailers get closer to schools.

2. BACKGROUND

In order to consider how youth may obtain marijuana after the implementation of retail sales

under I-502, I must first examine how the law provides access to adults. Of the eleven states that

have passed RMLs (plus the District of Columbia), only Washington and Illinois make it illegal

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for recreational consumers (i.e., not medical marijuana patients) to grow marijuana at home.5

Therefore, licensed retail outlets are the only places in Washington that adults may legally obtain

marijuana. Since it is illegal for people under 21 to obtain marijuana without a medical

authorization and retailers are punished heavily for selling to minors,6 a “shoulder tap” scenario

could arise in which youth proposition adults to buy marijuana at a retail store for them, as with

minors trying to obtain alcohol from adults.7

Thomas (2019) discusses how the state distributed retail licenses, which consisted of first

determining the number of licenses to allot to each county (based on a proxy for distance to the

nearest store within a county) and then determining the number of licenses to allot to cities

within each county (based on the proportion of the county population).8 Both state and local law

regulate where marijuana retailers may locate, especially in relation to where minors are likely to

be. As discussed in Ambrose et al. (2020), RCW 69.50.331(8) prohibits retailers from locating

within 1000 feet of elementary and secondary schools, child care centers, playgrounds,

recreation centers, libraries, public parks, public transit centers, and arcades that allow minors.9

I-502 allows cities and counties to reduce the number of retail licenses below the state’s

5 In Washington, it is a class C felony for adults to cultivate any amount of marijuana if they do not have a medical

marijuana authorization (excluding licensed I-502 producers) per the Revised Code of Washington (RCW)

69.50.401. 6 Prior to July 28, 2019, employees at marijuana retailers could be charged with a felony for selling marijuana to a

minor, however HB 1792 reduced this to a gross misdemeanor (excluding cases in which the employee knows that

the person they are selling to is under 21). For more information, see

(https://app.leg.wa.gov/billsummary?BillNumber=1792&Year=2019&Initiative=False) and RCW 69.50.475. 7 The penalties for an adult furnishing marijuana to someone under 21 are a class C felony (or B felony in certain

circumstances), per RCW 69.50.4013 and RCW 69.50.406(2). The penalty for providing alcohol to a minor (or

allowing its consumption on his or her premises) is a gross misdemeanor (RCW 66.44.270). There are some

exceptions to the rule for providing alcohol to a minor (e.g., parents/guardians may legally provide it to their

children if they supervise), however there are no exceptions to the law for providing marijuana to a minor (excluding

patients under 21 with a medical marijuana authorization). 8 For more details on retail license allocations, see Thomas (2019) and Caulkins and Dahlkemper (2013). 9 Washington Administrative Code (WAC) 314-55-050 mandates that this is the minimum straight-line distance

from the retailer’s property. Local governments may relax this requirement to 100 feet but not for playgrounds and

schools (Municipal Research and Services Center [MRSC], 2019).

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allocation or ban marijuana businesses, which means that there is substantial variation in

accessibility to marijuana retailers across the state (Dilley, Hitchcock, McGroder, Greto, &

Richardson, 2017).10 In addition, local zoning ordinances may be enacted to require shops to be

in a specific part of town, such as an industrial district (MRSC, 2019).

Among the studies on Washington youth before and after retail sales began, Dilley et al.

(2018) find significant decreases in marijuana use for 8th and 10th graders between 2010 and

2016; Johnson et al. (2019) find a significant decrease for 8th graders, a significant increase for

12th graders, but no change for 10th graders from 2004-2016; and Fleming et al. (2016) show that

marijuana use among 10th graders was largely stable from 2000-2014. In a study on working

Washington adolescents, Graves et al. (2019) find that working 12th graders’ marijuana use

increased from 2010 to 2016.11 Using survey data on Colorado high schoolers before and after

the first retail marijuana sales to adults (2013 and 2015), Brooks-Russell et al. (2019) find no

associated changes in adolescents’ lifetime marijuana use or past-month use as well as perceived

ease of access, perceived wrongfulness, or perceived disapproval from parents; however, they

observe a significant reduction in perceived harm.

Several papers have analyzed youth marijuana use in the context of MMLs (Anderson,

Hansen, & Rees, 2015; Anderson, Hansen, Rees, & Sabia, 2019; Coley, Hawkins, Ghiani,

Kruzik, & Baum, 2019; Johnson, Hodgkin, & Harris, 2017; Sarvet et al., 2018a). Using data

10 While it is possible that bans (and moratoria) of marijuana retailers could be used as an instrument for distance

because bans are time-varying, I believe that bans are an invalid instrument. Since a ban is enacted by a local

government (e.g., a city council), the views of those in charge will (to an extent) represent those of the community

they oversee. In the case of a ban, people are averse to marijuana. Therefore, the effect of a retail ban is not only

correlated with distance (by requiring an individual to drive further to a store), but it is also correlated with

marijuana use for youth because they are growing up in a community that is less tolerant toward marijuana use (and

they may share the same views of the drug). Hence, retail sales bans are endogenous. 11 Two of the possible reasons that the authors indicate are: 1) greater disposable income for youth who work and 2)

greater contact with adults at work who may use marijuana and be willing to purchase it for their younger co-

workers.

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from state Youth Risk Behavior Surveys (YRBS) from 1999-2015, Coley et al. (2019) find that

MMLs and decriminalization primarily led to decreases in youth marijuana use, although use at

the intensive margin was not significantly affected by either policy. Johnson et al. (2017) also

study the effects of MMLs with state YRBS from 1991-2011, observing a small decline in the

odds of youth use for states passing MMLs, the number of years since the MML was

implemented, and more permissive aspects of the law.12 Sarvet et al. (2018a) perform a meta-

analysis on studies focusing on MMLs and youth marijuana use, noting a general lack of effect.

Anderson et al. (2015) and more recently, Anderson et al. (2019) find no increases in youth

marijuana use after MMLs with the national and state YRBS.13 Anderson et al. (2019) also find

that youth in states that implement RMLs experience a decrease in the odds of marijuana use.

In addition to use outcomes, some studies have looked at youths’ attitudes toward

marijuana after the implementation of MMLs. From the 2004-2012 National Survey on Drug

Use and Health (NSDUH), Wen, Hockenberry, and Druss (2019) observe that MMLs yield a

reduction in young adults’ perceived risk of harm in using marijuana, as well as a slight decline

in the likelihood that adolescents believe that their parents accept marijuana use. Using the

1991-2015 MTF and 2002-2014 NSDUH, Sarvet et al. (2018b) find no increases in marijuana

use accompanying decreases in youths’ perception of harm for marijuana use across the U.S.

Since I am examining outcomes in relation to proximity to marijuana retailers, analyses

of proximity to alcohol and tobacco outlets provide examples of how youth substance use can be

affected by local availability. Accessibility to alcohol is associated with increases in certain

measures of youth drinking (Trapp, Knuiman, Hooper, & Foster, 2018; Young, Macdonald, &

12 However, Johnson et al. (2017) also observe increases in youth use for states with voluntary patient registration

and higher possession limits (at least 2.5 ounces of usable marijuana). 13 The YRBS data in Anderson et al. (2015) is from 1993-2011, while in Anderson et al. (2019) it is from 1993-

2017.

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Ellaway, 2013) and higher tobacco retail density leads to increases in youth smoking (Henriksen

et al., 2008; Larsen et al., 2017; Lipperman-Kreda et al., 2014). The use of e-cigarettes, as well

as other (non-cigarette) alternative tobacco products may be affected by local access to tobacco

retailers (Magid, Halpern-Felsher, Bradshaw, Ling, & Henriksen, 2019), but findings are mixed

(Cole, Aleyan, & Leatherdale, 2019). This is important for researchers to track, as Dai and

Siahpush (2019) find that youth who use marijuana are more likely to vape nicotine, marijuana,

and just flavoring; a finding that may be concerning with recent news of vaping-related illness

and deaths.14 If proximity to marijuana retailers induces more students to use marijuana, this

could also lead to greater use of e-cigarettes.

I contribute to the literature by exploiting within-school variation in distance to the

nearest marijuana retailer over time to examine whether proximity affects youth marijuana,

alcohol, and tobacco use. If the local presence of marijuana retailers leads to increases in youth

substance use, that would be cause for concern for policymakers in Washington, as well as other

states that may be contemplating legalizing marijuana for recreational purposes.

3. DATA

I use the 2014, 2016, and 2018 HYS state census datasets. The HYS, which is collected in

October of even years, asks a variety of questions regarding health behaviors of Washington

youth for grades 6, 8, 10, and 12.15 Since 6th graders are asked a simplified questionnaire (Form

C), which does not ask about two of my chosen demographic variables (living situation and

mother’s education) and does not have information on past-month electronic cigarette use in

14 https://www.seattletimes.com/seattle-news/washington-now-has-23-cases-of-vaping-related-lung-illness-health-

officials-say/ (accessed 2/19/20). 15 A limited number of responses are for other grades (grades 7, 9, and 11). These grades are surveyed in schools

from small districts (Washington State Health Care Authority et al., 2019). Since they compose a very small

proportion of the overall sample in comparison to grades 6, 8, 10, and 12, I do not analyze them.

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2014, I only examine grades 8, 10, and 12. The HYS is conducted using a random sample for

some schools, however non-sampled schools across the state may volunteer to take the survey

(known as “piggyback” schools). The state random sample schools comprise about 16 percent,

while the piggyback schools compose approximately 84 percent of the overall sample (for all

three years and all three grades combined).16 As others note (e.g., Graves et al., 2019), the

census dataset has been shown to be generalizable to the non-alternative public school youth of

Washington State in bias analyses (Washington State Department of Health et al., 2016;

Washington State Department of Social and Health Services et al., 2018).17 This allows me to

maximize the sample size in order to exploit more of the variation in distance to marijuana

retailers between schools across the state.

I analyze a variety of substance use outcomes: marijuana use in the past month, heavy

marijuana use (defined as 10 or more days)18 in the past month, whether the student drank

alcohol in the past month, if they binge drank (having five or more drinks in a row) in the past

two weeks, if they smoked cigarettes in the past month, and if they used e-cigarettes in the past

month. In addition to these measures of use, I also examine if perceptions of marijuana changed,

namely, whether it was hard to get, if students believe it is risky to try once or twice, and if they

believe that it is risky to use regularly (at least once or twice a week). Perceived ease of access is

important to track as the retail market develops because it can provide a sense of access to black

16 For more on sampling and the numbers used in this calculation, see pages 17-19 of the HYS manual (Washington

State Health Care Authority et al., 2019). 17 Although the bias analyses indicate that students who used alcohol, cigarettes, or marijuana may be

underrepresented in the 2014 census, as well as 10th graders who currently smoke cigarettes in the 2016 census, I use

the census data because it allows me to examine the distance to the nearest marijuana retailer for more schools, thus

exploiting greater variation in distance across the state. In addition, the census sample is much larger than the state-

selected random sample. 18 I define heavy use this way because 10 or more days was the maximum level for 2014. Although 2016 and 2018

have two more categories beyond 10-19 days (20-29 days and all 30 days), I wanted the data to be comparable

across all three years.

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market marijuana sold by dealers, as well as legal marijuana provided illegally to minors by

adults. Perceptions of harm in using the drug are also important to follow because greater

exposure to marijuana stores and advertisements after retail sales began may influence youth

marijuana use (see e.g., Fiala, Dilley, Everson, Firth, & Maher, 2020; Whitehill, Trangenstein,

Jenkins, Jernigan, & Moreno, 2020).

I link the HYS with marijuana retail sales data from the Liquor and Cannabis Board

(LCB), which provides both the total monthly sales volumes for each store, as well as its location

information through April 2019 (Washington State Liquor and Cannabis Board, 2017, 2018,

2019a, 2019b).19 From the LCB data, I determine which stores were operating as of the month

of the surveys.20 After geocoding the stores using the ArcGIS World Geocoding Service and

performing World Routing Service calculations, I obtain GIS measures of the driving time in

minutes (following the road network) from each school to the nearest operating marijuana

retailer.21

19 Unfortunately, some license numbers that were in the LCB sales data were not in the applicant (address) data.

Therefore, I combine older applicant data with current applicant data to obtain the missing retailers’ addresses. Any

licensee’s address that appeared in both applicant files was taken from the newer one. It is possible that the older

applicant file has obsolete store locations (leading to measurement error in my GIS variables for schools near the

affected stores). 20 Although I computed measures of average prices of marijuana products using data (Dilley, 2018) aggregated from

the former seed-to-sale dataset, BioTrackTHC (as in Ambrose et al. (2020)), I exclude these measures because I

would be unable to include the 2018 HYS respondents in my analysis. In addition to not being in possession of the

aggregated traceability dataset beyond August 2017, I have been told that the data from Leaf Data Systems (by MJ

Freeway, the company that replaced BioTrackTHC for Washington’s traceability system) is fraught with issues,

making it difficult to use. Despite this, I expect that part of the effect of retail density operates through price (i.e.,

prices are likely more competitive when many shops are close by each other). 21 My GIS data did not match with about 0.5% of the students’ schools, due to some lack of cohesion with the Office

of Superintendent of Public Instruction (OSPI, 2019) school directory. I also used an additional resource to facilitate

my GIS calculations, which geocodes the OSPI directory (Schneider, 2019). In addition to travel time, I also

compute the network distance in miles. The correlation between these two measures of proximity is 0.94 (for all

grades together, as well as each grade by itself).

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Although the distance from school to a marijuana retailer is correlated with the distance

to a retailer from home, some students may live farther from retailers than their peers.22

However, students may encounter marijuana retailers while heading to school or coming back

from it (when riding the bus or in a car, walking, etc.), even if their home is isolated from these

businesses, relative to their peers. If adults’ accessibility to retail marijuana leads to positive

spillovers in access for youth via parents, older siblings, or other adults in the household, then

the distance from home would be a better measure. Unfortunately, I do not have information on

the location of a student’s home.

Table 1 reports summary statistics for the key variables used in my analysis, consisting of

the sample used in the regressions reported in Table 2. The sample has no missing values for

marijuana use in the past month, gender, age, race/ethnicity, language spoken at home, living

situation, grades earned last year, and mother’s education.

Figures 1 and 2 show the trends in past-month marijuana use and heavy use, respectively.

Both figures indicate that 10th graders’ marijuana use is trending downward, while it is harder to

tell how use is changing for 8th graders; heavy use, however, appears to be declining for 8th

graders. For 12th graders, use on the extensive margin seems to be very stable, while heavy use

appears to be on a declining trend.

In the following three figures (Figures 3, 4, and 5), I present the typical perceptions of

marijuana. In Figure 3, observe that for all three grades, the belief that marijuana is “very hard”

or “sort of hard” to get is trending upward. This may indicate that the black market is shrinking

and that marijuana retailers (and their adult customers) are not prone to providing marijuana to

22 Students are assigned to specific schools based on where they live (i.e., being in the neighborhood of the school or

as determined by their school district), but they may transfer schools if approved by both the current and transfer

school districts. For more, see (https://www.k12.wa.us/student-success/support-programs/student-transfers).

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minors. Surprisingly, 12th graders—the group with the highest level of marijuana use and lowest

perceptions of harm in using it—exhibit the steepest upward trend for perceived difficulty in

obtaining the drug, while just the opposite is true for the lower grades. Figure 4 shows that

students of all three grades are experiencing an overall decline in the belief that trying marijuana

once or twice is of moderate or great risk, while in Figure 5, the overall trends in students’

beliefs that using marijuana regularly (at least once or twice per week) is of moderate or great

risk differ across all three grades. It appears that 8th graders’ perceived harm for regular

marijuana use is declining, 10th graders’ perceptions of risk are increasing, while 12th graders’

perceptions are mostly stable.

Figures 6 and 7 show the changes in proximity to marijuana retailers over time. Both

figures indicate that retailers have gotten much closer to schools as the market developed over

the years. The bins in each histogram of Figure 7 represent three-minute blocks of travel time;

there is a great deal of variation in the upper-left panel of this figure (October 2014), with some

schools over two hours from a retailer. Moving from top-right to bottom-right (two to four years

later), the distribution of travel time does not change much: the most notable difference being

that the closest two bins grow a few percent. This likely signifies that distant schools are not

experiencing much growth in the local retail environment and that retailers are getting only

marginally closer to the closest schools as the market becomes saturated.23 In my final figure

(Figure 8), I show the growth of Washington’s retail marijuana market over time. Stores first

23 Schools that are far away may be located in communities that have banned retailers or are in places small enough

to not have been allocated any. For example, schools in one city that banned retailers were over 30 minutes away

from the closest store for all three years of the survey.

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opened in July 2014, with 60 stores open in October 2014, 321 in October 2016, and 425 in

October 2018.24

4. EMPIRICAL METHODOLOGY

My primary hypothesis is that the diminishing distance to marijuana retailers will lead to an

increase in marijuana use, and possibly the use of alcohol and tobacco.25 I also hypothesize that

youths’ attitudes toward marijuana will be affected, such as finding it easier to obtain and less

risky to use both infrequently and frequently. This is because youth may believe that if it is safe

and acceptable for adults to buy and consume marijuana from local retail shops, then the risks of

adolescent marijuana use are exaggerated. In order to test my hypotheses, I estimate the

following two-way fixed effects model to capture the effect of travel time on youth substance

use:

Yist = β0 + β1 ln(Travelst) + β2 Densityst + β3 Xist + νs + ωt + εist (1)

where Yist measures a substance use outcome for student i at school s in October of year t;

Travelst denotes estimated drive time to the nearest operating I-502 marijuana retailer; Densityst

represents a retail density measure; Xist is a vector of individual-level controls, including gender,

age, race/ethnicity, language spoken at home, living situation (who the student resided with most

in the last 30 days), grades earned last year, and mother’s education; νs represents time-invariant

school effects; ωt represents year effects (i.e., October 2014, October 2016, or October 2018);

and εist is the error term.

24 Since I use the Traceability Contingency Reporting data for this paper to incorporate total monthly retail sales for

November 2017 and beyond, the data used in making Figure 8 is not solely what was used in making the figure

depicting the number of retailers in Ambrose et al. (2020). 25 According to a longitudinal study of 281 youth in RML states before RMLs to after retail sales were implemented,

RMLs are associated with greater odds of past-year alcohol and marijuana use, but not past-year cigarette use

(Bailey et al., 2020). While Choi, Dave, and Sabia (2019) find that MMLs reduce adult cigarette smoking at both

the extensive and intensive margins, Bhave and Murthi (2019) find that RML led to increases in cigarette sales in

Colorado.

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Since the natural logarithm produces negative numbers when computed for values less

than one (e.g., with travel times under one minute), I assign the log of these values to zero.26 My

chosen functional form allows me to interpret the coefficient on travel time in terms of relative

changes. By using the natural log of travel time, I am able to account for the fact that a 10-

minute reduction in drive time (say, from 60 to 50 minutes versus 30 to 20 minutes) will be

different, depending on the initial distance.

I control for retail density using the number of proximate sellers because the effect of

distance may be confounded with the number of retailers nearby (i.e., places with many retailers

nearby are likely to have a lower average distance to the nearest retailer).27 Due to the

previously established relationship between adolescent marijuana use and decreased academic

achievement (Silins et al., 2014), I control for grades earned in the past year. I control for

language spoken at home in order to account for some of the cultural differences among

adolescents’ families. I use time dummies to account for unobservable differences such as

changing attitudes toward marijuana after legalization, while school dummies account for

unobservable qualities of each school that persist throughout the period of the study. In

specifications combining all three grades, I use school-grade dummies to account for

unobservable differences between grades in each school, as well as differences between schools

across the state. All specifications use standard errors clustered at the school-level.28 I test my

hypotheses using linear probability models (LPMs).

5. RESULTS

26 Only one school in 2018 (comprising 94 respondents) had a travel time value below one minute. 27 Also, places with a relatively large number of retailers may be allocated more retail licenses or may be more

accepting of the marijuana industry and marijuana use in general. 28 Due to the number of fixed effects and clusters, in all regressions I use the multi-way fixed effects estimator

developed by Correia (2017). In Stata, the package is “reghdfe.”

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I begin my analysis of the effect of travel time on marijuana use in the past month, as well as

heavy use (10 or more days). From Table 2, I find no significant association between travel time

and either marijuana use outcome and the signs on each of the travel time coefficients are not the

same across all three grades. In addition, I find that only 8th graders’ marijuana use (in column

(2)) is affected by the number of stores within a 45 minute drive.29 This effect is small: an

additional ten stores within 45 minutes of school yields a 0.11 percentage point increase in the

probability of using marijuana in the past month (a 1.5% increase relative to the mean). The

absence of an effect of retail density on the other grades gives me pause, considering that they

are more likely to be engaging in substance use. Therefore, the results in this table do not

provide strong evidence that youth marijuana use is affected by the distance from their school to

the nearest operating marijuana retailer or the number of retailers nearby.30 As I move on, I

examine attitudes toward marijuana, as well as alcohol and tobacco use.

In Table 3, I perform the same analysis for perceptions of marijuana, namely whether

students think it is hard to get, if they think that trying the drug once or twice is risky, and if they

think that regular use (at least once or twice a week) is risky. I do not find any significant

associations between travel time to the nearest retailer and perceptions of marijuana. Although I

am not able to say with statistical confidence (due to large standard errors), it is interesting to

note that the coefficients on travel time are negative for all three grades in columns (1) through

(4). Therefore, as stores get closer to schools, on average, students find it harder to obtain

29 This effect for 8th graders also holds when I use other measures of retail density instead, such as the number of

stores within 120, 90, 60, or 30 minutes. The effects from these other measures follow an expected pattern: the

farther the threshold of distance, the smaller the effect in terms of magnitude. However, using measures of the

number of stores within 15, 10, or five minutes yields insignificant coefficients. 30 Although there may be concerns that ln(travel time) and the number of retailers within 45 minutes are collinear, I

also ran Tables 2-10 with these measures included separately (results available upon request). In the majority of

cases, the results are similar to when I include both measures. In some cases (in which the individual coefficients are

not significantly different from zero), the estimated magnitudes change.

17

marijuana. On the other hand, I find that the number of proximate sellers is inversely related to

perceived difficulty in obtaining marijuana; the coefficient is significant at the five percent level

for 8th graders.31 The coefficient on retail density in column (2) indicates that for an additional

ten retailers within 45 minutes of school, 8th graders experience a 0.27 percentage point decrease

in perceiving marijuana as hard to obtain (0.35% decrease relative to the mean). Since the vast

majority of 8th graders perceive marijuana as difficult to obtain, the effect of an increase in retail

density is very small. Next, I examine the effect of distance on alcohol and tobacco use.

In Table 4, I show results for if students drank alcohol in the past month and if they binge

drank in the past two weeks (having five or more drinks in a row). The same pattern of results

emerges, however the coefficient in column (8)—the effect of travel time on 12th graders’ binge

drinking in the past two weeks—is significant at the 10% level. The coefficient implies that a

33% reduction in drive time to the nearest retailer is associated with a 0.35% decrease in binge

drinking (a 2.0% decrease relative to the mean) among 12th graders.32 However, when I examine

the coefficients on retail density, I find that all of them are positive, with the coefficients in

columns (1), (5), and (7) significant at the five percent level.33 The retail density coefficients in

columns (1) and (7) imply a 10-store increase lead to 0.13 and 0.11 percentage point increases in

the probability of drinking and binge drinking for all grades combined. Once again, these are not

large effects (respective increases of 0.67% and 0.99% relative to the mean).

31 As with the retail density coefficient in column (2) of Table 2, I see a very similar pattern when I use alternative

density measures for column (2) in Table 3. Additionally, I see a negative effect for 90 and 120-minute density

measures (significant at the 10% level), while I see an even larger effect for 30 and 15-minute thresholds (significant

at the five percent level), but insignificant effects for 10 and five-minute thresholds. 32 These results are from computing Δy = β1 * ln([100+p%]/100), where p% is the percent change in travel time. So

for β1 = 0.00890, Δy = 0.00890 * ln(0.667) = -0.00361 (which is -0.00353 when Stata calculates it using a higher

degree of precision). 33 A very similar pattern as discussed in a previous footnote on Table 2’s retail density coefficients occurs here, but

for past-month alcohol use and binge drinking for all grades combined. However, 10th grade binge drinking is

affected by the number of retailers within 60, 45, 30, and 15 minutes (with the latter being the largest in magnitude),

but is not affected significantly using 120, 90, 10, or five-minute thresholds.

18

In Table 5, I examine my final set of outcomes for the main sample—tobacco use. In

columns (1) through (4) I report results for the effects of marijuana retail proximity and density

on whether students smoked any cigarettes in the past month, while in columns (5) through (8), I

do the same for past-month electronic cigarette use. There are only two significant travel time

coefficients—in columns (5) and (6) for all grades combined and 8th graders alone (at the five

percent level). Notice that these coefficients are much larger in magnitude than the others in the

table (with the exception of column (8)), while also being positive. These coefficients imply that

a 33% reduction in travel time yields 0.30 and 0.41 percentage point decreases in e-cigarette

usage (1.8% and 4.4% decreases relative to the mean for all grades combined and 8th graders,

respectively).

This result is the opposite of what I would expect—since marijuana retailers often sell

vape pens with cartridges containing concentrated cannabinoids (such as tetrahydrocannabinol or

THC), I would expect that closer proximity to shops could influence more youth to use these

kinds of devices. Although I find very little evidence that tobacco usage is affected by proximity

to marijuana retailers, I do observe a small effect of retail density on 8th graders and all grades’

cigarette smoking.34 The coefficient in column (2) implies that an additional 10 retailers within

45 minutes of school leads to a 0.10 percentage point increase in smoking (3.0% increase relative

to the mean).

Although some of my results provide evidence that increases in retail density are

associated with small increases in youth substance use, it could be that adolescents going to

school in areas with more retailers are more prone to using, as opposed to being induced to use

34 The number of retailers within 60, 30, and 15 minutes are also significantly associated with 8th graders’ smoking,

while only the 60- and 45-minute thresholds are significantly associated with all three grades’ smoking.

19

more by the presence of additional retailers. Therefore, I must be cautious in my interpretation

of these findings.35

Next, I perform subgroup analyses, first restricting the sample to students who reported

binge drinking in the past two weeks in Table 6, following Kerr et al. (2017). Kerr et al. (2017)

find that in Oregon after implementing its RML, marijuana use amongst college students

increased, relative to students in states without RMLs; however, this finding was only for those

who reported binge drinking in the past two weeks. Since high-school seniors and college

students are relatively close in terms of age, I expect the behavior of adolescents in the later

high-school grades to be affected similarly to that of college students. In columns (1), (3), and

(4) (for all grades, as well as 10th and 12th graders, respectively) of Table 6, I observe that the

coefficient on travel time is negative and significantly associated (at the 5% level or better) with

marijuana use in the past month. These coefficients imply that a 33% reduction in travel time

leads to 0.87, 1.3, and 1.2 percentage point increases in marijuana use (relative to the mean, these

are increases of 1.3%, 1.9%, and 1.8%). However, heavy marijuana use among binge drinking

students does not appear to respond to retail proximity. The results for use on the extensive

margin could indicate that students who binge drink have less difficulty obtaining marijuana in

addition to alcohol as marijuana retailers get closer to adults who are willing to purchase alcohol

for minors. The overall lack of an effect for retail density in this table indicates that the number

35 Following the geospatial density measure used in Everson et al. (2019), I also try using the sum of the inverse

travel times to the nearest five retailers as my explanatory variable of interest (albeit a continuous version) to see if

an alternative specification of retail density affects my results. The results for Tables 2-8 (available upon request),

which exclude ln(travel time) and other density measures, exhibit some similarities to my travel time results. In

many cases, the coefficient on this density measure is the opposite sign of that of ln(travel time), which is what I

would expect (i.e., being closer to five stores induces the same type of effect as a decrease in distance to the nearest

retailer). However, the coefficients in the vast majority of cases are not significantly different from zero and given

the construction of this retail density measure, the results are less intuitive. For more information on this type of

density measure, see Guide for Measuring Alcohol Outlet Density by the Centers for Disease Control and Prevention

(CDC, 2017).

20

of retailers nearby matters less than the distance to the closest retailer for this group of high-risk

students.

Although I examine a high-risk group of youth in Table 6, I perform additional subgroup

analyses as binge-drinking may be endogenous. A study on French adolescents surveyed in

2008 found that youth from wealthy families are more likely to experiment with marijuana but

are less likely to use it heavily than youth from families of lower socioeconomic status (SES;

Legleye, Beck, Khlat, Peretti-Watel, & Chau, 2012). Hoffmann (2017) examines the 2005-2006

Health Behaviour in School-Aged Children and finds that adolescent marijuana use is negatively

associated with living with both biological parents, regardless of other influences (such as time

spent with friends or family affluence). Vermeulen-Smit, Verdurmen, Engels, & Vollebergh

(2015) find in a 2011 survey of Dutch students and one of their parents that parental knowledge

of a child’s whereabouts reduces the odds of lifetime and past-month marijuana use among

adolescents. Miech and Chilcoat (2005) find that in the 1980s and 1990s, adolescents’ illegal

drug use in the United States became more prevalent among families with less educated mothers.

From the findings of these papers, I decided to cut the data by mother’s education (a proxy for

SES), as well as living situation.

I create a risk index, comprised of four levels: 1) students reporting that their mother

graduated from high school or received more education (i.e., not low SES) and that they live

with their parent(s), step-parents or legal guardian (which I will hereon abbreviate as living with

their parents), 2) their mother did not graduate high school but they live with their parents, 3)

their mother graduated high school or received more education, but they do not live with their

parents, and 4) their mother did not finish high school and they do not live with their parents.

This index is created with riskiness in ascending order. In Tables 7 and 8, I present these results:

21

Table 7 stratifies the data to only students with a risk index of 2, 3, or 4, while Table 8 further

stratifies to only those with a risk index of 3 or 4.

In both Tables 7 and 8, I observe that marijuana use among 12th graders is increasing as

marijuana retailers get closer to their schools. The coefficient on travel time in column (4) of

Table 7 indicates that a 33% reduction in travel time is associated with a 0.75 percentage point

increase in the probability of using marijuana in the past month (2.3% increase relative to the

mean). In Table 8, the coefficient on travel time in column (4) indicates an even stronger effect:

a 1.8 percentage point increase in use from the same reduction in distance (4.4% increase relative

to the mean); additionally, the coefficient on travel time in column (1) indicates that all three

grades combined experience a 0.80 percentage point increase in use (2.2% increase relative to

the mean). Table 8 also shows that heavy use for 12th graders is affected. The coefficient on

travel time in column (8) of Table 8 indicates that a 33% reduction in drive time yields a 1.3

percentage point increase in heavy use (6.1% increase relative to the mean). In both tables, none

of the coefficients on retail density are significantly different from zero. My results in Tables 7

and 8 indicate that the development of the recreational marijuana market in Washington has

facilitated use for students with lower parental involvement.

From my subgroup results, I find that proximity to marijuana retailers affects marijuana

use among youth who are at a higher risk of using the drug, such as students reporting recent

binge drinking, as well as students of lower SES and those not living with their parents. These

effects are mostly concentrated amongst 12th graders, who are more likely to engage in risky

behaviors, as well as live away from their parents.

6. ROBUSTNESS CHECKS

22

Since there is a 1000-foot minimum distance from schools to retailers mandated by law, plus

heavy penalties for providing marijuana to underage persons, it is unlikely that retailers would

purposefully locate near schools or consider their students as potential customers. However,

retailers may locate in places close to where students live. In Tables 9 and 10, I perform

falsification tests to examine whether stores seem to move in response to the demographic

characteristics of my sample or if students move in response to retailers.

I find no statistical evidence of a relationship between travel time to the nearest retailer

and gender, race/ethnicity, language spoken at home, living situation, grades earned in the last

year, or mother’s education. I also find little evidence that the number of retailers nearby a

school predicts any of these outcomes. The most prominent exception is in column (2) of Table

9, which indicates that an additional 10 retailers within 45 minutes of school leads to a 0.22

percentage point decrease in the probability of being white non-Hispanic (0.39% decrease

relative to the mean). The other main exception is in column (7) of Table 10: in this case an

additional 10 retailers within 45 minutes yields a 0.10 percentage point decrease in the

probability of a student’s mother having had some college or received some technical training

after high school (0.43% decrease relative to the mean). Neither of these effects are large

enough to convince me that places with retailers nearby are substantially different from others in

terms of demographic composition.

7. CONCLUSIONS

In this paper, I examine the impacts of proximity to marijuana retailers, as well as retail density

on youth marijuana use and other substance use outcomes. For my main sample, I do not find

evidence that youth marijuana use and perceptions of the drug are affected by proximity to

marijuana retailers and I find limited evidence that the number of proximate sellers has a small

23

impact on some of these outcomes. These findings align with those of studies examining

Colorado youth in the context of recreational marijuana sales (Brooks-Russell et al., 2019;

Harpin et al., 2018) and other studies using GIS measures of distance to medical marijuana

dispensaries (Shi, 2016; Shi et al., 2018), however I find no evidence that distance to recreational

marijuana retailers affects Washington adolescents’ perceived harm in using marijuana.

Additionally, alcohol and tobacco use are largely unaffected by proximity to marijuana

retailers, the main exception being a reduction in e-cigarette use accompanying a reduction in

distance; however, this result may be a manifestation of type I error. Upon stratifying the sample

to higher-risk subgroups, such as those reporting recent binge-drinking, as well as those with

lower SES and atypical living situations, I find evidence that these youth are more prone to using

marijuana as retailers get closer to their schools. Particularly, high-risk 12th graders are induced

to use marijuana as retailers get closer to their schools. My binge-drinking subgroup results

align with the findings of Kerr et al. (2017), especially considering that 12th graders are closest in

age to college students—the group that Kerr et al. (2017) examined.

My findings largely imply that distance from school to marijuana retailers does not have

an effect on marijuana-related outcomes for Washington adolescents. This lack of an effect, in

spite of increased access to marijuana for adults could be indicative of several factors. Since

fewer adolescents are using marijuana in Washington (Dilley et al., 2018) and fewer are being

admitted to treatment for marijuana in RML states (Mennis & Stahler, 2020), this could imply

less interest in using drugs among adolescents than before. Fiala et al. (2020) note that

Washington has much stricter regulations that govern advertising for marijuana retailers than

24

Oregon.36 The authors point out that the literature on alcohol and tobacco advertising agrees that

adolescent drinking and tobacco use increase with advertisements. Therefore, it is possible that

the lack of an effect of distance in my paper is due to advertising restrictions. Additionally, since

marijuana retailers are the only source of marijuana in the legal recreational market for adults,

minors are not allowed entry, and there are no provisions for social use outlets in Washington,

minors may be less exposed to marijuana (in person) than other substances.37

Regardless of whether youth have a weakened desire to use drugs, the specific provisions

of Washington’s RML could be affecting the behavior of adults, rather than adolescents.

Consistent with the reasoning in Anderson et al. (2019), if illegal marijuana sold to youth by

drug dealers becomes harder to procure because the retail market is undercutting the black

market, while at the same time adults become more hesitant to provide retail marijuana to youth

(e.g., if the penalties are too severe to risk getting caught or if more adults perceive that youth

will be harmed in using marijuana), youth may have a harder time obtaining marijuana and

therefore using it. Indeed, while small in magnitude and not significant, my results for perceived

difficulty of obtaining marijuana show that decreases in distance to retailers lead more

adolescents to perceive marijuana as being hard to get. Future research exploring these

mechanisms will be important as more places in the United States and other countries legalize

recreational marijuana for adults.

Conflict of Interest

36 They note that “Washington State…limits stores to 2 signs measuring a maximum of 1,600 square inches that may

contain only the trade name, location, and nature of the business” (p. 5). For more details on marijuana advertising

in Washington, see (https://lcb.wa.gov/mj2015/faq_i502_advertising). 37 Contrast this with adolescents going to say, a convenience store or grocery store (in which alcohol and tobacco

products are sold) or a restaurant (in which alcohol is served). Although minors may not purchase these products,

they still are exposed to the purchase and consumption of these products by adults.

25

The author reports no conflict of interest.

Acknowledgements

Research reported in this article was supported in part by the National Institute on Drug Abuse

(NIDA) of the National Institutes of Health under award number 1R01DA039293. I would like

to thank Julia Dilley and Susan Richardson for their assistance in obtaining the HYS data.

Disclaimer: The content is solely the responsibility of the author and does not necessarily

represent the official views of the National Institutes of Health.

Human Participant Protection

This study was reviewed by the Washington State Institutional Review Board (IRB): Project E-

060216-H: “Marijuana Legalization: Assessing the Impact of Local Regulatory Policies”.

26

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Appendix

36

Figure 1: Marijuana use in the past month

37

Figure 2: Heavy marijuana use in the past month

38

Figure 3: Hard to get marijuana

39

Figure 4: Risky to try marijuana once or twice

40

Figure 5: Risky to use marijuana regularly

41

Figure 6: Travel time to nearest operating retailer

42

Figure 7: Histograms of travel time to nearest operating retailer

43

Figure 8: Number of operating retailers

44

Table 1: Summary Statistics

8th Graders 10th Graders 12th Graders Variable N Mean S.D. N Mean S.D. N Mean S.D.

Used marijuana in past 30 days 122,971 0.0745 0.263 129,165 0.175 0.380 97,920 0.259 0.438

Heavy marijuana use (10+ days) in past 30 days 122,971 0.0190 0.136 129,165 0.0554 0.229 97,920 0.0962 0.295

Hard to get marijuana 60,419 0.778 0.415 63,888 0.500 0.500 48,641 0.353 0.478

Risky trying marijuana 60,611 0.503 0.500 64,023 0.360 0.480 48,727 0.260 0.438

Risky using marijuana regularly 60,583 0.743 0.437 64,010 0.646 0.478 48,704 0.531 0.499

Drank alcohol in past 30 days 122,761 0.0992 0.299 129,006 0.218 0.413 97,789 0.338 0.473

Binge drank in past two weeks 121,910 0.0475 0.213 128,289 0.109 0.311 97,347 0.179 0.384

Smoked cigarettes in past 30 days 122,543 0.0338 0.181 128,872 0.0663 0.249 97,729 0.106 0.308

Used e-cigarettes in past 30 days 61,716 0.0912 0.288 64,703 0.178 0.382 48,926 0.244 0.429

Independent variables

Travel time to nearest marijuana retailer (minutes) 122,971 11.8 10.8 129,165 12.2 11.4 97,920 12.3 12.0

Distance to nearest marijuana retailer (miles) 122,971 6.69 8.62 129,165 6.82 9.13 97,920 6.91 9.46

Number of stores within 120 minutes 122,971 109 95.2 129,165 108 95.1 97,920 105 94.4

Number of stores within 90 minutes 122,971 79.3 74.3 129,165 78.6 74.2 97,920 76.1 73.6

Number of stores within 60 minutes 122,971 48.9 49.1 129,165 47.7 48.4 97,920 45.9 47.6

Number of stores within 45 minutes 122,971 33.6 35.6 129,165 32.6 35.2 97,920 31.3 34.6

Number of stores within 30 minutes 122,971 17.3 20.1 129,165 16.6 19.8 97,920 16.2 19.6

Number of stores within 15 minutes 122,971 4.76 6.22 129,165 4.49 5.96 97,920 4.45 5.87

Number of stores within 10 minutes 122,971 2.17 3.21 129,165 1.85 2.84 97,920 1.86 2.82

Number of stores within five minutes 122,971 0.470 1.05 129,165 0.387 0.890 97,920 0.389 0.901

Male 122,971 0.465 0.499 129,165 0.464 0.499 97,920 0.486 0.500

Age

12 or younger 122,971 0.0127 0.112 129,165 0.00124 0.0352 97,920 0.00129 0.0358

13 122,971 0.759 0.427 129,165 0.000689 0.0262 97,920 0.000133 0.0115

14 122,971 0.223 0.416 129,165 0.0133 0.115 97,920 0.000266 0.0163

15 122,971 0.00428 0.0653 129,165 0.746 0.435 97,920 0.000960 0.0310

16 122,971 0.000138 0.0118 129,165 0.233 0.422 97,920 0.0140 0.117

17 122,971 0.0000895 0.00946 129,165 0.00534 0.0729 97,920 0.735 0.442

18 or older 122,971 0.000220 0.0148 129,165 0.000999 0.0316 97,920 0.249 0.432

Race/ethnicity

White non-Hispanic 122,971 0.509 0.500 129,165 0.560 0.496 97,920 0.583 0.493

Hispanic 122,971 0.174 0.379 129,165 0.176 0.381 97,920 0.177 0.381

American Indian or Alaska native non-Hispanic 122,971 0.0324 0.177 129,165 0.0215 0.145 97,920 0.0180 0.133

Asian or Asian American non-Hispanic 122,971 0.0908 0.287 129,165 0.0869 0.282 97,920 0.0824 0.275

Black or African American non-Hispanic 122,971 0.0421 0.201 129,165 0.0383 0.192 97,920 0.0379 0.191

Native Hawaiian or other Pacific Islander non-Hispanic 122,971 0.0180 0.133 129,165 0.0175 0.131 97,920 0.0168 0.129

45

Other non-Hispanic 122,971 0.0745 0.263 129,165 0.0428 0.202 97,920 0.0303 0.171

Multiracial non-Hispanic 122,971 0.0597 0.237 129,165 0.0576 0.233 97,920 0.0551 0.228

Language spoken in home

English 122,971 0.816 0.387 129,165 0.817 0.386 97,920 0.825 0.380

Spanish 122,971 0.0935 0.291 129,165 0.0913 0.288 97,920 0.0926 0.290

Other 122,971 0.0903 0.287 129,165 0.0913 0.288 97,920 0.0820 0.274

Living situation

Parent(s) and/or step-parent(s) 122,971 0.954 0.210 129,165 0.945 0.228 97,920 0.920 0.271

Relatives that are not parents 122,971 0.0263 0.160 129,165 0.0307 0.172 97,920 0.0376 0.190

Foster care parent(s) 122,971 0.00416 0.0644 129,165 0.00557 0.0744 97,920 0.00618 0.0784

Adult friend(s) of family 122,971 0.00292 0.0540 129,165 0.00547 0.0738 97,920 0.0105 0.102

Friends of yours with no adults present 122,971 0.00277 0.0526 129,165 0.00399 0.0630 97,920 0.00599 0.0772

On your own 122,971 0.00317 0.0562 129,165 0.00391 0.0624 97,920 0.00834 0.0910

Other 122,971 0.00685 0.0825 129,165 0.00533 0.0728 97,920 0.0109 0.104

Grades earned last year

Mostly As 122,971 0.503 0.500 129,165 0.463 0.499 97,920 0.386 0.487

Mostly Bs 122,971 0.310 0.463 129,165 0.307 0.461 97,920 0.345 0.475

Mostly Cs 122,971 0.129 0.335 129,165 0.161 0.367 97,920 0.204 0.403

Mostly Ds 122,971 0.0338 0.181 129,165 0.0444 0.206 97,920 0.0459 0.209

Mostly Fs 122,971 0.0233 0.151 129,165 0.0254 0.157 97,920 0.0193 0.137

Mother's education

Did not finish high school (HS) 122,971 0.136 0.343 129,165 0.144 0.351 97,920 0.152 0.359

Graduated from HS or GED 122,971 0.209 0.407 129,165 0.216 0.411 97,920 0.229 0.420

Had some college or technical training after HS 122,971 0.204 0.403 129,165 0.241 0.428 97,920 0.265 0.441

Graduated from a four-year college 122,971 0.279 0.448 129,165 0.259 0.438 97,920 0.231 0.421

Earned an advanced graduate degree 122,971 0.172 0.377 129,165 0.140 0.347 97,920 0.124 0.329

Notes: Sample reported above is what I use in Table 2 (with marijuana use in the past month as the dependent variable).

46

Table 2: The effects of travel time and retail density on marijuana use in the past month

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

Grade All 8th 10th 12th All 8th 10th 12th

If Used If Used If Used If Used Heavy Use Heavy Use Heavy Use Heavy Use

Ln(Travel time to nearest store) -0.000255 0.00150 -0.00365 0.00270 -0.000504 0.00108 -0.00293 0.00136

(0.00261) (0.00292) (0.00467) (0.00478) (0.00151) (0.00160) (0.00256) (0.00320)

# stores within 45m 3.62e-05 0.000110** 9.26e-06 -1.27e-05 -6.54e-06 3.10e-05 1.21e-05 -7.19e-05

(5.01e-05) (5.33e-05) (8.21e-05) (0.000106) (2.74e-05) (2.60e-05) (4.16e-05) (6.85e-05)

Observations 350,056 122,971 129,165 97,920 350,056 122,971 129,165 97,920

R-squared 0.117 0.088 0.094 0.073 0.083 0.050 0.073 0.075

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.163 0.0745 0.175 0.259 0.0540 0.0190 0.0554 0.0962

33% decrease in TT 0.0104 -0.0607 0.148 -0.109 0.0205 -0.0439 0.119 -0.0551

% change vs. mean 0.0634 -0.816 0.846 -0.422 0.379 -2.313 2.149 -0.572

Schools 826 481 427 426 826 481 427 426

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity,

language spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all three grades

include school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report the mean of the

dependent variable, the percentage point change corresponding with a 33% decrease in travel time, the percent change relative to the

mean, and the number of schools.

47

Table 3: The effects of travel time and retail density on perceptions of marijuana

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Grade All 8th 10th 12th All 8th 10th 12th All 8th 10th 12th Hard to Hard to Hard to Hard to Risky to Risky to Risky to Risky to Risky to Risky to Risky to Risky to

obtain obtain obtain obtain try 1-2x try 1-2x try 1-2x try 1-2x use often use often use often use often

Ln(Travel time) -0.00401 -0.000918 -0.00535 -0.00674 -0.00114 0.00643 -0.00593 -0.00454 -0.000374 0.00565 -0.00712 -0.000203

(0.00417) (0.00657) (0.00690) (0.00745) (0.00372) (0.00678) (0.00615) (0.00633) (0.00392) (0.00612) (0.00549) (0.00758)

# stores within 45m -0.000163* -0.000270** -2.99e-05 -0.000157 -1.16e-05 -0.000153 -1.71e-05 0.000196 7.86e-06 -0.000115 3.81e-05 0.000166 (8.34e-05) (0.000127) (0.000140) (0.000149) (8.31e-05) (0.000147) (0.000135) (0.000135) (7.16e-05) (0.000107) (0.000126) (0.000141)

Observations 178,072 62,473 65,703 49,896 177,112 62,101 65,351 49,660 176,959 62,039 65,301 49,619

R-squared 0.177 0.085 0.068 0.052 0.098 0.078 0.061 0.047 0.132 0.126 0.111 0.085 School & year FEs Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Controls Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.557 0.777 0.501 0.354 0.382 0.501 0.360 0.260 0.646 0.740 0.644 0.529 33% decrease in TT 0.163 0.0372 0.217 0.273 0.0461 -0.261 0.240 0.184 0.0152 -0.229 0.289 0.00822

% change vs. mean 0.292 0.0479 0.433 0.773 0.121 -0.520 0.667 0.709 0.0235 -0.310 0.448 0.0155

Schools 819 476 412 419 819 476 412 419 819 476 413 419

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity, language spoken at home, living situation, grades

earned last year, and mother's education. Specifications combining all three grades include school-grade fixed effects, rather than school fixed effects. In the last four rows of the

table, I report the mean of the dependent variable, the percentage point change corresponding with a 33% decrease in travel time, the percent change relative to the mean, and the number of schools.

48

Table 4: The effects of travel time and retail density on recent alcohol use

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

Grade All 8th 10th 12th All 8th 10th 12th

Drank Any Drank Any Drank Any Drank Any Binge Drank Binge Drank Binge Drank Binge Drank

Ln(Travel time) 0.00180 -0.000240 -0.000173 0.00722 0.00282 0.000781 0.000336 0.00870*

(0.00266) (0.00308) (0.00393) (0.00609) (0.00209) (0.00239) (0.00333) (0.00510)

# of stores within 45m 0.000132** 7.10e-05 0.000131 0.000180 0.000106** 3.82e-05 0.000156** 0.000110

(5.34e-05) (5.51e-05) (8.94e-05) (0.000126) (4.31e-05) (4.13e-05) (6.98e-05) (0.000102)

Observations 350,540 123,100 129,390 98,050 348,206 122,134 128,525 97,547

R-squared 0.100 0.054 0.055 0.044 0.078 0.063 0.062 0.045

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.198 0.0882 0.205 0.324 0.107 0.0479 0.109 0.180

33% decrease in TT -0.0729 0.00971 0.00703 -0.293 -0.114 -0.0317 -0.0136 -0.353

% change vs. mean -0.369 0.110 0.0342 -0.903 -1.065 -0.661 -0.125 -1.962

Schools 826 481 427 426 826 481 427 426

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity,

language spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all three grades

include school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report the mean of the

dependent variable, the percentage point change corresponding with a 33% decrease in travel time, the percent change relative to the

mean, and the number of schools.

49

Table 5: The effects of travel time and retail density on tobacco use in the past month

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

Grade All 8th 10th 12th All 8th 10th 12th

Smoked Smoked Smoked Smoked Used Used Used Used

Cigarettes Cigarettes Cigarettes Cigarettes E-cigarettes E-cigarettes E-cigarettes E-cigarettes

Ln(Travel time) 0.000867 0.000619 0.00193 0.000312 0.00743** 0.0100** 0.00342 0.00943

(0.00188) (0.00223) (0.00289) (0.00385) (0.00377) (0.00440) (0.00602) (0.00662)

# stores within 45m 7.60e-05** 0.000104*** 4.21e-05 6.09e-05 -6.55e-06 1.19e-06 3.19e-05 -1.81e-06

(3.24e-05) (3.73e-05) (5.11e-05) (6.87e-05) (7.29e-05) (9.19e-05) (0.000109) (0.000137)

Observations 350,950 123,164 129,543 98,243 175,628 61,822 64,795 49,011

R-squared 0.095 0.057 0.088 0.099 0.106 0.083 0.089 0.087

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.0672 0.0346 0.0675 0.108 0.166 0.0917 0.178 0.245

33% decrease in TT -0.0351 -0.0251 -0.0784 -0.0126 -0.301 -0.406 -0.139 -0.382

% change vs. mean -0.523 -0.725 -1.162 -0.117 -1.812 -4.430 -0.778 -1.563

Schools 826 481 427 426 820 475 413 419

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity,

language spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all three

grades include school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report the mean

of the dependent variable, the percentage point change corresponding with a 33% decrease in travel time, the percent change

relative to the mean, and the number of schools.

50

Table 6: Effects of travel time and retail density on marijuana use for binge-drinking students

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

Grade All 8th 10th 12th All 8th 10th 12th

If Used If Used If Used If Used Heavy Use Heavy Use Heavy Use Heavy Use

Ln(Travel time) -0.0215*** 0.0292 -0.0315** -0.0295*** -0.00430 0.00911 -0.0179 0.00153

(0.00794) (0.0188) (0.0146) (0.0111) (0.00765) (0.0180) (0.0121) (0.0105)

# stores within 45m -0.000107 -7.12e-06 0.000139 -0.000337 -0.000174 0.000157 7.85e-06 -0.000378*

(0.000168) (0.000449) (0.000282) (0.000236) (0.000151) (0.000359) (0.000253) (0.000219)

Observations 37,158 5,755 13,949 17,454 37,158 5,755 13,949 17,454

R-squared 0.077 0.135 0.071 0.063 0.102 0.130 0.101 0.098

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.646 0.599 0.656 0.654 0.259 0.222 0.264 0.266

33% decrease in TT 0.872 -1.185 1.278 1.196 0.174 -0.369 0.724 -0.0619

% change vs. mean 1.349 -1.977 1.948 1.828 0.674 -1.664 2.740 -0.232

Schools 760 388 376 401 760 388 376 401

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity,

language spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all

three grades include school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report

the mean of the dependent variable, the percentage point change corresponding with a 33% decrease in travel time, the

percent change relative to the mean, and the number of schools.

51

Table 7: Effects of travel time and retail density on marijuana use for students with risk index > 1

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

Grade All 8th 10th 12th All 8th 10th 12th

If Used If Used If Used If Used Heavy Use Heavy Use Heavy Use Heavy Use

Ln(Travel time to nearest store) -0.00549 0.00212 -0.000224 -0.0185** -0.00243 0.00388 -0.000411 -0.0102

(0.00582) (0.00815) (0.0103) (0.00915) (0.00424) (0.00467) (0.00743) (0.00802)

# stores within 45m 3.44e-05 4.76e-06 0.000128 -2.25e-05 1.77e-05 8.21e-05 0.000107 -0.000119

(0.000112) (0.000168) (0.000188) (0.000193) (7.94e-05) (9.64e-05) (0.000138) (0.000161)

Observations 64,683 20,702 23,585 20,396 64,683 20,702 23,585 20,396

R-squared 0.127 0.100 0.116 0.113 0.114 0.076 0.103 0.114

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.259 0.169 0.276 0.330 0.102 0.0509 0.106 0.149

33% decrease in TT 0.223 -0.0858 0.00909 0.750 0.0987 -0.157 0.0167 0.413

% change vs. mean 0.861 -0.507 0.0330 2.275 0.970 -3.089 0.157 2.777

Schools 800 446 394 403 800 446 394 403

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity,

language spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all three grades

include school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report the mean of the

dependent variable, the percentage point change corresponding with a 33% decrease in travel time, the percent change relative to the

mean, and the number of schools.

52

Table 8: Effects of travel time and retail density on marijuana use for students with risk index > 2

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

Grade All 8th 10th 12th All 8th 10th 12th

If Used If Used If Used If Used Heavy Use Heavy Use Heavy Use Heavy Use

Ln(Travel time to nearest store) -0.0196** 0.0170 -0.0183 -0.0451*** -0.0116 0.00738 -0.00389 -0.0315**

(0.00910) (0.0158) (0.0148) (0.0157) (0.00775) (0.00958) (0.0118) (0.0144)

# stores within 45m 7.68e-05 0.000151 0.000245 -7.29e-05 6.80e-06 0.000301 -8.27e-06 -6.50e-05

(0.000204) (0.000351) (0.000334) (0.000350) (0.000161) (0.000192) (0.000291) (0.000321)

Observations 20,473 5,642 7,072 7,759 20,473 5,642 7,072 7,759

R-squared 0.159 0.167 0.151 0.131 0.148 0.136 0.138 0.136

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.354 0.241 0.372 0.420 0.159 0.0838 0.164 0.210

33% decrease in TT 0.794 -0.689 0.743 1.828 0.470 -0.299 0.158 1.276

% change vs. mean 2.242 -2.855 1.998 4.358 2.949 -3.568 0.961 6.070

Schools 761 401 361 384 761 401 361 384

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity, language

spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all three grades include

school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report the mean of the dependent

variable, the percentage point change corresponding with a 33% decrease in travel time, the percent change relative to the mean, and

the number of schools.

53

Table 9: Falsification tests

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

Grade All All

All All English Lives With

Male White At Home Parents

Ln(Travel time) -0.00377 0.00455 -0.00196 0.000331

(0.00278) (0.00289) (0.00154) (0.00139)

# stores within 45m -2.19e-06 -0.000216*** -2.32e-05 -4.48e-05*

(5.53e-05) (6.01e-05) (3.69e-05) (2.64e-05)

Observations 350,056 350,056 350,056 350,056

R-squared 0.039 0.260 0.325 0.070

School & year FEs? Yes Yes Yes Yes

Controls? Yes Yes Yes Yes

Mean of DV 0.470 0.548 0.819 0.941

33% decrease in TT 0.153 -0.185 0.0796 -0.0134

% change vs. mean 0.325 -0.337 0.0971 -0.0143

Schools 826 826 826 826

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by

school. Controls include gender, age, race/ethnicity, language spoken at

home, living situation, grades earned last year, and mother's education.

Specifications combining all three grades include school-grade fixed

effects, rather than school fixed effects. In the last four rows of the table,

I report the mean of the dependent variable, the percentage point change

corresponding with a 33% decrease in travel time, the percent change

relative to the mean, and the number of schools.

54

Table 10: Additional falsification tests

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

Grade All All All All All All All All

Mostly Mostly Mostly Mostly Mom Did Mom Mom Went Mom Graduated

Bs Cs Ds Fs Not Finish HS Graduated HS To College 4-Year College

Ln(Travel time) -0.000664 0.00323 -0.00140 -0.00119 -0.000353 -0.000270 -0.00113 0.00312

(0.00268) (0.00219) (0.00117) (0.000849) (0.00162) (0.00203) (0.00210) (0.00198)

# stores within 45m -3.37e-05 -5.94e-05 8.19e-06 -3.30e-05** -5.31e-07 -7.38e-05* -0.000102** 3.66e-06

(6.37e-05) (4.35e-05) (2.35e-05) (1.64e-05) (3.34e-05) (3.90e-05) (4.29e-05) (4.93e-05)

Observations 350,056 350,056 350,056 350,056 350,056 350,056 350,056 350,056

R-squared 0.029 0.061 0.032 0.038 0.237 0.051 0.037 0.090

School & year FEs? Yes Yes Yes Yes Yes Yes Yes Yes

Controls? Yes Yes Yes Yes Yes Yes Yes Yes

Mean of DV 0.319 0.162 0.0411 0.0230 0.143 0.217 0.235 0.258

33% decrease in TT 0.0269 -0.131 0.0568 0.0482 0.0143 0.0109 0.0459 -0.127

% change vs. mean 0.0845 -0.809 1.382 2.101 0.0998 0.0504 0.196 -0.490

Schools 826 826 826 826 826 826 826 826

*** p<0.01, ** p<0.05, * p<0.1.

Notes: Estimates obtained using OLS with standard errors clustered by school. Controls include gender, age, race/ethnicity, language

spoken at home, living situation, grades earned last year, and mother's education. Specifications combining all three grades include

school-grade fixed effects, rather than school fixed effects. In the last four rows of the table, I report the mean of the dependent variable,

the percentage point change corresponding with a 33% decrease in travel time, the percent change relative to the mean, and the number

of schools.