Abstract
Purpose:
The commercialization of recreational cannabis has led to increased concern over adolescent cannabis use in the U.S. Given that price-based regulations have the potential to reduce cannabis consumption and mitigate the adverse effects associated with cannabis use, this study aims to assess the price elasticity of cannabis use among U.S. adolescents.
Methods:
Data were drawn from the repeated cross-sectional Monitoring the Future Survey conducted between 2015 and 2022. A total of 40,277 adolescents from ten states that had commercialized recreational cannabis at the time of survey were included in the analysis. Two cannabis use outcomes were assessed, including current cannabis use and frequent cannabis use. Logistic regressions were performed to estimate the associations of these outcomes with legal cannabis prices and cannabis taxes. To address potential endogeneity in prices, we applied the generalized method of moments estimator, using cannabis tax as an instrumental variable.
Results:
An increase in cannabis prices was associated with a lower likelihood of current cannabis use, with estimated price elasticity ranging from −0.33 to −0.21 (p-values for most model specifications smaller than 0.05). Neither the association between cannabis prices and frequent cannabis use, nor the associations between cannabis taxes and current use or frequent use, were statistically significant.
Discussion:
This study found a significant, though inconsistent, negative association between legal cannabis prices and current cannabis use among adolescents. The estimated price elasticity ranged from −0.33 to −0.21. Further research utilizing long-term data is recommended.
Keywords: price elasticity, recreational cannabis, recreational cannabis commercialization, cannabis current use, cannabis frequent use, adolescent
Introduction
Cannabis use among adolescents has become an increasingly pressing public health issue in the U.S. In 2023, more than 10% individuals aged 12 to 20 reported using cannabis in the past 30 days, and over 54% of first-time cannabis users were underage (1). Extensive research has documented a range of adverse outcomes associated with cannabis use, including cognitive impairments, dependence on cannabis and other illicit substances, acute toxicity, lung cancer and an increased risk of cannabis use disorders (2–4). For adolescents, early cannabis use may lead to long-term consequences, such as academic challenges, diminished future employment prospect and a higher risk of developing mental health conditions like depression and psychosis (5, 6), which can persist into adulthood.
Despite cannabis remaining illicit at the federal level, 20 states had legalized and commercialized recreational cannabis by 2023. By removing legal barriers, expanding access, and reshaping public perceptions, the commercialization may contribute to increased problematic use and adverse health consequences.
Research on alcohol and tobacco suggested that price regulations were among the most effective policy tools for controlling substance use (7, 8). Taxation, as a key element of price regulation, can alter purchasing behaviors by raising the cost of products. Strong evidence indicated that taxes, such as excise taxes and sales taxes, on tobacco and alcohol products were associated with decreased consumption, reduced initiation, and increased cessation (9, 10). Imposing excise taxes on cannabis products may similarly make them less affordable, thereby reducing overall consumption (11). Price elasticity of demand refers to the proportional change in demand in response to a change in price (e.g. the percentage change in consumption resulting from a 10% price increase) (12). It reflects consumers’ sensitivity to price and is a key concept for evaluating the impact of price-based regulations. A product is considered price elastic when the proportional decrease in demand exceeds the proportional increase in price (i.e. the absolute value of elasticity is greater than one) and is price inelastic when demand changes less than the change in price. When demand is inelastic, tax revenues would increase as price increases. Accurately estimating the price elasticity of cannabis use is crucial for understanding how pricing or taxation may influence consumer behavior and tax revenues.
Although previous studies explored the price elasticity for cannabis use behaviors (13, 14), almost all focused on illicit markets prior to the legalization of recreational cannabis sales. For the adult population, some studies estimated that the price elasticity of illicit cannabis use ranged from −1.51 to 0.17 (15–19), while others suggested that the price elasticity was not significantly different from zero (20). Some more recent research estimated the price elasticity of legal cannabis sales using data collected after recreational cannabis commercialization, with estimated elasticities ranging from −3.5 to −0.43 (11, 21–23). These studies used aggregated data with little inference on individual-level behavioral changes. To date, only one study has evaluated the price elasticity of current cannabis use status among adults in states with legalized recreational markets, reporting elasticity estimates between –0.66 and –0.59. However, these associations were not statistically significant (24).
Unlike adults, adolescents are prohibited from directly accessing legalized recreational cannabis markets. Nevertheless, they may still obtain cannabis through quasi-legal or informal channels, such as peers or family members, which are facilitated by weak law enforcement. This indirect market structure may influence adolescents’ price responsiveness by distancing them from formal market dynamics. On one hand, adolescents might be more price sensitive due to their generally limited financial power (25). On the other hand, their limited purchasing experience may reduce sensitivity to price changes, leading to less informed decisions and narrower product preferences (26). Additionally, their reliance on indirect sources, where prices are shaped more by social networks than market forces, may further reduce price responsiveness, complicating predictions of their price elasticity.
To date, only three studies have examined the price elasticity of cannabis use among U.S. adolescent population. One study, which used cannabis price data from the Illegal Drug Price/Purity Report (IDPPR) published by the Drug Enforcement Administration from 1990 to 1997, found adolescent cannabis use was not price responsive (20). Another study, analyzing price data from both IDPPR and the System to Retrieve Information from Drug Evidence (STRIDE) between 1982 and 1998, estimated the price elasticity for past-year cannabis use among adolescents to range from −0.47 to −0.06, and for past-month use, from −0.69 to −0.002 (27). Additionally, one study that used illicit cannabis price data from STRIDE between 2002 and 2007 estimated the price elasticity of demand for cannabis among adolescents aged 12–17 to be −1.01(18).
Several research gaps remained in the literature on adolescent cannabis use. First, prior studies relied on price data that were either self-reported or lacked representativeness of actual markets. Second, studies focused on illicit markets prior to cannabis commercialization. Illicit markets typically featured for limited accessibility, law enforcement penalties, and inherent irregularities, which make their findings less applicable to legal cannabis markets. Third, the price variables in these studies were likely subject to endogeneity issues, where both the price variable and the behavioral outcome variables were affected by confounding factors that were not controlled for, or caused by reverse causality, where higher demand may drive higher prices.
To address these research gaps, we conducted this study to examine the price elasticity of cannabis use among adolescents in states with recreational cannabis commercialization. This study contributes to the existing literature in several ways. First, we constructed representative, objectively measured state-level cannabis price data. Second, we focused on legal markets following cannabis commercialization. Third, we addressed potential endogeneity in the price variable by using cannabis tax as an instrument variable. The findings may provide important policy implications regarding the impact of cannabis taxation and pricing strategies on adolescent cannabis use behaviors.
Methods
Data source and study sample
The Monitoring the Future (MTF) study is a repeated cross-sectional survey that collects data from nationally representative samples of U.S. students in the 8th, 10th, and 12th grades. Annually, this study employs a multi-stage random sampling method to collect data from 130 schools, including approximately 10,000 students per grade across the contiguous U.S. It provides comprehensive data on adolescent substance use, risk perceptions, and behavioral trends, making it a valuable resource for understanding adolescent behaviors over time.
This study analyzed MTF data from 2015 to 2022, corresponding to the period during which cannabis price data were available. The analysis focused exclusively on states where recreational cannabis had been commercialized at the time of each survey (see Supplementary Table S1 for data availability) and included only years following legalization in those states, aiming to examine behavioral responses to the prices in legal cannabis markets. After excluding 2,647 respondents with missing cannabis use data, the final analytic sample included 40,277 adolescents across ten states.
Data access and results disclosure were reviewed and approved by the Inter-university Consortium for Political and Social Research at the University of Michigan. This study was approved by the Institutional Review Board at the University of California San Diego.
Measures
Individual outcomes: current cannabis use and current frequent cannabis use
This study had two outcomes of interest, both of which were binary indicators: 1) current cannabis use, and 2) current frequent cannabis use. Adolescents were asked how many times they had used cannabis in the past 30 days, with response options ranging from “0 occasions” to “40 or more occasions”. Respondents who reported using cannabis on one or more occasions were classified as current users. Respondents who reported using cannabis ten or more occasions in the past 30 days were further classified as current frequent users (28).
Cannabis-related predictor: state-level cannabis wholesale prices
Cannabis Benchmarks is a leading provider of business and transactional data in the North America cannabis market. It regularly tracks and collects data on legal cannabis transactions in states where cannabis has been legalized for medical and/or recreational purposes. These transactions occur at the wholesale level before any taxes are applied, and involve licensed operators, such as cultivators, extractors/producers, and dispensaries. Prices are constructed by removing outliers from transactional data point, aggregating data from multiple sources, and then standardizing to reflect wholesale cannabis prices per ounce. Currently, Cannabis Benchmarks only tracks wholesale prices for cannabis flower, without adjustments for potency variations. The data have been validated against published wholesale cannabis prices from governmental reports and used in publications that derived wholesale prices for legal cannabis (24, 29).
Cannabis-related predictor: state-level cannabis excise taxes
Given the varying tax bases and the multiple stages at which cannabis excise taxes are imposed, we standardized these tax structures by converting them into a common per-ounce tax rate for cannabis flower (29) (see Appendix Note 1 for details on the tax conversion process).
State-level substance policy covariates
This study incorporated three state-level substance policy covariates, which were state-level tax rates for cigarettes, e-cigarettes and beer. We obtained cigarette excise tax per pack of 20 cigarettes from the State Tobacco Activities Tracking and Evaluation System of the Centers for Disease Control and Prevention. We obtained e-cigarette tax per fluid milliliter that were standardized by Cotti et al (30). We obtained standardized beer tax per gallon from the Alcohol Policy Information System of National Institute on Alcohol Abuse and Alcoholism. State-level sociodemographic covariates were not considered due to the small sample size of states.
Individual sociodemographic covariates
Individual sociodemographic covariates included gender, race/ethnicity, grade year, academic performance, urbanicity of residence, parental educational attainments, and adolescents’ living arrangements.
Statistical analysis
We summarized statistics for the outcomes and covariates. We estimated the price elasticity of 1) current cannabis use among all adolescents; 2) frequent cannabis use among current users only; and 3) frequent cannabis use among all adolescents in the sample. Specifically, we used frequent cannabis use among current users to examine how price changes contributed intensive cannabis use. We used frequent cannabis use among all respondents to assess how price changes influenced both the initiation and intensity of cannabis use. In our analyses, the first set of regressions used cannabis wholesale prices as the main predictor, and the second set of regressions used cannabis excise taxes as the main predictor, in that taxes are a crucial price-based instrument and could be largely passed on to consumers (11). Logistic regressions were used for estimation. To address potential endogeneity in cannabis prices, in the third set of regressions we used cannabis taxes as an instrument for cannabis prices and applied the generalized method of moment estimators.
Various model specifications were applied for each set of regressions to test the robustness of the findings. In Models 1 and 2, we controlled for two-way fixed effects, including both state and year fixed effects. In Models 3 and 4, we replaced year-fixed effects with year trend to account for temporal changes. To address the impact of COVID-19 pandemic, Models 5 and 6 further replaced year-fixed effects with a COVID-specific trend. The main differences between Model 1 and Model 2, Model 3 and Model 4, and Model 5 and Model 6 lied in the inclusion of state-level substance policy covariates. All models controlled for individual sociodemographic covariates. Both cannabis prices and taxes were log-transformed before entering the regressions. Standard errors were clustered at the state level. For each estimate, we also calculated the implied price or tax elasticities.
Among all the regressions, Model 2 was preferred because it included both individual-level sociodemographic characteristics and state-level substance policy covariates. This specification also accounts for time-invariant state-level factors and captures year-specific unobserved variations common across states.
Results
Table 1 provides summary statistics for the study sample. The overall prevalence of current cannabis use among U.S. adolescent was approximately 14.43% during the study period. Among current users, 36.01% were frequent users, representing 5.20% of the entire sample. The average wholesale price of cannabis was $119.16 per ounce, and the average cannabis excise tax was around $42.69 per ounce.
Table 1.
Summary statistics on sample characteristics (N=40,277)
| Mean (%) | 95% Confidence Interval | |
|---|---|---|
| Individual level Cannabis Use Outcomes | ||
| Current cannabis use | 14.43 | (14.089, 14.78) |
| Frequent cannabis use (10+ times) among current cannabis users | 36.010 | (34.77, 37.24) |
| Frequent cannabis use (10+ times) among all respondents | 5.20 | (4.98, 5.41) |
| State-level cannabis price and tax | ||
| Wholesale price adjusted for inflation 2022 ($/oz) | 119.16 | (100.56, 137.76) |
| Cannabis excise taxes adjusted for inflation 2022 ($/oz) | 42.69 | (36.77, 48.60) |
| Individual level sociodemographic characteristics | ||
| Gender: male | 47.76 | (47.28, 48.25) |
| Gender: female | 46.79 | (46.30, 47.28) |
| Gender: other choices/missing values | 5.45 | (5.23, 5.67) |
| Race/Ethnicity: Hispanics | 27.54 | (27.11, 27.98) |
| Race/Ethnicity: Non-Hispanic Whites | 38.50 | (38.026, 38.98) |
| Race/Ethnicity: Non-Hispanic Blacks | 5.80 | (5.58, 6.033) |
| Race/Ethnicity: missing values | 28.15 | (27.71, 28.59) |
| Grade year: 8th grade | 34.67 | (34.21, 35.14) |
| Grade year: 10th grade | 35.26 | (34.79, 35.73) |
| Grade year: 12th grade | 30.064 | (29.62, 30.51) |
| Grade: A | 39.49 | (38.99, 39.94) |
| Grade: B | 37.31 | (36.85, 37.79) |
| Grade: C or D | 19.64 | (19.25, 20.024) |
| Grade: missing values | 3.57 | (3.40, 3.76) |
| Urbanicity: urban | 85.25 | (84.91, 85.60) |
| Father’s educational attainment: high school or below | 35.34 | (34.88, 35.81) |
| Father’s educational attainment: some college | 11.79 | (11.48, 12.11) |
| Father’s educational attainment: bachelor or above | 33.85 | (33.39, 34.31) |
| Father’s educational attainment: unknown/missing values | 19.013 | (18.63, 19.37) |
| Mother’s educational attainment: high school or below | 29.22 | (28.78, 29.67) |
| Mother’s educational attainment: some college | 14.30 | (13.96, 14.64) |
| Mother’s educational attainment: bachelor or above | 42.65 | (42.16, 43.13) |
| Mother’s educational attainment: unknown/missing values | 13.83 | (13.49, 14.17) |
| Live with: both parents | 72.58 | (72.14, 73.018) |
| Live with: one parent/alone | 22.73 | (22.32, 23.13) |
| Live with: other/missing values | 4.69 | (4.49, 4.99) |
| State-level tobacco and beer policies | ||
| Cigarette tax adjusted for inflation 2022 ($/pack) | 2.64 | (2.35, 2.93) |
| E-Cigarette tax adjusted for inflation 2022 ($/fluid milliliter) | 0.42 | (0.22, 0.66) |
| Beer tax adjusted for inflation 2022 ($/gallon) | 0.29 | (0.20, 0.38) |
Notes: Statistics for cannabis prices, cannabis taxes, cigarette taxes, e-cigarette taxes and beer taxes were generated at state-year level.
Table 2 presents the results from the logistic regressions, using cannabis wholesale prices as the main predictor. In our preferred specification, Panel A, which focused on current cannabis use as the main outcome, showed that the price elasticity of adolescent current cannabis use was approximately −0.13. This estimate, however, was not statistically significant at the 5% level. Models 3 through 6 represent alternative specifications that controlled for the year trend and COVID-specific trend, respectively. These models suggested an increase in cannabis prices was associated with a lower likelihood of current cannabis use, with estimated price elasticities ranging from −0.33 (95% confidence interval (CI): −0.63, −0.03) to −0.31 (95% CI: −0.55, −0.07). In Panels B and C, where current frequent cannabis use was the primary outcome, cannabis price was not a significant predictor of frequent cannabis use, either among current users or across the entire sample of respondents. Complete regression results are available in Supplementary Tables S2 through S4.
Table 2.
Cannabis price as main predictor: Logistic regression without instrumental variable
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| Panel A: Outcome—Current cannabis use status (N=40,277) | ||||||
| Cannabis price | −0.030 (−0.083, 0.023) | −0.019 (−0.069, 0.031) | −0.048* (−0.091, −0.0040) | −0.046* (−0.089, −0.0035) | −0.048** (−0.083, −0.013) | −0.045* (−0.079, −0.010) |
| Implied price elasticity | −0.21 | −0.13 | −0.33 | −0.32 | −0.33 | −0.31 |
| Panel B: Outcome—Current cannabis frequent use status among current cannabis users (N=5,813) | ||||||
| Cannabis price | −0.013 (−0.21, 0.18) | 0.010 (−0.16, 0.18) | 0.0010 (−0.12, 0.12) | 0.027 (−0.085, 0.14) | 0.030 (−0.13, 0.18) | 0.049 (−0.10, 0.20) |
| Implied price elasticity | −0.036 | 0.028 | 0.0028 | 0.075 | 0.083 | 0.14 |
| Panel C: Outcome—Current cannabis frequent use status among all respondents (N=40,277) | ||||||
| Cannabis price | −0.010 (−0.053, 0.034) | −0.0030 (−0.044, 0.038) | −0.016 (−0.044, 0.011) | −0.013 (−0.039, 0.013) | −0.012 (−0.034, 0.0095) | −0.010 (−0.030, 0.011) |
| Implied price elasticity | −0.19 | −0.058 | −0.31 | −0.25 | −0.23 | −0.19 |
| Individual level sociodemographic controls | Y | Y | Y | Y | Y | Y |
| State-level substances policy controls | N | Y | N | Y | N | Y |
| State fixed effects | Y | Y | Y | Y | Y | Y |
| Year fixed effects | Y | Y | N | N | N | N |
| Year trend | N | N | Y | Y | N | N |
| COVID-specific trend | N | N | N | N | Y | Y |
Notes:
p<0.01,
p<0.05. Cannabis prices were log-transformed. Marginal effects and 95% CIs were reported. Complete regression results are available in Supplementary Tables S2 through S4.
Table 3 shows the results from the reduced-form logistic regressions, using cannabis taxes as the main predictor. In Model 2 Panel B, where frequent cannabis use was the primary outcome, the tax elasticity of adolescent frequent cannabis use among current users was approximately 0.25 (95% CI: 0.03, 0.47). In Panels A and C, cannabis tax was not a significant predictor of either current cannabis use, or frequent cannabis use among all respondents. Complete regression results are available in Supplementary Tables S5 through S7.
Table 3.
Cannabis tax as main predictor: Logistic regression without instrumental variable
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
|---|---|---|---|---|---|---|
| Panel A: Outcome—Current cannabis use status (N=40,277) | ||||||
| Cannabis tax | 0.012 (−0.014, 0.037) | 0.014 (−0.011, 0.038) | −0.029 (−0.075, 0.017) | −0.028 (−0.073, 0.018) | −0.018 (−0.061, 0.026) | −0.020 (−0.062, 0.023) |
| Implied tax elasticity | 0.083 | 0.097 | −0.20 | −0.19 | −0.12 | −0.14 |
| Panel B: Outcome—Current cannabis frequent use status among current cannabis users (N=5,813) | ||||||
| Cannabis tax | 0.093 (−0.010, 0.19) | 0.090* (0.010, 0.17) | 0.025 (−0.056, 0.11) | −0.044 (−0.028, 0.12) | 0.083 (−0.030, 0.20) | 0.090 (−0.021, 0.20) |
| Implied tax elasticity | 0.26 | 0.25 | 0.069 | −0.12 | 0.23 | 0.25 |
| Panel C: Outcome—Current cannabis frequent use status among all respondents (N=40,277) | ||||||
| Cannabis tax | 0.016 (−0.0026, 0.035) | 0.016 (−0.0010, 0.033) | −0.010 (−0.031, 0.017) | −0.0042 (−0.028, 0.019) | 0.010 (−0.013, 0.024) | 0.0049 (−0.011, 0.021) |
| Implied tax elasticity | 0.31 | 0.31 | −0.13 | −0.10 | 0.11 | 0.10 |
| Individual level sociodemographic controls | Y | Y | Y | Y | Y | Y |
| State-level substances policy controls | N | Y | N | Y | N | Y |
| State fixed effects | Y | Y | Y | Y | Y | Y |
| Year fixed effects | Y | Y | N | N | N | N |
| Year trend | N | N | Y | Y | N | N |
| COVID-specific trend | N | N | N | N | Y | Y |
Notes:
p<0.05. Cannabis taxes were log-transformed. Marginal effects and 95% CIs were reported. Complete regression results are available in Supplementary Tables S5 through S7.
Table 4 outputs the results from the generalized method of moment estimators, where cannabis tax was used as an instrument for cannabis prices. In the first stage, the results demonstrated that cannabis tax was a strong and valid predictor of cannabis price. The F-statistics for all models exceeded the threshold of 10, indicating a sufficient correlation between cannabis tax and price, thus confirming the validity of this approach. As shown in Panel A, in the second stage, our preferred specification suggested that the price elasticity of adolescent current cannabis use was approximately 0.23, though it was not statistically significant. The alternative specification, Model 4, which controlled for year trends, indicated a negative association between cannabis prices and adolescent current cannabis use, with a price elasticity of −0.21 (95% CI: −0.35, −0.08). In Panel B, our preferred specification estimated the price elasticity for adolescent frequent cannabis use among current users at 0.53, although this estimate remained statistically insignificant. When controlling for the COVID-specific trend, Model 6 suggested a price elasticity of 0.36 (95% CI: 0.05, 0.67). In Panel C, the results indicated that cannabis price was not a significant predictor of frequent cannabis use among all respondents. Complete regression results are available in Supplementary Tables S8 through S10.
Table 4.
Current cannabis use estimation: Generalized Method of Moments Regression with Cannabis Tax as Instrumental Variable
| First-stage: | ||||||
|---|---|---|---|---|---|---|
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
| Panel A: Outcome—Cannabis price (N=40,277) | ||||||
| Cannabis tax | 0.49*** (0.48, 0.50) | 0.49*** (0.49, 0.50) | 0.76*** (0.76, 0.77) | 0.75*** (0.74, 0.75) | 0.70*** (0.69, 0.71) | 0.73*** (0.72, 0.73) |
| F-statistics | 13347.40 | 18248.40 | 99731.10 | 92113.30 | 39983.10 | 51720.30 |
| Panel B: Outcome—Cannabis price (N=5,813) | ||||||
| Cannabis tax | 0.48*** (0.46, 0.50) | 0.49*** (0.47, 0.51) | 0.73*** (0.72, 0.74) | 0.71*** (0.69, 0.72) | 0.67*** (0.65, 0.69) | 0.69*** (0.67, 0.70) |
| F-statistics | 2181.72 | 2530.61 | 14842.40 | 10271.80 | 6098.02 | 6362.34 |
| Panel C: Outcome—Cannabis price (N=40,277) | ||||||
| Cannabis tax | 0.49*** (0.48, 0.50) | 0.49*** (0.49, 0.50) | 0.76*** (0.76, 0.77) | 0.75*** (0.74, 0.75) | 0.70*** (0.69, 0.71) | 0.73*** (0.72, 0.73) |
| F-statistics | 13347.40 | 18248.40 | 99731.10 | 92113.30 | 39983.10 | 51720.30 |
| Individual level sociodemographic controls | Y | Y | Y | Y | Y | Y |
| State-level substances policy controls | N | Y | N | Y | N | Y |
| State fixed effects | Y | Y | Y | Y | Y | Y |
| Year fixed effects | Y | Y | N | N | N | N |
| Year trend | N | N | Y | Y | N | N |
| COVID-specific trend | N | N | N | N | Y | Y |
| Second-stage: | ||||||
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | |
| Panel A: Outcome—Current cannabis use status (N=40,277) | ||||||
| Cannabis price | 0.027 (−0.031, 0.085) | 0.033 (−0.027, 0.092) | −0.035*** (−0.054, −0.016) | −0.031** (−0.050, −0.011) | −0.023 (−0.056, 0.010) | −0.020 (−0.048, 0.0077) |
| Implied price elasticity | 0.19 | 0.23 | −0.24 | −0.21 | −0.16 | −0.14 |
| Panel B: Outcome—Current cannabis frequent use status among current cannabis users (N=5,813) | ||||||
| Cannabis price | 0.20 (−0.0019, 0.40) | 0.19 (−0.0089, 0.39) | 0.035 (−0.037, 0.11) | 0.064 (−0.014, 0.14) | 0.13* (0.018, 0.25) | 0.13* (0.017, 0.24) |
| Implied price elasticity | 0.56 | 0.53 | 0.10 | 0.18 | 0.36 | 0.36 |
| Panel C: Outcome—Current cannabis frequent use status among all respondents (N=40,277) | ||||||
| Cannabis price | 0.033 (−0.0060, 0.071) | 0.035 (−0.0053, 0.075) | −0.010 (−0.020, 0.0044) | −0.0049 (−0.018, 0.0077) | 0.010 (−0.012, 0.028) | 0.013 (−0.0074, 0.034) |
| Implied price elasticity | 0.63 | 0.67 | −0.15 | −0.10 | 0.15 | 0.25 |
| Individual level sociodemographic controls | Y | Y | Y | Y | Y | Y |
| State-level substances policy controls | N | Y | N | Y | N | Y |
| State fixed effects | Y | Y | Y | Y | Y | Y |
| Year fixed effects | Y | Y | N | N | N | N |
| Year trend | N | N | Y | Y | N | N |
| COVID-specific trend | N | N | N | N | Y | Y |
Notes:
p<0.001,
p<0.01,
p<0.05. Cannabis prices were log-transformed. Cannabis taxes were log-transformed and used as instrumental variable. Complete regression results are available in Supplementary Tables S8 through S10.
Discussion
This study aimed to estimate the price elasticity of cannabis in states that have legalized recreational cannabis sales. In the preferred model, which included two-way fixed effects, no significant association was found between legal cannabis price and adolescent cannabis use. This finding is consistent with a previous work on adolescents that found null association (20). One possible explanation is that our study estimated cannabis use behaviors in response to legal market prices. Despite cannabis retail is legal in these states, adolescents were not able to purchase cannabis products from licensed dispensaries, because the minimum legal purchasing age is 21 and almost all dispensaries enforce age verification to prevent underage access (31). Moreover, due to the common perception that illicit cannabis is more affordable than legal options (19), adolescent consumers may still obtain cannabis from illicit cannabis markets or social networks, which were less sensitive to price variations in legal markets. Second, as demonstrated in existing literature (32), cannabis elasticity at the extensive margin (i.e. use status) was generally inelastic. Compared to outcomes like consumption quantity or sales at the intensive margin, use status is usually less responsive to price changes. Third, research on addictive goods indicated that long-term price responses tend to be more sensitive than short-term ones (33). Therefore, it is possible that changes in cannabis use status may take time to adjust in response to price fluctuations. Fourth, as revealed in tobacco research on adolescent use (34, 35), most adolescents who used cigarettes or e-cigarettes were experimenting instead of establishing a frequent use pattern. This may explain the insignificant estimates regarding cannabis frequent use in our study. Additionally, this study used data from the early years of cannabis commercialization, which may reflect legal markets that were premature and evolving rapidly.
In alternative specifications, a negative association was observed between legal cannabis prices and adolescent current cannabis use, when year fixed effects were not controlled for. The estimated price elasticity ranged from −0.33 to −0.21, indicating that higher legal cannabis prices were associated with lower likelihood of current cannabis use among adolescents. This finding was comparable to an existing study, which identified cannabis price elasticity of current use to range from −0.69 to −0.002 (27).
We also observed that the absolute value of the price elasticity of frequent use was greater than that of current use. This finding is supported by a previous study on tobacco use, which suggested that more intensive adolescent use tended to be more responsive to price changes (36). Infrequent users among adolescents are more likely to obtain substances through social sharing or peer exchanges rather than direct purchases, which are less influenced by price changes. In contrast, frequent users may make regular purchases from sources including legal markets, and thus exhibit greater sensitivity to price changes.
Our findings bring important policy implications. While a negative association was observed between legal prices and adolescent current cannabis use, price-based policies alone may not be sufficient to influence cannabis use among adolescents. Policymakers may also consider comprehensive strategy that go beyond price regulation to effectively address adolescent cannabis use. Additionally, adolescents’ access to cannabis through social channels, such as peer-to-peer exchanges or family members, may complicate the effectiveness of price-based policies. Therefore, regulations that address these social dynamics are crucial. It is also important to consider broader societal regulations and targeted education programs that challenge the normalization of cannabis use within peer groups and families to help prevent adolescent cannabis use.
This study has limitations. First, the MTF survey has data constraints. Cannabis use was self-reported by adolescents, which might be subject to recall and social desirability biases. The survey did not indicate whether cannabis was obtained from legal or illicit sources, nor whether it was acquired through non-monetary means. Moreover, we could not determine the route of administration or distinguish between use of flower and non-flower products. In addition, MTF data are not representative at the state level. Despite these limitations, the MTF survey remains a valid and important data source for research on adolescent cannabis use (37). Second, the cannabis prices used in this study reflect only wholesale prices for flower products. Price data for non-flower products, which have grown in popularity, were unavailable. We were also unable to measure retail prices, which include in-store promotions and taxes and are more likely to directly influence consumer behavior. Third, this study spanned 2015 to 2022, primarily capturing the early phase of recreational cannabis market development across ten states. Thus, there was limited price variation across states and over time. Fourth, we estimated the price elasticity of cannabis use at the extensive margin rather than the intensive margin. Fifth, this study is limited by variation in state-level medical cannabis policies, enforcement, and market conditions prior to recreational legalization. While state fixed effects account for time-invariant differences, they may not fully capture pre-existing factors such as lax enforcement or early adolescent access through medical channels. As the transition to recreational markets may represent different policy shifts across states, observed associations should be interpreted with caution. Future research could address this limitation by incorporating more detailed measures of pre-existing state-level confounding factors, including nuanced medical cannabis policy provisions and market characteristics. Additionally, we included a limited number of state-level controls to avoid potential multicollinearity that could arise from incorporating too many covariates. Lastly, our findings may not be generalizable to other states, different time points, regions where cannabis remains illicit or to older age groups.
This study examined the price elasticity of cannabis use, focusing on both current and frequent use among adolescents in states with recreational cannabis commercialization. Findings suggest a significant, though inconsistent, negative association between legal cannabis prices and current cannabis use, with price elasticity estimated between −0.33 and −0.21. Given the continued expansion and evolution of cannabis markets, further research utilizing long-term data is recommended.
Supplementary Material
Implications and contribution.
This study contributes to the understanding of how cannabis prices influence adolescent cannabis use. For the first time, it examines the price elasticity of legal cannabis prices on adolescent use. The observed negative association provides insights for potential price-based policy regulations.
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