Abstract
Objective
Behavioral economics suggest that cannabis reinforcing value (cannabis demand) may be influenced by external, contextual factors such as the social reward that might accompany cannabis use and the presence of opportunity costs (e.g., a next-day responsibility that cannabis use might adversely impact). The present study examined the effect of social context and opportunity cost on cannabis demand and explored whether relations were moderated by cannabis use severity.Method
Adults with past-week cannabis use recruited from Amazon's Mechanical Turk (N = 310; 53.5% female, 79.4% White) completed four purchase tasks, in which participants reported how much cannabis they would purchase across escalating prices, to index cannabis demand under varying contexts: (a) solitary, typical responsibilities; (b) social, typical responsibilities; (c) solitary, substantial responsibilities; and (d) social, substantial responsibilities.Results
The presence of peers significantly increased demand intensity (consumption at zero price) and Omax (maximum expenditure) relative to the solitary conditions. Substantial responsibilities significantly decreased intensity, breakpoint (price at which consumption is fully suppressed), and Pmax (price at which maximum expenditure occurs) and increased elasticity (greater price sensitivity). Demand was most inelastic in the social, typical responsibilities condition relative to other conditions. Cannabis use severity was associated with less elastic demand in the solitary, typical responsibilities condition. Those with higher cannabis use severity reported larger differences in demand intensity and Omax between solitary and social conditions, and in demand elasticity between typical and substantial responsibility conditions.Conclusions
Results are consistent with previous research illustrating social and opportunity costs as determinants of cannabis use behavior. (PsycInfo Database Record (c) 2023 APA, all rights reserved).Free full text
The Effects of Social Context and Opportunity Cost on the Behavioral Economic Value of Cannabis
Abstract
Objective:
Behavioral economics suggests that cannabis reinforcing value (cannabis demand) may be influenced by external, contextual factors such as the social reward that might accompany cannabis use and the presence of opportunity costs (e.g., a next-day responsibility that cannabis use might adversely impact). The current study examined the effect of social context and opportunity cost on cannabis demand and explored whether relations were moderated by cannabis use severity.
Methods:
Adults with past-week cannabis use recruited from Amazon’s Mechanical Turk (N=310; 53.5% female, 79.4% White) completed four purchase tasks, in which participants reported how much cannabis they would purchase across escalating prices, to index cannabis demand under varying contexts: (1) solitary, typical responsibilities; (2) social, typical responsibilities; (3) solitary, substantial responsibilities; and (4) social, substantial responsibilities.
Results:
The presence of peers significantly increased demand intensity (consumption at zero price) and Omax (maximum expenditure) relative to the solitary conditions. Substantial responsibilities significantly decreased intensity, breakpoint (price at which consumption is fully suppressed), and Pmax (price at which maximum expenditure occurs) and increased elasticity (greater price sensitivity). Demand was most inelastic in the social, typical responsibilities condition relative to other conditions. Cannabis use severity was associated with less elastic demand in the solitary, typical responsibilities condition. Those with higher cannabis use severity reported larger differences in demand intensity and Omax between solitary and social conditions, and in demand elasticity between typical and substantial responsibility conditions.
Conclusions:
Results are consistent with previous research illustrating social and opportunity costs as determinants of cannabis use behavior.
Introduction
Cannabis (i.e., marijuana) is the most commonly used federally illicit substance in the United States (National Survey on Drug Use and Health, 2018). Although most use only occasionally, 42% of those who use cannabis report daily use (Azofeifa et al., 2016), and roughly 6% of Americans meet criteria for lifetime cannabis use disorder (CUD; NSDUH, 2018). Cannabis use can be associated with acute health and social consequences, and some who use frequently experience chronic impairment and compulsive use (Arria et al., 2015; Terry-McElrath et al., 2022). It is critical to better understand the processes that may lead to chronic and harmful cannabis use.
Behavioral economics has contributed to understanding why people continue to use cannabis and other drugs despite negative consequences and suggests that drug use is most likely when the availability and reinforcing value of drugs exceeds that of potential drug-free reinforcers in a choice context (Tucker et al., 2022). Behavioral economics is a choice theory of addiction suggesting that drug use is a cost/benefit analysis; in short, cannabis use will be most likely when the benefits are greater, and the costs minimal, compared to other options in the choice context (Rachlin et al., 1981). Cannabis value, also known as cannabis demand, can be quantified within this framework using marijuana purchase tasks (MPT; Aston et al., 2015, 2021; Collins et al., 2014), which ask respondents to report hypothetical consumption in a scenario at escalating prices. Although cannabis demand demonstrates consistent associations with cannabis use, problems, and CUD, evidence also suggests that momentary fluctuations in cannabis demand can occur dependent upon the presence of constraints (i.e., any characteristic that may influence the consumption of cannabis) in a discrete choice context, including the presence of friends and opportunity costs, such as having a next-morning responsibility (Acuff, Soltis, et al., 2020; Joyner et al., 2019).
Social Factors
Social and public use of cannabis is increasingly tolerated as cannabis for medical and recreational use continues to be legalized across the United States. Indeed, like alcohol and other drug use, the vast majority of cannabis use occurs in social settings (Terry-McElrath et al., 2021), and social connection appears to be robustly associated with frequent cannabis use (Bonar et al., 2017). However, non-medical solitary cannabis use also appears to confer some risk. Solitary cannabis use is associated with negative affect (Buckner et al., 2016; Mason et al., 2020), higher levels of coping motives (Mason et al., 2020), and increased prospective risk for CUD (Creswell et al., 2015; Mason et al., 2020). Although solitary cannabis use may not be harmful for some because they may use alone to manage symptoms (e.g., chronic pain, sleep), for others solitary cannabis use may be a unique marker of cannabis use severity because the motivation to use is attributable to the pharmacological effects of cannabis alone, rather than the combination of pharmacological and social reward obtained through social cannabis use. Disaggregating social versus solitary motivation to use cannabis may help identify those at higher risk for cannabis-related problems.
Recent work shows that the presence of alcohol-consuming peers increases alcohol demand as measured using the hypothetical purchase task procedure (Acuff, Soltis, et al., 2020). Further, and consistent with behavioral economic theory, participants with high levels of alcohol problems in that study demonstrated less sensitivity to changes in social contexts (i.e., smaller percent reduction in drinking from the social to solitary condition, those with greater use severity are less sensitive to changes in the social context). It is unclear if these results would directly translate to cannabis.
Opportunity Costs
Behavioral economic theory suggests that behavioral allocation is sensitive to opportunity costs, or the loss of potential rewards due to the selection of another reward option. The cost/benefit ratio may favor cannabis use under conditions in which a valuable alternative is unavailable but may shift when an alternative is introduced, increasing the opportunity cost to be greater than the benefit gained from cannabis use (Lamb & Ginsburg, 2018). In behavioral economic research, opportunity costs have primarily been operationalized as next-day responsibilities (Gentile et al., 2012; Joyner et al., 2019; Skidmore & Murphy, 2011). Research shows that when a next day responsibility is introduced (e.g., next day class, test, work), many individuals reduce their alcohol use (Murphy et al., 2014; Skidmore & Murphy, 2011), and diminished sensitivity to this constraint has been shown to be associated with greater alcohol use severity (Joyner et al., 2019). Particularly relevant for the current study, Ferguson et al. (2021) administered two MPTs to individuals who endorsed regular cannabis use via an online survey: one with no next-day responsibilities, and the other whereby the participant was presented with a scenario in which they had a job interview the next morning. The results revealed that next-day responsibilities decreased alcohol demand.
Current Study
Research suggests that the presence of peers increases, whereas opportunity costs decrease, behavioral economic alcohol demand. However, no study has examined the impact of social context on cannabis demand, nor the interactive effects of social context and opportunity costs on demand for any substance. The goal of the current study is to evaluate the potential impact of the social context and responsibilities on demand for cannabis to advance theoretical models of CUD and to inform prevention and treatment approaches. Using a 2 (social vs. solitary) × 2 (typical responsibility week vs. substantial responsibility week) counterbalanced within-subject design, we examined differences in cannabis demand based on the presence and absence of both peers and opportunity costs among adults who use cannabis sampled through Amazon Mechanical Turk (mTurk). We hypothesized that (1) cannabis demand will be greater in conditions with friends compared to conditions in which the participant is using cannabis alone, (2) cannabis demand will be greater in conditions with typical levels of responsibility compared to conditions in which the level of responsibilities is substantially higher than normal, (3) these conditions will interact, such that cannabis demand will be greatest in the condition with friends and typical responsibilities, and lowest when responsibilities are substantial and when primarily using alone, and (4) participants with greater cannabis use severity will be less sensitive to solitary and opportunity cost constraints compared to those with no/minimal cannabis use severity. We also hypothesized that sensitivity to constraints, and therefore differences in cannabis use severity, will be more pronounced in demand elasticity, which theoretically reflects sensitivity to price constraints and is the best candidate demand index for capturing sensitivity to other constraints (Acuff & Murphy, 2021).
Method
Participants
Participants (N=310) were recruited from mTurk, a reliable and valid way to collect behavioral economic and addiction-related data (Strickland et al., 2019; Strickland & Stoops, 2019). Inclusion criteria were (1) age 21 or older, (2) self-reported past week cannabis use, (3) 30 or more lifetime cannabis use episodes, and (4) residency in the United States (verified by mTurk). Participants were prohibited from completing the survey more than once. A 2-step screening process was implemented consistent with recommendations to improve validity (Hydock, 2018; Wessling et al., 2017). First, participants completed a brief screener with questions related to various health behaviors (to avoid false endorsement of cannabis use) and attention/validity checks. In the screener, potential participants were shown the following text: “The purpose of this research is to better understand patterns of a range of behaviors and experiences, drinking and drug use, experienced drinking and drug use outcomes, and attitudes and thoughts regarding various activities and life experiences. You are being invited to take part in this research study because you are 21 or older.” We included questions about demographics, lifetime and recency of a range of substances, fast food consumption, e-cigarette use, and television consumption. We also included questions about consumption of a drug that does not exist. Only participants who met criteria and passed validity checks were allowed to complete the main study assessment. A total of 3,058 people competed the screening survey. Of these, 60.8% (n = 1,860) were not eligible because they did not report recent cannabis use. An additional 22.9% (n = 699) reported that they had consumed the fake drug. Of those that passed the fake drug screening item, all provided valid responses to the open-ended question about fast food. Thus, 16.3% (n = 499) of participants who completed the screening survey were given access to the final survey.
For data inclusion in the primary survey, participants were required to correctly answer three out of four attention check items, an item asking about frequency of use of a fake drug, and an open-ended question to ensure English proficiency and that the respondent was not a bot (Hauser & Schwarz, 2016). Before excluding nonattentive and invalid responders, 332 people completed the full survey. Twenty participants did not correctly answer the validity check (6%) and 2 participants did not correctly respond to at least 3 out of 4 attention checks (0.6%). There were no differences in age, sex, race, or Hispanic ethnicity between those who passed the attention and validity checks and those who did not (results of sensitivity analyses can be found in Supplemental Table S2). These rates are superior to those typically found in mTurk data, likely attributable to the two-staged screening process (Jones et al., 2022). This sample size provided 80% power to detect a small-to-medium effect size difference (f = 0.20) in cannabis demand across the five MPT conditions, which is smaller than effect sizes found in previous work examining contextual determinants of alcohol demand (Acuff, Soltis, et al., 2020). The sample size also provided 80% power to detect a significant small correlation between cannabis consumption and demand indices (r = 0.15). The analyses were not preregistered.
Procedure
All study procedures were approved by the University of Memphis Institutional Review Board (FWA00006815). A brief (~1 minute) screening survey assessed demographics, substance use, and other health behaviors (e.g., eating), for which respondents were compensated 10 cents. Qualifying participants accessed and completed the full survey (measures described below). Participants completed a standard marijuana purchase task (MPT), followed by each of the four modified MPTs1 counterbalanced across participants, and measures assessing cannabis use, social factors, depression and anxiety, and measures of behavioral economic discounting. The current analysis focuses on the marijuana demand data and cannabis use frequency and severity. Participants were paid $3.50 for this survey, in line with previous research using crowdsourced samples (Horton & Chilton, 2010).
Measures
Marijuana Purchase Tasks.
Five hypothetical MPTs assessed cannabis demand under different contextual conditions (see supplemental materials). First, we presented a standard MPT with standard task constraints setting the economy (e.g., cannot purchase from other sources). Our study used modified MPTs exploring purchasing over a week, rather than a single occasion, based on a qualitative analysis of inconsistencies between previous purchase tasks and how respondents actually purchase cannabis in the real world (Aston et al., 2021). Participants reported how many grams of marijuana they would purchase for a typical week in which they used cannabis at each of 20 prices ranging from $0-$60.
Next, we manipulated social context and opportunity cost in a within-subject 2 × 2 design, resulting in four MPTs counterbalanced across participants: social/typical responsibilities, solitary/typical responsibilities, social/substantial responsibilities, and solitary/substantial responsibilities. These conditions were manipulated by adding specific task instructions (e.g., At each price, choose how many grams you would use over a typical week in which you were going to be only smoking[alone versus with friends]). Participants were required to answer three questions about the vignette before they continued to ensure they understood the instructions for each condition.
Cannabis Use Frequency and Severity.
We measured cannabis consumption with the Daily Sessions, Frequency, Age of Onset, and Quantity of Cannabis Inventory (DFAQ-CU; Cuttler & Spradlin, 2017). We used item three as a measure of cannabis use frequency: “Which of the following best captures the average frequency you currently use marijuana?” Participants responded on a scale from 0 (I do not use marijuana) to 12 (more than once per day). Participants completed the CUD Identification Test - Revised (CUDIT-R) to measure use severity (Adamson et al., 2010). Each item, scored on a scale from 0 to 4, is summed to create a total score. Psychometric research has demonstrated that the CUDIT-R has high sensitivity and specificity in predicting cannabis use disorder (Adamson et al., 2010). Further, CUDIT-R scores demonstrate robust predictive validity of relevant measures of cannabis use severity such as cannabis use frequency (Adamson et al., 2010).
Data Analysis
First, we extracted demand indices from MPTs. Data were screened for inconsistency using a standard three-point algorithm (Trend = 0.025; Bounce = 0.10; Reversal from zero = 1; Stein et al., 2015). Some participants did not pass due to reporting of a single number across prices (e.g., all 1s, all 0s; Standard MPT: 4.8%; Social Typical MPT: 4.8%; Social Substantial MPT: 9.0%; Alone Typical MPT: 4.8%; Alone Substantial MPT: 10.8%). These participants were retained for calculation of observed indices, but not for derived elasticity. All other data met criteria for consistency according to these standards across all MPTs. The current study examined four observed indices from each MPT condition: intensity (consumption with no constraint, or when cannabis is free), breakpoint (price at which consumption reaches zero), Omax (maximum expenditure), and Pmax (price at which maximum expenditure occurs). We also derived elasticity (the rate of change in consumption as a function of price) using the exponentiated demand equation (Koffarnus et al., 2015). Elasticity data were calculated using the Demand Curve Analyzer software (Gilroy et al., 2018), which is freely available online at: https://github.com/miya mot0/DemandCalculatorQT. In the current study, k (0.98) was calculated by subtracting the log10-transformed average consumption at the highest price from the log10-transformed average consumption at the lowest price using the raw data from all five MPTs (Koffarnus et al., 2015).
Outliers of observed and derived data were detected and winsorized at the price level (0.01 units; 18 data points in total) (Tabachnick & Fidell, 2013). The only variables that were skewed and kurtotic (i.e., values outside of −2 and 2; Trochim & Donnelly, 2006), were demand elasticity for all MPT conditions. These variables were normalized with a log transformation; however, conditions for all elasticity variables remained skewed and kurtotic (there were no differences in results across transformed and untransformed elasticity variables; Supplemental Table S3). Correlations between the standard MPT and both indices of cannabis use and severity and demand indices derived from the modified MPTs are reported in the supplemental materials.
We used linear effect mixed models to test our first three hypotheses by exploring individual main effects for social context (i.e., alone, with friends) and level of responsibilities (i.e., typical responsibilities, substantial responsibilities) as well as a social context × responsibility interaction. To test our fourth hypothesis, we included the CUDIT total score in a model with social context, responsibilities, and the social context × responsibilities interaction term, resulting in three two-way interaction terms and one three-way interaction term. For all analyses, we used an unstructured correlation structure. Further, we controlled for age, sex assigned at birth, perceived household financial status, cannabis use frequency, and medical cannabis use status in all analyses. All analyses were conducted in SAS 9.4 (SAS Institute Inc.; Cary, NC, USA). A significance level of p < .05 was used to indicate significance. We also report p-values controlling for the False Discovery Rate (FDR) across all five effects for each given independent variable (Benjamini & Hochberg, 1995). Supplemental Table S3 reports primary analyses with the invalid respondents included. Supplemental Table S4 reports relative fit indices and intraclass correlations for each model.
Results
Descriptive Statistics and Demand Curve Model Fit
Table 1 reports descriptive statistics. The sample was split evenly between male and female participants and was mostly White. Table 2 reports means, standard deviations, and model fits across MPT conditions. The exponentiated demand equation demonstrated a good fit to the individual level data and an excellent fit for the aggregated data.
Table 1
Descriptive Statistics (N = 310).
| Variable | M/Med (SD) | Percent |
|---|---|---|
| Age | 37.25 (11.56) | |
| Sex (Female) | 53.5% | |
| Gender | ||
| 49.7% | ||
| 47.4% | ||
| 2.9% | ||
| Race | ||
| 79.4% | ||
| 0.6% | ||
| 3.9% | ||
| 7.4% | ||
| 5.8% | ||
| 2.9% | ||
| Hispanic (Yes) | 14.2% | |
| Student status (Yes) | 18.8% | |
| Employment status | ||
| 22.3% | ||
| 54.5% | ||
| 23.2% | ||
| Perceived Household Financial Status | ||
| 12.9% | ||
| 46.1% | ||
| 29.7% | ||
| 24.2% | ||
| Individual Income | ||
| 11.6% | ||
| 17.1% | ||
| 23.2% | ||
| 13.5% | ||
| 10.0% | ||
| 9.4% | ||
| 5.2% | ||
| 3.2% | ||
| 6.8% | ||
| CUDIT Score | 9.99 (6.07) | |
| Cannabis Use Frequency (Median) | 5–6 Days a Week | |
| State Legal Status | ||
| 24.2% | ||
| 27.7% | ||
| 46.8% | ||
| Medical Use (% Yes) | 26.1% | |
| DASS Depression Subscale Total Score | 10.46 (11.11) | |
| DASS Anxiety Subscale Total Score | 8.41 (8.90) |
Note. M = Mean; Med = Median; SD = Standard Deviation; CUDIT = Cannabis Use Disorder Identification Test.
Table 2
Means and Standard Deviations for Demand Indices Across MPT Conditions.
| Condition | Intensity (Grams/week) | Breakpoint (USD) | Omax (USD) | Pmax (USD) | Elasticity | M/Med R2 | Aggregate R2 |
|---|---|---|---|---|---|---|---|
| M (SD) | M (SD) | M (SD) | M (SD) | M (SD) | |||
| Social Typical | 22.88 (29.37) | 39.78 (20.30) | 153.20 (183.25) | 27.27 (19.32) | .0059 (.0194) | .83/.87 | .98 |
| Social Substantial | 20.42 (28.54) | 37.81 (21.49) | 145.84 (189.26) | 25.66 (19.00) | .0065 (.0266) | .81/.84 | .99 |
| Alone Typical | 20.05 (27.56) | 38.87 (20.32) | 133.02 (156.13) | 27.22 (19.58) | .0050 (.0118) | .83/.86 | .98 |
| Alone Substantial | 18.26 (27.38) | 36.98 (21.68) | 131.20 (176.52) | 25.41 (19.42) | .0040 (.0113) | .80/.84 | .98 |
Note. MPT = Marijuana Purchase Task; M = Mean; Med = Median, SD = Standard deviation; USD = US Dollars.
Effects of Social Context and Responsibilities on Demand
Table 3 reports the results of all linear effects models. Intensity was significantly greater when among friends compared to when using alone. Intensity was significantly lower in the substantial responsibilities’ conditions compared to the typical responsibility conditions. There was not a significant social context × responsibility interaction effect on intensity.
Table 3
Results of linear effects models in predicting demand indices: Effects of Condition and Interactions with CUDIT Scores
| Estimate | S.E. | df | t | p-value | p-value FDR | |
|---|---|---|---|---|---|---|
| Intensity | ||||||
| Social | 1.93 | 0.73 | 901 | 2.66 | .008 | .04 |
| Responsibility | 2.04 | 0.73 | 901 | 2.81 | .005 | .01 |
| Social × Responsibility | 0.48 | 1.03 | 901 | .47 | .64 | .93 |
| CUDIT | 0.79 | 0.28 | 338 | 2.79 | .006 | .02 |
| CUDIT × Social | 0.33 | 0.12 | 898 | 3.06 | .002 | .01 |
| CUDIT × Responsibility | 0.79 | 0.28 | 898 | 2.76 | .006 | .03 |
| CUDIT × Responsibility × Social | −0.13 | 0.17 | 898 | −.80 | .42 | .42 |
| Breakpoint | ||||||
| Social | 0.59 | 0.50 | 901 | 1.16 | .25 | .42 |
| Responsibility | 1.91 | 0.50 | 901 | 3.78 | < .001 | <.001 |
| Social × Responsibility | 0.24 | 0.71 | 901 | 0.33 | .74 | .93 |
| CUDIT | 0.57 | 0.22 | 328 | 2.56 | .01 | .02 |
| CUDIT × Social | 0.05 | 0.08 | 898 | 0.65 | .52 | .65 |
| CUDIT × Responsibility | 0.13 | 0.08 | 898 | 1.51 | .13 | .22 |
| CUDIT × Responsibility × Social | −0.19 | 0.12 | 898 | −1.65 | .10 | .25 |
| O max | ||||||
| Social | 12.17 | 5.33 | 902 | 2.28 | .02 | .05 |
| Responsibility | 2.91 | 5.34 | 902 | 0.55 | .59 | .59 |
| Social × Responsibility | 6.94 | 7.55 | 901 | 0.92 | .36 | .90 |
| CUDIT | 5.67 | 1.74 | 358 | 3.25 | .001 | .005 |
| CUDIT × Social | 2.02 | 0.86 | 898 | 2.34 | .02 | .05 |
| CUDIT × Responsibility | 0.29 | 0.87 | 898 | 0.34 | .74 | .74 |
| CUDIT × Responsibility × Social | 1.15 | 1.22 | 898 | 0.94 | .35 | .42 |
| P max | ||||||
| Social | 0.14 | 0.75 | 915 | 0.18 | .85 | .85 |
| Responsibility | 1.77 | 0.75 | 915 | 2.38 | .02 | .03 |
| Social × Responsibility | 0.007 | 1.06 | 915 | 0.01 | .99 | .99 |
| CUDIT | 0.44 | 0.21 | 395 | 2.13 | .03 | .04 |
| CUDIT × Social | −0.04 | 0.12 | 912 | −0.33 | .74 | .74 |
| CUDIT × Responsibility | 0.13 | 0.12 | 912 | 1.05 | .30 | .38 |
| CUDIT × Responsibility × Social | −0.19 | 0.17 | 912 | −1.11 | .27 | .42 |
| Elasticity | ||||||
| Social | 0.001 | 0.001 | 816 | 0.74 | .46 | .58 |
| Responsibility | 0.001 | 0.001 | 821 | 0.88 | .38 | .48 |
| Social × Responsibility | −0.003 | 0.002 | 817 | −2.07 | .04 | .20 |
| CUDIT | −0.0001 | 0.0002 | 499 | −0.35 | .73 | .73 |
| CUDIT × Social | −0.0003 | 0.0002 | 817 | −1.54 | .12 | .20 |
| CUDIT × Responsibility | −0.0005 | 0.0002 | 817 | −2.82 | .01 | .03 |
| CUDIT × Responsibility × Social | 0.001 | 0.0002 | 814 | 2.81 | .005 | .03 |
Note. All models controlled for age, sex assigned at birth, perceived financial status, cannabis use frequency, and medical cannabis use status. CUDIT = cannabis use disorder identification test-revised; S.E. = standard error; df = degrees of freedom; FDR = False Discovery Rate.
There was no effect of the social context on breakpoint; however, breakpoint was lower in the typical responsibility condition compared to the substantial responsibility condition. There was not a significant social context × responsibility interaction effect on breakpoint.
Omax was greater in the social, compared to the solitary, condition. However, there was not a significant main effect for responsibility, nor did social context interact with responsibility.
There was no effect of the social context on Pmax. However, Pmax was greater in the typical responsibilities condition compared to the substantial responsibilities condition. There was not a significant social context × responsibility interaction effect on Pmax.
There was no main effect for the social condition or for the substantial responsibility condition on demand elasticity. There was, however, a significant social context × responsibility interaction. Probing the interaction suggested more inelastic demand for the typical responsibility social condition relative to all other conditions. The social context × responsibility interaction effect was no longer significant when controlling for the false discovery rate.
Interaction Between Cannabis Use Severity, Social Context, and Responsibilities on Demand
A higher CUDIT total score was significantly associated with greater intensity. There was also a CUDIT × responsibilities interaction. As CUDIT scores increased, intensity in both the typical and substantial responsibilities conditions increased; however, the slope was greater in the typical responsibilities condition, such that there was greater intensity in the typical, compared to the substantial, responsibility condition among those with higher CUDIT scores. There was also a significant social by CUDIT score interaction. As CUDIT scores increased, intensity increased in both the social and solitary conditions; however, the slope was greater in the social condition, such that there was greater intensity in the social, compared to the solitary, condition among those with higher CUDIT scores. The CUDIT score × responsibility × social interaction was not significant for intensity.
A higher CUDIT total score was significantly associated with greater breakpoint. No interaction between condition and CUDIT total score was significant for breakpoint.
A higher CUDIT total score was also significantly associated with greater Omax. The interaction between CUDIT total score and social context was also significant. Probing the interaction revealed a similar effect to that observed for intensity. As CUDIT scores increase, Omax increases in both the social and solitary conditions; however, the slope is greater in the social condition, such that there is greater demand Omax in the social, compared to the solitary, condition among those with higher CUDIT scores. The social context × CUDIT interaction effect was no longer significant when controlling for the false discovery rate. No other interaction between condition and the CUDIT total score was significant for Omax.
A higher CUDIT total score was significantly associated with greater Pmax. No interaction between condition and CUDIT total score was significant for breakpoint.
CUDIT total score was not significantly associated with elasticity. There was not a significant interaction between CUDIT score and social context. However, the interaction between the CUDIT total score and responsibilities was significant. Elasticity in both the typical and substantial responsibilities conditions was lower at greater CUDIT scores; however, the slope was greater in the typical responsibilities condition, such that there was lower elasticity in the typical, compared to the substantial, responsibilities conditions with higher CUDIT scores. Finally, the three-way interaction between the CUDIT total score, responsibilities, and social context was significant. Lower elasticity was associated with higher CUDIT scores; however, the slope was significantly greater in the solitary and typical responsibilities condition compared to other conditions.
Discussion
Behavioral economic frameworks suggest that the reinforcing value of cannabis is related to both the pharmacological properties of the substance and the larger choice context in which the drug is available (Hogarth & Field, 2020). As a result, variations in environmental contexts, including the social context and the presence of constraints associated with cannabis use, should shift motivation to consume cannabis. The current study manipulated the level of responsibility and the presence or absence of friends, using behavioral economic hypothetical purchase tasks.
Consistent with previous research focused on alcohol-related decision making, the presence of peers generally increased demand relative to a solitary condition (Acuff, Soltis, et al., 2020). Comparing across the typical responsibilities conditions, cannabis demand was decreased by 12–15% for intensity, Omax, Pmax, and elasticity indices, which is smaller compared to alcohol (36% average reduction across all indices; Acuff et al., 2020). In linear mixed models, intensity and Omax both decreased in a solitary, compared to social, condition, whereas the social context did not impact breakpoint, Pmax, or elasticity. Breakpoint and elasticity, however, both reflect an element of persistence, suggesting that social context had less influence on price sensitivity than on maximum consumption level and expenditure. These results generally suggest a social effect on the reward value of cannabis, albeit on a smaller scale relative to the social effects observed for alcohol. It is important to note that the effect of the social context on Omax did not retain significance when controlling for the FDR.
Previous research has explored the effects of next day responsibilities, rather than on a more molar pattern of greater responsibilities across a week. The effect of responsibilities on cannabis demand in the current study was consistent with a growing body of literature demonstrating that demand is generally lower under conditions of greater occupational or academic responsibility (Berman & Martinetti, 2017; Ferguson et al., 2021; Gentile et al., 2012; Joyner et al., 2019). Despite these results, the size of the effects are smaller relative to those effects of next day responsibilities on alcohol, which is consistent with another recent study exploring the effects of responsibilities on cannabis demand (Ferguson et al., 2021). This may be due to differences between alcohol and cannabis in the profiles of adverse effects and perceived risk of harm associated with use. Greater alcohol consumption results in hangovers, poor sleep, and fatigue, whereas many who use cannabis report positive use motivations such as to improve sleep (Bonn-Miller et al., 2014; Schierenbeck et al., 2008). Thus, motivation to use may persist if cannabis use is perceived as relatively compatible with responsibilities.
Our results suggest that the effects of the social context and opportunity costs may be additive, rather than interactive. The exception to this is elasticity, which was lowest (least price sensitive); when the individual had typical levels of responsibility and was with friends. Elasticity represents sensitivity to cost (typically only to financial cost in studies with human participants), and these results are consistent with previous assertions that elasticity may be more accurately reflective of contextual value when other, non-financial “costs” are adequately incorporated (Acuff, Amlung, et al., 2020; Acuff & Murphy, 2021). These results support a molar theories of behavior (Tucker et al., 2022) by demonstrating that cannabis use is not solely determined by the pharmacological profile of cannabis or characteristics of the individual but is also dependent upon constraints in the environment that may only become clear across an analysis of patterns of behavior over varying constraints.
Conditions of reduced “constraints” of cannabis use were associated with greater demand across several indices among those with higher CUDIT scores, but not among those with lower CUDIT scores. Although previous studies have demonstrated a relation between CUD severity and cannabis value (Aston et al., 2015), no study has illuminated the mechanism. We believe that CUD may manifest as a pattern of cost insensitivity, in which increasing cost constraints have a reduced impact, and/or as a pattern of benefit hypersensitivity, in which the addition of benefits associated with use are enhanced. People with and without CUD may be as equally likely to use cannabis when under certain conditions; however, differences can emerge as constraints shift, revealing either decreased cost, or increased benefit, sensitivity. Our results are consistent with an interpretation of benefit hypersensitivity: the primary difference between those with and without CUD emerged under conditions of loosened constraint in the form of an added benefit (i.e., presence of friends). These results are also consistent with a recent daily diary study finding that those with greater inelasticity (i.e., those who are theoretically less likely to reduce consumption as cost increases) reported greater within-person differences in the likelihood of heavy drinking in social, compared to solitary, contexts relative to those with more elastic demand (Acuff et al., 2021).
A recent merging of behavioral economics and values-based decision making (Field et al., 2020) may provide a mechanism for benefit hypersensitivity. From this perspective, internal and external “signals” dictate value of the various options in each discrete choice context similar to a cost/benefit ratio; as value signals accumulate (i.e., evidence accumulation), one choice option reaches a response threshold, at which point the corresponding behavior is executed. Those with CUD may already be on the threshold of preference for cannabis and are thus susceptible to changes in the context that pair with and increase the reinforcing value of cannabis (e.g., social connection). These findings suggest that it may be as important, and perhaps more effective, to reduce the benefits of cannabis as it is to increase the costs.
We used a novel experimental paradigm and robust statistical methods to isolate the effects of the presence of peers and responsibilities on cannabis demand. However, the study was not without limitations. First, we evaluated only one type of social connection, and future studies should explore whether the effect differs depending on the relationship. Second, we also explored differences in cannabis demand between typical and substantial responsibility weeks, however, we did not include a low responsibility condition, which may be a better test of the cost insensitivity/benefit hypersensitivity effects of CUD and can be explored in future work. Third, our study used hypothetical purchase tasks, and the results should be confirmed with real-world purchase tasks. Alternatively, studies could be conducted in naturalistic settings using ecological momentary assessment. Fourth, while we did use the exponentiated demand equation, which allows for zeros to be included in the calculation of elasticity, we only used those reporting non-zero demand to calculate elasticity, which removes those with potentially the greatest elasticity (those that reduced use when a new constraint was introduced). In other words, if the individual reports all zero values across the full task (from free price to the most expensive price point), then their data cannot be used to calculate elasticity because the equation requires at least some variability in responding across prices. Although this is a methodological necessity, it restricts the range of the sample. This is a disadvantage relevant specifically to derived indices relative to observed. Fifth, the study was cross-sectional and does not provide any information about how these effects may change over time. Future research may help understand how the impact of the social context and of opportunity costs on value may change as CUD progresses.
Conclusion
The current study extends research on the effects of constraints on the value of cannabis by exploring interactive effects the social context and responsibilities (i.e., opportunity cost). The presence of peers increases, and greater responsibilities decrease, cannabis demand, and higher CUDIT scores were associated with greater sensitivity to changes in constraints. These results highlight the importance of the choice context in understanding patterns of cannabis-related behavior and suggests that targeting the social environment or next day opportunity costs, perhaps in the context of just-in-time interventions, may reduce cannabis use.
Demand Curves for each MPT Condition
Note. Demand curves for each of the primary MPT conditions. Demand intensity, or grams purchased at zero cost, was highest for the social/typical responsibility condition, and lowest for the solitary/substantial responsibility condition. Across all conditions, grams purchased decreased as prices increased.
Interactions between social context and responsibilities conditions in the MPT by CUDIT score.
Note. Figures depicting two-way interactions between MPT conditions and the CUDIT total score. (A) Intensity rises as CUDIT scores rise; at low CUDIT scores, intensity is similar in the social and solitary condition. At high CUDIT scores, cannabis demand intensity is greater in the social compared to the solitary condition. (B) Intensity rises as CUDIT scores rise; at low CUDIT scores, intensity is similar in the substantial and typical responsibilities condition. At high CUDIT scores, cannabis demand intensity is greater in the typical compared to the substantial responsibilities condition. (C) Omax rises as CUDIT scores rise; at low CUDIT scores, Omax is similar in the social and solitary condition. At high CUDIT scores, cannabis demand Omax is greater in the social compared to the solitary condition. (D) In the substantial responsibilities condition, demand elasticity is relatively similar regardless of the participant’s CUDIT score. However, in the typical responsibilities condition, those with low CUDIT scores have greater elasticity compared to those with high CUDIT scores.
Three-way Interaction between social context condition, responsibilities condition, and the CUDIT score predicting Demand Elasticity.
Note. Figure demonstrating the interaction between the presence of friends, responsibilities, and the CUDIT score in predicting demand elasticity. Generally, lower elasticity was associated with higher CUDIT scores; however, the slope was significantly greater in the solitary and typical responsibilities condition compared to other conditions.
Acknowledgments
This work was supported by grants from the National Institute on Alcohol Abuse and Alcoholism (R01 AA024930-01, M-PIs: JGM and MacKillop; R21 AA027679, M-PIs: JGM and MacKillop; F31 AA027140, PI: SFA), the National Institute on Drug Abuse (T32 DA007288, PI: McGinty), and the National Institute on General Medical Sciences (P20GM130414, Project Lead: Aston). The funding agencies played no role in the design or execution of the study. SFA, JGM, and JCS contributed to data collection. SFA wrote the first draft of the manuscript and completed statistical analyses. All authors contributed substantially to the design of the study, measurement, conceptualization, and editing. No conflicts declared. Analysis code available upon request.
Footnotes
1Although “cannabis” is the more appropriate scientific term, the term “marijuana” is more commonly used among those who use the substance. For this reason, we reference “marijuana” in the measure, and have retained the original name, the “marijuana purchase task.”
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Funding
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NIAAA NIH HHS (3)
Grant ID: R21 AA027679
Grant ID: F31 AA027140
Grant ID: R01 AA024930
NIDA NIH HHS (1)
Grant ID: T32 DA007288
NIGMS NIH HHS (1)
Grant ID: P20 GM130414
National Institute of General Medical Sciences (1)
Grant ID: P20 GM130414
National Institute on Alcohol Abuse and Alcoholism (2)
Grant ID: R01 AA024930; R21 AA027679
Grant ID: F31 AA027140
National Institute on Drug Abuse (1)
Grant ID: T32 DA007288