Analytical Problems and Suggestions in the Analysis of Behavioral Economic Demand Curves

Behavioral economic relationship between cannabis and cigarettes: Evidence from hypothetical purchase tasks

BACKGROUND

In the United States (U.S), cannabis policies have been increasingly liberalized whereas tobacco policies have been increasingly stringent. Given the high prevalence of cannabis and tobacco dual use, there are concerns that a policy regulating one substance may unintendedly influence the other. This study examined the responsiveness of the demand for cannabis joints and cigarettes when price varied.

METHODS

The study included 338 adult participants (21+) who used both cannabis and tobacco and lived in one of the U.S. states with recreational cannabis legalized by the time of interview in 2019. They completed hypothetical purchase tasks to indicate the quantity desired of cannabis joints and cigarette packs 1) when only one substance was available with escalating prices and 2) when both substances were concurrently available with escalating prices of cannabis joints and a fixed price of cigarette packs. We estimated 1) the own-price elasticity of demand for each substance using nonlinear exponential demand model, and 2) the cross-price elasticity of demand at aggregate level using nonlinear exponential demand model and at individual level using log-linear demand model.

RESULTS

The estimates for the rate of change of own-price elasticity (α) were 0.0011 (SE = 0.000039, p < 0.001) for cannabis joints and 0.00095 (SE = 0.000037, p < 0.001) for cigarette packs. The aggregate-level estimates of cross-price elasticity (I = 13.032, SE = 0.34, p < 0.001; β = 0.0029, SE = 0.0021, p > 0.05) suggest an independent relationship between the two substances. At individual level, 78.70% of the participants treated the two substances as independent, 17.46% as complements, and 3.85% as substitutes.

CONCLUSIONS

For most adults who used both cannabis and tobacco in the U.S., cannabis joints and cigarettes had an independent relationship. Policies regulating the price of cannabis may not have large unintended consequences on cigarette use.

Demand management approaches in services sector and influence on company performance

Purpose

The purpose of this paper is to empirically examine the impact of supply chain risks (SCRs) and demand management strategies (DMSs) on the company performance in order to study the use of DMSs in delivering improved results even in the presence of SCRs. The SCRs considered under the study are as follows: demand variability, constrained capacity and quality of services delivery, and competitive performance, customer satisfaction and financial performance are the measures considered for company performance.

Design/methodology/approach

This study is based on a survey of 439 businesses in India representing 10 groups of services industries (information technology/IT enabled services, business process outsourcing, IT infrastructure, logistics/transportation, healthcare, hospitality, personal services, consulting, education and training, consumer products and retail), using structural equation modeling (SEM) methods.

Findings

The findings reveal that presence of demand variability risk has significant influence upon the use of demand planning and forecasting, controlling customer arrival during peaks and shifting demand to future. Mismatch of capacity against demand (unused capacity) leads to the use of techniques to influence business during lean periods, thereby resulting in enhanced supply chain (SC) and financial performance. Controlling customer arrival during peaks to shift the demand to lean periods leads to enhanced financial performance. Presence of delivery quality risk does not significantly influence the use of DMS. Also, short-term use of customer and business handling techniques does not exert significant influence on company performance.

Research limitations/implications

The study has limitations as follows: (1) respondents are primarily from India while representing global organizations, (2) process/service redesign to relieve capacity as a DMS is not considered and (3) discussion on capacity management strategies (CMSs) is also excluded.

Practical implications

SC managers can be resourceful in shifting the peak demand to future with the application of techniques to control customer arrival during peaks. The managers can also help enhance business by influencing business through offers, incentives and promotions during lean periods to use available capacity and improve company performance.

Originality/value

This study is one of the first empirical works to explore how presence of SCRs influences the use of DMS and impacts the three types of company performance. The study expands current research on demand management options (DMOs) by linking three dimensions of company performance based on the data collected from ten different groups of service industry.

Behavioral economic demand modeling chronology, complexities, and considerations: Much ado about zeros

Behavioral economic demand has been shown to have high utility in quantifying the value or consumption of a commodity. Demand describes the relationship between cost and consumption of a commodity, and tends to be curvilinear with consumption approaching zero as the cost increases to a sufficiently high cost to suppress consumption completely. Over a period spanning greater than three decades, behavioral economists have made great strides in the modeling of demand and addressing analytical challenges, although this work is not complete and unresolved challenges remain. The analytical challenges associated with modeling zeros both when they arise as consumption values of zero and when consumption at zero cost is assessed have been a substantial part of this evolution in models. The goals of this methodological review are to provide a historical overview of the major behavioral economic demand models that have been proposed, describe some of the common difficulties with analyzing behavioral economic demand, and discuss general considerations for the analysis of demand. In an environment with evolving and multiple competing analytical practices, we conclude that researchers can maximize scientific rigor by embracing transparency in their analysis choices and employing techniques such as sensitivity analyses to determine if their analysis choices impact the conclusions of their experiments.

Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction

Behavioral economic demand methodology is increasingly being used in various fields such as substance use and consumer behavior analysis. Traditional analytical techniques to fitting demand data have proven useful yet some of these approaches require preprocessing of data, ignore dependence in the data, and present statistical limitations. We term these approaches "fit to group" and "two stage" with the former interested in group or population level estimates and the latter interested in individual subject estimates. As an extension to these regression techniques, mixed-effect (or multilevel) modeling can serve as an improvement over these traditional methods. Notable benefits include providing simultaneous group (i.e., population) level estimates (with more accurate standard errors) and individual level predictions while accommodating the inclusion of "nonsystematic" response sets and covariates. These models can also accommodate complex experimental designs including repeated measures. The goal of this article is to introduce and provide a high-level overview of mixed-effects modeling techniques applied to behavioral economic demand data. We compare and contrast results from traditional techniques to that of the mixed-effects models across two datasets differing in species and experimental design. We discuss the relative benefits and drawbacks of these approaches and provide access to statistical code and data to support the analytical replicability of the comparisons.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40614-021-00299-7.

The effect of next day responsibilities and an adaptive purchase task on cannabis demand

BACKGROUND

The marijuana purchase task (MPT) is a commonly used behavioral economic measure of relative cannabis value (i.e. demand) that presents specific methodological concerns due to non-standardized measurement, variability in modality of use, and evolving legalization policies. Refinement of the task is critical to improve task ecological validity and accurate measurement of cannabis demand. The present study examined the construct validity of an adaptive MPT that allowed for participant selection of their preferred cannabis-based product and unit of measurement and the effect of next-day responsibilities on demand.

METHODS

Participants reporting at least monthly cannabis use (N = 186, 40.3 % women, Mage = 33.59) were recruited via Amazon Mechanical Turk and completed the original MPT and our adaptive form with 2 next-day responsibilities scenarios (no responsibilities vs. morning job interview) for each MPT. Cannabis use motives, problems, and risk for cannabis use disorder were also assessed.

RESULTS

Cannabis demand was sensitive to next-day responsibility, with higher hypothetical consumption observed in the no responsibilities condition. Responsibility-related decreases in Omax (F(1,185) = 4.83, p = .029, η2p = .03) were significantly greater on the adaptive MPT than the original MPT. Demand indices derived from the adaptive MPT were significantly correlated with cannabis problems (rbreakpoint = .19, rPmax = 0.18, relasticity=-0.18) and motives (rrange=-.32-.25), and demand metrics from the original MPT.

CONCLUSIONS

Results provide preliminary support for the construct validity of an adaptive MPT and suggest that early-morning work responsibilities may reduce cannabis demand. Continued research is needed to further refine this task and determine implications for cannabis use disorder intervention and prevention approaches.

Decreased cocaine demand following contingency management treatment

A hypothetical cocaine purchasing task (CocPT) was used to assess changes in cocaine demand in the context of contingency management (CM) treatment for cocaine use disorder (CUD). Participants (N = 89) were treatment-seeking individuals with CUD receiving 4 weeks of abstinence-based, high-magnitude CM. Treatment response (vs. non-response) was operationally defined as the submission of 6 consecutive cocaine-negative urine samples across two weeks. The CPT was assessed at baseline, week 2, and week 5. Demand data were well described by the exponentiated demand model, and baseline demand indices (Q0, Pmax, breakpoint, essential value) were significantly associated with self-report measures of cocaine use. The probability of being a zero-responder reporting zero cocaine consumption at all prices significantly increased over the course of treatment, and was greater among treatment responders vs. non-responders. Among non-zero demand data, decreases in Omax, Pmax, breakpoint, and essential value were observed over the course of CM treatment, favoring responders. To our knowledge, this is the first study to assess change in cocaine demand in the context of CM treatment targeting cocaine abstinence. Our results support the utility of cocaine demand as a measure for both identifying individuals with greater treatment need and tracking relapse risk over the course of treatment.

Assessment of demand for methamphetamine and cigarettes among individuals with methamphetamine use disorder

Methamphetamine (MA) is a highly addictive stimulant with recent upward trends in prevalence and associated public health problems. Drug demand, as assessed by hypothetical purchasing tasks, has been useful in addictions research and may help our understanding of the factors influencing MA use. However, no studies have assessed MA demand using current models of demand. The purpose of the current study was to assess demand for MA using a hypothetical drug purchasing task. Given high rates of cigarette smoking among MA users, it was of interest also to assess and compare demand for MA relative to cigarettes. Participants consisted of non-treatment-seeking volunteers with MA use disorder (N = 18), of whom 17 reported daily smoking. Results showed the exponentiated demand model provided a good fit to consumption data. Results from Bayesian generalized linear modeling demonstrated multiple positive relationships (posterior probability ≥75%) between self-reported drug use (days MA used in the past 30 days, cigarettes smoked per day) and indices of demand for each drug (Qo, Omax, Pmax, and break point). Comparing MA to cigarettes, results from Bayesian generalized linear mixed modeling revealed greater abuse liability for MA compared to cigarettes (posterior probability ≥99%) based on α and essential value. Overall, the findings of the current study support the feasibility and validity of the exponentiated demand model for assessing demand for drugs among individuals with MA use disorder. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A zero-bounded model of operant demand

Contemporary approaches for evaluating the demand for reinforcers use either the Exponential or the Exponentiated model of operant demand, both derived from the framework of Hursh and Silberberg (2008). This report summarizes the strengths and complications of this framework and proposes a novel implementation. This novel implementation incorporates earlier strengths and resolves existing shortcomings that are due to the use of a logarithmic scale for consumption. The Inverse Hyperbolic Sine (IHS) transformation is reviewed and evaluated as a replacement for the logarithmic scale in models of operant demand. Modeling consumption in the "log₁₀ -like" IHS scale reflects relative changes in consumption (as with a log scale) and accommodates a true zero bound (i.e., zero consumption values). The presence of a zero bound obviates the need for a separate span parameter (i.e., k) and the span of the model may be more simply defined by maximum demand at zero price (i.e., Q₀ ). Further, this reformulated model serves to decouple the exponential rate constant (i.e., α) from variations in span, thus normalizing the rate constant to the span of consumption in IHS units and permitting comparisons when spans vary. This model, called the Zero-bounded Exponential (ZBE), is evaluated using simulated and real-world data. The direct reinstatement ZBE model showed strong correspondence with empirical indicators of demand and with a normalization of α (ZBEn) across empirical data that varied in reinforcing efficacy (dose, time to onset of peak effects). Future directions in demand curve analysis are discussed with recommendations for additional replication and exploration of scales beyond the logarithm when accommodating zero consumption data.

Refining the marijuana purchase task: Using qualitative methods to inform measure development

Behavioral economic demand for cannabis (i.e., relative reinforcing value) can be measured via marijuana purchase tasks (MPTs). However, commodity ambiguities pose challenges and design concerns exist regarding current MPTs. The aim of this 2-phase study was to modify and improve a MPT using qualitative methods. Phase I: Focus groups were conducted with regular (i.e., average use ≥ once/week) cannabis users (n = 31; 6-7 per group M[SD] age = 26 [7]; 28% female). Focus groups followed a semistructured agenda, and executive summaries were made concerning key MPT themes. Feedback was used to refine the MPT. Phase II: Cognitive interviews using the refined MPT were conducted with regular cannabis users (n = 20; M[SD] age = 28 [8]; 50% female). Phase I: Focus group analyses highlighted 4 critical areas for MPT improvement: (a) unit of purchase, (b) cannabis quality, (c) time duration specified for use episode, and (d) price. Participants suggested using grams as the unit of purchase, tailoring cannabis quality to the individual, and clarifying intended episode length. Phase II: Cognitive interviewing indicated additional areas for task refinement, resulting in a second iteration of the MPT based on the 2 phases. Qualitative research in both phases suggested a number of substantive modifications to the MPT format. MPT modifications are expected to improve comprehension, ecological validity, and general construct validity. Findings highlight the importance of careful instructional set development for drug purchase tasks for heterogeneous products that do not have standard units of consumption. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Behavioral economic demand as a unifying language for addiction science: Promoting collaboration and integration of animal and human models

The intersection of pharmacological, psychological, and economic theory within behavioral economics has helped advance an understanding of substance use disorder. A notable contribution of this approach is the conceptualization of reinforcement from a behavioral economic demand perspective. Demand analyses provide a multidimensional view of reinforcement in which distinct behavioral mechanisms are measured that impact decision making and drug consumption. This review describes the state of research on behavioral economic demand as a common language for addiction science researchers across varied model systems and stages of a translational continuum. We first provide an overview of the theoretical concepts and procedures used to evaluate demand in animal and human models. The potential for demand to serve as a common language for diverse research groups in psychopharmacology and addiction science (e.g., those evaluating neurobehavioral outcomes, medications development, clinical practice) is then described. An overview is also provided of existing empirical studies that, while small in number, suggest good linguistic and conceptual overlap between animal and human demand models when studying biological, environmental, and pharmacological individual difference vulnerabilities underlying drug-taking behavior. Refinement of methodological procedures and incorporation of more nuanced environmental features should help improve correspondence between animal and human demand studies as well as clinical translation of such findings. Our hope is that this review and commentary ultimately serves as inspiration for new collaborative efforts involving behavioral economic demand between animal and human researchers who share a common goal of improving substance use treatment outcomes and broader psychological wellbeing. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Pilot trial investigating a brief behavioral economic intervention as an adjunctive treatment for alcohol use disorder

OBJECTIVE

Behavioral economic research suggests that increasing the salience of a delayed reward may improve capacity for delaying gratification and increase behavior allocated toward obtaining larger, delayed substance-free reward rather than smaller, more immediate reward such as alcohol use. This study aimed to improve the efficacy of outpatient alcohol use disorder (AUD) treatment by adding elements that target behavioral economic mechanisms of change.

METHOD

Forty-one (N = 41) adults in outpatient AUD treatment were recruited and 37 participants were retained at follow-up. Following baseline assessment, participants received either the Substance Free Activity Session (SFAS), a single-session behavioral economic-informed intervention focused on increasing future orientation and engagement in values-based substance-free activities or a health education control intervention. Participants in both conditions received weekly prompts (via text or email) relevant to their respective intervention for four weeks. Participants (68.3% male; 70.7% Caucasian, M age = 38.24, SD = 12.69) reported an average of 3.95 (SD = 4.72) binge drinking episodes (4/5 drinks per occasion for a woman/man) and 5.05 (SD = 5.32) drinks per drinking day 30-days prior to treatment entry.

RESULTS

The study provided initial support for the feasibility and acceptability of implementing the SFAS within a treatment setting. Participants reported high levels of satisfaction with the SFAS (M = 9.08 (SD = 0.94), on a scale of 1-10). At 3-month follow-up, the SFAS was associated with reductions in the proportion of activity participation and enjoyment (reinforcement) related to substance-use relative to substance-free activities and in alcohol demand compared to control.

CONCLUSION

These preliminary results provide initial support for targeting behavioral economic mechanisms of change in an outpatient AUD treatment with a single-session intervention plus remote delivery of booster prompts.

Baseline cocaine demand predicts contingency management treatment outcomes for cocaine-use disorder

Cocaine use disorder (CUD) is a significant public health issue. Behavioral interventions such as contingency management (CM) have been demonstrated to be highly effective in promoting cocaine abstinence. However, identifying individual characteristics associated with cocaine relapse may help improve treatment outcomes. Cocaine demand is a behavioral economic measure that shares a scientific foundation with CM. In the current study, we assessed baseline cocaine demand using a hypothetical cocaine purchasing task. Participants (N = 58) consisted of treatment-seeking individuals with CUD. All participants received 1 month of CM treatment for cocaine abstinence, and treatment responders were defined as presenting 6 consecutive cocaine negative urine samples from thrice weekly clinic visits. Demand data were well described by the exponentiated demand model. Indices of demand (intensity of demand [Q₀], elasticity [α]) were significantly associated with recent (last 30 days) cocaine use. Importantly, linear regression revealed that CM treatment nonresponders presented significantly higher Q₀ (p = .025). Subsequent quantile regression analyses examining the relationship between CM treatment response and Q₀ revealed statistically reliable effects of being a nonresponder across 3 of the lower percentiles (i.e., 15, 25, and 30). Overall, these findings provide further support for the utility of exponentiated demand model. To our knowledge, this is the first study to demonstrate an association between baseline demand and contingency management response and systematically extend the findings of prior demand research to a novel drug class, cocaine. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

The R package beezdemand: Behavioral Economic Easy Demand

beezdemand: Behavioral Economic Easy Demand, a novel package for performing behavioral economic analyses, is introduced and evaluated. beezdemand extends the statistical program to facilitate many of the analyses performed in studies of behavioral economic demand. The package supports commonly used options for modeling operant demand and performs data screening, fits models of demand, and calculates numerous measures relevant to applied behavioral economists. The free and open source beezdemand package is compared to commercially available software (i.e., GraphPad Prism™) using peer-reviewed and simulated data. The results of this study indicated that beezdemand provides results consistent with commonly used commercial software but provides a wider range of methods and functionality desirable to behavioral economic researchers. A brief overview of the package is presented, its functionality is demonstrated, and considerations for its use are discussed.

The Demand Curve Analyzer: Behavioral economic software for applied research

Free and open-source software for applying models of operant demand called the Demand Curve Analyzer (DCA) was developed and systematically evaluated for use in research. The software was constructed to streamline the use of recommended screening measures, prepare suitable scaling parameters, fit one of several models of operant demand, and provide publication-quality figures. The DCA allows users to easily import price and consumption data into spreadsheet-based controls and to perform statistical modeling with the aid of a graphical user interface. The results from computer simulations and reanalyses of published study data indicated that the DCA provides results consistent with commercially available software that has been traditionally used to apply these analyses (i.e., GraphPadTM Prism). Further, the DCA provides additional functionality that other statistical packages do not include. Practical issues and future directions related to the determination of scaling parameter k, screening for nonsystematic data, and the incorporation of more advanced behavioral economic methods are also discussed.

Understanding alcohol motivation using the alcohol purchase task: A methodological systematic review

BACKGROUND

The Alcohol Purchase Task (APT) is a behavioral economic assessment of alcohol demand (i.e., motivation for consumption during escalating levels of response cost) using simulated marketplace survey techniques. While the APT is often used and widely cited, to date, there has yet to be a systematic review elucidating the variability in administering and analyzing the APT. The purpose of the current paper is to address this knowledge gap in the literature by cataloging the various purchase task methodologies and providing recommendations and future areas of inquiry.

METHODS

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was utilized (Prospero: No. CRD42017072159). Searches through Google Scholar, PsychINFO, PubMed, and SpringerLink databases identified 47 empirical articles referencing the use of an APT and published through the year 2016. Articles were coded for demographic and procedural characteristics, structural characteristics of the APT itself, and characteristics of data analysis.

RESULTS

Results indicate substantial variation within categories and suggest that there is no standard approach to administering the APT or analyzing the responses generated from it. The results underscore the need for researchers to report as much information as possible related to administration, instructions, price structuring, and analytical approach, as we found that many articles did not provide these details.

CONCLUSION

Enhancing the transparency of APT methods and analyses in published reports will aid in reproducibility as well as future meta-analytic studies of alcohol demand that could lead to the development of best-practice recommendations for this procedure.

A Bayesian hierarchical model for demand curve analysis

Drug self-administration experiments are a frequently used approach to assess the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Cigarette and e-liquid demand and substitution in e-cigarette-naïve smokers

Behavioral economic methods allow experimental manipulation of price and examination of its effects on tobacco product purchasing. These methods may be used to examine tobacco product abuse liability and to prospectively model possible effects of price regulation. In the present study, we examined multiple measures of behavioral economic demand for cigarettes and e-liquid for use in a second-generation electronic cigarette (e-cigarette) in e-cigarette-naïve cigarette smokers. Twenty-five smokers received an e-cigarette (eGo ONE CT), sampled study e-liquid (24 mg/mL nicotine), and completed recurring sessions in which they used an experimental income to purchase real-world supplies of cigarettes and/or e-liquid. Participants also completed self-report measures of drug effects/liking. When products were available alone, we observed lower demand for e-liquid than for cigarettes. This effect was magnified when cigarettes and e-liquid were available concurrently. In additional assessments, e-liquid served as a partial substitute for cigarettes, but cigarettes did not serve as a substitute for e-liquid. Finally, participants rated e-liquid more poorly than cigarettes on several dimensions of drug effects/liking (any effects, liking, desire, and probability of continued use). We conclude that e-cigarette-naïve smokers value cigarettes more highly than e-liquid across multiple contexts and measurements. Nonetheless, participants still valued e-liquid positively and purchased it frequently, both as a substitute for cigarettes and independently of cigarettes. To understand the variables that influence transitions from exclusive smoking to either dual cigarette/e-cigarette use or exclusive e-cigarette use, future work should systematically examine the role of duration of e-liquid exposure. (PsycINFO Database Record

A Bayesian hierarchical model for demand curve analysis

Drug self-administration experiments are a frequently used approach to assessing the abuse liability and reinforcing property of a compound. It has been used to assess the abuse liabilities of various substances such as psychomotor stimulants and hallucinogens, food, nicotine, and alcohol. The demand curve generated from a self-administration study describes how demand of a drug or non-drug reinforcer varies as a function of price. With the approval of the 2009 Family Smoking Prevention and Tobacco Control Act, demand curve analysis provides crucial evidence to inform the US Food and Drug Administration's policy on tobacco regulation, because it produces several important quantitative measurements to assess the reinforcing strength of nicotine. The conventional approach popularly used to analyze the demand curve data is individual-specific non-linear least square regression. The non-linear least square approach sets out to minimize the residual sum of squares for each subject in the dataset; however, this one-subject-at-a-time approach does not allow for the estimation of between- and within-subject variability in a unified model framework. In this paper, we review the existing approaches to analyze the demand curve data, non-linear least square regression, and the mixed effects regression and propose a new Bayesian hierarchical model. We conduct simulation analyses to compare the performance of these three approaches and illustrate the proposed approaches in a case study of nicotine self-administration in rats. We present simulation results and discuss the benefits of using the proposed approaches.

Reinforcing value and hypothetical behavioral economic demand for food and their relation to BMI

Food is a primary reinforcer, and food reinforcement is related to obesity. The reinforcing value of food can be measured by establishing how hard someone will work to get food on progressive-ratio schedules. An alternative way to measure food reinforcement is a hypothetical purchase task which creates behavioral economic demand curves. This paper studies whether reinforcing value and hypothetical behavioral demand approaches are assessing the same or unique aspects of food reinforcement for low (LED) and high (HED) energy density foods using a combination of analytic approaches in females of varying BMI. Results showed absolute reinforcing value for LED and HED foods and relative reinforcing value were related to demand intensity (r's = 0.20-0.30, p's < 0.01), and demand elasticity (r's = 0.17-0.22, p's < 0.05). Correlations between demographic, BMI and restraint, disinhibition and hunger variables with the two measures of food reinforcement were different. Finally, the two measures provided unique contributions to predicting BMI. Potential reasons for differences between the reinforcing value and hypothetical purchase tasks were actual responding versus hypothetical purchasing, choice of reinforcers versus purchasing of individual foods in the demand task, and the differential role of effort in the two tasks. Examples of how a better understanding of food reinforcement may be useful to prevent or treat obesity are discussed, including engaging in alternative non-food reinforcers as substitutes for food, such as crafts or socializing in a non-food environment, and reducing the value of immediate food reinforcers by episodic future thinking.

A two-part mixed effects model for cigarette purchase task data

The Cigarette Purchase Task is a behavioral economic assessment tool designed to measure the relative reinforcing efficacy of cigarette smoking across different prices. An exponential demand equation has become a standard model for analyzing purchase task data, but its utility is compromised by its inability to accommodate values of zero consumption. We propose a two-part mixed effects model that keeps the same exponential demand equation for modeling nonzero consumption values, while providing a logistic regression for the binary outcome of zero versus nonzero consumption. Therefore, the proposed model can accommodate zero consumption values and retain the features of the exponential demand equation at the same time. As a byproduct, the logistic regression component of the proposed model provides a new demand index, the "derived breakpoint", for the price above which a subject is more likely to be abstinent than to be smoking. We apply the proposed model to data collected at baseline from college students (N = 1,217) enrolled in a randomized clinical trial utilizing financial incentives to motivate tobacco cessation. Monte Carlo simulations showed that the proposed model provides better fits than an existing model. We note that the proposed methodology is applicable to other purchase task data, for example, drugs of abuse.

Clinical models of decision making in addiction

As research on decision making in addiction accumulates, it is increasingly clear that decision-making processes are dysfunctional in addiction and that this dysfunction may be fundamental to the initiation and maintenance of addictive behavior. How drug-dependent individuals value and choose among drug and nondrug rewards is consistently different from non-dependent individuals. The present review focuses on the assessment of decision-making in addiction. We cover the common behavioral tasks that have shown to be fruitful in decision-making research and highlight analytical and graphical considerations, when available, to facilitate comparisons within and among studies. Delay discounting tasks, drug demand tasks, drug choice tasks, the Iowa Gambling Task, and the Balloon Analogue Risk Task are included.

Binary components of food reinforcement: Amplitude and persistence

BACKGROUND

Demand curves provide an index of how reinforcing a food is. Research examining the latent structure of alcohol and tobacco reinforcement identified two underlying components of reinforcement, amplitude and persistence. No research has assessed latent structure of food reinforcement and how these factors are related to BMI.

SUBJECTS AND METHODS

Participants were 297 adults from two studies that completed food purchasing tasks to assess the following measures of relative reinforcing efficacy (RRE) of food: intensity (Q₀): purchases made when the food was free or of very minimal price, O_max: maximum expenditure (purchases*price), P_max: price point where maximum expenditure was observed, breakpoint: first price where 0 purchases are made, and demand elasticity (α): quantitative non-linear relationship between purchasing and price. Principal components analysis was used to examine the factor structure of RRE for food across samples and types of food.

RESULTS

Both studies revealed two factor solutions, with P_max, O_max, breakpoint and α loading on factor 1 (persistence) and intensity (Q₀) loading on factor 2 (amplitude) across both high and low energy dense foods. Persistence reflects an aggregate measure of price sensitivity and amplitude reflects the preferred volume of consumption (how long vs. how much). The two factors accounted for between 91.7 and 95.4% of the variance in food reinforcement. Intensity for high energy dense foods predicted BMI for both studies (r = 0.18 and r = 0.22, p's < 0.05).

CONCLUSIONS

The latent factor structure was similar across two significantly different independent samples and across low and high energy dense snack foods. In addition, the amplitude of the demand curve, but not persistence, was related to BMI. These results suggest specific aspects of food reinforcement that can be targeted to alter food intake.

Bleak Present, Bright Future: Online Episodic Future Thinking, Scarcity, Delay Discounting, and Food Demand

Obesity is associated with steep discounting of the future and increased food reinforcement. Episodic future thinking (EFT), a type of prospective thinking, has been observed to reduce delay discounting (DD) and improve dietary decision making. In contrast, negative income shock (i.e., abrupt transitions to poverty) has been shown to increase discounting and may worsen dietary decision-making. Scalability of EFT training and protective effects of EFT against simulated negative income shock on DD and demand for food were assessed. In two experiments we showed online-administered EFT reliably reduced DD. Furthermore, EFT reduced DD and demand for fast foods even when challenged by negative income shock. Our findings suggest EFT is a scalable intervention that has implications for improving public health by reducing discounting of the future and demand for high energy dense food.

Further examination of the temporal stability of alcohol demand

Demand, or the amount of a substance consumed as a function of price, is a central dependent measure in behavioral economic research and represents the relative valuation of a substance. Although demand is often utilized as an index of substance use severity and is assumed to be relatively stable, recent experimental and clinical research has identified conditions in which demand can be manipulated, such as through craving and stress inductions, and treatment. Our study examines the 1-month reliability of the alcohol purchase task in a sample of heavy drinking college students. We also analyzed reliability in subgroup of individuals whose consumption decreased, increased, or stayed the same over the 1-month period, and in individuals with moderate/severe Alcohol Use Disorder (AUD) vs. those with no/mild AUD. Reliability was moderate in the full sample, high in the group with stable consumption, and did not differ appreciably between AUD groups. Observed indices and indices derived from an exponentiated equation (Koffarnus et al., 2015) were generally comparable, although P_max observed had very low reliability. Area under the curve, O_max derived, and essential value showed the greatest reliability in the full sample (rs=0.75-0.77). These results provide evidence for the relative stability over time of demand and across AUD groups, particularly in those whose consumption remains stable.

Individual and combined effects of cannabis and tobacco on drug reward processing in non-dependent users

RATIONALE

Cannabis and tobacco are often smoked simultaneously in joints, and this practice may increase the risks of developing tobacco and/or cannabis use disorders. Currently, there is no human experimental research on how these drugs interact on addiction-related measures.

OBJECTIVES

This study aimed to investigate how cannabis and tobacco, each alone and combined together in joints, affected individuals' demand for cannabis puffs and cigarettes, explicit liking of drug and non-drug-related stimuli and craving.

METHOD

A double-blind, 2 (active cannabis, placebo cannabis) × 2 (active tobacco, placebo tobacco) crossover design was used with 24 non-dependent cannabis and tobacco smokers. They completed a pleasantness rating task (PRT), a marijuana purchase task (MPT) and a cigarette purchase task (CPT) alongside measures of craving pre- and post-drug administration.

RESULTS

Relative to placebo cannabis, active cannabis reduced liking of cannabis-associated stimuli and increased response time to all stimuli except cigarette-related stimuli. Relative to placebo cannabis, active cannabis decreased demand for cannabis puffs (trends for breakpoint and elasticity) and cigarettes (breakpoint, P _max, O _max) on several characteristics of the purchase tasks. We found no evidence that active tobacco, both alone or combined with cannabis, had an effect on liking, demand or craving.

CONCLUSIONS

Acutely, cannabis reduced liking of cannabis-related stimuli and demand for cannabis itself. Acute cannabis also reduced demand for cigarettes on the CPT. Acute tobacco administration did not affect demand or pleasantness ratings for cigarettes themselves or cannabis. In non-dependent cannabis and tobacco co-users, tobacco did not influence the rewarding effects of cannabis.

The effects of perceived quality on behavioral economic demand for marijuana: A web-based experiment

BACKGROUND

Given the growing legalization of recreational marijuana use and related increase in its prevalence in the United States, it is important to understand marijuana's appeal. We used a behavioral economic (BE) approach to examine whether the reinforcing properties of marijuana, including "demand" for marijuana, varied as a function of its perceived quality.

METHODS

Using an innovative, Web-based marijuana purchase task (MPT), a sample of 683 young-adult recreational marijuana users made hypothetical purchases of marijuana across three qualities (low, mid and high grade) at nine escalating prices per joint, ranging from $0/free to $20.

RESULTS

We used nonlinear mixed effects modeling to conduct demand curve analyses, which produced separate demand indices (e.g., P_max, elasticity) for each grade of marijuana. Consistent with previous research, as the price of marijuana increased, marijuana users reduced their purchasing. Demand also was sensitive to quality, with users willing to pay more for higher quality/grade marijuana. In regression analyses, demand indices accounted for significant variance in typical marijuana use.

CONCLUSIONS

This study illustrates the value of applying BE theory to young adult marijuana use. It extends past research by examining how perceived quality affects demand for marijuana and provides support for the validity of a Web-based MPT to examine the appeal of marijuana. Our results have implications for policies to regulate marijuana use, including taxation based on the quality of different marijuana products.

Naturalistic assessment of demand for cigarettes, snus, and nicotine gum

RATIONALE

Behavioral economic measures of demand provide estimates of tobacco product abuse liability and may predict effects of policy-related price regulation on consumption of existing and emerging tobacco products.

OBJECTIVE

In the present study, we examined demand for snus, a smokeless tobacco product, in comparison to both cigarettes and medicinal nicotine. We used both a naturalistic method in which participants purchased these products for use outside the laboratory, as well as laboratory-based self-administration procedures.

METHODS

Cigarette smokers (N = 42) used an experimental income to purchase their usual brand of cigarettes and either snus or gum (only one product available per session) across a range of prices, while receiving all products they purchased from one randomly selected price. In a separate portion of the study, participants self-administered these products during laboratory-based, progressive ratio sessions.

RESULT

Demand elasticity (sensitivity of purchasing to price) was significantly greater for snus than cigarettes. Elasticity for gum was intermediate between snus and cigarettes but was not significantly different than either. Demand intensity (purchasing unconstrained by price) was significantly lower for gum compared to cigarettes, with no significant difference observed between snus and cigarettes. Results of the laboratory-based, progressive ratio sessions were generally discordant with measures of demand elasticity, with significantly higher "breakpoints" for cigarettes compared to gum and no significant differences between other study products. Moreover, breakpoints and product purchasing were generally uncorrelated across tasks.

CONCLUSIONS

Under naturalistic conditions, snus appears more sensitive to price manipulation than either cigarettes or nicotine gum in existing smokers.

Comparing exponential and exponentiated models of drug demand in cocaine users

Drug purchase tasks provide rapid and efficient measurement of drug demand. Zero values (i.e., prices with zero consumption) present a quantitative challenge when using exponential demand models that exponentiated models may resolve. We aimed to replicate and advance the utility of using an exponentiated model by demonstrating construct validity (i.e., association with real-world drug use) and generalizability across drug commodities. Participants (N = 40 cocaine-using adults) completed Cocaine, Alcohol, and Cigarette Purchase Tasks evaluating hypothetical consumption across changes in price. Exponentiated and exponential models were fit to these data using different treatments of zero consumption values, including retaining zeros or replacing them with 0.1, 0.01, or 0.001. Excellent model fits were observed with the exponentiated model. Means and precision fluctuated with different replacement values when using the exponential model but were consistent for the exponentiated model. The exponentiated model provided the strongest correlation between derived demand intensity (Q0) and self-reported free consumption in all instances (Cocaine r = .88; Alcohol r = .97; Cigarette r = .91). Cocaine demand elasticity was positively correlated with alcohol and cigarette elasticity. Exponentiated parameters were associated with real-world drug use (e.g., weekly cocaine use) whereas these correlations were less consistent for exponential parameters. Our findings show that selection of zero replacement values affects demand parameters and their association with drug-use outcomes when using the exponential model but not the exponentiated model. This work supports the adoption of the exponentiated demand model by replicating improved fit and consistency and demonstrating construct validity and generalizability. (PsycINFO Database Record

A modified exponential behavioral economic demand model to better describe consumption data

Behavioral economic demand analyses that quantify the relationship between the consumption of a commodity and its price have proven useful in studying the reinforcing efficacy of many commodities, including drugs of abuse. An exponential equation proposed by Hursh and Silberberg (2008) has proven useful in quantifying the dissociable components of demand intensity and demand elasticity, but is limited as an analysis technique by the inability to correctly analyze consumption values of zero. We examined an exponentiated version of this equation that retains all the beneficial features of the original Hursh and Silberberg equation, but can accommodate consumption values of zero and improves its fit to the data. In Experiment 1, we compared the modified equation with the unmodified equation under different treatments of zero values in cigarette consumption data collected online from 272 participants. We found that the unmodified equation produces different results depending on how zeros are treated, while the exponentiated version incorporates zeros into the analysis, accounts for more variance, and is better able to estimate actual unconstrained consumption as reported by participants. In Experiment 2, we simulated 1,000 datasets with demand parameters known a priori and compared the equation fits. Results indicated that the exponentiated equation was better able to replicate the true values from which the test data were simulated. We conclude that an exponentiated version of the Hursh and Silberberg equation provides better fits to the data, is able to fit all consumption values including zero, and more accurately produces true parameter values.

What is the Proper Sample Size for Studies of Periodontal Treatment?

Small sample sizes are a common problem in biomedical research, and the periodontal literature is no exception. It is a problem leading to not only reduced statistical power but also an inappropriate statistical inference of a treatment effect. Using statistical methods with an insufficient sample size may give rise to an increased chance of falsely detecting treatment efficacy. This article provides some guidelines to cope with the small sample size problem. The authors discuss adequate sample sizes in several statistical tests and then suggest alternative statistical methods that are valid with a small sample size.

Further validation of a marijuana purchase task

BACKGROUND

A valid measure of the relative economic value of marijuana is needed to characterize individual variation in the drug's reinforcing value and inform evolving national marijuana policy. Relative drug value (demand) can be measured via purchase tasks, and demand for alcohol and cigarettes has been associated with craving, dependence, and treatment response. This study examined marijuana demand with a marijuana purchase task (MPT).

METHODS

The 22-item self-report MPT was administered to 99 frequent marijuana users (37.4% female, 71.5% marijuana use days, 15.2% cannabis dependent).

RESULTS

Pearson correlations indicated a negative relationship between intensity (free consumption) and age of initiation of regular use (r=-0.34, p<0.001), and positive associations with use days (r=0.26, p<0.05) and subjective craving (r=0.43, p<0.001). Omax (maximum expenditure) was positively associated with use days (r=0.29, p<0.01) and subjective craving (r=0.27, p<0.01). Income was not associated with demand. An exponential demand model provided an excellent fit to the data across users (R(2)=0.99). Group comparisons based on presence or absence of DSM-IV cannabis dependence symptoms revealed that users with any dependence symptoms showed significantly higher intensity of demand and more inelastic demand, reflecting greater insensitivity to price increases.

CONCLUSIONS

These results provide support for construct validity of the MPT, indicating its sensitivity to marijuana demand as a function of increasing cost, and its ability to differentiate between users with and without dependence symptoms. The MPT may denote abuse liability and is a valuable addition to the behavioral economic literature. Potential applications to marijuana pricing and tax policy are discussed.

A behavioral economic approach to assessing demand for marijuana

In the United States, marijuana is the most commonly used illicit drug. Its prevalence is growing, particularly among young adults. Behavioral economic indices of the relative reinforcing efficacy (RRE) of substances have been used to examine the appeal of licit (e.g., alcohol) and illicit (e.g., heroin) drugs. The present study is the first to use an experimental, simulated purchasing task to examine the RRE of marijuana. Young-adult (M age = 21.64 years) recreational marijuana users (N = 59) completed a computerized marijuana purchasing task designed to generate demand curves and the related RRE indices (e.g., intensity of demand-purchases at lowest price; Omax-max. spent on marijuana; Pmax-price at which marijuana expenditure is max). Participants "purchased" high-grade marijuana across 16 escalating prices that ranged from $0/free to $160/joint. They also provided 2 weeks of real-time, ecological momentary assessment reports on their marijuana use. The purchasing task generated multiple RRE indices. Consistent with research on other substances, the demand for marijuana was inelastic at lower prices but became elastic at higher prices, suggesting that increases in the price of marijuana could lessen its use. In regression analyses, the intensity of demand, Omax, and Pmax, and elasticity each accounted for significant variance in real-time marijuana use. These results provide support for the validity of a simulated marijuana purchasing task to examine marijuana's reinforcing efficacy. This study highlights the value of applying a behavioral economic framework to young-adult marijuana use and has implications for prevention, treatment, and policies to regulate marijuana use.