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

Yu, Jihnhee; Liu, Liu; Vincent, Paula C.; Epstein, Leonard H.

doi:10.1080/00273171.2013.862491

Cited by 41 publications

(59 citation statements)

References 39 publications

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“…Furthermore, alternative statistical techniques exist for the analysis of behavioral economic data. Collins and colleagues (2014) successfully implemented a novel approach using a nonlinear mixed effects model to analyze MPT data from their research (Yu et al, 2014). In this regard, there are a number of strategies for the analysis of demand data, each with strengths and limitations; therefore the optimal strategy remains an open question.…”

Section: Discussionmentioning

confidence: 99%

Further validation of a marijuana purchase task

Aston

Metrik

MacKillop

2015

Drug and Alcohol Dependence

141

154

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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 (R2=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.

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Section: Discussionmentioning

confidence: 99%

Further validation of a marijuana purchase task

Aston

Metrik

MacKillop

2015

Drug and Alcohol Dependence

141

154

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“…Given the limitations just described, Yu, Liu, Collins, Vincent, and Epstein (2013) have provided evidence for an alternative approach to analyzing data from behavioral economic demand curve tasks, which is based on the nonlinear mixed effects model. Modifying the model proposed by Hursh et.…”

Section: Methodsmentioning

confidence: 99%

A behavioral economic approach to assessing demand for marijuana.

Collins¹,

Vincent²,

Yu³

et al. 2014

Experimental and Clinical Psychopharmacology

Self Cite

135

108

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In the U.S., 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 its 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.

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“…To compare the robustness of the three methods when model is mis-specified, we simulated data with 100 rats under the model below10

\log Q_{i j} = \log l + b \log p_{j} - a p_{j} + ε_{i j}

i = 1, 2, … 100 and j = 1, 2, … 10. In the above equation, Q ij is the consumption for the i th rat at the j th price point.…”

Section: Simulation Studiesmentioning

confidence: 99%

“…A number of models have been proposed to perform demand curve analysis; 9,10 the most recent and popularly used model is the exponential demand curve, which has the following form

\log Q = \log Q_{0} + K (e^{- α P} - 1)

…”

Section: Introductionmentioning

confidence: 99%

A Bayesian hierarchical model for demand curve analysis

Chu

et al. 2016

Stat Methods Med Res

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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.

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

Cited by 41 publications

References 39 publications

Further validation of a marijuana purchase task

Further validation of a marijuana purchase task

A behavioral economic approach to assessing demand for marijuana.

A Bayesian hierarchical model for demand curve analysis

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