The price elasticity of marijuana demand: evidence from crowd-sourced transaction data

被引:22
|
作者
Davis, Adam J. [1 ]
Geisler, Karl R. [2 ]
Nichols, Mark W. [3 ]
机构
[1] ADM Energy, 3239 Ramos Circle, Sacramento, CA 95827 USA
[2] New Mexico State Univ, Dept Econ Appl Stat & Int Business, Las Cruces, NM 88003 USA
[3] Univ Nevada, Dept Econ, Reno, NV 89557 USA
关键词
Elasticity; Marijuana demand; Instrumental variable estimation; ALCOHOL; CANNABIS; DECRIMINALIZATION; SUBSTITUTES; CONSUMPTION; COMPLEMENTS; STUDENTS; TOBACCO; COCAINE;
D O I
10.1007/s00181-015-0992-1
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper uses crowd-sourced transaction data from a cross section of the USA to examine demand for marijuana. State and regional variations in consumption, price, and quality are also explored. Our data are a unique cross section of over 23,000 actual marijuana transactions where price, quantity, and quality are reported, allowing for an estimation of the full demand elasticity rather than the participation elasticity. In addition, we account for the endogeneity of price by using instrumental variable estimation to calculate price elasticity. Price elasticity of demand estimates ranges between 0.67 and 0.79. Noticeable price differences are found between high-, medium-, and low-quality marijuana, with high-quality marijuana, at $13.77 per gram, 144 % greater than low-quality marijuana, at $5.63 a gram. Significant price variation is also found by medical marijuana status and census region, although this variation depends critically on the quality of the marijuana.
引用
收藏
页码:1171 / 1192
页数:22
相关论文
共 50 条
  • [21] Social-distancing fatigue: Evidence from real-time crowd-sourced traffic data
    Shearston, Jenni A.
    Martinez, Micaela E.
    Nunez, Yanelli
    Hilpert, Markus
    SCIENCE OF THE TOTAL ENVIRONMENT, 2021, 792
  • [22] Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges
    Ellrott, Kyle
    Buchanan, Alex
    Creason, Allison
    Mason, Michael
    Schaffter, Thomas
    Hoff, Bruce
    Eddy, James
    Chilton, John M.
    Yu, Thomas
    Stuart, Joshua M.
    Saez-Rodriguez, Julio
    Stolovitzky, Gustavo
    Boutros, Paul C.
    Guinney, Justin
    GENOME BIOLOGY, 2019, 20 (01)
  • [23] Learning of Performance Measures from Crowd-Sourced Data with Application to Ranking of Investments
    Harris, Greg
    Panangadan, Anand
    Prasanna, Viktor K.
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PART I, 2015, 9077 : 538 - 549
  • [24] Player Interaction with Procedurally Generated Game Play from Crowd-Sourced data
    Arnab, Sylvester
    Klopfenstein, Lorenz Cuno
    Lewis, Mark
    Delpriori, Saverio
    Bogliolo, Alessandro
    Clarke, Samantha
    CHI PLAY'19: EXTENDED ABSTRACTS OF THE ANNUAL SYMPOSIUM ON COMPUTER-HUMAN INTERACTION IN PLAY, 2019, : 333 - 339
  • [25] Reproducible biomedical benchmarking in the cloud: lessons from crowd-sourced data challenges
    Kyle Ellrott
    Alex Buchanan
    Allison Creason
    Michael Mason
    Thomas Schaffter
    Bruce Hoff
    James Eddy
    John M. Chilton
    Thomas Yu
    Joshua M. Stuart
    Julio Saez-Rodriguez
    Gustavo Stolovitzky
    Paul C. Boutros
    Justin Guinney
    Genome Biology, 20
  • [26] A Crowd-Sourced Data Based Analytical Framework for Urban Planning
    Li Dong
    Long Ying
    China City Planning Review, 2015, 24 (01) : 49 - 57
  • [27] Gluten Contamination of Restaurant Food: Analysis of Crowd-Sourced Data
    Lerner, Benjamin A.
    Lynn Phan Vo
    Yates, Shireen
    Rundle, Andrew G.
    Green, Peter H. R.
    Lebwohl, Benjamin
    AMERICAN JOURNAL OF GASTROENTEROLOGY, 2018, 113 : S658 - S658
  • [28] Road Grade Estimation Using Crowd-Sourced Smartphone Data
    Gupta, Abhishek
    Hu, Shaohan
    Zhong, Weida
    Sadek, Adel
    Su, Lu
    Qiao, Chunming
    2020 19TH ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN 2020), 2020, : 313 - 324
  • [29] Robust CNNs for detecting collapsed buildings with crowd-sourced data
    Gibson, Matthew J.
    Kaushik, Dhruv
    Sowmya, Arcot
    2019 JOINT URBAN REMOTE SENSING EVENT (JURSE), 2019,
  • [30] Route Recommendations to Business Travelers Exploiting Crowd-Sourced Data
    Collerton, Thomas
    Marrella, Andrea
    Mecella, Massimo
    Catarci, Tiziana
    MOBILE WEB AND INTELLIGENT INFORMATION SYSTEMS, MOBIWIS 2017, 2017, 10486 : 3 - 17