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

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Abstract

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.

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Notes

  1. On November 6, 2012, Colorado voters passed Amendment 64 and Washington voters approved Initiative 502; Alaska voters approved Ballot Measure 2 and the District of Columbia passed Initiative 71 on November 4, 2014.

  2. See Pacula and Lundberg (2014) for an in depth exportation of the existing literature.

  3. The Web site has a drop-down menu for quantity with the following options: 1 gram, 5 grams, 10 grams, 1/8 ounce, 1/4 ounce, 1/2 ounce, or 1 ounce. The quality field is also a drop-down menu with low, medium, and high options. Prices are entered freely.

  4. Malivert and Hall (2013) also use data from www.priceofweed.com, though they only use one month of data and do not report price elasticity.

  5. Prices over $100 a gram and $0 per gram were thought to be unrealistic. Including these observations does not dramatically change our findings. OLS elasticity estimates average \(-\)0.530, while the instrumental variable estimates range from \(-\)0.656 to \(-\)0.783. Similarly, dropping Alaska and Hawaii made no difference in our elasticity estimates, but their inclusion unnecessarily complicates the instruments chosen in our instrumental variable estimation.

  6. Observations are weighted by the number of observations in each state, specifically the inverse of the probability that each state is on our sample.

  7. Because variation in price and quantity may reflect changes in supply as well as demand, price is endogenous in our demand equation, requiring Instrumental Variable estimation to verify that the demand elasticity is accurately estimated. See Angrist and Kreuger (2001) for an overview of instrumental variable techniques.

  8. Available at http://www.whitehouse.gov/ondcp/state-laws-related-to-marijuana.

  9. The Cragg–Donald Wald F statistics are 108.46, 75.21, 115.26, and 68.26 for columns (4), (5), (6), and (7), respectively. These values indicate that none of the IV specifications suffer from weak instruments.

  10. Though nearly all of the results in Table 6 are consistent with our other findings, the high-quality estimates from the IV estimation with just two instruments lose significance. We suspect this is due to weighting and clustering on the state while also using only state-specific instruments.

  11. States corresponding with each Census region are as follows: New England (ME, NH, VT, MA, CT, RI); Mid-Atlantic (NY, PA, NJ); South Atlantic (WV, MD, DE, VA, NC, SC, GA, FL); East South Central (MS, AL, TN, KY); East North Central (WI, MI, IL, IN, OH); West North Central (ND,SD, NE, KS, MO, IA, MN); West South Central (OK, AR, LA, TX); Mountain (MT, ID, WY, UT, CO, AZ, NM, NV); and Pacific (WA, OR, CA).

  12. We are very grateful to an anonymous referee for bringing this issue and explanation to our attention.

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Acknowledgments

We are very grateful to two anonymous referees whose comments greatly improved this paper, especially with regard to its placement within existing literature. We also thank Dr. Robert Kunst, Editor, for his comments and encouragement. Finally, we thank the founders of priceofweed.com, Cory and Andy, for providing the data used in this study.

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Authors and Affiliations

  1. ADM Energy, 3239 Ramos Circle, Sacramento, CA, 95827, USA

    Adam J. Davis

  2. Department of Economics Applied Statistics, and International Business, New Mexico State University, Las Cruces, NM, 88003, USA

    Karl R. Geisler

  3. Department of Economics, University of Nevada, Reno, Reno, NV, 89557, USA

    Mark W. Nichols

Authors
  1. Adam J. Davis
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  2. Karl R. Geisler
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  3. Mark W. Nichols
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Correspondence to Mark W. Nichols.

Appendix

Appendix

see Tables 5 and 6.

Table 5 First-stage estimates (weighted and clustered IV) Dependent variable: log of price per gram
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Table 6 Weighted and cluster robust OLS and IV estimates of marijuana demand by quality dependent variable: log of grams of marijuana purchased
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Davis, A.J., Geisler, K.R. & Nichols, M.W. The price elasticity of marijuana demand: evidence from crowd-sourced transaction data. Empir Econ 50, 1171–1192 (2016). https://doi.org/10.1007/s00181-015-0992-1

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