The Price of Illicit Drugs: 1981 through the - The White House

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Corresponding predicted real prices per pure gram for this set of nominal expenditures were obtained by deflating and then inverting these predicted mean amounts. The average retail price (in 2000 dollars) for a given city and quarter was then obtained as the weighted average of the set of 10 prices, with weights coming from the DUF-based cocaine and heroin price distributions described above. For a given quarter, the 30 city means were then multiplied by their respective DAWN weights and the sum of these contributions provided a weighted estimate for the mean price and mean purity in the U.S. for that quarter. The national results are show in Table 5 and Figures 2 and 5. 5. A Model for Purity Given sufficient data, we could have estimated the mean purity for each city in each quarter by its sample mean. For a given quarter, a weighted average of these 30 city (29 large U.S. cities and the Rest of U.S.) sample means would have provided a sensible quarterly estimate for the mean purity in the U.S. for that quarter. However, given 30 cities and 78 quarters, this would require 2,340 sample means for each level of distribution for each drug, and this requirement goes well beyond the resources of the STRIDE database. For each level of distribution for cocaine, Table A10 contains the percentages of these cell means that do not have data. This section describes a feasible alternative to simple tabulations. In order to estimate 2,340 cell means from a database containing less than 2,340 cells of data, some form of modeling is necessary. The linear model provides a straightforward possibility: E(purity itj ) = + city i + time t (3) V(purity itj ) = 2 (4) However, both of these specifications are implausible for the purity data. First, the mean purity must lie in the unit interval, but (3) does not impose this restriction. Second, the variance of purity depends on its mean (it is higher around 0.5 than 0.9), but this is not embodied in (4). The first problem was particularly important for our data, as the linear model gave impossible estimates for purity for several cities in several quarters. We avoided both problems by working with a quasi- A-12
inomial model and regarding a purity measurement as a realization from a quasi-binomial experiment. Table A10. Percentage of Cell Means with no Data Cocaine Number of Cells Missing Percent Cells Missing 0 to 1 Pure Grams 1174 50.17% 1 to 10 Pure Grams 573 24.49% 10 to 100 Pure Grams 310 13.25% Greater than 100 pure grams 1347 57.56% Heroin Number of Cells Missing Percent Cells Missing 0 to 0.1 Pure Grams 1138 48.63% 0.1 to 1 Pure Grams 923 39.44% 1 to 10 Pure Grams 1206 51.54% Greater than 10 pure grams 1460 62.39% Methamphetamine Number of Cells Missing Percent Cells Missing 0 to 10 Pure Grams 1673 71.50% 10 to 100 Pure Grams 1800 76.92% Greater than 100 pure grams 2126 90.85% Marijuana Number of Cells Missing Percent Cells Missing 0 to 10 Pure Grams 2079 88.85% 10 to 100 Pure Grams 2092 89.40% 100 to 1000 Pure Grams 2012 85.98% Greater than 1000 pure grams 2209 94.40% A DEA laboratory obtained a purity measurement for a given purchase by analyzing a small sample of the purchase. To construe this measurement as an outcome of a binomial experiment (even though the actual measurement process may have proceeded along different lines), let the small sample constitute a three dimensional grid of m cells, and let y be the number of pure cells. Then y follows a binomial(m, p) distribution, and p = y/m is the observed proportion of pure cells, that is, the purity. The analysis of purity can now be carried out via a binomial A-13
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The Price of Illicit Drugs: 1981 th
Page 5 and 6:
Table of Contents Introduction.....
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Table C8. Annual Heroin Price and P
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Acknowledgements The authors and st
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transit zone programs almost certai
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The System To Retrieve Information
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Secondly, even if the raw data over
Page 18 and 19: same month, and an increase of $0.0
Page 20 and 21: purchases, at least as reflected by
Page 22 and 23: difficulty this causes is that STRI
Page 24 and 25: References Box G.E., Jenkins G.M.,
Page 27 and 28: Figure 1: Estimated Cocaine Price P
Page 29 and 30: Figure 3: Estimated Cocaine Purity
Page 31 and 32: Figure 5: Comparison of Heroin Reta
Page 33 and 34: Figure 7: Estimated Methamphetamine
Page 35: Figure 9: Estimated Marijuana Price
Page 38 and 39: Table 1: Estimated Quarterly Price
Page 54 and 55: Table 7: Estimated Annual Price per
Page 56 and 57: Table 9: Estimated Annual Price per
Page 58 and 59: Figure A1. Flow Chart for STRIDE an
Page 60 and 61: the subsequent removal of extreme r
Page 62 and 63: From the viewpoint of the model, th
Page 64 and 65: Table A5. Mean Price and Parameter
Page 66 and 67: 200 180 160 Number of Observat ions
Page 70 and 71: model because each purity observati
Page 72 and 73: Figure A4. Predicted Weights for Co
Page 74 and 75: Figure A5. Monthly Retail and Whole
Page 76 and 77: The approach used to obtain the Sou
Page 78 and 79: Table B1: Estimated DAWN Weights fo
Page 106 and 107: Table B5: Comsumer Price Index, Fir
Page 108 and 109: Table C1. Annual Cocaine Price and
Page 110 and 111: Table C3. Annual Cocaine Price and
Page 112 and 113: Table C5. Annual Heroin Price and P
Page 114 and 115: Table C7. Annual Heroin Price and P
Page 116 and 117: Table C9. Annual Methamphetamine Pr
Page 118 and 119:
Table C11. Annual Methamphetamine P
Page 120 and 121:
Table C13. Annual Marijuana Price f
Page 122:
Table C15. Annual Marijuana Price f
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The Price of Illicit Drugs: 1981 through the - The White House

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