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tom/ justin / dani

DEA $ $ $ Spending; Necessary?. tom/ justin / dani. Introduction. Our Purpose. From looking at the available data on drug usage, we want to prove that the constant increase in spending by the DEA is unnecessary.

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tom/ justin / dani

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  1. DEA $$$ Spending; Necessary? tom/justin/dani

  2. Introduction

  3. Our Purpose • From looking at the available data on drug usage, we want to prove that the constant increase in spending by the DEA is unnecessary. • The linear trend, when compared to that of the drug usage, will make no positive impact for the DEA.

  4. The DEA:who are they? • More specifically: the Drug Enforcement Administration • Law enforcement agency under the US Department of Justice

  5. The DEA: what is their mission? • The mission of the Drug Enforcement Administration (DEA) is to enforce the controlled substances laws and regulations of the United States and bring to the criminal and civil justice system of the United States, or any other competent jurisdiction, those organizations and principal members of organizations, involved in the growing, manufacture, or distribution of controlled substances appearing in or destined for illicit traffic in the United States; and to recommend and support non-enforcement programs aimed at reducing the availability of illicit controlled substances on the domestic and international markets.

  6. The DEA: what dothey do? • Drug smuggling and usage within the United States • Lead agency for domestic enforcement • Coordinate and pursue US drug investigations abroad

  7. The DEA:spending situation? • Current budget: $2,602 Million • Split up amongst the various categories • Constantly increasing every year

  8. The DEA:employment situation? • Total Employees: 10,784 • Special Agents: 5,233 • Support Staff: 5,551

  9. Drug Use:what is it? • Using non-harmful dosage of a substance recreationally • Used with the intention of creating or enhancing a recreational experience • Used with eliminated risk of negatively affecting other aspects of one's life • Drug abuse is when you are using a substance in a harmful dosage

  10. Drug Use:cocaine? • Powerful addictive stimulant that directly affects the brain • One of the oldest drugs known • Abused substance – 100 years • Source, coca leaves - thousands

  11. Drug Use:heroin? • Highly addictive and rapidly acting opiate • Morphine – principal component of naturally occurring substance opium • Injected, snorted, smoked • White – eastern, black or brown - western

  12. Drug Use:marijuana? • Mind-altering substance produced from a plant with the scientific name, Cannabis sativa. • Active chemical, THC, induces relaxation and heightening of the senses • Dried, shredded leaves, stems, seeds and flowers • Green, Brown or Gray • Lower quality– all parts, higher quality – bud and flowering top

  13. Drug Use:methamphetamine? • Synthetic stimulant that is highly addictive • Produces euphoric effects, sense of well-being – 24 hours • Inexpensive, relatively east to produce • Crystallized or rock-like-chunks • White, yellow, brown, gray, orange, and pink

  14. Drug Use:hallucinogens? • Substance that produces profound distortions in a person’s perception of reality • See images, hear sounds, and feel sensations that seem real but do not exist • Cause motions to swing wildly and real-world sensations to assume unreal, sometimes frightening aspects • LSD is and the most widely used in this class of drugs • Around for thousands of years, from Arctic to the Tropics

  15. What are the trendsin drug use???

  16. Drug Use:from 1991-2008 • Analyzed 5 age groups from a monitoringthefuture.org report • 8th grade, 10th grade, 12th grade, College, Young Adult • Looked at 5 of the most well known illegal drugs • Marijuana, Cocaine, Crack, Heroin, Hallucinogens

  17. Drug Use:eighth grade • Add info

  18. Drug Use:most significant? • Best linear regression: Any ~ Marijuana + Hallucinogens • Marijuana most significant

  19. Drug Use:individual drug by years • All polynomial • Each one mirrors the graph of Any vs. Year • Especially marijuana

  20. Drug Use:tenth grade

  21. Drug Use:most significant? • Best linear regression: Any ~ Marijuana + Cocaine + Crack + Hallucinogens • Marijuana most significant, but cocaine is close

  22. Drug Use:individual drug by years • All polynomial • Each one mirrors the graph of Any vs. Year • Especially marijuana

  23. Drug Use:twelfth grade

  24. Drug Use:most significant? • Best linear regression: Any ~ Marijuana + Crack + Hallucinogens • Marijuana most significant, but crack is close

  25. Drug Use:individual drug by years • All polynomial • Each one mirrors the graph of Any vs. Year • Especially marijuana

  26. Drug Use:college

  27. Drug Use:most significant? • Best linear regression: Any ~ Marijuana + Cocaine + Crack + Hallucinogens • Marijuana most significant, although cocaine is close

  28. Drug Use:individual drug by years • Different than expected • Only marijuana and crack appear polynomial

  29. Drug Use:young adult

  30. Drug Use:most significant? • Best linear regression: Any ~ Marijuana + Cocaine + Hallucinogens • Marijuana most significant variable

  31. Drug Use:individual drug by years • Marijuana close to expected trend • Crack and heroin vary very little

  32. What can we determine? • Drug use has changed as a polynomial • Peaked around the year 2000 for almost all age groups • Most significant drugs: • Marijuana • Hallucinogens • Insignificant drug? • Heroin

  33. How are the DEA budget and drug use related???

  34. Budget and Drug Use:

  35. Pearson’s product-moment correlation: • p-value = .2158 • correlation = .306667

  36. Marijuana: • At peak of polynomial, budget increases as marijuana usage continues to drop • For 8 years

  37. Cocaine: • More closely related to budget than other drugs

  38. Hallucinogens: • Ended peak earlier than average drug use • Negatively correlated to budget

  39. Have DEA actions affected drug use???

  40. Drug Seizures:

  41. Impact of Marijuana Seizures on Use: • summary(lm(ts(Marijuana) ~ ts(weed_kg)) • Coefficients: • Estimate Std. Error t value Pr(>|t|) • 4.410e-06 4.714e-06 0.936 0.363 • Residual standard error: 2.345 on 16 degrees of freedom • Multiple R-squared: 0.05187, Adjusted R-squared: -0.007391 • F-statistic: 0.8753 on 1 and 16 DF, p-value: 0.3634 • Not very helpful, looks more like exponential

  42. Impact of Marijuana Seizures on Use (cont.) • summary(dyn$lm(ts(Marijuana) ~ lag(ts(weed_kg),2) + lag(ts(I(weed_kg^2)), 2))) • Estimate Std. Error t value Pr(>|t|) • 6.900e-05 1.687e-05 4.090 0.00128 ** • -8.021e-11 2.063e-11 -3.888 0.00187 ** • Residual standard error: 1.745 on 13 degrees of freedom • Multiple R-squared: 0.5653 • Adjusted R-squared: 0.4985 • F-statistic: 8.454 on 2 and 13 DF, p-value: 0.004446 • Lagged by 2 years creates best model • Reasonable that effects of busts are not immediate

  43. Impacts of Other Drug Seizure on Use: • summary(dyn$lm(ts(Hallucinogens) ~ lag(ts(hall_doses),1))) • # R-squared: 0.2241 • # P: 0.06823 • summary(dyn$lm(ts(Heroin) ~ lag(ts(heroin_kg),0))) • # R-squared: 0.2864 • # P: 0.01292 • summary(dyn$lm(ts(Cocaine) ~ lag(ts(coke_kg),1))) • # R-squared: 0.02374 • # P: 0.2569 • Possible impact from heroin/hallucinogens • No benefit from exponentials • Benefit of the doubt given to best 0-2 year impact • Cocaine busts appear to have no effect on use

  44. Impact of Arrestson Drug Use: • summary(dyn$lm(ts(Any) ~ lag(ts(arrests),1))) • # R-squared: 0.5071 • # P: 0.0005508 • summary(dyn$lm(ts(Marijuana) ~ lag(ts(arrests),1))) • # R-squared: 0.5045 • # P: 0.001231 • summary(dyn$lm(ts(Hallucinogens) ~ lag(ts(arrests),0))) • # R-squared: 0.7976 • # P: 5.639e-5 • summary(dyn$lm(ts(Heroin) ~ lag(ts(arrests),1))) • # R-squared: 0.6273 • # P: 0.0001550 • summary(dyn$lm(ts(Cocaine) ~ lag(ts(arrests),0))) • # R-squared: 0.4472 • # P: 0.001442

  45. What impacts arrests pre-1999? Budget • summary(dyn$lm(ts(arrests) ~ lag(ts(budget),1))) • Coefficients: • Estimate Std. Error t value Pr(>|t|) • 26.190 2.966 8.830 0.000117 *** • Multiple R-squared: 0.9285, Adjusted R-squared: 0.9166 • F-statistic: 77.97 on 1 and 6 DF, p-value: 0.0001172 Employees • summary(dyn$lm(ts(arrests) ~ lag(ts(employees),1))) • Coefficients: • Estimate Std. Error t value Pr(>|t|) • 8.458 4.542e-01 18.62 1.55e-06 *** • Multiple R-squared: 0.983, Adjusted R-squared: 0.9802 • F-statistic: 346.7 on 1 and 6 DF, p-value: 1.548e-06

  46. What impacts arrests post-1999? Budget • summary(dyn$lm(ts(arrests) ~ lag(ts(budget),1))) • Coefficients: • Estimate Std. Error t value Pr(>|t|) • -13.512 3.721 -3.631 0.008388 ** • Multiple R-squared: 0.6532, Adjusted R-squared: 0.6036 • F-statistic: 13.18 on 1 and 7 DF, p-value: 0.008388 Employees • summary(dyn$lm(ts(arrests) ~ lag(ts(employees),1))) • Coefficients: • Estimate Std. Error t value Pr(>|t|) • -5.996 1.158 -5.178 0.00128 ** • Multiple R-squared: 0.793, Adjusted R-squared: 0.7634 • F-statistic: 26.81 on 1 and 7 DF, p-value: 0.001284

  47. Conclusion

  48. Questions and Comments???

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