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

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 do they 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 trends in drug use???

16. Drug Use: from 1991-2008 Analyzed 5 age groups from a monitoringthefuture.org report –8 th grade, 10 th grade, 12 th 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 Arrests on 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(arrest s) ~ 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 ???