Economic Models of CJS

1. And other thoughts Economic Models of CJS

2. News

3. Outline • CA Criminal Justice System: Cost Constraint Vs. The Capital Constraint • The Effect of Income on Loss Rate • Decision theory and the jury system • Victimization Rates by Income, robbery VS. burglary; public goods VS. private goods

4. Crime in California • Causality and Control • Corrections: Dynamics and Economics

5. The Capital Constraint

6. Prison Dynamics and Economics • Admissions * mean years served = prisoners

7. Relationships Between Stocks and Flows: Coordinating CJS • In equilibrium: • Inflow = Outflow • The outflow is proportional to the stock • Outflow = k * Stock • constant of proportionality, k, equals one divided by mean time served • Admits * mean years served = stock of prisoners

8. The Stock of Prisoners Inflow Outflow Stock of Prisoners New Admissions from Court Released to Parole Coordinating CJS

9. Coordinating CJS Constraint: Admits per year*Average years served = Prisoners Admits per Year 45 degrees Average Years Served

11. California Department of Corrections: Total Felon Admissions http://www.cdc.state.ca.us/reports/populatn.htm

13. Prison Realities • We can not build prisons fast enough to increase capacity soon enough • The public wants more convicts sent to prison • But prisons are full • So, what happens?

14. Consequence • Release violent offenders • Innocent children are kidnapped, raped and murdered: example-Polly Klass

15. Consequence • Polly’s father campaigns for three strikes law

16. Consequence • More convicts are sent to prison

18. Capital constraint: Coordinating CJS • admits per capita per year * average years served = prisoners per capita • Prisoners per capita is limited by prison capacity • If you increase admits per capita per year, then average years served decreases until prison capacity catches up

19. Annual Budget Constraint

20. Prison Dynamics and Economics • Admissions * mean years served = prisoners • Dynamics • Production Possibility Frontier • Economics

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23. Abstraction (Model) of the Criminal Justice System New Admits Enforcement Prosecution Defense Courts State Prisons Mean Years Served

24. Tradeoff Between Criminal Justice System Outputs Admits per Year per capita  average years served tan = admits per year per capita/average years served

25. Resource constraint • expenditure per capita on CJS = expenditure per capita on enforcement, prosecution, and adjudication plus expenditure per capita on corrections • admits per year per capita depends on expenditures per capita on enforcement, etc. • average years served depends on expenditures per capita on corrections

26. Admits per Capita Average Years Served production function production function Expenditures per capita on Enforcement Expenditures per capita on Corrections Expenditures per capita on Corrections Total Expenditures per capita on Criminal Justice System Expenditures per capita on Enforcement

27. Admits per capita Production Function Expenditures per capita, Enforcement Average Years Served Production Function Total Expenditure per capita on CJScapita on CJS Expenditures per capita, Corrections

28. Abstraction (Model) of the Criminal Justice System New Admits Enforcement Prosecution Defense Courts State Prisons Mean Years Served

29. Admits per capita Expenditures per capita, Enforcement Average Years Served Production Function Total Expenditure per capita on CJScapita on CJS Expenditures per capita, Corrections

30. A Shifting Mix In Criminal Justice System Outputs Facts 1. spend more 2. Admit more 3. shorter time served  Admits per Year per capita, AD  Prison Capacity Constraint   average years served, S tan = admits per year per capita/average years served

31. 1994 1986 1952 1975

32. The Effect of Income on Loss Rate • Income = expenditure on public goods plus expenditure on private goods • Y = CJS + M • Crime control technology, aka crime abatement curve • Derivation of the crime-consumption possibility frontier • Preferences for a good, M, and a bad, OF

33. M Y = M + CJS CJS 450 OF CJS Crime control technology

34. M Y = M + CJS CJS 450 OF CJS Crime control technology

35. M Y = M + CJS CJS 450 OF CJS Crime control technology

36. M Crime-consumption possibility frontier Y = M + CJS CJS 450 OF CJS Crime control technology

37. M Crime-consumption possibility frontier Y = M + CJS preferences CJS 450 OF CJS Crime control technology

38. M Crime-consumption possibility frontier Y = M + CJS preferences CJS 450 OF CJS Crime control technology

39. M Crime-consumption possibility frontier Y = M + CJS preferences poor rich CJS 450 OF CJS Crime control technology

40. M Crime-consumption possibility frontier Y = M + CJS preferences poor rich CJS 450 OF CJS Crime control technology

41. M Crime-consumption possibility frontier Y = M + CJS preferences poor rich CJS 450 OF CJS Crime control technology

42. M Crime-consumption possibility frontiers Y = M + CJS poor rich CJS 450 OF CJS Crime control technology

43. M Crime-consumption possibility frontiers Y = M + CJS rich poor rich poor CJS 450 OF CJS Crime control technology

44. M Crime-consumption possibility frontiers Y = M + CJS rich poor rich poor CJS 450 OF rich poor CJS Crime control technology

45. Expenditures per Capita Total cost = expenditures per capita Crime Control Technology $200 South Dakota $100 North Dakota Total cost = damages to victims poor $0 0 0.025 Index crimes per capita 0.050 Offenses Per Capita Total cost = $200 per capita = damages to victims = loss rate*0.05 so loss rate = $4,000 per Index Crime in South Dakota

46. CJS $ Per FBI Index Offense Vs. Income

47. Poor Communities • Poor communities have less safety and less other goods and services • Poorer communities care less about crime, i.e. have a lower loss rate, because they have other priorities, namely food , shelter, and clothing

48. Decision Theory & Jury System • Decision making and uncertainty • If a person enters a plea of innocent and is a defendant in a jury trial, you do not know whether he/she is guilty or innocent • The evidence is reviewed and a decision is rendered by the jury, still not knowing for sure whether the defendant is guilty or innocent • It is possible for juries to make mistakes, i.e. let a guilty person go free or convict an innocent person

49. Distribution of Evidence Quality

50. Distribution of Evidence Quality for the Innocent, mean=35 on a scale of 0=innocent, 100=guilty