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Part I Strategies to Estimate Deterrence Part II Optimization of the Criminal Justice System Llad Phillips 1 Testing crime control Llad Phillips 2 Schematic of the Criminal Justice System Causes ? Weak Link Offense Rate Per Capita Crime Generation Expenditures Expected Cost of Punishment (detention, deterrence) Crime Control Llad Phillips 3 Suicide AS A Proxy For Causes of Homicide Llad Phillips 4 FBI Vs. Vital Statistics 24 Murder Rate Per 100,000 from FBI Uniform Crime Reports; Death by Cause, Suicide and Homicide, Vital Statistics from US Statistical Abstract 20 16 12 8 4 0 1930 1940 1950 1960 1970 1980 1990 2000 2010 MURDER_RATE VITALSTATS_HOMICIDE VITALSTATS_SUICIDE Llad Phillips 5 Homicide Rate & Suicide Rate Murder Rate Correlated with the Suicide Rate, 1960-2007 11 MURDER_RATE_PER_100K 10 9 8 7 6 5 4 9 10 11 12 13 14 SUICIDE_RATE_PER_100K Llad Phillips 6 Schematic Model Controls: Imprisonment rate Clearance ratio Causes Homicide Llad Phillips 7 MURDER_RATE_PER_100K 12 10 The story over time 8 6 4 55 60 65 70 75 80 85 90 95 00 05 10 95 00 05 10 05 10 CR_HOMICIDE 100 90 80 70 60 55 60 65 70 75 80 85 90 USIMPRISON_RATE_PER_100K 600 500 400 300 200 100 0 55 Llad Phillips 60 65 70 75 80 85 90 95 00 8 A Control Story: US _ Clearance ratio for homicide was falling from 1960 0n _ _ This could explain the rising homicide rate from 1965-1975 Imprisonment rate was pretty stable until 1980 when it started rising _ This could explain the falling homicide rate from 1995-2009 Llad Phillips 9 Empirical Study of Certainty and Severity Murder Rate Regressed on Clearance Ratio and Imprisoment Rate 12 10 8 2 6 1 4 0 -1 -2 1960 1965 1970 1975 1980 Residual Llad Phillips 1985 1990 Actual 1995 2000 2005 Fitted 10 Empirical Study of Certainty, Severity, & Causality Murder Rate Regressed on Clearance Ratio, Imprisonment Rate & Suicide Rate 12 10 8 .8 6 .4 4 .0 -.4 -.8 1970 1975 1980 1985 Residual Llad Phillips 1990 Actual 1995 2000 2005 Fitted 11 Outline _ _ _ Human Capital & Other News Studying for the Midterm Deterrence: _ _ Evidence pro Evidence con Llad Phillips 12 Human Capital news Llad Phillips 13 About 60% Of 9th graders Get a diploma somewhere Llad Phillips 14 The high Hurdle? Algebra Llad Phillips 15 Studying For the Midterm _ http://econ.ucsb.edu/ Llad Phillips 16 Llad Phillips 17 Llad Phillips 18 Llad Phillips 19 Llad Phillips 20 Llad Phillips 21 Deterrence: conceptual issues _ _ Controlling for causality Simultaneity Llad Phillips 22 Get Expect Source: Llad Phillips Report to the Nation on Crime and Justice 23 Schematic of the Criminal Justice System Causes ? Control for Causality Weak Link Offense Rate Per Capita Crime Generation Expenditures Expected Cost of Punishment (detention, deterrence) Crime Control Llad Phillips 24 Schematic of the Criminal Justice System Causes ? Weak Link Offense Rate Per Capita Recognize Simultaneity Crime Generation Expenditures Expected Cost of Punishment (detention, deterrence) Crime Control Llad Phillips 25 News Over the Weekend Deep Recession high Unemployment rate Keynesian Economics drop money from a helicopter? Or invest in infrastructure? Transportation Energy independence green _ Llad Phillips 26 Greening the Earth _ Greening UCSB _ Rec-Cen Llad Phillips 27 Human development Index and Electricity Use Llad Phillips 28 Production Function UN Human Development Index & Electricity Consumption 1 0.9 0.8 0.7 Index 0.6 0.5 0.4 0.3 0.2 0.1 0 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 Annual Kwhr Per capita Llad Phillips 29 Llad Phillips 30 Llad Phillips 31 Llad Phillips 32 Policy Comment About Economic Development _ An Obama Keynesian strategy: invest in infrastructure _ Past investments in infrastructure _ _ _ _ _ Llad Phillips Canals Railroads Paved roads Airways ? 33 Cesare Marchetti “Fifty-Year Pulsation In Human Affairs” Futures 17(3):376-388 (1986) www.cesaremarchetti.org/archive/scan/ MARCHETTI-069.pdf _ Example: the construction of railroad miles is logistically distributed Llad Phillips 34 FREQUENCY 0.3 0.2 0.1 10% 90% 1890 0.0 -10 Llad Phillips -5 1859 0 1921 5 Mean RAILMILES constructed 35 Cesare Marchetti Llad Phillips 36 Llad Phillips 37 Cesare Marchetti: Energy Technology: Coal, Oil, Gas, Nuclear 52 years Llad Phillips 57 years 56 years 38 Cesare Marchetti Llad Phillips 39 Theodore Modis Figure 4. The data points represent the percentage deviation of energy consumption in the US from the natural growth-trend indicated by a fitted S-curve. The gray band is an 8% interval around a sine wave with period 56 years. The black dots and black triangles show what happened after the graph was first put together in 1988.[7] Presently we are entering a “spring” season. WWI occurred in late “summer” whereas WWII in late “winter”. Llad Phillips 40 Homicide and Non-negligent Manslaaaughter, Rates Per 100,000 16 California 14 12 USA 10 8 6 4 2 0 1900 1920 1940 HOMICIDECA Llad Phillips 1960 1980 2000 HOMICIDEUSA 41 CA Homicide Rate Per 100,000 & Misery Rate in % 25 20 15 10 5 0 1900 1920 1940 1960 HOMICIDECA Llad Phillips 1980 2000 MISERY 42 Causality? Misery Index Offense Rate Mystery Force Llad Phillips 43 Regress CAINDXPC = a + b*MISERY + e(t) where e(t) = 0.96*e(t-1) + u(t) 0.04 0.03 0.004 0.02 0.002 0.01 0.000 0.00 -0.002 -0.004 55 60 65 70 75 Residual Llad Phillips 80 85 Actual 90 95 00 05 Fitted 44 Schematic of the Criminal Justice System Causes ? Control for Causality Weak Link Offense Rate Per Capita Crime Generation Expenditures Expected Cost of Punishment (detention, deterrence) Crime Control Llad Phillips 45 California Prisoners: 1851-1945 10000 8000 6000 4000 2000 0 60 70 80 90 00 10 20 30 40 CAPRISONERS Llad Phillips 46 detrend = caprisoners - 19.656 - 48.569*time 6000 1930 5000 4000 3000 2000 1000 1900 0 -1000 1851 60 70 80 90 00 10 DETREND Llad Phillips 20 30 40 1945 47 Regression of CAINDXPC on MISERY and CAPRPC 0.04 0.03 0.006 0.02 0.004 0.002 0.01 0.000 0.00 -0.002 -0.004 55 60 65 70 75 Residual Llad Phillips 80 85 Actual 90 95 00 05 Fitted 48 Llad Phillips 49 Part I Strategies to Estimate Deterrence Llad Phillips 50 Questions About Crime Why is it difficult to empirically demonstrate the control effect of deterrence on crime? What is the empirical evidence that raises questions about deterrence? What is the empirical evidence that supports deterrence? Llad Phillips 51 Evidence Against the Death Penalty Being a Deterrent Contiguous States Maine: no death penalty Vermont: death penalty New Hampshire: death penalty Little Variation in the Homicide Rate Source: Study by Thorsten Sellin in Hugo Bedau, The Death Penalty in America Llad Phillips 52 Isaac Ehrlich Study of the Death Penalty: 1933-1969 Homicide Control Rate Per Capita Variables probability of arrest probability of conviction given charged Probability of execution given conviction Causal Variables labor force participation rate unemployment rate percent population aged 14-24 years permanent income trend Llad Phillips 54 Ehrlich Results: Elasticities of Homicide with respect to Controls Control Elasticity Average Value of Control 0.90 Prob. of Arrest -1.6 Prob. of Conviction Given Charged Prob. of Execution Given Convicted -0.5 0.43 -0.04 0.026 Source: Isaac Ehrlich, “The Deterrent Effect of Capital Punishment Critique of Ehrlich by Death Penalty Opponents Time period used: 1933-1968 period of declining probability of execution Ehrlich did not include probability of imprisonment given conviction as a control variable Causal variables included are unconvincing as causes of homicide Llad Phillips 56 U.S. Llad Phillips United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ 57 U.S. United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ Llad Phillips 58 What is the Empirical Evidence that Supports Deterrence? Domestic violence and police intervention Experiments Traffic Black Spots Focused Llad Phillips with control groups enforcement efforts 59 Traffic Black Spots Blood Alley Highway San Marcos Pass Highway Llad Phillips 126 154 60 San Marcos Pass Experiment Increase Highway Patrols Increase Arrests Total accidents decrease Injury accidents decrease Accidents involving drinking under the influence decrease Llad Phillips 61 Llad Phillips 62 Los Angeles Traffic Map Domestic Violence & Police Intervention Llad Phillips 64 1993-2005 Llad Phillips 65 Female Victims of Violent Crime, 1973-2003 Llad Phillips 66 Homicides of Intimates, 1976-2005 Llad Phillips 67 Female Victims of Violent Crime In 1994 1 homicide for every 23,000 women (12 or older) females represented 23% of homicide victims in US 9 out of 10 female victims were murdered by males 1 rape for every 270 women 1 robbery for every 240 women 1 assault for every 29 women Llad Phillips 68 Victims of Lone Offenders* Annual Average Numbers Known Female Male 2,715,000 2,019,400 Intimate 1,008,000 143,400 Relative 304,500 122,000 Acquaintance 1,402,500 1,754,000 Stranger 802,300 1,933,100 United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ Llad Phillips 70 Llad Phillips 71 Average Annual Rate of Violent Victimizations Per 1000 Females Family Income Less than $10,000 $10,000 - $14,999 $15,000 - $19,999 $20,000 - $29,999 $30,000 - $49,999 $50,000 or more Llad Phillips Total 57 47 42 38 31 25 Intimate 20 13 11 10 5 5 72 Llad Phillips 73 Declining Trends in Intimate Violence: Homicide Llad Phillips 74 Nonfatal Violent victimization Rates Llad Phillips 75 United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ Llad Phillips 76 United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ Llad Phillips 77 United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ Llad Phillips 78 Llad Phillips 79 Nonfatal intimate Victimization Rates By Age Llad Phillips 80 Llad Phillips 81 Female victimization rates by relationship Llad Phillips 82 Llad Phillips 83 Llad Phillips 84 Llad Phillips 85 Intimate homicides by weapon type Llad Phillips 86 Domestic Violence in California http://caag.state.ca.us/ Llad Phillips 87 Llad Phillips 88 Domestic Violence Rates in California: 1988-1998 1988: 113.6 per 100.000 1998: 169.9 per 100,000 Llad Phillips 89 Domestic Violence in California 1988: 94% Male Arrests 1998: 83.5% Male Arrests Llad Phillips 90 Police Intervention with Experimental Controls A 911 call from a family member the case is randomly assigned for “treatment” A police patrol responds and visits the household police calm down the family members based on the treatment randomly assigned, the police carry out the sanctions Llad Phillips 91 Why is Treatment Assigned Randomly? To control for unknown causal factors assign known numbers of cases, for example equal numbers, to each treatment with this procedure, there should be an even distribution of difficult cases in each treatment group Llad Phillips 92 911 call (characteristics of household Participants unknown) Random Assignment code blue code gold patrol responds patrol responds settles the household verbally warn the husband Llad Phillips settles the household take the husband to jail for the night 93 Part II Optimization of the Criminal Justice System Llad Phillips 94 Questions About Statistical Studies of Deterrence _ Do we know enough about the factors that cause crime? _ _ Can we find variables that will control for variation in crime generation? We have better measures for the factors that control crime than for the factors that cause crime. _ Unknown variation in crime generation may mask the effects of crime control. Llad Phillips 95 Schematic of the Criminal Justice System Causes ? Weak Link Offense Rate Per Capita Crime Generation Expenditures Expected Cost of Punishment (detention, deterrence) Crime Control Llad Phillips 96 Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 2 3 offense rate per capita Source: Report to the Nation on Crime and Justice Causal factors control Source: Report to the Nation on Crime and Justice Expenditures per Capita Crime Control Technology South Dakota North Dakota $100 $0 0 2500 Index crimes per 100,000 people Offenses Per Capita Optimization of the Criminal Justice System (CJS) Minimize damages to victims plus the costs of control, subject to the crime control technology damages to victims per capita = loss rate per offense * offense rate per capita Costs of control = per capita expenditures on CJS Total cost = damages + expenditures Llad Phillips 101 Expenditures per Capita Total cost = expenditures per capita Crime Control Technology $200 South Dakota North Dakota $100 $0 0 2500 Index crimes per 100,000 people Offenses Per Capita Expenditures per Capita Total cost = expenditures per capita Crime Control Technology $200 South Dakota North Dakota $100 Total cost = damages to victims $0 0 2500 Index crimes per 100,000 people Offenses Per Capita 5000 Index offenses per 100,000 people = 0.05 per capita Expenditures per Capita Total cost = expenditures per capita Crime Control Technology $200 South Dakota North Dakota $100 Total cost = damages to victims $0 0 0.025 0.050 Offenses Per Capita Index crimes 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 Llad Phillips 104 Cost to Victims in US, 1993 Offense Robbery Loss Rate Reported Damages, Offenses Billions $ $13,000 659,757 $8.6 Auto Theft Burglary $4,000 1,561,047 $6.2 $1,500 2,834,808 $4.3 Larceny $370 7,820,909 $2.4 Total Source: Nat ional Instit ute of Justice, $21.5 Victim Costs and Consequences (1996) Llad Phillips Source: Phillips: Lecture One 17 Expenditures per capita Total cost = expenditures per capita High Family of Total Cost Curves $100 Low 2500 Index crimes per 100,000 people Llad Phillips Total cost = damages to victims Offenses Per Capita 106 Expenditures per Capita Total cost = expenditures per capita Crime Control Technology $100 South Dakota North Dakota Total cost = damages to victims 2500 Index crimes per 100,000 people Llad Phillips Offenses Per Capita 107 Application of the Economic Paradigm Specify the feasible options the states of the world: Crime control technology Value loss the options rate per offense Optimize Pick the lowest cost point on the crime control technology Llad Phillips 108 That’s all folks! Crime Generation 1. variation of offense rate per capita with expected cost of punishment 2. Shift in the relationship with a change in causal factors Offense rate per capita crime generation function Expected cost(severity) of punishment Crime Generation 1. variation of offense rate per capita with expected cost of punishment 2. Shift in the relationship with a change in causal factors Offense rate per capita crime generation function High causal conditions Low causal conditions Expected cost(severity) of punishment Production Function for the Criminal Justice System (CJS) 1. Variation in expected costs of punishment with criminal justice system expenditure per capita Expected costs of punishment production function Criminal Justice System expenditures per capita Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS per capita expenditures on CJS offense rate per capita Production Function Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS square per capita expenditures on CJS Production Function 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square per capita expenditures on CJS Production Function 1 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square per capita expenditures on CJS Production Function 1 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square per capita expenditures on CJS Production Function 1 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square per capita expenditures on CJS Production Function 1 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square 2 per capita expenditures on CJS Production Function 1 2 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square 2 per capita expenditures on CJS Production Function 3 1 2 450 offense rate per capita Crime Generation expected cost of punishment Four-Way Diagram: Crime Generation & Crime Control per capita expenditures on CJS 1 square 2 per capita expenditures on CJS Production Function 3 1 2 450 offense rate per capita Crime Generation expected cost of punishment Female Victims of Violent Crime Llad Phillips 123 Llad Phillips 124 Llad Phillips 125 Llad Phillips 126 Long Swings in the Homicide Rate in the US: 1900-1980 Source: Report to the Nation on Crime and Justice Long Swings in The Homicide Rate United States Bureau of Justice Statistics http://www.ojp.usdoj.gov/bjs/ Llad Phillips 128 California Homicide Rate Per 100,000: 1952-2003 16 14 12 Rate 10 8 6 4 2 0 1950 1960 1970 1980 1990 2000 2010 Year Llad Phillips 129