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Llad Phillips 1
Part IStrategies to Estimate Deterrence
Part IIOptimization of the Criminal
Justice System
Part IStrategies to Estimate Deterrence
Part IIOptimization of the Criminal
Justice System
Llad Phillips 2
OutlineOutline_ Human Capital & Other NewsHuman Capital & Other News_ Studying for the MidtermStudying for the Midterm_ Deterrence: Deterrence:
_ Evidence proEvidence pro_ Evidence con Evidence con
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Human Capital newsHuman Capital news
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About 60%Of 9th gradersGet a diplomasomewhere
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The highHurdle?Algebra
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Studying For the MidtermStudying For the Midterm
_ http://econ.ucsb.edu/
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Deterrence: conceptual issuesDeterrence: conceptual issues_ Controlling for causality_ Simultaneity
Llad Phillips 13Source: Report to the Nation on Crime and Justice
Expect
Get
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Crime Generation
Crime Control
OffenseRate PerCapita
ExpectedCost ofPunishment
Schematic of the Criminal Justice System
Causes ?
(detention,deterrence)
Expenditures
Weak Link
Control for Causality
Llad Phillips 15
Crime Generation
Crime Control
OffenseRate PerCapita
ExpectedCost ofPunishment
Schematic of the Criminal Justice System
Causes ?
(detention,deterrence)
Expenditures
Weak Link
RecognizeSimultaneity
Llad Phillips 16
News Over the WeekendNews Over the Weekend_ Deep RecessionDeep Recession
high Unemployment ratehigh Unemployment rate
Keynesian EconomicsKeynesian Economics
drop money from a helicopter?drop money from a helicopter?
Or invest in infrastructure?Or invest in infrastructure?
TransportationTransportation
EnergyEnergy
independenceindependence
greengreen
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Greening the EarthGreening the Earth
_ Greening UCSBGreening UCSB_ Rec-CenRec-Cen
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Human development Index and Electricity Use
Human development Index and Electricity Use
Llad Phillips 19
UN Human Development Index & Electricity Consumption
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0 2000 4000 6000 8000 10000 12000 14000 16000 18000
Annual Kwhr Per capita
Ind
ex
Production FunctionProduction Function
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Policy Comment About Economic Development
Policy Comment About Economic Development
_ An Obama Keynesian strategy: invest in infrastructure_ Past investments in infrastructure
_ Canals_ Railroads_ Paved roads_ Airways_ ?
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Cesare Marchetti“Fifty-Year Pulsation In Human Affairs”
Futures 17(3):376-388 (1986)www.cesaremarchetti.org/archive/scan/
MARCHETTI-069.pdf
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
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0.0
0.1
0.2
0.3
-10 -5 0 5
RAILMILES
FR
EQ
UE
NC
Y
Mean
constructed
90%10%
1859
1890
1921
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Cesare MarchettiCesare Marchetti
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Cesare Marchetti: Energy Technology: Coal, Oil, Gas, Nuclear
Cesare Marchetti: Energy Technology: Coal, Oil, Gas, Nuclear
52 years 57 years 56 years
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Cesare MarchettiCesare Marchetti
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Theodore ModisTheodore 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”.
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0
2
4
6
8
10
12
14
16
1900 1920 1940 1960 1980 2000
HOMICIDECA HOMICIDEUSA
California
USA
Homicide and Non-negligent Manslaaaughter, Rates Per 100,000
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0
5
10
15
20
25
1900 1920 1940 1960 1980 2000
HOMICIDECA MISERY
CA Homicide Rate Per 100,000 & Misery Rate in %
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Causality?Causality?
Misery Index Offense Rate
Mystery Force
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-0.004
-0.002
0.000
0.002
0.004
0.00
0.01
0.02
0.03
0.04
55 60 65 70 75 80 85 90 95 00 05
Residual Actual Fitted
Regress CAINDXPC = a + b*MISERY + e(t)where e(t) = 0.96*e(t-1) + u(t)
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Crime Generation
Crime Control
OffenseRate PerCapita
ExpectedCost ofPunishment
Schematic of the Criminal Justice System
Causes ?
(detention,deterrence)
Expenditures
Weak Link
Control for Causality
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0
2000
4000
6000
8000
10000
60 70 80 90 00 10 20 30 40
CAPRISONERS
California Prisoners: 1851-1945
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-1000
0
1000
2000
3000
4000
5000
6000
60 70 80 90 00 10 20 30 40
DETREND
detrend = caprisoners - 19.656 - 48.569*time
1851 1945
1900
1930
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-0.004
-0.002
0.000
0.002
0.004
0.006
0.00
0.01
0.02
0.03
0.04
55 60 65 70 75 80 85 90 95 00 05
Residual Actual Fitted
Regression of CAINDXPC on MISERY and CAPRPC
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Part IStrategies to Estimate Deterrence
Part IStrategies to Estimate Deterrence
Llad Phillips 41
Questions About CrimeQuestions About Crime
Why is it difficult to empirically Why is it difficult to empirically demonstrate the control effect of deterrence demonstrate the control effect of deterrence on crime?on crime?
What is the empirical evidence that raises What is the empirical evidence that raises questions about deterrence?questions about deterrence?
What is the empirical evidence that supports What is the empirical evidence that supports deterrence?deterrence?
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Evidence Against the Death Penalty Being a DeterrentEvidence Against the Death Penalty Being a Deterrent Contiguous StatesContiguous States
Maine: no death penaltyMaine: no death penalty Vermont: death penaltyVermont: death penalty New Hampshire: death penaltyNew Hampshire: death penalty
Little Variation in the Homicide RateLittle Variation in the Homicide Rate Source: Study by Thorsten Sellin in Hugo Source: Study by Thorsten Sellin in Hugo
Bedau, Bedau, The Death Penalty in AmericaThe Death Penalty in America
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Isaac Ehrlich Study of the Death Penalty: 1933-1969Isaac Ehrlich Study of the Death Penalty: 1933-1969 Homicide Rate Per CapitaHomicide Rate Per Capita
Control VariablesControl Variables probability of arrestprobability of arrest probability of conviction given charged probability of conviction given charged Probability of execution given convictionProbability of execution given conviction
Causal VariablesCausal Variables labor force participation ratelabor force participation rate unemployment rateunemployment rate percent population aged 14-24 yearspercent population aged 14-24 years permanent incomepermanent income trendtrend
Ehrlich Results: Elasticities of Homicide with respect to ControlsEhrlich Results: Elasticities of Homicide with respect to Controls
Control Elasticity Average Valueof Control
Prob. of Arrest -1.6 0.90
Prob. of ConvictionGiven Charged
-0.5 0.43
Prob. of ExecutionGiven Convicted
-0.04 0.026
Source: Isaac Ehrlich, “The Deterrent Effect of Capital Punishment
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Critique of Ehrlich by Death Penalty OpponentsCritique of Ehrlich by Death Penalty Opponents Time period used: 1933-1968Time period used: 1933-1968
period of declining probability of executionperiod of declining probability of execution Ehrlich did not include probability of Ehrlich did not include probability of
imprisonment given conviction as a control imprisonment given conviction as a control variablevariable
Causal variables included are unconvincing Causal variables included are unconvincing as causes of homicideas causes of homicide
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U.S.
United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
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U.S.
United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
Llad Phillips 49
What is the Empirical Evidence that Supports Deterrence?What is the Empirical Evidence that Supports Deterrence? Domestic violence and police interventionDomestic violence and police intervention
Experiments with control groupsExperiments with control groups Traffic Black SpotsTraffic Black Spots
Focused enforcement effortsFocused enforcement efforts
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Traffic Black SpotsTraffic Black Spots
Blood AlleyBlood Alley Highway 126Highway 126
San Marcos PassSan Marcos Pass Highway 154Highway 154
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San Marcos Pass ExperimentSan Marcos Pass Experiment
Increase Highway PatrolsIncrease Highway Patrols Increase ArrestsIncrease Arrests
Total accidents decreaseTotal accidents decrease Injury accidents decreaseInjury accidents decrease Accidents involving drinking under the Accidents involving drinking under the
influence decreaseinfluence decrease
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Los Angeles Traffic Map
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Domestic Violence & Police Intervention
Domestic Violence & Police Intervention
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1993-20051993-2005
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Female Victims of Violent Crime, 1973-2003
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Homicides of Intimates, 1976-2005Homicides of Intimates, 1976-2005
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Female Victims of Violent CrimeFemale Victims of Violent Crime
In 1994In 1994 1 homicide for every 23,000 women (12 or older)1 homicide for every 23,000 women (12 or older)
females represented 23% of homicide victims in USfemales represented 23% of homicide victims in US 9 out of 10 female victims were murdered by males9 out of 10 female victims were murdered by males
1 rape for every 270 women1 rape for every 270 women 1 robbery for every 240 women1 robbery for every 240 women 1 assault for every 29 women 1 assault for every 29 women
Victims of Lone Offenders*Annual Average NumbersVictims of Lone Offenders*Annual Average Numbers
Female Male
Known 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
* Excludes Homicide
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United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
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Average Annual Rate of Violent Victimizations Per 1000 FemalesAverage Annual Rate of Violent Victimizations Per 1000 FemalesFamily Income Total IntimateLess than $10,000 57 20$10,000 - $14,999 47 13$15,000 - $19,999 42 11$20,000 - $29,999 38 10$30,000 - $49,999 31 5$50,000 or more 25 5
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Declining Trends in Intimate Violence: Homicide
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Nonfatal Violent victimization RatesNonfatal Violent victimization Rates
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United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
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United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
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United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
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Nonfatal intimate Victimization Rates By AgeNonfatal intimate Victimization Rates By Age
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Female victimization rates by relationshipFemale victimization rates by relationship
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Intimate homicides by weapon typeIntimate homicides by weapon type
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Domestic Violence in California
http://caag.state.ca.us/
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Domestic Violence Rates in California: 1988-19981988: 113.6 per 100.0001998: 169.9 per 100,000
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Domestic Violence in California1988: 94% Male Arrests1998: 83.5% Male Arrests
Llad Phillips 81
Police Intervention with Experimental ControlsPolice Intervention with Experimental Controls A 911 call from a family memberA 911 call from a family member
the case is randomly assigned for “treatment”the case is randomly assigned for “treatment” A police patrol responds and visits the A police patrol responds and visits the
householdhousehold police calm down the family memberspolice calm down the family members based on the treatment randomly assigned, the based on the treatment randomly assigned, the
police carry out the sanctionspolice carry out the sanctions
Llad Phillips 82
Why is Treatment Assigned Randomly?Why is Treatment Assigned Randomly? To control for unknown causal factorsTo control for unknown causal factors
assign known numbers of cases, for example assign known numbers of cases, for example equal numbers, to each treatmentequal numbers, to each treatment
with this procedure, there should be an even with this procedure, there should be an even distribution of distribution of difficultdifficult cases in each treatment cases in each treatment groupgroup
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911 call(characteristics of household Participants unknown)
Random Assignment
code blue code gold
patrol responds patrol responds
settles the household settles the household
verbally warn the husband take the husband to jail for the night
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Part IIOptimization of the Criminal
Justice System
Part IIOptimization of the Criminal
Justice System
Llad Phillips 85
Questions About Statistical Studies of DeterrenceQuestions About Statistical Studies of Deterrence_ Do we know enough about the factors that cause Do we know enough about the factors that cause
crime?crime?_ Can we find variables that will control for variation in Can we find variables that will control for variation in
crime generation?crime generation?
_ We have better measures for the factors that We have better measures for the factors that control crime than for the factors that cause crime.control crime than for the factors that cause crime._ Unknown variation in crime generation may mask the Unknown variation in crime generation may mask the
effects of crime control.effects of crime control.
Llad Phillips 86
Crime Generation
Crime Control
OffenseRate PerCapita
ExpectedCost ofPunishment
Schematic of the Criminal Justice System
Causes ?
(detention,deterrence)
Expenditures
Weak Link
per capita expenditures on CJS
offense rate per capita
Four-Way Diagram: Crime Generation & Crime Control
1
2
3
Source: Report to the Nation on Crime and Justice
Source: Report to the Nation on Crime and Justice
control
Causalfactors
Expenditures per Capita
Offenses Per Capita
Crime Control Technology
South Dakota North Dakota
2500 Index crimesper 100,000 people
$100
$00
Llad Phillips 91
Optimization of the Criminal Justice System (CJS)Optimization of the Criminal Justice System (CJS) Minimize damages to victims plus the costs Minimize damages to victims plus the costs
of control, subject to the crime control of control, subject to the crime control technologytechnology damages to victims per capita = loss rate per damages to victims per capita = loss rate per
offense * offense rate per capitaoffense * offense rate per capita Costs of control = per capita expenditures on CJSCosts of control = per capita expenditures on CJS Total cost = damages + expendituresTotal cost = damages + expenditures
Expenditures per Capita
Offenses Per Capita
Crime Control Technology
South Dakota North Dakota
2500 Index crimesper 100,000 people
$100
Total cost = expenditures per capita
$200
$00
Expenditures per Capita
Offenses Per Capita
Crime Control Technology
South Dakota North Dakota
2500 Index crimesper 100,000 people
$100
Total cost = expenditures per capita
Total cost = damages to victims
$200
$00
5000 Index offenses per 100,000 people = 0.05 per capita
Llad Phillips 94
Expenditures per Capita
Offenses Per Capita
Crime Control Technology
South Dakota North Dakota
0.025 Index crimesper capita
$100
Total cost = expenditures per capita
Total cost = damages to victims
0.050
Total cost = $200 per capita = damages to victims = loss rate*0.05so loss rate = $4,000 per Index Crime in South Dakota
$200
$00
Llad Phillips 17
Cost to Victims in US, 1993Offense Loss Rate Reported
OffensesDamages,Billions $
Robbery $13,000 659,757 $8.6
AutoTheft
$4,000 1,561,047 $6.2
Burglary $1,500 2,834,808 $4.3
Larceny $370 7,820,909 $2.4
Total $21.5
Source: National Institute of Justice, Victim Costs and Consequences (1996)
Source: Phillips: Lecture One
Llad Phillips 96
Expenditures per capita
Offenses Per Capita
2500 Index crimesper 100,000 people
$100
Total cost = expenditures per capita
Total cost = damages to victims
Family of Total Cost CurvesHigh
Low
Llad Phillips 97
Expenditures per Capita
Offenses Per Capita
Crime Control Technology
South Dakota North Dakota
2500 Index crimesper 100,000 people
$100
Total cost = expenditures per capita
Total cost = damages to victims
Llad Phillips 98
Application of the Economic ParadigmApplication of the Economic Paradigm Specify the feasible optionsSpecify the feasible options
the states of the world: Crime control the states of the world: Crime control technologytechnology
Value the optionsValue the options loss rate per offenseloss rate per offense
OptimizeOptimize Pick the lowest cost point on the crime control Pick the lowest cost point on the crime control
technologytechnology
That’s all folks!
Crime Generation1. variation of offense rate per capita with expected cost of punishment2. Shift in the relationship with a change in causal factors
Offenserate percapita
Expected cost(severity) of punishment
crime generation function
Crime Generation1. variation of offense rate per capita with expected cost of punishment2. Shift in the relationship with a change in causal factors
Offenserate percapita
Expected cost(severity) of punishment
crime generation function
High causal conditions
Low causal conditions
Production Function for the Criminal Justice System (CJS)1. Variation in expected costs of punishment with criminal justice system expenditure per capita
Expected costs ofpunishment
Criminal Justice System expenditures per capita
production function
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
450
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
2
2
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
2
2
3
per capita expenditures on CJS
offense rate per capita
expected cost of punishment
Crime Generation
Four-Way Diagram: Crime Generation & Crime Control
per capita expenditures on CJS
ProductionFunction
square
4501
1
2
2
3
Llad Phillips 113
Female Victims of Violent CrimeFemale Victims of Violent Crime
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Long Swings in the Homicide Rate in the US: 1900-1980
Source: Report to the Nation on Crime and Justice
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United States Bureau of Justice Statisticshttp://www.ojp.usdoj.gov/bjs/
Long Swings inThe Homicide Rate
Llad Phillips 119
California Homicide Rate Per 100,000: 1952-2003
0
2
4
6
8
10
12
14
16
1950 1960 1970 1980 1990 2000 2010
Year
Rat
e