Transcript Part I Strategies to Estimate Deterrence Part II

Part I
Strategies to Estimate Deterrence
Part II
Optimization of the Criminal
Justice System
Llad Phillips
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Outline
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Human Capital & Other News
Studying for the Midterm
Deterrence:
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Evidence pro
Evidence con
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Human Capital news
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About 60%
Of 9th graders
Get a diploma
somewhere
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The high
Hurdle?
Algebra
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Studying For the Midterm
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http://econ.ucsb.edu/
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Deterrence: conceptual issues
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Controlling for causality
Simultaneity
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Get
Expect
Source:
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Phillips
Report to the Nation on Crime and Justice
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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
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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
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News Over the Weekend
Deep Recession
high Unemployment rate
Keynesian Economics
drop money from a helicopter?
Or invest in infrastructure?
Transportation
Energy
independence
green
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Greening the Earth
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Greening UCSB
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Rec-Cen
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Human development Index and
Electricity Use
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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
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Policy Comment About Economic
Development
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An Obama Keynesian strategy: invest in
infrastructure
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Past investments in infrastructure
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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
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Example: the construction of railroad miles
is logistically distributed
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FREQUENCY
0.3
0.2
0.1
10%
90%
1890
0.0
-10
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-5 1859
0
1921 5
Mean
RAILMILES constructed
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Cesare Marchetti
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Cesare Marchetti: Energy
Technology: Coal, Oil, Gas, Nuclear
52 years
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57 years 56 years
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Cesare Marchetti
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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”.
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Homicide and Non-negligent Manslaaaughter, Rates Per 100,000
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California
14
12
USA
10
8
6
4
2
0
1900
1920
1940
HOMICIDECA
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1960
1980
2000
HOMICIDEUSA
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CA Homicide Rate Per 100,000 & Misery Rate in %
25
20
15
10
5
0
1900
1920
1940
1960
HOMICIDECA
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1980
2000
MISERY
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Causality?
Misery Index
Offense Rate
Mystery Force
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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
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60
65
70
75
Residual
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85
Actual
90
95
00
05
Fitted
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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
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California Prisoners: 1851-1945
10000
8000
6000
4000
2000
0
60
70
80
90
00
10
20
30
40
CAPRISONERS
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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
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20
30
40
1945
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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
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60
65
70
75
Residual
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85
Actual
90
95
00
05
Fitted
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Part I
Strategies to Estimate Deterrence
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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?
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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
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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
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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
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U.S.
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United States Bureau of Justice Statistics
http://www.ojp.usdoj.gov/bjs/
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U.S.
United States Bureau of Justice Statistics
http://www.ojp.usdoj.gov/bjs/
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What is the Empirical Evidence
that Supports Deterrence?
 Domestic
violence and police intervention
Experiments
 Traffic
Black Spots
Focused
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with control groups
enforcement efforts
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Traffic Black Spots
 Blood
Alley
Highway
 San
Marcos Pass
Highway
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154
50
San Marcos Pass Experiment
 Increase
Highway Patrols
 Increase Arrests
Total
accidents decrease
Injury accidents decrease
Accidents involving drinking under the
influence decrease
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Los Angeles Traffic Map
Domestic Violence & Police
Intervention
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1993-2005
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Female Victims of Violent Crime, 1973-2003
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Homicides of Intimates, 1976-2005
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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
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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/
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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
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Total
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42
38
31
25
Intimate
20
13
11
10
5
5
62
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Declining Trends in Intimate Violence: Homicide
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Nonfatal Violent victimization Rates
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United States Bureau of Justice Statistics
http://www.ojp.usdoj.gov/bjs/
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United States Bureau of Justice Statistics
http://www.ojp.usdoj.gov/bjs/
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United States Bureau of Justice Statistics
http://www.ojp.usdoj.gov/bjs/
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Nonfatal intimate Victimization Rates By Age
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Female victimization rates by relationship
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Intimate 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: 1988-1998
1988: 113.6 per 100.000
1998: 169.9 per 100,000
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Domestic Violence in California
1988: 94% Male Arrests
1998: 83.5% Male Arrests
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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
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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
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911 call
(characteristics of household Participants unknown)
Random Assignment
code blue
code gold
patrol responds
patrol responds
settles the household
verbally warn the husband
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settles the household
take the husband to jail
for the night
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Part II
Optimization of the Criminal
Justice System
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Questions About Statistical
Studies of Deterrence
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Do we know enough about the factors that
cause crime?
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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.
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Unknown variation in crime generation may
mask the effects of crime control.
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Schematic of the Criminal Justice System
Causes ?
Weak Link
Offense
Rate Per
Capita
Crime Generation
Expenditures
Expected
Cost of
Punishment
(detention,
deterrence)
Crime Control
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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
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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
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Cost to Victims in US, 1993
Offense
Robbery
Loss Rate Reported
Offenses
$13,000
659,757
Damages,
Billions $
$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: National Institute of Justice,
$21.5
Victim Costs and Consequences(1996)
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Source: Phillips: Lecture One
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Expenditures per capita
Total cost = expenditures per capita
High
Family of Total Cost Curves
$100
Low
2500 Index crimes
per 100,000 people
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Total cost = damages
to victims
Offenses Per Capita
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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
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Offenses Per Capita
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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
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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
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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/
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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
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