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Economic Opportunity and Crime

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Title: Economic Opportunity and Crime


1
Economic Opportunity and Crime
2
Economics 160
Lecture 5 Professor Votey Crime Generation
Youth and Women
NotesVotey, Lecture 3, 37
3
Consider the Circular Flow Process (again)
  • Depicting ( more elaborately) The Social Costs of
    Crime

This is the Social Cost Of Crime
Victim Costs
4
The Circular Flow Model in Symbolic
Notation
  • Crime Generation OF g( CR, SV,
    SE) (1)
  • CRClearance Ratio
  • SVSeverity of Sentence
  • SESoc. Econ. Conditions
  • Crime Control(Lect. 3) CR f( OF, L )
    (2)
  • OFCrime Load on the System
  • L Law Enforcement Resources
  • Societys Objective Min. SC r . OF w . L
    (3)
  • where r loss rate / Offense
  • w resource price (police wage)
  • We might think of this as a social control model.
  • How does it relate to our notions of individual
    behavior?

Note the circularity of the
relationships
Notes p. 37
5
Recall Jeremy Benthams Notion of
Individual Utility Maximization
6
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize

7
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E ( B )
    - E ( C ) B . P ( B ) - C . P ( C )

8
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E ( B )
    - E ( C ) B . P ( B ) - C . P ( C ) and
    will commit a crime

9
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E ( B )
    - E ( C ) B . P ( B ) - C . P ( C ) and
    will commit a crime if E ( NB ) gt 0

10
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E ( B )
    - E ( C ) B . P ( B ) - C . P ( C
    ) and will commit a crime if E ( NB ) gt 0
  • Consider a potential criminal with two options

11
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E ( B )
    - E ( C ) B . P ( B ) - C . P ( C
    ) and will commit a crime if E ( NB ) gt 0
  • Consider a potential criminal with two options
    A Crime E(NB(Crime)) Take . P(Not
    Jail))-Jail . P(Jail)
    where P(Not Jail) 1 - P(Jail)

12
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E ( B )
    - E ( C ) B . P ( B ) - C . P ( C
    ) and will commit a crime if E ( NB ) gt 0
  • Consider a potential criminal with two options
    A Crime E(NB(Crime)) Take . P(Not
    Jail))-Jail . P(Jail) where Not Jail 1 -
    P(Jail) An Honest Job E(NB(Job)) wage
    . P(E)-U . P(U) where EEmployed, UUnempl,
    and P(E) 1- P(U)

13
Recall Jeremy Benthams Notion of
Individual Utility Maximization
  • The Individual will maximize E (NB ) E
    ( B ) - E ( C ) B . P ( B ) - C .
    P ( C ) and will commit a crime if E ( NB
    ) gt 0
  • Consider a potential criminal with two options
    A Crime E(NB(Crime)) Take .
    P(Not Jail))-Jail . P(Jail) where Not Jail
    1 - P(Jail) An Honest Job
    E(NB(Job)) wage . P(E)-U . P(U) where
    EEmployed, UUnempl, and P(E) 1- P(U)
  • A Rational Individual will pick the Best Option

14
Note that Using Benthams Analysis
suggests a two pronged set of policy alternatives
Raise the Cost of Jail (length of sentence)
and / or
Increase P(Arrest), P(ConvictionArrest),
P(JailConviction)
thru Crime Control
Social Choice
thru Crime Generation
Lower P(Being Unemployed) and / or
Raise Wages
15
Two Views or maybe three
  • The Rational Man Approach to Crime Control ¹
    (Benthams Logic )
  • Most Modern Criminologists 2
    (Rejecting Bentham)
  • The Liberal Rational Man 3 (Benthams Logic
    Extended)
  • ¹ Deterrence Works Use the threat of Punishment
  • ² Deterrence Doesnt Work (Rely on the
    Imprisonment Model)
  • ³ Deterrence Works, but so do Economic
    Opportunities (In Todays World this
    might have been Benthams View)

16
Some Personal Questions in
Regard to Career Choice
17
Some Personal Questions in
Regard to Career Choice
Not for the record
18
The Charles Schultz Perspective
19
At this point, we are Back to
Positive Economics
  • A little bit like detective work
  • A detectives job is to solve a crime so that the
    prosecutor can deal with the criminal
  • Our task was to explain criminal behavior
  • So that Public Policy could be modified to
    reduce the likelihood of crime
  • The same sort of stimulus was facing Steven
    Levitt when he wrote his book

20
Consider Crimes Committed by Youth We Note
That
  • Crime involvement greatest among youth

21
FBI,
Uniform Crime Reports
Cities of the U.S.,
By Type of Offense,
By Age
22
Consider Crimes Committed by Youth We Note
That
  • Crime involvement greatest among youth
  • Historically Crime has been predominantly a
    male phenomenon

23
Relatively few offenders are female
  • Females
  • in group
  • All arrests (adults
  • and juveniles) 17
  • Index crime arrests 21
  • Violent crime arrests 11
  • Property crime arrests 24
  • Larceny 31
  • Non larceny 8
  • Report to the Nation, 2nd Edit., p. 46
  • (Incarceration Data from 1984)

24
Consider Crimes Committed by Youth We Note
That
  • Crime involvement greatest among youth
  • Historically Crime has been predominantly a
    male phenomenon
  • (I will talk further about womens
    increasing involvement in crime.)

25
Consider Crimes Committed by Youth We Note
That
  • Crime involvement greatest among youth
  • Historically Crime has been predominantly a
    male phenomenon
  • Crime is more prevalent in the cities

26
Who are the victims of violent crime?
  • Rates per 1,000 persons
  • age 12 and older____
  • Residence (1984) Robbery Assault
    Rape
  • Central City 11 31 1
  • Suburban 5 24 1
  • Rural 3 19 1
  • Report to the Nation, 2nd Edit., p. 27

27
Consider Crimes Committed by Youth We Note
That
  • Crime involvement greatest among youth
  • Historically Crime has been predominantly a
    male phenomenon.
  • Crime is more prevalent in the cities
  • Non-whites are more than proportionately involved

28
Notes p.40
29
Consider Crimes Committed by Youth We Note
That
  • Crime involvement greatest among youth
  • Historically Crime has been predominantly a
    male phenomenon.
  • Crime is more prevalent in the cities
  • Non-whites are more than proportionately
    involved
  • In our earliest analysis of youth participation
    in crime, we believed that a primary cause was
    lack of economic opportunities

30
Consider the picture of economic
opportunities for youth
  • Youth unemployment rates are high relative to
    those of older workers. Unempl. Rate Persons
    actively seeking work Labor Force

31
Notes p. 41
32
Consider the picture of economic
opportunities for youth
  • Youth unemployment rates are high relative to
    those of older workers. Unempl. Rate Persons
    actively seeking work Labor Force
  • What has been the effect of higher
    unemployment rates for youth ?

33
Consider the picture of economic
opportunities for youth
  • Youth unemployment rates are high relative to
    those of older workers. Unempl. Rate Persons
    actively seeking work Labor Force
  • What has been the effect of higher
    unemployment rates for youth ? 1. A decline in
    their Labor Force Participation Rates Age
    Specific No. Empl. or Seeking Work (Age)
    LFPR Population (Age)

34
Recall that, in my previous lecture I showed
that a factor in the growth crime was a decline
in police effectiveness starting in the
mid-fifties. Here we see another factor
that may be important, This is labor market
data (BLS)
The decline in the Labor Force
Participation Rate
This is something Philip Cook Didnt Understand
Notes p. 42
35
An Important Elaboration Here
  • Prof. Phillips showed video of Phil Cook, Duke
    Univ, saying unemployment didnt have much to do
    with crime patterns.
  • There was something he didnt understand. He
    wasnt alone in not understanding the link
    between jobs and crime.

36
Consider the picture of economic
opportunities for youth
  • Youth unemployment rates are high relative to
    those of older workers. Unempl. Rate Persons
    actively seeking work Labor Force
  • What has been the effect of higher
    unemployment rates for youth ? 1. A decline in
    their Labor Force Participation Rates Age
    Specific No. Empl. or Seeking Work (Age)
    LFPR Population (Age) 2.
    Youth invest in schooling to get a better job,
    stay out of the labor force temporarily.

37
Notes p. 43
38
More Recent Data on the Labor Market and
Schooling
  • Measure Population
    Year___________________
  • _______ _Males, 18-19 1968 1979 1982 1984
    1988 1998_2000 UR White
    7.9 19.0
    10.4
  • Non-white 12.3 29.6
    25.0
  • LFPR White 65.7 74.5
    69.0
  • Non-white 63.3 57.8
    43.8
  • School Combined 60.4 47.8
  • Enrollments
  • ______,all ages
  • UR Combined 3.6 5.8
    11.0 7.5 7.0 4.4 4.0
  • LFPR 59.6
    63.3 67.1 67.2
    Source Employment and Training Report of the
    President, various issues 2000 data, www.bls.gov


39
Testing the Hypothesis that Crime Rates for
youth are related to economic
opportunities
The Population of 18-19 year olds
This figure in Notes, p.38
AS
IP
Persons committing crimes
EMPL
NLF
UNEM
These relationships can be stated in terms of
probabilities
40
AS
IP
EMPL
NLF
UNEM
41
Our Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
Notes, p.38
We start by simply describing the relationships
illustrated in the Venn Diagram of Fig. 3.6 as a
probability statement
42
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
Notes, p.32
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement

AS
IP
EMPL
NLF
UNEM
43
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)

AS
IP
EMPL
NLF
UNEM
44
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)

AS
IP
EMPL
NLF
UNEM
45
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)

AS
IP
EMPL
NLF
UNEM
46
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)

AS
IP
EMPL
NLF
UNEM
47
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)

AS
IP
EMPL
NLF
UNEM
48
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)

AS
IP
EMPL
NLF
UNEM
49
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)

AS
IP
EMPL
NLF
UNEM
50
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components

51
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components
  • (OF/ Pop)Age,Sex,Race (CrimeRate
    EMP)Prob(EMP)Age,Sex,Race

52
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components
  • (OF/ Pop)Age,Sex,Race (CrimeRate
    EMP)Prob(EMP)Age,Sex,Race
  • (Crime Rate
    UNEM)Prob(UNEM)Age,Sex,Race

53
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components
  • (OF/ Pop)Age,Sex,Race (CrimeRate
    EMP)Prob(EMP)Age,Sex,Race
  • (Crime Rate
    UNEM)Prob(UNEM)Age,Sex,Race
  • (CrimeRate NLF)Prob(NLF)Age,Sex,Rac
    e P(Other)

54
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components
  • (OF/ Pop)Age,Sex,Race (CrimeRate
    EMP)Prob(EMP)Age,Sex,Race
  • (Crime Rate
    UNEM)Prob(UNEM)Age,Sex,Race
  • (CrimeRate NLF)Prob(NLF)Age,Sex,Rac
    e P(Other)

Or, in terms of the estimation relationship in
the text
55
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components
  • (OF/ Pop)Age,Sex,Race (CrimeRate
    EMP)Prob(EMP)Age,Sex,Race
  • rE x (1 - m)
  • (Crime Rate
    UNEM)Prob(UNEM)Age,Sex,Race
  • rU x m
  • (CrimeRate NLF)Prob(NLF)Age,Sex,Race
    P(Other)
  • rN x (1 - r) e

56
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - as a probability statement
  • We start by simply describing the relationships
    illustrated in the Venn Diagram of Fig. 3.6 as a
    probability statement
  • P(Commit Crime) P(Commit Crime and EMPL)
  • P(Commit Crime and UNEM)
  • P(Commit Crime and NLF) P(other)
  • in terms of the components
  • (OF/ Pop)Age,Sex,Race (CrimeRate
    EMP)Prob(EMP)Age,Sex,Race
  • Key for symbols rE x
    (1 - m)
  • in Text (Crime Rate
    UNEM)Prob(UNEM)Age,Sex,Race
  • m Unempl. Rate rU x
    m
  • r LFPR (CrimeRate
    NLF)Prob(NLF)Age,Sex,Race P(Other)
  • e error term rN x (1 - r)
    e

57
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
58
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white

59
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN

60
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • Crime rate for those employed greater than crime
    rate for.....

61
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE

62
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains
  • (R2)
  • (In Regression
  • Analysis)

63
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains 87 of variation of Larceny OF
    rate
  • (R2)
  • (In Regression
  • Analysis)

64
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains 87 of variation of Larceny OF
    rate
  • (R2) 82 Burglary
  • (In Regression
  • Analysis)

65
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains 87 of variation of Larceny OF
    rate
  • (R2) 82 Burglary
  • (In Regression 55 Robbery
  • Analysis)

66
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains 87 of variation of Larceny OF
    rate
  • (R2) 82 Burglary
  • (In Regression 55 Robbery
  • Analysis) 79
    Auto Theft

67
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains 87 of variation of Larceny OF
    rate
  • (R2) 82 Burglary
  • (In Regression 55 Robbery
  • Analysis) 79
    Auto Theft

Why the difference between whites and non-whites
?
68
The Hypothesis Crimes by youth are a function
of lack of legitimate economic
opportunities - empirical results
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 separated white, non-white
  • Results for whites rU gt rE gt rN
  • for non-whites rN gt rU gt rE
  • Model Explains 87 of variation of Larceny OF
    rate
  • (R2) 82 Burglary
  • (In Regression 55 Robbery
  • Analysis) 79
    Auto Theft

Why the difference between whites and non-whites
? We hypothesized that a greater proportion of
the whites who were NLF were enrolled in school,
whereas a greater proportion of non-whites were
discouraged workers.
69
Testing the hypothesis that black-white
differences were due to differences in school
enrollment rates
  • The Data U. S. cities, 1952-1967
  • Focus Males, 18-19 ( not separated by race or
    ethnicity)
  • Results are for two offenses burglary, robbery
  • Results rDNLF gt rSNLF
  • rDU gt rDNLF gt r DE
  • rSE rSU rSNLF
  • where E enrolled in school
  • D dropped out of school
  • Clearly, for this age group during these years,
    those enrolled had lower imputed offense rates
    than those dropped out of school, and the
    relative criminality of dropouts were similar to
    the ordering for whites, once the factor of
    school enrollment is eliminated. There was
    little difference in criminality among labor
    market classifications for those enrolled.

70
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that

71
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring

72
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring 2.
    that you steal once a month

73
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring 2.
    that you steal once a month 3. that the
    probability of being caught in any year was 1

74
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring 2.
    that you steal once a month 3. that the
    probability of being caught in any year was
    1 4. that you could earn 200,000 per year for
    this work

75
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring 2.
    that you steal once a month 3. that the
    probability of being caught in any year was
    1 4. that you could earn 200,000 per year for
    this work
  • Probability never caught in ten years

76
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring 2.
    that you steal once a month 3. that the
    probability of being caught in any year was
    1 4. that you could earn 200,000 per year for
    this work
  • Probability never caught in ten years P(Never
    Caught10YEARS) (1-.01)1 x (1-.01)2 - - -
    -(1-.01)10
  • (.99)10 .9044

77
A verbal quiz regarding the choice model
  • Suppose someone could convince you that he had a
    job for you that 1. was illegal, requiring 2.
    that you steal once a month 3. that the
    probability of being caught in any year was
    1 4. that you could earn 200,000 per year for
    this work
  • Probability never caught in ten years P(Never
    Caught10YEARS) (1-.01)1 x (1-.01)2 - - -
    -(1-.01)10
  • (.99)10 .9044
  • Expected Income(10 Years) 10 x 100,000 x
    .9044
  • 1,808,800

78
What if you are caught ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely
    somewhere in between)

79
How many of you would take the job ?
80
How many of you would take the job ?
  • Knowing that the penalty if caught 1st
    Offense Max 2 years, State Prison Min
    Suspended Sentence Probation, 5 years
    (most likely somewhere in between)
  • Why, yes?

81
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely
    somewhere in between)
  • Why, yes? Easy Money.

82
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no?

83
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question

84
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question
  • Moral Compliance with the Law

85
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question
  • Moral Compliance with the Law 1. Raised in a
    religion

86
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question
  • Moral Compliance with the Law 1. Raised in a
    religion 2. Still have a religion

87
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question
  • Moral Compliance with the Law 1. Raised in a
    religion 2. Still have a religion 3. Frequency
    of church attendance

88
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question
  • Moral Compliance with the Law 1. Raised in a
    religion 2. Still have a religion 3. Frequency
    of church attendance
  • Crime Generation

89
How many of you would take the job ?
  • The penalty if caught 1st Offense Max 2
    years, State Prison Min Suspended Sentence
    Probation, 5 years (most likely somewhere in
    between)
  • Why, yes? Easy Money.
  • Why, no? The What would my mother (girl
    friend, boy friend) think? question
  • Moral Compliance with the Law 1. Raised in a
    religion 2. Still have a religion 3. Frequency
    of church attendance
  • Crime Generation OF g( CR, SV, SE, MC )

90
Public Realization of Womens Increasing
Involvement with Crime
Wall Street Journal,Thur. Jan.25,1990
91
Public Realization of Womens Increasing
Involvement with Crime
Wall Street Journal,Thur. Jan.25,1990
92
Between 1979 and 1988, the number of women
ar-rested for violent crimes went up 41.5 versus
23.1 for men. The trend is even starker among
teen-agers.
93
Womens Increasing Participation in Crime
Notes, p. 47
Embezzlement
Robbery
Burglary
Homicide
Crime Rates for Women
94
The Work/Leisure Trade-off for Women
See Notes, p 49
Preferences
Income
Available Market Income
8Hr. Std. Work Day
A
B
Income Shortfall
D
C
b
a
8hrs.work
12 hrs.
Work
Leisure
24 Hours
Time Endowment
Desired Work Hours at Market Wage
95
We can add another complication to a
job seekers objectives
The conventional labor market standardizing on 8
hour jobs creates a situation we call
underemployment for the individual we have
depicted here.
Underemployment may contribute to an
individuals willingness to consider crime as a
source of income
96

The Work/Leisure Trade-off adding a new
constraint Committed Leisure
See Notes, p 49
Income
Available Market Income
Binding Constraint
8 hr. Job
Desired Work Day
C
A
24 Hours
Committed Leisure
Time Endowment
Notes pp.49-51
97
Here, the conventional labor market creates a
state of overemployment for the
individual we have depicted
in our analysis
As Family responsibilites for single parent
women increase,
the constraints narrow further.
98
The Changing Labor Market Status of Women
99

The Work/Leisure Trade-off a more
constraining Committed Leisure
See Notes, p 50
Income
Available Market Income
Binding Constraint
8 hr. Job
Desired Work Day
C
A
24 Hours
Committed Leisure
Time Endowment
100

The Work/Leisure Trade-off for Women a more
constraining Committed Leisure
Income
Available Market Income
Binding Constraint
8 hr. Job
Desired Work Day

A
24 Hours
Committed Leisure
Time Endowment
Notes, p.50
101
The appeal of the crime solution
becomes even greater.
102

The Work/Leisure Trade-off for Women a more
constraining Committed Leisure
Income
Available Market Income
Binding Constraint
8 hr. Job
Desired Work Day

A
b
a
24 Hours
Committed Leisure
Time Endowment
Crime may permit optimal hours of work
and a higher monetary return
103
And this could be true
in both cases of
underemployment
and overemployment.
104
The Incentive Effects of Current Welfare Rules
depend on a full employment economy

105
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs

106
The Incentive Effects of Current Welfare
Rules depend on a full employment economy
  • The Demand for Jobs 1. Numbers

107
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics

108
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case

109
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers
  • 2. Characteristics Underemployment
    Case Longer Hours (Overtime work)

110
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time jobs

111
The Incentive Effects of Current Welfare Rules
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case

112
The Incentive Effects of Current Welfare Rules
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time Jobs

113
The Incentive Effects of Current Welfare Rules
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time
    Jobs Flextime Working out of ones home

114
The Incentive Effects of Current Welfare Rules
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time
    Jobs Flextime Working out of ones home
  • The Alternatives

115
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time
    Jobs Flextime Working out of ones home
  • The Alternatives 1. Job Creation

116
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time
    Jobs Flextime Working out of ones home
  • The Alternatives 1. Job Creation Economic
    Growth

117
Effects of Recent Change in Welfare Rules
Depend on the State of the Economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time
    Jobs Flextime Working out of ones home
  • The Alternatives 1. Job Creation Economic
    Growth Incentives

118
US Civilian Labor Force - Thousands
US Labor Force Participation Rate
All Employees, Thousands
Unemployment Levels
What is happening with U S Labor
Not in the Labor Force, Thousands
119
The economy was doing well, but compared to
what?
Employment Levels 1991 to 2001
2002-2006
120
The Incentive Effects of Current Welfare Rules
depend on a full employment economy
  • The Demand for Jobs 1. Numbers 2.
    Characteristics Underemployment Case Longer
    Hours (Overtime work) Part time
    jobs Overemployment Case Part time
    Jobs Flextime Working out of ones home
  • The Alternatives 1. Job Creation Economic
    Growth Incentives 2. Crime ??

121
Professor Phillips
Next Time
  • Deterrence and the
  • Death Penalty

Notes, Phillips 3, p51
122
Points to remember
  • Who are the most crime prone elements of society?
    Why?
  • How do they fit into a model of crime generation
    and control? Can we explain the why?
  • Why do we think blacks responded to crime in a
    different pattern from whites?
  • What has been happening with respect to women and
    crime? Again, why?
  • Why didnt crime go up when the country changed
    the welfare rules?
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