economic opportunity and crime

Economic Opportunity and CrimeEconomic Opportunity and Crime

Economics 160 Economics 160

Notes:Votey, Lecture 3, 37

Lecture 5

Professor Votey

Crime Generation: Youth and Women

Depicting ( more elaborately) Depicting ( more elaborately) The Social Costs of CrimeThe Social Costs of Crime

Victim Costs +

Consider the Circular Flow Process: (again)Consider the Circular Flow Process: (again)

This is theThis is theSocial CostSocial CostOf CrimeOf Crime

The Circular Flow Model in Symbolic Notation The Circular Flow Model in Symbolic Notation

Crime Generation:Crime Generation: OF = g( CR, SV, SE) (1)OF = g( CR, SV, SE) (1) CR=Clearance RatioCR=Clearance Ratio SV=Severity of SentenceSV=Severity of Sentence SE=Soc. & Econ. Conditions SE=Soc. & Econ. Conditions

Crime Control:Crime Control:(Lect. 3)(Lect. 3) CR = f( OF, L )CR = f( OF, L ) (2) (2) OF=Crime Load on the SystemOF=Crime Load on the System L =Law Enforcement ResourcesL =Law Enforcement Resources

Society’s ObjectiveSociety’s Objective Min. SC = r Min. SC = r .. OF + w OF + w .. L (3) L (3) where r = loss rate / Offensewhere r = loss rate / Offense w = resource price (police wage)w = resource price (police wage) We might think of this as a social control modelWe might think of this as a social control model . . How does it relate to our notions of individual behavior?How does it relate to our notions of individual behavior?

Note the Note the circularity of thecircularity of the relationshipsrelationships

Notes p. 37Notes p. 37

Recall Jeremy Bentham’s Notion of Individual Utility MaximizationRecall Jeremy Bentham’s Notion of Individual Utility Maximization


The Individual will maximize:The Individual will maximize:


The Individual will maximize:The Individual will maximize:E (NB ) = E ( B ) - E ( C )E (NB ) = E ( B ) - E ( C )

= $B = $B .. P ( B ) - $C P ( B ) - $C .. P ( C ) P ( C )



= $B = $B .. P ( B ) - $C P ( B ) - $C .. P ( C ) P ( C )and will commit a crimeand will commit a crime



= $B = $B .. P ( B ) - $C P ( B ) - $C .. P ( C ) P ( C )and will commit a crime and will commit a crime if if E ( NB ) > 0E ( NB ) > 0



= $B = $B .. P ( B ) - $C P ( B ) - $C .. P ( C ) P ( C )and will commit a crime if E ( NB ) > 0and will commit a crime if E ( NB ) > 0

Consider a potential criminal with two options:Consider a potential criminal with two options:




Consider a potential criminal with two options:Consider a potential criminal with two options: A Crime:A Crime:

E(NB(Crime))= $Take E(NB(Crime))= $Take . . P(Not Jail))-$Jail P(Not Jail))-$Jail . . P(Jail)P(Jail)

where P(Not Jail) = 1 - P(Jail)where P(Not Jail) = 1 - P(Jail)




Consider a potential criminal with two options:Consider a potential criminal with two options: A Crime:A Crime:

E(NB(Crime))= $Take E(NB(Crime))= $Take . . P(Not Jail))-$Jail P(Not Jail))-$Jail . . P(Jail)P(Jail)where Not Jail = 1 - P(Jail)where Not Jail = 1 - P(Jail)

An Honest Job:An Honest Job:

E(NB(Job)) = $wage E(NB(Job)) = $wage . . P(E)-$U P(E)-$U . . P(U)P(U)where E=Employed, U=Unempl, and P(E) = 1- P(U)where E=Employed, U=Unempl, and P(E) = 1- P(U)


The Individual will maximize:The Individual will maximize: E (NB ) = E ( B ) - E ( C ) E (NB ) = E ( B ) - E ( C )

= $B = $B .. P ( B ) - $C P ( B ) - $C .. P ( C ) P ( C ) and will commit a crime if E ( NB ) > 0 and will commit a crime if E ( NB ) > 0

Consider a potential criminal with two options:Consider a potential criminal with two options: A CrimeA Crime::

E(NB(Crime))= $Take E(NB(Crime))= $Take . . P(Not Jail))-$Jail P(Not Jail))-$Jail . . P(Jail)P(Jail) where Not Jail = 1 - P(Jail) where Not Jail = 1 - P(Jail)

An Honest JobAn Honest Job::

E(NB(Job)) = $wage E(NB(Job)) = $wage . . P(E)-$U P(E)-$U . . P(U)P(U) where E=Employed, U=Unempl, and P(E) = 1- P(U) where E=Employed, U=Unempl, and P(E) = 1- P(U)

A Rational Individual will pick the Best OptionA Rational Individual will pick the Best Option

Note that Using Bentham’s Analysis suggests a two pronged set of policy alternativesNote that Using Bentham’s Analysis suggests a two pronged set of policy alternatives

Social Choice

thru Crime Control

thru Crime Generation

Raise the Cost of Jail (length of sentence) and / or

Increase P(Arrest), P(Conviction|Arrest), P(Jail|Conviction)

Lower P(Being Unemployed)and / or

Raise Wages

Two Views – or maybe threeTwo Views – or maybe three The Rational Man Approach to Crime ControlThe Rational Man Approach to Crime Control ¹¹

(Bentham’s Logic )(Bentham’s Logic )

Most Modern Criminologists Most Modern Criminologists 22 (Rejecting Bentham)(Rejecting Bentham)

The Liberal Rational ManThe Liberal Rational Man 33

(Bentham’s Logic Extended)(Bentham’s Logic Extended)

¹ ¹ Deterrence Works – Use the threat of PunishmentDeterrence Works – Use the threat of Punishment

² ² Deterrence Doesn’t Work –Deterrence Doesn’t Work –((Rely on the Imprisonment Model)Rely on the Imprisonment Model)

³ ³ Deterrence Works, but so do Economic Opportunities Deterrence Works, but so do Economic Opportunities

(In Today’s World this might have been Bentham’s View) (In Today’s World this might have been Bentham’s View)

Some Personal Questions in Regard to Career Choice




Not for the record

The Charles Schultz Perspective

At this point, we are – Back to Positive EconomicsAt this point, we are – Back to Positive Economics

A little bit like detective workA little bit like detective work A detective’s job is to solve a crimeA detective’s job is to solve a crime

so that the prosecutor can deal with the criminalso that the prosecutor can deal with the criminal Our task was to explain criminal behaviorOur task was to explain criminal behavior

So that Public Policy could be modified|So that Public Policy could be modified|to reduce the likelihood of crimeto reduce the likelihood of crime

The same sort of stimulus was facing The same sort of stimulus was facing Steven Levitt when he wrote his bookSteven Levitt when he wrote his book

Consider Crimes Committed by Youth:We Note That:


Crime involvement greatest among youthCrime involvement greatest among youth

FBIFBI,, Uniform Crime ReportsUniform Crime ReportsCities of the U.S.,Cities of the U.S.,By Type of Offense,By Type of Offense, By AgeBy Age



Crime involvement greatest among youthCrime involvement greatest among youth Historically Crime has been predominantly a maleHistorically Crime has been predominantly a male

phenomenonphenomenon

Relatively few offenders are femaleRelatively few offenders are female

%% FemalesFemales

in groupin groupAll arrests (adults All arrests (adults and juveniles)and juveniles) 17% 17% Index crime arrestsIndex crime arrests 21 21

Violent crime arrestsViolent crime arrests 11 11Property crime arrestsProperty crime arrests 24 24 LarcenyLarceny 31 31 Non larcenyNon larceny 8 8

Report to the Nation, 2nd Edit.Report to the Nation, 2nd Edit., p. 46, p. 46(Incarceration Data from 1984)(Incarceration Data from 1984)




phenomenonphenomenon

(I will talk further about women’s increasing (I will talk further about women’s increasing involvement in crime.) involvement in crime.)




phenomenonphenomenon Crime is more prevalent in the citiesCrime is more prevalent in the cities

Who are the victims of violent crime?Who are the victims of violent crime?

Rates per 1,000 personsRates per 1,000 persons

age 12 and older____age 12 and older____

Residence Residence (1984) (1984) RobberyRobbery AssaultAssault RapeRape

Central CityCentral City 11 11 31 31 1 1

SuburbanSuburban 5 5 24 1 24 1

RuralRural 3 19 1 3 19 1

Report to the Nation, 2nd Edit.Report to the Nation, 2nd Edit., p. 27, p. 27




phenomenon.phenomenon. Crime is more prevalent in the citiesCrime is more prevalent in the cities Non-whites are more than proportionately involvedNon-whites are more than proportionately involved

Notes p.40Notes p.40




phenomenon.phenomenon. Crime is more prevalent in the citiesCrime is more prevalent in the cities Non-whites are more than proportionately involvedNon-whites are more than proportionately involved

In our earliest analysis of youth participation in crime,In our earliest analysis of youth participation in crime,we believed that a primary cause was lack of we believed that a primary cause was lack of economic opportunitieseconomic opportunities

Consider the picture of economic opportunities for youth


Youth unemployment rates are high relative to those of Youth unemployment rates are high relative to those of older workers.older workers.

Unempl. Rate = Unempl. Rate = Persons actively seeking workPersons actively seeking workLabor ForceLabor Force




Unempl. Rate = Unempl. Rate = Persons actively seeking workPersons actively seeking workLabor ForceLabor Force

What has been the effect of higher unemploymentWhat has been the effect of higher unemploymentrates for youth rates for youth ??




Unempl. Rate Unempl. Rate = = Persons actively seeking workPersons actively seeking work

Labor ForceLabor Force What has been the effect of higher unemploymentWhat has been the effect of higher unemployment

rates for youth rates for youth ??1. A decline in their Labor Force Participation Rates1. A decline in their Labor Force Participation Rates

Age Specific Age Specific == No. Empl. or Seeking Work (Age)No. Empl. or Seeking Work (Age)

LFPR Population (Age) LFPR Population (Age)

Recall that, in my previous lectureRecall that, in my previous lecture I showed that a factor in the growthI showed that a factor in the growthcrime was a decline in police effectivenesscrime was a decline in police effectivenessstarting in the mid-fifties. starting in the mid-fifties. Here we see another factor that may be Here we see another factor that may be important, This is labor market data (BLS)important, This is labor market data (BLS)


This is something Philip CookThis is something Philip CookDidn’t UnderstandDidn’t Understand

The decline in the Labor Force The decline in the Labor Force

Participation RateParticipation Rate

An Important Elaboration HereAn Important Elaboration Here

Prof. Phillips showed video of Phil Cook, Prof. Phillips showed video of Phil Cook, Duke Univ, saying unemployment didn’t Duke Univ, saying unemployment didn’t have much to do with crime patterns.have much to do with crime patterns.

There was something he didn’t understand.There was something he didn’t understand.He wasn’t alone in not understanding the He wasn’t alone in not understanding the link between jobs and crime.link between jobs and crime.




Unempl. Rate Unempl. Rate = = Persons actively seeking workPersons actively seeking work

Labor ForceLabor Force What has been the effect of higher unemploymentWhat has been the effect of higher unemployment

rates for youth rates for youth ??1. A decline in their Labor Force Participation Rates1. A decline in their Labor Force Participation Rates

Age Specific Age Specific == No. Empl. or Seeking Work (Age)No. Empl. or Seeking Work (Age)

LFPR Population (Age) LFPR Population (Age)2. Youth invest in schooling to get a better job, stay out2. Youth invest in schooling to get a better job, stay out of the labor force temporarily. of the labor force temporarily.

More Recent Data on the Labor Market and SchoolingMore Recent Data on the Labor Market and Schooling

Measure Measure Population Population Year Year______________________________________

______________ __Males, Males, 18-1918-19 1968 1979 1982 1984 1988 1998 1968 1979 1982 1984 1988 1998__20002000 UR % UR % WhiteWhite 7.9 19.0 7.9 19.0 10.410.4

Non-white 12.3 29.6 25.0Non-white 12.3 29.6 25.0 LFPR% LFPR% White 65.7 74.5 69.0White 65.7 74.5 69.0 Non-white 63.3 57.8 43.8Non-white 63.3 57.8 43.8 School Combined 60.4 47.8 School Combined 60.4 47.8 EnrollmentsEnrollments ____________,all ages,all ages UR Combined 3.6 5.8 11.0 7.5 7.0UR Combined 3.6 5.8 11.0 7.5 7.0 4.4 4.04.4 4.0 LFPR “ 59.6 63.3LFPR “ 59.6 63.3 67.1 67.267.1 67.2

Source: Source: Employment and Training Report of the PresidentEmployment and Training Report of the President , various issues; 2000 data, www.bls.gov, various issues; 2000 data, www.bls.gov

ASAS IPIP

Testing the Hypothesis that Crime Rates for youth are related to economic opportunities

The Populationof 18-19year olds

This figure in Notes, p.38

Personscommittingcrimes

These relationshipscan be stated interms of probabilities

EMPL

UNEMUNEMNLFNLF

ASAS IPIP

EMPL

UNEMUNEMNLFNLF

Our Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities

- as a probability statement: Notes, p.38

Our Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities


We start by simply describing the relationships illustrated in the Venn Diagram of Fig. 3.6 as a probability statement:

The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities




We start by simply describing the relationships illustrated in the We start by simply describing the relationships illustrated in the Venn Diagram of Fig. 3.6 as a probability statement:Venn Diagram of Fig. 3.6 as a probability statement:

ASAS IPIP

EMPL

UNEMUNEMNLFNLF


- as a probability statement:



We start by simply describing the relationships illustrated in the Venn We start by simply describing the relationships illustrated in the Venn Diagram of Fig. 3.6 as a probability statement:Diagram of Fig. 3.6 as a probability statement:

P(Commit Crime) = P(Commit Crime P(Commit Crime) = P(Commit Crime and and EMPL) +EMPL) +

ASAS IPIP

EMPL

UNEMUNEMNLFNLF

The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- as a probability statement:




P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +

ASAS IPIP

EMPL

UNEMUNEMNLFNLF

The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:





P(Commit Crime P(Commit Crime andand NLF) + P(other) NLF) + P(other)ASAS IPIP

EMPL

UNEMUNEMNLFNLF






P(Commit Crime P(Commit Crime andand NLF) + P(other) NLF) + P(other)

in terms of the components:in terms of the components:








(OF/ Pop)[Age,Sex,Race] = (CrimeRate EMP)Prob(EMP)[Age,Sex,Race] +(OF/ Pop)[Age,Sex,Race] = (CrimeRate EMP)Prob(EMP)[Age,Sex,Race] +











(Crime Rate UNEM)Prob(UNEM)[Age,Sex,Race] +(Crime Rate UNEM)Prob(UNEM)[Age,Sex,Race] +










(CrimeRate NLF)Prob(NLF)[Age,Sex,Race] + P(Other)(CrimeRate NLF)Prob(NLF)[Age,Sex,Race] + P(Other)











Or, in terms of the estimation relationship in the text:Or, in terms of the estimation relationship in the text:








(OF/ Pop)[Age,Sex,Race] = (CrimeRate EMP)Prob(EMP)[Age,Sex,Race](OF/ Pop)[Age,Sex,Race] = (CrimeRate EMP)Prob(EMP)[Age,Sex,Race]

rrE E x (1 - x (1 - ) +) + (Crime Rate UNEM)Prob(UNEM)[Age,Sex,Race] +(Crime Rate UNEM)Prob(UNEM)[Age,Sex,Race] +

rrUU x x


rrNN x (1 - x (1 - )) + +











Key for symbolsKey for symbols rrE E x (1 - x (1 - ) +) +in Text:in Text: (Crime Rate UNEM)Prob(UNEM)[Age,Sex,Race] + (Crime Rate UNEM)Prob(UNEM)[Age,Sex,Race] +

Unempl. RateUnempl. Rate rrUU x x

LFPRLFPR (CrimeRate NLF)Prob(NLF)[Age,Sex,Race] + P(Other) (CrimeRate NLF)Prob(NLF)[Age,Sex,Race] + P(Other)

error termerror term rrNN x (1 - x (1 - )) + +


- empirical results:







The Data: U. S. cities, 1952-1967The Data: U. S. cities, 1952-1967

Focus: Males, 18-19; separated: white, non-whiteFocus: Males, 18-19; separated: white, non-white

The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:




Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN






Crime rate for those employed greater than crime rate for.....Crime rate for those employed greater than crime rate for.....








for non-whites for non-whites rrNN > r > rU U > r> rEE







Model Explains Model Explains

(R(R22)) (In Regression(In Regression

Analysis)Analysis)







Model Explains Model Explains 87%87% of variation of Larceny OF rate of variation of Larceny OF rate

(R(R22))

(In Regression(In Regression

Analysis)Analysis)








(R(R22)) 8282 Burglary Burglary(In Regression(In Regression

Analysis)Analysis)








(R(R22)) 8282 Burglary Burglary

(In Regression(In Regression 5555 Robbery Robbery

Analysis)Analysis)










Analysis)Analysis) 7979 Auto Theft Auto Theft










Analysis)Analysis) 7979 Auto Theft Auto TheftWhy the difference between whites and non-whites ?












Analysis)Analysis) 7979 Auto Theft Auto TheftWhy 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.

Testing the hypothesis that black-white differenceswere due to differences in school enrollment rates:Testing the hypothesis that black-white differenceswere due to differences in school enrollment rates:


Focus: Males, 18-19 ( not separated by race or ethnicity)Focus: Males, 18-19 ( not separated by race or ethnicity)

Results are for two offenses: burglary, robberyResults are for two offenses: burglary, robbery

Results:Results: rrDNLFDNLF > r > rSNLFSNLF

rrDUDU > r > rDNLF DNLF > r > r DEDE

rrSE SE ~~ rrSU SU ~~ rrSNLFSNLF

where E where E = enrolled in school= enrolled in school

D = dropped out of schoolD = dropped out of school

Clearly, for this age group during these years, those enrolled had lower imputed offense 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 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. 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 There was little difference in criminality among labor market classifications for those enrolled.enrolled.

A verbal quiz regarding the choice model:A verbal quiz regarding the choice model:

Suppose someone could convince you that he had a job for Suppose someone could convince you that he had a job for you thatyou that



1. was illegal, requiring1. was illegal, requiring



1. was illegal, requiring1. was illegal, requiring2. that you steal once a month2. that you steal once a month



1. was illegal, requiring1. was illegal, requiring2. that you steal once a month2. that you steal once a month3. that the probability of being caught in any year was 1%3. that the probability of being caught in any year was 1%



1. was illegal, requiring1. was illegal, requiring2. that you steal once a month2. that you steal once a month3. that the probability of being caught in any year was 1%3. that the probability of being caught in any year was 1%4. that you could earn $200,000 per year for this work4. that you could earn $200,000 per year for this work


Suppose someone could convince you that he had a job forSuppose someone could convince you that he had a job for you thatyou that


Probability never caught in ten years:Probability never caught in ten years:




Probability never caught in ten years:Probability never caught in ten years:P(Never CaughtP(Never Caught10YEARS10YEARS)) = (1-.01)= (1-.01)11 xx (1-.01) (1-.01)22 - - - -(1-.01) - - - -(1-.01)1010

= (.99)= (.99)1010 = = ..90449044




Probability never caught in ten years:Probability never caught in ten years:P(Never CaughtP(Never Caught10YEARS10YEARS)) = = (1-.01)(1-.01)11 xx (1-.01) (1-.01)22 - - - -(1-.01) - - - -(1-.01)1010

= (.99)= (.99)1010 = = .9044.9044

Expected Income(10 Years)Expected Income(10 Years) = = 10 10 xx $100,000 $100,000 xx .9044 .9044

= $1,808,800 = $1,808,800

What if you are caught ?What if you are caught ?

The penalty if caught: 1st Offense:The penalty if caught: 1st Offense:Max: 2 years, State PrisonMax: 2 years, State PrisonMin: Suspended Sentence + Probation, 5 yearsMin: Suspended Sentence + Probation, 5 years (most likely somewhere in between) (most likely somewhere in between)

How many of you would take the job ?How many of you would take the job ?


Knowing that the penalty if caught: 1st Offense:Knowing that the penalty if caught: 1st Offense:Max: 2 years, State PrisonMax: 2 years, State PrisonMin: Suspended Sentence + Probation, 5 yearsMin: Suspended Sentence + Probation, 5 years (most likely somewhere in between) (most likely somewhere in between)

Why, yes?Why, yes?


The penalty if caught: 1st Offense:The penalty if caught: 1st Offense:Max: 2 years, State PrisonMax: 2 years, State PrisonMin: Suspended Sentence + Probation, 5 yearsMin: Suspended Sentence + Probation, 5 years (most likely somewhere in between) (most likely somewhere in between)

Why, yes?Why, yes? Easy Money.Easy Money.


The penalty if caught: 1st Offense:The penalty if caught: 1st Offense:Max: 2 years, State PrisonMax: 2 years, State PrisonMin: Suspended Sentence + Probation, 5 yearsMin: Suspended Sentence + Probation, 5 years

(most likely somewhere in between) (most likely somewhere in between) Why, yes? Why, yes? Easy Money.Easy Money. Why, no?Why, no?



(most likely somewhere in between) (most likely somewhere in between) Why, yes? Why, yes? Easy Money.Easy Money.

Why, no?Why, no? TheThe “What would my mother (girl friend, “What would my mother (girl friend,boy friend) think? boy friend) think? questionquestion




Why, no? Why, no? TheThe “What would my mother (girl friend, “What would my mother (girl friend,boy friend) think? boy friend) think? questionquestion

Moral Compliance with the LawMoral Compliance with the Law




Why, no? Why, no? TheThe “What would my mother (girl friend,“What would my mother (girl friend,boy friend) think? boy friend) think? questionquestion

Moral Compliance with the LawMoral Compliance with the Law1. Raised in a religion1. Raised in a religion





Moral Compliance with the LawMoral Compliance with the Law1. Raised in a religion1. Raised in a religion2. Still have a religion2. Still have a religion



(most likely somewhere in between) (most likely somewhere in between) Why, yes? Why, yes? Easy Money.Easy Money. Why, no? TheWhy, no? The “What would my mother (girl friend, “What would my mother (girl friend,

boy friend) think? boy friend) think? questionquestion Moral Compliance with the LawMoral Compliance with the Law

1. Raised in a religion1. Raised in a religion2. Still have a religion2. Still have a religion3. Frequency of church attendance3. Frequency of church attendance





Moral Compliance with the LawMoral Compliance with the Law1. Raised in a religion1. Raised in a religion2. Still have a religion2. Still have a religion3. Frequency of church attendance3. Frequency of church attendance

Crime Generation:Crime Generation:




Why, no? Why, no? TheThe “What would my mother (girl friend,“What would my mother (girl friend,boy friend) think? boy friend) think? questionquestion

Moral Compliance with the LawMoral Compliance with the Law1. Raised in a religion1. Raised in a religion2. Still have a religion2. Still have a religion3. Frequency of church attendance3. Frequency of church attendance

Crime Generation:Crime Generation: OF = g( CR, SV, SEOF = g( CR, SV, SE, , MCMC ) )

Public Realization of Women’s Increasing Involvement with Crime

Public Realization of Women’s Increasing Involvement with Crime

Wall StreetJournal,Thur.Jan.25,1990

Between 1979 and 1988, the number of women ar-Between 1979 and 1988, the number of women ar-rested for violent crimes went up 41.5% versus 23.1% rested for violent crimes went up 41.5% versus 23.1% for men. The trend is even starker among teen-agers.for men. The trend is even starker among teen-agers.

Women’s Increasing Participation in CrimeWomen’s Increasing Participation in Crime

Embezzlement

Robbery

Burglary

Homicide

Crime Rates for Women

Notes, p. 47Notes, p. 47

24 Hours

Available Market Income $ Income

Preferences

The Work/Leisure Trade-off for Women

Desired Work Hours at Market Wage

8Hr. Std. Work DayA

C

IncomeShortfall D

B

Time EndowmentWork8hrs.work12 hrs.LeisureLeisure

See See NotesNotes, p 49, p 49

We can add another complication to a job seeker’s objectivesWe can add another complication to a job seeker’s objectives

The conventional labor market standardizing The conventional labor market standardizing on 8 hour jobs creates a situation we call on 8 hour jobs creates a situation we call

underemploymentunderemployment for the individual we have depicted here.for the individual we have depicted here.

Underemployment may contribute to an Underemployment may contribute to an individual’s willingness to consider crime individual’s willingness to consider crime as a as a source of income source of income

The Work/Leisure Trade-off adding a new constraint: Committed Leisure

$ Income

24 Hours

Available Market Income

Time Endowment Committed Leisure

8 hr. JobDesired Work Day

C

A

Binding Constraint


NotesNotes pp.49-51 pp.49-51

As Family responsibilites for As Family responsibilites for single parent women increase,single parent women increase,

the constraints narrow further.the constraints narrow further.

Here, the conventional labor market Here, the conventional labor market createscreates a state ofa state of overemployment overemployment for the individual we have depicted for the individual we have depicted in our analysisin our analysis

The Changing Labor Market Status of WomenThe Changing Labor Market Status of Women

The Work/Leisure Trade-off a more constraining: Committed Leisure

$ Income

24 Hours




C

A

Binding Constraint


The Work/Leisure Trade-off for Women:a more constraining Committed Leisure

$ Income

24 Hours




A

Binding Constraint

NotesNotes, p.50, p.50

The appeal of the crime The appeal of the crime solutionsolution becomes even becomes even greater.greater.

The Work/Leisure Trade-off for Women:a more constraining Committed Leisure

$ Income

24 Hours




A

Binding Constraint

Crime may permit optimal hours of workCrime may permit optimal hours of work and a higher monetary returnand a higher monetary return

And this could be trueAnd this could be true

in both cases ofin both cases of

underemploymentunderemployment

and overemployment.and overemployment.

The Incentive Effects of Current Welfare Rules depend on a full employment economyThe Incentive Effects of Current Welfare Rules depend on a full employment economy

The Incentive Effects of Current Welfare Rules depend on a full employment economyThe Incentive Effects of Current Welfare Rules depend on a full employment economy

The Demand for JobsThe Demand for Jobs

The Incentive Effects of Current Welfare Rulesdepend on a full employment economy

The Incentive Effects of Current Welfare Rulesdepend on a full employment economy


1. Numbers1. Numbers

The Incentive Effects of Current Welfare Rules depend on a full employment economy



1. Numbers1. Numbers2. Characteristics2. Characteristics





Underemployment Case:Underemployment Case:




1. Numbers 1. Numbers

2. Characteristics2. CharacteristicsUnderemployment Case:Underemployment Case:

Longer Hours (Overtime work)Longer Hours (Overtime work)





Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs

The Incentive Effects of Current Welfare Rules:The Incentive Effects of Current Welfare Rules:




Overemployment CaseOveremployment Case





Overemployment CaseOveremployment CasePart time JobsPart time Jobs


The Demand for JobsThe Demand for Jobs1. Numbers1. Numbers2. Characteristics2. Characteristics


Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home






The AlternativesThe Alternatives







The AlternativesThe Alternatives1. Job Creation1. Job Creation








Economic GrowthEconomic Growth

Effects of Recent Change in Welfare Rules Depend on the State of the EconomyEffects of Recent Change in Welfare Rules Depend on the State of the Economy






Economic GrowthEconomic GrowthIncentivesIncentives

US Civilian Labor Force - ThousandsUS Civilian Labor Force - Thousands US Labor Force Participation RateUS Labor Force Participation Rate

All Employees, ThousandsAll Employees, Thousands Unemployment LevelsUnemployment Levels

Not in the Labor Force, ThousandsNot in the Labor Force, Thousands

What is happening with U S LaborWhat is happening with U S Labor

The economy was doing well,but compared to what?

The economy was doing well,but compared to what?

Employment Levels 1991 to 2001Employment Levels 1991 to 2001

2002-20062002-2006



The Demand for JobsThe Demand for Jobs1. Numbers1. Numbers2. Characteristics2. Characteristics




Economic GrowthEconomic GrowthIncentivesIncentives

2. Crime ??2. Crime ??

Professor PhillipsProfessor Phillips

Deterrence and theDeterrence and the

Death PenaltyDeath Penalty

Next Time

NotesNotes, Phillips 3, p51, Phillips 3, p51

Points to rememberPoints to remember

Who are the most crime prone elements of Who are the most crime prone elements of society? Why?society? Why?

How do they fit into a model of crime generation How do they fit into a model of crime generation and control? Can we explain the why?and control? Can we explain the why?

Why do we think blacks responded to crime in a Why do we think blacks responded to crime in a different pattern from whites?different pattern from whites?

What has been happening with respect to women What has been happening with respect to women and crime? Again, why?and crime? Again, why?

Why didn’t crime go up when the country Why didn’t crime go up when the country changed the welfare rules?changed the welfare rules?

economic opportunity and crime

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