Just out: Calvin’s Lemonade has filed in Federal Court for Bankruptsy. (see below)
Previously, the Bailout Review Committee’s response had been: A suggestion that the firm might be a perfect candidate for Investment banking or the brokerage business-- following their earlier rejection that stated, in part, “If your firm’s status had been “Too big to fail” , we might have considered it.”
Economic Opportunity and CrimeEconomic Opportunity and Crime
Economic Opportunity and CrimeEconomic Opportunity and CrimeA comment on the reason A comment on the reason No. 216 is in Prison:No. 216 is in Prison:
Up until a year ago last Up until a year ago last December, my home had been December, my home had been burglarized 3 times. My burglarized 3 times. My insurance company, after the 3insurance company, after the 3rdrd time, had suggested that we time, had suggested that we install an alarm, telling me we install an alarm, telling me we would get a discount on our would get a discount on our insurance.insurance.On the afternoon of Dec 24 at On the afternoon of Dec 24 at 4:30pm, a burglar broke in, not 4:30pm, a burglar broke in, not setting off the alarm. But, when setting off the alarm. But, when he entered the master bedroom, he entered the master bedroom, it went off. The burglar fled. it went off. The burglar fled. When the police arrived 6 When the police arrived 6 minutes later, nothing appeared minutes later, nothing appeared to have been taken. I had a few to have been taken. I had a few panes of glass to replace.panes of glass to replace.
Self Defense measures PAY !Self Defense measures PAY !
11stst Lesson of the Day Lesson of the Day
Economics 160 Economics 160
Notes:Votey, Lecture 3, 27
Lecture 5
It’s me again, 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. 27Notes p. 27
Recall Jeremy Bentham’s Notion of Individual Utility MaximizationRecall Jeremy Bentham’s Notion of Individual Utility Maximization
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:
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:E (NB ) = E ( B ) - E ( C )E (NB ) = E ( B ) - E ( C )
= $B = $B .. P ( B ) - $C P ( B ) - $C .. P ( C ) P ( C )
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: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 crimeand will commit a crime
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: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 and will commit a crime if if E ( NB ) > 0E ( NB ) > 0
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: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 ) > 0and will commit a crime if E ( NB ) > 0
Consider a potential criminal with two options:Consider a potential criminal with two options:
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: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 ) > 0and will commit a crime if E ( NB ) > 0
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)
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: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 ) > 0and will commit a crime if E ( NB ) > 0
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)
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: 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)Two of our early supporters, criminologists, didn’t reject Bentham’s viewTwo of our early supporters, criminologists, didn’t reject Bentham’s view
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)
Not Don Cressey, Dan GlaserNot Don Cressey, Dan Glaser
Some Personal Questions in Regard to Career Choice
Some Personal Questions in Regard to Career Choice
Some Personal Questions in Regard to Career Choice
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:
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
Based onBased on
p. 29p. 29
Consider Crimes Committed by Youth:We Note That:
Consider Crimes Committed by Youth:We Note That:
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)
Consider Crimes Committed by Youth:We Note That:
Consider Crimes Committed by Youth:We Note That:
Crime involvement greatest among youthCrime involvement greatest among youth Historically Crime has been predominantly a maleHistorically Crime has been predominantly a male
phenomenonphenomenon
(I will talk further about women’s increasing (I will talk further about women’s increasing involvement in crime.) involvement in crime.)
Consider Crimes Committed by Youth:We Note That:
Consider Crimes Committed by Youth:We Note That:
Crime involvement greatest among youthCrime involvement greatest among youth Historically Crime has been predominantly a maleHistorically Crime has been predominantly a male
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
Consider Crimes Committed by Youth:We Note That:
Consider Crimes Committed by Youth:We Note That:
Non-whites are more thanNon-whites are more than proportionately involvedproportionately involved**Crime involvement greatest among youthCrime involvement greatest among youthCrime is more prevalent in the citiesCrime is more prevalent in the citiesHistorically Crime has been predominantly a maleHistorically Crime has been predominantly a male phenomenon. phenomenon.
**The studies, that we conducted, began in the late 60’s and the data The studies, that we conducted, began in the late 60’s and the data available distinguished between whites and non-whites. Later, available available distinguished between whites and non-whites. Later, available data accounted for Hispanics as well. data accounted for Hispanics as well.
Notes p.30Notes p.30
In our earliest analysis of youth participation in In our earliest analysis of youth participation in crime, we believed that a primary cause was lack crime, we believed that a primary cause was lack of economic opportunities.of economic opportunities.
Supporting that, later studies revealed that when Supporting that, later studies revealed that when economic opportunities were taken into account, economic opportunities were taken into account, black participation in crime was found to be less black participation in crime was found to be less significant to non-signicant. significant to non-signicant.
Race variables, in effect, were proxies for a state Race variables, in effect, were proxies for a state of limited economic opportunities. of limited economic opportunities.
Crime is inevitably higher in poor Crime is inevitably higher in poor neighborhoods and such variation also is likely neighborhoods and such variation also is likely to reflect income differentials between central to reflect income differentials between central cities and other residential locations.cities and other residential locations.
Consider the picture of economic opportunities for youth
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.
Unemployment Rate = Unemployment Rate = Persons actively seeking workPersons actively seeking work Labor Force Labor Force
Notes p. 31Notes p. 31
Consider the picture of economic opportunities for youth
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
What has been the effect of higher unemploymentWhat has been the effect of higher unemploymentrates for youth rates for youth ??
Consider the picture of economic opportunities for youth
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 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 growth ofI showed that a factor in the growth of crime was a decline in police effectivenesscrime was a decline in police effectiveness starting in the mid-fifties. starting in the mid-fifties. Here we see another factor that is Here we see another factor that is important, This is labor market data (BLS).important, This is labor market data (BLS).
Notes p. 32Notes p. 32
Philip Cook didn’t understand Philip Cook didn’t understand that the unemployment rate that the unemployment rate doesn’t tell the full story doesn’t tell the full story
The decline in the Labor Force The decline in the Labor Force
Participation RateParticipation Rate
This is a clue to understandingThis is a clue to understanding Crime participationCrime participation
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 Phil Cook didn’t There was something Phil Cook didn’t understand.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.
Consider the picture of economic opportunities for youth
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 work
Labor Force Labor 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 investing in schooling to get a better job, stay out2. Youth investing in schooling to get a better job, stay out of the labor force temporarily. But those who can’t afford of the labor force temporarily. But those who can’t afford
more education may resort to crime more education may resort to crime
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 the factors affecting youth in this period.the factors affecting youth in this period.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.
Consider the picture of economic opportunities for youth
Consider the picture of economic opportunities for youth
Youth unemployment rates are high relative to those of older Youth unemployment rates are high relative to those of older workers.workers.
Unempl. Rate Unempl. Rate = = Persons actively seeking workPersons actively seeking work Labor Force Labor Force
What has been the effect of higher unemploymentWhat has been the effect of higher unemploymentrates 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, stay2. Youth invest in schooling to get a better job, stay out of the labor force temporarily. out of the labor force temporarily.
Notes p. 40Notes p. 40
I was getting some BLS data over the weekend to demonstrate thisI was getting some BLS data over the weekend to demonstrate this
But, in the middle of that, their data base access was cut But, in the middle of that, their data base access was cut off.off.
The data did show that the series for whites and non-The data did show that the series for whites and non-whites (the BLS designations) show that youth responded whites (the BLS designations) show that youth responded differently to the labor market conditions.differently to the labor market conditions.
The fact was that market changes affected them differentlyThe fact was that market changes affected them differently And, consequently, their participation in crime was And, consequently, their participation in crime was
different. The changes continued to be according to what different. The changes continued to be according to what we had anticipatedwe had anticipated
And enrollment rates at higher education grew more And enrollment rates at higher education grew more slowly for non-whites, ultimately declining for both as slowly for non-whites, ultimately declining for both as living costs roseliving costs rose
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.41
Our Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- as a probability statement: Notes, p.41
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
- as a probability statement: Notes, p.41
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- as a probability statement: Notes, p.41
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
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- as a probability statement:
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 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:
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 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:
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 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
ASAS IPIP
EMPL
UNEMUNEMNLFNLF
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- as a probability statement:
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 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) +
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:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
P(Commit Crime P(Commit Crime andand NLF) + P(other) NLF) + P(other)ASAS IPIP
EMPL
UNEMUNEMNLFNLF
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
P(Commit Crime P(Commit Crime andand NLF) + P(other) NLF) + P(other)ASAS IPIP
EMPL
UNEMUNEMNLFNLF
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
P(Commit Crime P(Commit Crime andand NLF) + P(other) NLF) + P(other)ASAS IPIP
EMPL
UNEMUNEMNLFNLF
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
P(Commit Crime P(Commit Crime andand NLF) + P(other) NLF) + P(other)
in terms of the components:in terms of the components:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
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] +
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- as a probability statement:
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 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
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] +
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
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)
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- as a probability statement:
The Hypothesis: Crimes by youth are a function oflack of legitimate economic opportunities- 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
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:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- as a probability statement:
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 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
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]
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
(CrimeRate NLF)Prob(NLF)[Age,Sex,Race] + P(Other)(CrimeRate NLF)Prob(NLF)[Age,Sex,Race] + P(Other)
rrNN x (1 - x (1 - )) + +
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- as a probability statement:
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 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) +
P(Commit Crime P(Commit Crime and and UNEM) +UNEM) +
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] +
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 - )) + +
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- 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:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
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.....
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
Model Explains Model Explains
(R(R22)) (In Regression(In Regression
Analysis)Analysis)
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
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)
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
Model Explains Model Explains 87%87% of variation of Larceny OF rate of variation of Larceny OF rate
(R(R22)) 8282 Burglary Burglary(In Regression(In Regression
Analysis)Analysis)
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
Model Explains Model Explains 87%87% of variation of Larceny OF rate of variation of Larceny OF rate
(R(R22)) 8282 Burglary Burglary
(In Regression(In Regression 5555 Robbery Robbery
Analysis)Analysis)
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
Model Explains Model Explains 87%87% of variation of Larceny OF rate of variation of Larceny OF rate
(R(R22)) 8282 Burglary Burglary
(In Regression(In Regression 5555 Robbery Robbery
Analysis)Analysis) 7979 Auto Theft Auto Theft
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities- 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
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
Model Explains Model Explains 87%87% of variation of Larceny OF rate of variation of Larceny OF rate
(R(R22)) 8282 Burglary Burglary
(In Regression(In Regression 5555 Robbery Robbery
Analysis)Analysis) 7979 Auto Theft Auto TheftWhy the difference between whites and non-whites ?
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- empirical results:
The Hypothesis: Crimes by youth are a function of lack of legitimate economic opportunities
- empirical results:
The Data: U. S. cities, 1952-1967The Data: U. S. cities, 1952-1967
Focus: Males, 18-19; separated: white, non-white (Notes p. 42)Focus: Males, 18-19; separated: white, non-white (Notes p. 42)
Results: for whites: Results: for whites: rrUU > r > rEE > r > rNN
for non-whites for non-whites rrNN > r > rU U > r> rEE
Model Explains Model Explains 87%87% of variation of Larceny OF rate of variation of Larceny OF rate
(R(R22)) 8282 Burglary Burglary
(In Regression(In Regression 5555 Robbery Robbery
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:
The Data: U. S. cities, 1952-1967The Data: U. S. cities, 1952-1967
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, robbery (Notes p.42)Results are for two offenses: burglary, robbery (Notes p.42)
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.
Our hypothesis about why crime levels are not explained by unemployment rates is vindicated
Our hypothesis about why crime levels are not explained by unemployment rates is vindicated
Blacks who sought out jobs in times of high unemployment Blacks who sought out jobs in times of high unemployment initially would look for workinitially would look for work
With high unemployment, they would often not find jobs.With high unemployment, they would often not find jobs. They would stop looking for jobsThey would stop looking for jobs And, in so doing, would be taken off the unemployment lists And, in so doing, would be taken off the unemployment lists
because they were no longer looking for jobs.because they were no longer looking for jobs. Thus weakening the correlation between unemployment Thus weakening the correlation between unemployment
rates and criminal offenses.rates and criminal offenses. So, Why didn’t Phil cook figure this out ?So, Why didn’t Phil cook figure this out ? – – or read some of our papers that were in journals heor read some of our papers that were in journals he
should have been reading? should have been reading?
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
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
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, requiring2. that you steal once a month2. that you steal once a month
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, 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%
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, 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
A verbal quiz regarding the choice model:A verbal quiz regarding the choice model:
Suppose someone could convince you that he had a job forSuppose someone could convince you that he had a job for you thatyou that
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
Probability never caught in ten years:Probability never caught in ten years:
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, 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
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
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, 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
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 $200,000 $200,000 xx .9044 .9044
= $1,808,800 = $1,808,800
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)
How many of you would take the job ?How many of you would take the job ?
Why, yes?Why, yes? Easy Money, Low RiskEasy Money, Low Risk
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
Where does it come from?Where does it come from? Religion?Religion? Family counseling?Family counseling? A personal sense of honor?A personal sense of honor?
We tried to test for the 1st suggestionWe tried to test for the 1st suggestion
The data was limitedThe data was limited
Raised in a religionRaised in a religion
Still have a religionStill have a religion
Frequency of church attendanceFrequency of church attendance
Where does this fit in ?Where does this fit in ?
Crime GenerationCrime 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
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. 46Notes, p. 46
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 43, p 43
Mkt. wageMkt. wage
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
See See NotesNotes, p 44, p 44
A New ConstraintA New Constraint
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
Notes p. 47Notes p. 47
The Work/Leisure Trade-off a more constraining: Committed Leisure
$ Income
24 Hours
Available Market Income
Time Endowment Committed Leisure
8 hr. JobDesired Work Day
C
A
Binding Constraint
See See NotesNotes, p 45, p 45
The Work/Leisure Trade-off for Women:a more constraining Committed Leisure
$ Income
24 Hours
Available Market Income
Time Endowment Committed Leisure
8 hr. JobDesired Work Day
A
Binding Constraint
NotesNotes, p.45, p.45
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
Available Market Income
Time Endowment Committed Leisure
8 hr. JobDesired Work Day
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
Positive Incentive Effects with Current Welfare Ruleswill depend on a full employment economyPositive Incentive Effects with Current Welfare Ruleswill 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
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers will depend on1. Numbers will depend on
2. Characteristics2. CharacteristicsUnderemployment Case:Underemployment Case:
Longer Hours (Overtime work)Longer Hours (Overtime work)
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economyThe Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment Case
The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economyThe Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time Jobs
:The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
:The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home
: The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy: The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home
The AlternativesThe Alternatives
The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economyThe Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home
The AlternativesThe Alternatives1. Job Creation1. Job Creation
The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economyThe Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home
The AlternativesThe Alternatives1. Job Creation1. Job Creation
Economic GrowthEconomic Growth
The Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economyThe Incentive Effects of Current Welfare Ruleswill depend on a fuller employment economy
The Demand for JobsThe Demand for Jobs
1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home
The AlternativesThe Alternatives1. Job Creation1. Job Creation
Economic GrowthEconomic GrowthIncentivesIncentives
Civilian labor force participation rateCivilian labor force participation rate
PercentPercent UnemployedUnemployed (thousands)(thousands)
What Has Been Happening to the U. S. Economy ?What Has Been Happening to the U. S. Economy ?
Civilian labor force participation rate (Percent)
Not in the Labor ForceNot in the Labor Force (thousands) (thousands)
U.S. Employment Levels 1992 – 2008, thousandsU.S. Employment Levels 1992 – 2008, thousands
TrendsTrends
The Incentive Effects of Current Welfare Rules depend on a full employment economy The Incentive Effects of Current Welfare Rules depend on a full employment economy
The Demand for JobsThe Demand for Jobs1. Numbers1. Numbers2. Characteristics2. Characteristics
Underemployment Case:Underemployment Case:Longer Hours (Overtime work)Longer Hours (Overtime work)Part time jobsPart time jobs
Overemployment CaseOveremployment CasePart time JobsPart time JobsFlextimeFlextimeWorking out of one’s homeWorking out of one’s home
The AlternativesThe Alternatives1. Job Creation1. Job Creation
Economic GrowthEconomic GrowthIncentivesIncentives
2. Crime ??2. Crime ??
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 How do they fit into a model of crime generation and control? Can we explain the generation and control? Can we explain the why?why?
Why do we think blacks responded to crime Why do we think blacks responded to crime in a different pattern from whites?in a different pattern from whites?
What has been happening with respect to What has been happening with respect to women and crime? Again, why?women and crime? Again, why?
Look up the ranking of the U.S., i.e., relative to the other industrial nationsLook up the ranking of the U.S., i.e., relative to the other industrial nations
Over the most recent forty years, we have all learned a lot, BUT:Over the most recent forty years, we have all learned a lot, BUT: Our distribution of income has gotten more concentrated to the topOur distribution of income has gotten more concentrated to the top Our Our relativerelative position in health care has diminished position in health care has diminished At every level of education except graduate studies, our position in At every level of education except graduate studies, our position in
world world rankingranking has fallen. has fallen. In this week, our nation has been focusing on change, you had In this week, our nation has been focusing on change, you had
better be hoping that it works.better be hoping that it works. While the overall level of crime rates has come down dramatically, While the overall level of crime rates has come down dramatically, The proportion of our population is prison leads the free world, The proportion of our population is prison leads the free world,
(which doesn’t explain the decline of rates for property crimes)(which doesn’t explain the decline of rates for property crimes) We lead all of the nations we respect in homicide rates, mostly due We lead all of the nations we respect in homicide rates, mostly due
to the availability of weapons.to the availability of weapons. And we have the highest rate and magnitude of gun deaths of any And we have the highest rate and magnitude of gun deaths of any
nation for which there is reliable data.nation for which there is reliable data. We need changeWe need change!!
Professor PhillipsProfessor Phillips
Deterrence and theDeterrence and the
Death PenaltyDeath Penalty
Next Time
NotesNotes, Phillips 3, p50, Phillips 3, p50