1 lecture 5: economics and psychology of crime economics 1035 sendhil mullainathan fall 2006

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1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Page 1: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

1

Lecture 5: Economics and Psychology of

CrimeEconomics 1035

Sendhil MullainathanFall 2006

Page 2: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

2

Overview of Crime Section

• This Lecture:– Basic facts about Crime in US – Economics of Crime– Psychology of what drives criminal behavior

• Next Lecture:– Policies for Crime reduction– Understanding a concrete phenomena: decline in

crime– Details of capture and sentencing in next group of

lectures

Page 3: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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• Basic Facts about Crime

• Thought experiment: impact of incarceration

• Economic Model of Crime

• Psychology of Crime

Page 4: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Measuring Crime

• 1997: – 13.5 million crimes reported to police. – 5079 crimes per 100,000– Source: FBI Uniform Crime Reporting

Program

• What to think of reliability of this statistic?

Page 5: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Measuring Crime

• 1997: – 13.5 million crimes reported to police.– 5079 crimes per 100,000

• What to think of reliability of this statistic?– All crimes reported?

• How else to measure?

Page 6: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Measuring Crime

• Individual victimization surveys• 1997:

– 13.5 million crimes reported to police.– 5079 crimes per 100,000– 36.8 million crimes reported by victims– 13845 crimes per 100,000

• Source: National Victimization Survey

• Discrepancy of about three times• Why discrepancy?

Page 7: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why discrepancy?

• Under reporting of crimes– Benefit of reporting can be small. Police

can’t/won’t do anything?– Cost of reporting can be moderate– Someone close– Multiple victims per crime

• Are there situations were these factors are more or less relevant?

Page 8: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Discrepancy has implications

• Some crimes show greater or lesser discrepancy– Auto theft– Murder

• What about over time?

Page 9: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Self reports of Crime

Page 10: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Police versus Self Reports

Page 11: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Discrepancy can change over time

• Why a change over time?– Shift in composition of crimes to better

reported ones– Better reporting of crime

• 1/3 of discrepancy

– Increased filing of police reports?

• So did crime go down or not?

Page 12: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Another Source of Data

• Self reports– Example: – 2.6% of adults reported that they had

committed a felony in the past year

• Why use this data?

Page 13: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Who commits crimes?

• You guys do

• 72% of persons arrested were aged 13-34– 32% of the population

Page 14: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why?

• Why age difference?

Page 15: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Who comits crimes?

• You GUYS do

Page 16: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why?

• Why a gender difference?

Page 17: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Who Commits Crimes?

• Large Racial differences

Page 18: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Who Commits Crimes?

• Large Racial differences

Page 19: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why?

• Why a racial difference?

Page 20: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Who Commits Crimes?

• Large educational differences

• 1991 survey of prison inmates: – 2/3 had not graduated from high school– Note: Many obtained GED

• 12% of all male high school dropouts were incarcerated in 1993. More today.

• Ok, so not you guys.

Page 21: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why Educational Difference?

Page 22: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Who Commits Crimes?

• A few people do. Crimes are conentrated• Original study:

– Philadelphia, 1945. 18% of delinquents comm. 52% of crimes.

• Recent estimates– California: upper half of offenders commit 10.6

crimes per year

• Implication:– High recidivism

Page 23: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why high concentration?

Page 24: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Geographic Concetraiton of Crime

City Index Murder Rape Robbery Assault

New York, N.Y. 3,286.70 8.2 19.1 351.5 471.3

Los Angeles, Calif. 5,029.30 15.6 37.4 456.1 879

Chicago, Ill.   22.9   633.3 877.2

Houston, Tex. 7,106.60 13.4 47.3 496.6 614.9

Philadelphia, Pa. 6,183.10 20.4 66.8 632.5 690

Phoenix, Ariz. 7,681.80 15.3 29.3 338.7 387.4

San Diego, Calif. 4,048.00 4 27.4 138.7 424

Dallas, Tex. 9,132.10 19.7 54.3 685.3 703.1

San Antonio, Tex. 8,243.30 8.5 42 183.3 581.6

Las Vegas, Nev. 4,524.20 11.9 40 328.1 295.4

Detroit, Mich. 9,431.60 41.3 68.2 742 1,338.90

San Jose, Calif. 2,754.50 2.4 36 77.9 492.7

Honolulu, Hawaii 5,469.90 2.3 33.1 112.8 128.8

Indianapolis, Ind. 5,143.50 14 55.4 349.1 512

Page 25: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Why Geographic Concentration?

Page 26: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Time Series of Crime

Page 27: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Arrests

Page 28: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Time Series of Crime

• Huge crime wave in 60s, 70s

• Big reduction in crime in late 90s

• Huge increase in arrests of crime

Page 29: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Implication of Arrests

• What ought to have happened as people were arrested?

Page 30: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Implication of Arrests

• What ought to have happened as people were arrested?

• Recall: high concentration of crimes

• Crime should have dropped to zero.

• Why didn’t it?

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Implication of Arrests

• What ought to have happened as people were arrested?

• Recall: high concentration of crimes• Crime should have dropped to zero.• Why didn’t it?

– Criminality increasing– Other criminals coming in

• Need a framework for understanding supply (& “Demand” for Crime).

Page 32: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Criminal Behavior as choice

• Net Benefits of committing crime

• Compare to 0?

PenaltyCaughtBenefitCaughtNot

)()()1( SUpCUp

Page 33: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Criminal Behavior as choice

• Net Benefits of committing crime

• Opportunity Cost

PenaltyCaughtBenefitCaughtNot

)()()1( SUpCUp

eMarket Wag

)(WU

Page 34: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Demand and Supply Framework

• Supply of crime– Benefits of crime

• Wage rates, unemployment

• “Demand” for crime– Prevention activities

• Threat of incarceration

Page 35: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Equilibrium

Benefits from Crime

CrimeSupply

Demand

Page 36: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Change in Penalties

Benefits from Crime

Crime Demand

Supply

Page 37: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Incapacitation effect

How to think about incapacitation effect here?

Benefits from Crime

CrimeDemand Supply

Page 38: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Incapacitation effect

How to think about incapacitation effect here?

Page 39: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Incapacitation effect

How to think about incapacitation effect here?

Benefits from Crime

CrimeDemand Supply

Page 40: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Empirical Evidence for Model

• Relation between Unemployment and Crime– Some in time series/state panel

• Too small to explain variation in crime• High estimate: 1 point increase in unemployment

leads to 1.1% drop in property crime

• Unemployed more likely to commit crimes– Some evidence– Transitional Aid Research Project (return to

later)

Page 41: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Empirical Evidence for Model

• Why such a low link?• Not a tradeoff?

– Experienced drug dealers often hold legal jobss

• Why?

– Only those on verge of incarceration show great reduction in crime

• Effect of Sanctions– Return to later

Page 42: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Explaining other facts

• Education?

• Race?

• Gender?

• Concentration amongst few people?

• Geographic concentration?

• Time Series?

Page 43: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Psychology of Criminal Behavior

Page 44: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Overview of Ideas

• Psychology of the “Situation”– Impulsivity in decisions– Imitation and Conformity– Situational factors driving behavior

• Psychology of Biology of the “Criminal”– Hormonal studies– Genetics: Twin Studies– Personality measurement

Page 45: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• Subject brought into room.

• Told he is studying visual perception

• Simple task. Match length of bars

Page 46: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Which line matches?

Page 47: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• Subject is next to last person on each trial

• Others confederates. Give incorrect answers on 12 of 18 trials

Page 48: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Findings

• 37 of 50 subjects conformed to majority at least once

• 14 conformed on more than 6 of 12 trials

• Mean subject conformed 4 of 12 trials

• Other findings:– Small amounts of non-conformity by

confederates is enough

Page 49: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Bandura, Ross and Ross

• Setup

• 36 boys and 36 girls in nursery school– 37 to 69 months

• Eight experimental groups, one control – Half exposed to aggressive models, half not– Male, females– Same sex, not same sex

Page 50: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• Individually brought into experimental room to come and join game

• Subjects would play in corner. Various small toys

• During that, model escorted to corner:– Small table and chair, tinker toy set, mallet

and 5 foot inflated Bobo doll

Page 51: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• Model would either play with tinker toys

• Or play with tinker toys and then attack Bobo doll– Punch Bobo in the nose– Hit with mallet– Aggressive phases such as “Kick him”

• Subjects would then after 10 minutes be asked to go to another room

Page 52: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• Mild aggression arousal– Showed some toys.– After playing for a few minutes, told “These are the

best times, not for everyone. Reserved for other children”

– Why?

• Told they could play with other toys in another room– Variety of toys. Some aggressive, non aggressive

rooms

• DV: Aggression in playing with toys

Page 53: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Findings

• Big differences between aggression and non agression– Both physical and verbal aggression– Also for aggression on characteristics not

related to Bobo doll

• Equal for Boys and girls• Sample size too small to test for gender

match effects• Problems?

Page 54: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Deeper point

• Imitative and peer influences

• Can be found in many many contexts

• Implications?

Page 55: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Situational Drivers of Behavior

• Darley and Batson

• Recruited seminary students for a study on religious education

• Completed personality questionnaires

• Experimental procedures in one building

• Then told to go to another building

Page 56: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• On the way, encounter a man slumped in alleyway (victims condition is unknown—hurt or drunk?).

• Subject moans and coughs as they walk by…clearly needing help

• Manipulations:– Subjects in a rush or not– Task: give lecture on seminary jobs or story of

Good Samaritan

Page 57: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Setup

• DV:– Helping scale

• Asked to answer helping behavior questionnaire at second site

Page 58: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Results

• 40% offered some help– 63% in low hurry– 45% medium hurry– 10% high hurry– Helping relevant message: 53%– Task relevant message: 29%

• No correlation between personality and behavior

Page 59: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Other findings

• Kitty Geneovese incident

• Laboratory studies of helping behavior

• Broad finding:– Small situational factors can influence helping

behavior

• Why not study aggression?

Page 60: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Milgram experiment

• Should be familiar to all

• Subjects asked to administer shock in learning task

• Increase voltage for failure

• Experimenter intervenes if subjects wish to stop– “Experiment must go on”– Four successive prods

Page 61: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Results

• 65% of participants administered final shcok

• Why no control?

• Many replications

• Stunning findings

• Human subjects considerations?

Page 62: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Implications

• Situational factors have such large effects on

Page 63: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Impulsivity in Decisions

• Will talk about later

• High discount rates

• Numerous self control problem

• Implications for criminality?

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Summary

• Behavior driven largely by the situation

• Criminality is a consequence of small situational factors

• Such as?

Page 65: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Hormonal Drivers of Behavior

• Recall interesting facts:– Crime concentrated in youth– Crime concentrated in men

• Could there be a biological driver of crime? – Specifically might hormonal factors have an

effect?

Page 66: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Evidence from other species

• Fact 1: Male aggression not universal– Ex: Spotted hyenas. Females are highly aggressive.

Dominate males.

• Fact 2: Some impact of testosterone level and aggression. Only up to a point– Big effects of early testosterone during development– Big effects for first time aggression– Some of the work

• Fact 3: Prepubertal castration reduces aggression

Page 67: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Evidence from humans

• Some evidence of anti-androgen drugs reducing recidivism amongst sex offenders– Reduce testosterone levels or hormone

receptor dynamics

• XYY more likely to commit crimes

• No clear relation between hormone levels and aggressive behavior.– Some evidence for adolescents

Page 68: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Inheritability

• Simple hypothesis: – Criminality is (partly) a predisposed trait

• Motivated again by genetic differences

• How to test?

Page 69: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Family studies

• Typical finding: Criminal behavior correlated within families– Example: One study found that if father is

criminal 40% chance of son being criminal too– Aside: How might you practically conduct

such a study?

• Consistently found in most data sets?• Problems?• How might you solve them?

Page 70: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Twin Studies

• Typical twin study compares:– Monozygotic twins

• One egg, later two embryos, “Identical twins”

– Dizygotic twins• Two eggs, two embryos, “Fraternal twins”

– Siblings

• Method:– Collect data on twins and siblings– Correlate between twins or siblings

• How do you expect to rank these three?

Page 71: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Typical Findings

• Example: 1986 Rushto et. Al.– 572 adult twin pairs– Questionnaire on altruistic and aggressive

tendencies

• Correlations:– .40 for MZ– .04 for DZ pairs

• Problems?

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Key Problems

• Measurement of aggression?– Self-administered– Others

• Must clarify MZ, DZ

• Why so low correlation for DZ?

• Replication?

• Raised together?

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Overall Findings

• Meta review of 100 such studies

• Half the total variation attributed to genetics– Note: Aggression is being studied – Only sometimes behavior (e.g. delinquency

behavior)

• Why?

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Adoption studies

• Example- Crowe– Adopted children whose biological mothers

had criminal record– Adopted children whose mothers did not

• 50% of children in group 1 had a criminal record

• 5% in group 2• Aside: Why mothers?• Problems?

Page 75: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Problems

• Adoption to parents random?– Small correlation between criminality of

biological and adoptive parents

Page 76: 1 Lecture 5: Economics and Psychology of Crime Economics 1035 Sendhil Mullainathan Fall 2006

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Personality Measurement

• Eysenck: Biosocial theory of criminality– Three personality traits: Extraversion,

Neuroticism and Psychoticism

• High on any one dimension-> criminality– Some support from large scale research– Weak correlations– Test-retest problems

• Implications:– Psychotherapeutic work

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How to reconcile these two view?

• Criminal behavior a characteristic of the person?

• A characteristic of the situation?

• Two extreme views