Crime in Cities
Brendan O’Flaherty & Rajiv Sethi
Frontiers of Urban Economics, Columbia University
O’Flaherty & Sethi
Crime in Cities
Columbia University
1 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
2 / 55
Crime and Space
What are crimes?
O’Flaherty & Sethi
Crime in Cities
Columbia University
3 / 55
Crime and Space
What are crimes?
Crimes are activities that governments have threatened to punish
Significant variation in coverage across time and space
Examples: blasphemy, sodomy, prostitution, alcohol consumption
O’Flaherty & Sethi
Crime in Cities
Columbia University
3 / 55
Crime and Space
What are crimes?
Crimes are activities that governments have threatened to punish
Significant variation in coverage across time and space
Examples: blasphemy, sodomy, prostitution, alcohol consumption
Crimes with a spatial component
O’Flaherty & Sethi
Crime in Cities
Columbia University
3 / 55
Crime and Space
What are crimes?
Crimes are activities that governments have threatened to punish
Significant variation in coverage across time and space
Examples: blasphemy, sodomy, prostitution, alcohol consumption
Crimes with a spatial component
Focus on index crimes; mala in se
Murder, rape, robbery, assault, larceny, burglary, motor vehicle theft
Other street crime (such as drug selling) also has spatial component
We omit fraud, embezzlement, cybercrime, terrorism
O’Flaherty & Sethi
Crime in Cities
Columbia University
3 / 55
Crime and Space
What are crimes?
Crimes are activities that governments have threatened to punish
Significant variation in coverage across time and space
Examples: blasphemy, sodomy, prostitution, alcohol consumption
Crimes with a spatial component
Focus on index crimes; mala in se
Murder, rape, robbery, assault, larceny, burglary, motor vehicle theft
Other street crime (such as drug selling) also has spatial component
We omit fraud, embezzlement, cybercrime, terrorism
Most personal crimes also have a strategic component
O’Flaherty & Sethi
Crime in Cities
Columbia University
3 / 55
Some Data
O’Flaherty & Sethi
Crime in Cities
Columbia University
4 / 55
Crime Rate (per 100,000 inhabitants), 1986=100
140
120
100
80
60
40
20
2012
2010
2008
2006
2004
2002
2000
1998
1996
1994
1992
1990
1988
1986
1984
1982
1980
1978
1976
1974
1972
1970
1968
1966
1964
1962
1960
0
Years
Murder
O’Flaherty & Sethi
Robbery
Crime in Cities
Burglary
MV Theft
Columbia University
5 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
6 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
7 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
8 / 55
Crime and Location
O’Flaherty & Sethi
Crime in Cities
Columbia University
9 / 55
Crime and Location
Inter-Metropolitan Variation
Robbery, motor vehicle theft, murder; more prevalent in larger cities
Possible effects of density: more disputes, easier victim selection
Rape, burglary, larceny appear uncorrelated with city size
O’Flaherty & Sethi
Crime in Cities
Columbia University
9 / 55
Crime and Location
Inter-Metropolitan Variation
Robbery, motor vehicle theft, murder; more prevalent in larger cities
Possible effects of density: more disputes, easier victim selection
Rape, burglary, larceny appear uncorrelated with city size
Intra-Metropolitan Variation
Crime is highly concentrated within cities (much more so than across)
Murder, robbery, motor vehicle theft more concentrated than poverty
Burglary and theft are much less concentrated
O’Flaherty & Sethi
Crime in Cities
Columbia University
9 / 55
Crime and Location
Inter-Metropolitan Variation
Robbery, motor vehicle theft, murder; more prevalent in larger cities
Possible effects of density: more disputes, easier victim selection
Rape, burglary, larceny appear uncorrelated with city size
Intra-Metropolitan Variation
Crime is highly concentrated within cities (much more so than across)
Murder, robbery, motor vehicle theft more concentrated than poverty
Burglary and theft are much less concentrated
Within city hot spots are sites of disproportionate incidence
Sherman’s (1989) Minneapolis study: 3% of locations, 50% of calls
Crime concentrated at intersections/addresses, not neighborhoods
Stable concentration despite adjustment by victims, police, businesses
O’Flaherty & Sethi
Crime in Cities
Columbia University
9 / 55
Theoretical Approaches
O’Flaherty & Sethi
Crime in Cities
Columbia University
10 / 55
Characteristics versus Incentives
Cesare Lombroso (1878): criminals are born different
Cesare Beccaria’s (1764): rewards and punishments determine crime
O’Flaherty & Sethi
Crime in Cities
Columbia University
11 / 55
Characteristics versus Incentives
Cesare Lombroso (1878): criminals are born different
Cesare Beccaria’s (1764): rewards and punishments determine crime
Lombroso argued that characteristics were physically identifiable
Modern versions include environment and time-varying characteristics
O’Flaherty & Sethi
Crime in Cities
Columbia University
11 / 55
Characteristics versus Incentives
Cesare Lombroso (1878): criminals are born different
Cesare Beccaria’s (1764): rewards and punishments determine crime
Lombroso argued that characteristics were physically identifiable
Modern versions include environment and time-varying characteristics
Two claims of criminogenic characteristics: strong and weak
Weak: relatively stable traits predict who commits crime
Strong: prevalence of characteristics affects volume of crime
O’Flaherty & Sethi
Crime in Cities
Columbia University
11 / 55
Which Characteristics Matter?
O’Flaherty & Sethi
Crime in Cities
Columbia University
12 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
Legality of Abortion (reduced births 4-5%, increased pregnancies)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
Legality of Abortion (reduced births 4-5%, increased pregnancies)
Education (preschool and school quality, high school completion)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
Legality of Abortion (reduced births 4-5%, increased pregnancies)
Education (preschool and school quality, high school completion)
Personality Traits (hyper-vigilance to threat cues, hostile attribution)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
Legality of Abortion (reduced births 4-5%, increased pregnancies)
Education (preschool and school quality, high school completion)
Personality Traits (hyper-vigilance to threat cues, hostile attribution)
Physiological Traits (brain structure and function)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
Legality of Abortion (reduced births 4-5%, increased pregnancies)
Education (preschool and school quality, high school completion)
Personality Traits (hyper-vigilance to threat cues, hostile attribution)
Physiological Traits (brain structure and function)
In utero experience (smoking, alcohol, hunger)
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Characteristics Correlated with Offending
Age and Gender (inmates and arrestees mostly male and young)
Childhood Lead Exposure (via impulsiveness, aggression, IQ, ADHD)
Psychological disorders (schizophrenia, major depressive disorder)
Family Structure (single parent households)
Legality of Abortion (reduced births 4-5%, increased pregnancies)
Education (preschool and school quality, high school completion)
Personality Traits (hyper-vigilance to threat cues, hostile attribution)
Physiological Traits (brain structure and function)
In utero experience (smoking, alcohol, hunger)
Race and Ethnicity effects not accounted for by characteristic distributions
O’Flaherty & Sethi
Crime in Cities
Columbia University
13 / 55
Incentives
O’Flaherty & Sethi
Crime in Cities
Columbia University
14 / 55
The Incentive Approach
Cesare Beccaria’s (1738-1794): On Crimes and Punishments
Crime depends on rewards and punishments
Expected punishment should be set to equal expected reward
O’Flaherty & Sethi
Crime in Cities
Columbia University
15 / 55
The Incentive Approach
Cesare Beccaria’s (1738-1794): On Crimes and Punishments
Crime depends on rewards and punishments
Expected punishment should be set to equal expected reward
Approach revived by Becker (1968)
Effectiveness of punishment depends on certainty and severity
O’Flaherty & Sethi
Crime in Cities
Columbia University
15 / 55
The Incentive Approach
Cesare Beccaria’s (1738-1794): On Crimes and Punishments
Crime depends on rewards and punishments
Expected punishment should be set to equal expected reward
Approach revived by Becker (1968)
Effectiveness of punishment depends on certainty and severity
Beccaria: certainty is more effective (hope dispels apprehension)
Beccaria also suggested celerity or swiftness of punishment matters
O’Flaherty & Sethi
Crime in Cities
Columbia University
15 / 55
The Incentive Approach
Cesare Beccaria’s (1738-1794): On Crimes and Punishments
Crime depends on rewards and punishments
Expected punishment should be set to equal expected reward
Approach revived by Becker (1968)
Effectiveness of punishment depends on certainty and severity
Beccaria: certainty is more effective (hope dispels apprehension)
Beccaria also suggested celerity or swiftness of punishment matters
Punishments can reduce crime through deterrence or incapacitation
Incarceration operates through both effects
O’Flaherty & Sethi
Crime in Cities
Columbia University
15 / 55
Deterrence Effects of Certainty and Severity
O’Flaherty & Sethi
Crime in Cities
Columbia University
16 / 55
Deterrence Effects of Certainty and Severity
The Argentina mobilization (Di Tella and Schargrodsky, 2004)
Argentina 1994: Bombing of Jewish Community Center
Police presence increased at all Jewish and Muslim institutions
Car thefts fell by 75% on affected blocks
No evidence of displacement to other locations
O’Flaherty & Sethi
Crime in Cities
Columbia University
16 / 55
Deterrence Effects of Certainty and Severity
The Argentina mobilization (Di Tella and Schargrodsky, 2004)
Argentina 1994: Bombing of Jewish Community Center
Police presence increased at all Jewish and Muslim institutions
Car thefts fell by 75% on affected blocks
No evidence of displacement to other locations
The Italian Collective Pardon (Drago et al., 2009)
A 2006 prison release in Italy induced heterogeneity in severity
Overcrowding resulted in early release
Reoffenders faced restoration of remaining sentences
Those facing more severe punishments found less likely to re-offend
O’Flaherty & Sethi
Crime in Cities
Columbia University
16 / 55
Race and Ethnicity
Large racial disparities in arrests, incarceration, and victimizations
Not due to lower likelihood of punishment conditional on offending
Opportunity costs may be part of the explanation
But other factors are in play, especially for homicide and robbery
O’Flaherty & Sethi
Crime in Cities
Columbia University
17 / 55
Race and Ethnicity
Large racial disparities in arrests, incarceration, and victimizations
Not due to lower likelihood of punishment conditional on offending
Opportunity costs may be part of the explanation
But other factors are in play, especially for homicide and robbery
Need to consider strategic incentives arising from categorization
O’Flaherty & Sethi
Crime in Cities
Columbia University
17 / 55
Race and Ethnicity
Large racial disparities in arrests, incarceration, and victimizations
Not due to lower likelihood of punishment conditional on offending
Opportunity costs may be part of the explanation
But other factors are in play, especially for homicide and robbery
Need to consider strategic incentives arising from categorization
Crimes are strategically and informationally distinct
O’Flaherty & Sethi
Crime in Cities
Columbia University
17 / 55
Robbery
O’Flaherty & Sethi
Crime in Cities
Columbia University
18 / 55
Strategic Considerations
Robbery involves victim selection, resistance, forced compliance
O’Flaherty & Sethi
Crime in Cities
Columbia University
19 / 55
Strategic Considerations
Robbery involves victim selection, resistance, forced compliance
Unobserved heterogeneity in both victim and offender populations
O’Flaherty & Sethi
Crime in Cities
Columbia University
19 / 55
Strategic Considerations
Robbery involves victim selection, resistance, forced compliance
Unobserved heterogeneity in both victim and offender populations
Visible markers used to infer likelihood of resistance and violence
Those least likely to resist targeted at highest rates
Likelihood of resistance, violence will be identity contingent
O’Flaherty & Sethi
Crime in Cities
Columbia University
19 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
20 / 55
Deterrence and Selection
More effective deterrence (or better labor market) lowers crime
O’Flaherty & Sethi
Crime in Cities
Columbia University
21 / 55
Deterrence and Selection
More effective deterrence (or better labor market) lowers crime
But who exit differ systematically from those who remain
O’Flaherty & Sethi
Crime in Cities
Columbia University
21 / 55
Deterrence and Selection
More effective deterrence (or better labor market) lowers crime
But who exit differ systematically from those who remain
For robbery, those who remain more likely to force compliance
O’Flaherty & Sethi
Crime in Cities
Columbia University
21 / 55
Deterrence and Selection
More effective deterrence (or better labor market) lowers crime
But who exit differ systematically from those who remain
For robbery, those who remain more likely to force compliance
Prediction: robberies are more violent when less frequent
Evidence from NCVS supports this
O’Flaherty & Sethi
Crime in Cities
Columbia University
21 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
22 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
23 / 55
Racial Stereotypes and Robbery
Victim resistance is itself a form of deterrence
O’Flaherty & Sethi
Crime in Cities
Columbia University
24 / 55
Racial Stereotypes and Robbery
Victim resistance is itself a form of deterrence (Wright and Decker, 1997):
I rob mostly whites... I usually don’t have no problem [with
resistance], none at all. [Whites] got this stereotype, this myth, that a
black person with a gun or knife is like Idi Amin or Hussein. And [a]
person [who believes] that will do anything [you say]
O’Flaherty & Sethi
Crime in Cities
Columbia University
24 / 55
Racial Stereotypes and Robbery
Victim resistance is itself a form of deterrence (Wright and Decker, 1997):
I rob mostly whites... I usually don’t have no problem [with
resistance], none at all. [Whites] got this stereotype, this myth, that a
black person with a gun or knife is like Idi Amin or Hussein. And [a]
person [who believes] that will do anything [you say]
Whites accept the fact that they’ve been robbed... some blacks would
rather die than give you they bucks and you damn near have to be
killing [them] to get it
O’Flaherty & Sethi
Crime in Cities
Columbia University
24 / 55
Racial Stereotypes and Robbery
Victim resistance is itself a form of deterrence (Wright and Decker, 1997):
I rob mostly whites... I usually don’t have no problem [with
resistance], none at all. [Whites] got this stereotype, this myth, that a
black person with a gun or knife is like Idi Amin or Hussein. And [a]
person [who believes] that will do anything [you say]
Whites accept the fact that they’ve been robbed... some blacks would
rather die than give you they bucks and you damn near have to be
killing [them] to get it
Victim selection not based on anticipated cash holdings:
most white people have about two dollars on them, and credit cards,
something like that
whites, they have credit cards and checkbooks on them... they get
robbed, they cancel it
all they got is plastic and checks
O’Flaherty & Sethi
Crime in Cities
Columbia University
24 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
25 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
26 / 55
Implications of Victim Selection
O’Flaherty & Sethi
Crime in Cities
Columbia University
27 / 55
Implications of Victim Selection
Rates of Offending:
Stereotypes of violence lower resistance
This raises returns to offending
Racial disparities in robbery offending differ from burglary or theft
O’Flaherty & Sethi
Crime in Cities
Columbia University
27 / 55
Implications of Victim Selection
Rates of Offending:
Stereotypes of violence lower resistance
This raises returns to offending
Racial disparities in robbery offending differ from burglary or theft
Violence Conditional on Resistance
Black victims avoided by less desperate offenders
Offender pool faced by white victims is less desperate on average
Conditional on resistance, whites face violence less often
O’Flaherty & Sethi
Crime in Cities
Columbia University
27 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
28 / 55
Differential Victimization and Segregation
O’Flaherty & Sethi
Crime in Cities
Columbia University
29 / 55
Differential Victimization and Segregation
Standard approaches to segregation: preferences or discrimination
Differential victimization provides alternative explanation
O’Flaherty & Sethi
Crime in Cities
Columbia University
29 / 55
Differential Victimization and Segregation
Standard approaches to segregation: preferences or discrimination
Differential victimization provides alternative explanation
Whites rationally resist at lower rates, thus targeted more often
Conditional on income, will choose structurally safer locations
O’Flaherty & Sethi
Crime in Cities
Columbia University
29 / 55
Differential Victimization and Segregation
Standard approaches to segregation: preferences or discrimination
Differential victimization provides alternative explanation
Whites rationally resist at lower rates, thus targeted more often
Conditional on income, will choose structurally safer locations
Racial disparities in victimization rates highest at intermediate income
O’Flaherty & Sethi
Crime in Cities
Columbia University
29 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
30 / 55
Homicide
O’Flaherty & Sethi
Crime in Cities
Columbia University
31 / 55
Homicide
The only serious crime potentially motivated by preemption
O’Flaherty & Sethi
Crime in Cities
Columbia University
32 / 55
Homicide
The only serious crime potentially motivated by preemption
If I go downstairs to investigate a noise at night, with a gun in my hand,
and find myself face to face with a burglar who has a gun in his hand,
there is a danger of an outcome that neither of us desires. Even if he
prefers to just leave quietly, and I wish him to, there is danger that he may
think I want to shoot, and shoot first. Worse, there is danger that he may
think that I think he wants to shoot. Or he may think that I think he
thinks I want to shoot. And so on.
O’Flaherty & Sethi
Crime in Cities
Columbia University
32 / 55
Homicide
The only serious crime potentially motivated by preemption
If I go downstairs to investigate a noise at night, with a gun in my hand,
and find myself face to face with a burglar who has a gun in his hand,
there is a danger of an outcome that neither of us desires. Even if he
prefers to just leave quietly, and I wish him to, there is danger that he may
think I want to shoot, and shoot first. Worse, there is danger that he may
think that I think he wants to shoot. Or he may think that I think he
thinks I want to shoot. And so on. “Self-Defense” is ambiguous, when one
is only trying to preclude being shot in self-defense.
Schelling (1960)
O’Flaherty & Sethi
Crime in Cities
Columbia University
32 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
33 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
34 / 55
Homicide
Homicide rates depend on investments in lethality
Newark: murders rose while shootings fell
O’Flaherty & Sethi
Crime in Cities
Columbia University
35 / 55
Homicide
Homicide rates depend on investments in lethality
Newark: murders rose while shootings fell
Need model of endogenous lethality with preemptive motive
O’Flaherty & Sethi
Crime in Cities
Columbia University
35 / 55
Homicide
Homicide rates depend on investments in lethality
Newark: murders rose while shootings fell
Need model of endogenous lethality with preemptive motive
Three lethality levels: unarmed, low, high with different costs
Unobserved heterogeneity in costs of killing
Preemptive motive implies strategic complementarity in lethality
Bayes-Nash equilibria have threshold structure (cost maps to lethality)
Can have multiple equilibria and a murder multiplier
O’Flaherty & Sethi
Crime in Cities
Columbia University
35 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
36 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
37 / 55
Homicide
When killing is common, climate of fear prevails
O’Flaherty & Sethi
Crime in Cities
Columbia University
38 / 55
Homicide
When killing is common, climate of fear prevails
Preemptive motive looms large, feedback to high homicide rates
O’Flaherty & Sethi
Crime in Cities
Columbia University
38 / 55
Homicide
When killing is common, climate of fear prevails
Preemptive motive looms large, feedback to high homicide rates
Small reductions hard to sustain, large reductions possible
But require coordinated change in expectations
Example: Operation Ceasefire
O’Flaherty & Sethi
Crime in Cities
Columbia University
38 / 55
Racial Disparities in Victimization and Offending
Fear depends on observable characteristics of others
O’Flaherty & Sethi
Crime in Cities
Columbia University
39 / 55
Racial Disparities in Victimization and Offending
Fear depends on observable characteristics of others
Individuals who are feared will be preemptively killed more often
And individuals with more fear will kill more often
O’Flaherty & Sethi
Crime in Cities
Columbia University
39 / 55
Racial Disparities in Victimization and Offending
Fear depends on observable characteristics of others
Individuals who are feared will be preemptively killed more often
And individuals with more fear will kill more often
Historically, penalties lower if victim is black
Raises incentives for pre-emptive killing
Effect is especially strong if both parties fear being killed pre-emptively
O’Flaherty & Sethi
Crime in Cities
Columbia University
39 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
40 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
41 / 55
Stand-your-Ground Laws
Broaden notion of justifiable homicide
Makes threatened individuals more dangerous
Hence more likely to be killed preemptively
Evidence seems to support this (Cheng and Hoekstra, 2013)
O’Flaherty & Sethi
Crime in Cities
Columbia University
42 / 55
Street Vice
O’Flaherty & Sethi
Crime in Cities
Columbia University
43 / 55
Street Vice
Prostitution, illegal gambling, drug selling
O’Flaherty & Sethi
Crime in Cities
Columbia University
44 / 55
Street Vice
Prostitution, illegal gambling, drug selling
Diffuse demand but highly concentrated supply
Models of spatial competition can be adapted to this
Fixed costs of protection, transportation carries risks
O’Flaherty & Sethi
Crime in Cities
Columbia University
44 / 55
Street Vice
Prostitution, illegal gambling, drug selling
Diffuse demand but highly concentrated supply
Models of spatial competition can be adapted to this
Fixed costs of protection, transportation carries risks
Greater demand density implies more sellers per unit distance
Central cities will have lower prices and higher seller density
Competition from city lowers suburban prices and seller density
Strong enough effect can eliminate suburban markets
O’Flaherty & Sethi
Crime in Cities
Columbia University
44 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
45 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
46 / 55
Race and Street Vice
What accounts for racial disparities in exposure to street vice?
O’Flaherty & Sethi
Crime in Cities
Columbia University
47 / 55
Race and Street Vice
What accounts for racial disparities in exposure to street vice?
Drug selling generates negative externalities: robbery, homicide
Courts not used for dispute resolution; pervasive threat of violence
Drug sellers are attractive robbery targets
Marginal costs of killing lower for drug sellers
Negative externalities cause exit of the more affluent non-users
O’Flaherty & Sethi
Crime in Cities
Columbia University
47 / 55
Race and Street Vice
What accounts for racial disparities in exposure to street vice?
Drug selling generates negative externalities: robbery, homicide
Courts not used for dispute resolution; pervasive threat of violence
Drug sellers are attractive robbery targets
Marginal costs of killing lower for drug sellers
Negative externalities cause exit of the more affluent non-users
Preferences over racial composition can then give rise to segregation
Street vice correlated with race through neighborhood dynamics
Not because vice originates in or moves to black neighborhoods
O’Flaherty & Sethi
Crime in Cities
Columbia University
47 / 55
Some Lessons for Law Enforcement
Street vice requires coordinated expectations between buyers, sellers
Most desirable locations will be occupied by best protected sellers
With well settled expectations, violence can be limited
Disruption by law enforcement can trigger battles for best locations
Law enforcement should target least lucrative locations first
O’Flaherty & Sethi
Crime in Cities
Columbia University
48 / 55
Police Killings
O’Flaherty & Sethi
Crime in Cities
Columbia University
49 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
50 / 55
O’Flaherty & Sethi
Crime in Cities
Columbia University
51 / 55
Safe and Risky Encounters
Is equality of arrest and victimization rates the right benchmark?
O’Flaherty & Sethi
Crime in Cities
Columbia University
52 / 55
Safe and Risky Encounters
Is equality of arrest and victimization rates the right benchmark?
Only if objectively threatening encounters have equal frequency
O’Flaherty & Sethi
Crime in Cities
Columbia University
52 / 55
Safe and Risky Encounters
Is equality of arrest and victimization rates the right benchmark?
Only if objectively threatening encounters have equal frequency
Suppose equal frequency of risky encounters and no police bias
Proportion unarmed among victims should match proportion armed
Guardian and Washington Post data can be used to test this
O’Flaherty & Sethi
Crime in Cities
Columbia University
52 / 55
Washington Post 2015 Data
400
350
300
250
200
150
100
50
0
gun
O’Flaherty & Sethi
knife
vehicle
Crime in Cities
toy weapon
unarmed
Columbia University
53 / 55
The Guardian 2015 Data
350
300
250
200
150
100
50
0
gun
O’Flaherty & Sethi
knife
Crime in Cities
vehicle
unarmed
Columbia University
54 / 55
Mass Incarceration
Why is mass incarceration tolerated?
O’Flaherty & Sethi
Crime in Cities
Columbia University
55 / 55
Mass Incarceration
Why is mass incarceration tolerated?
Could it be because of its racial character?
O’Flaherty & Sethi
Crime in Cities
Columbia University
55 / 55
Mass Incarceration
Why is mass incarceration tolerated?
Could it be because of its racial character?
Essentialist interpretations of equilibrium phenomena
O’Flaherty & Sethi
Crime in Cities
Columbia University
55 / 55
Mass Incarceration
Why is mass incarceration tolerated?
Could it be because of its racial character?
Essentialist interpretations of equilibrium phenomena
Facilitated by cognitive load, historical superstructure of beliefs
O’Flaherty & Sethi
Crime in Cities
Columbia University
55 / 55
Mass Incarceration
Why is mass incarceration tolerated?
Could it be because of its racial character?
Essentialist interpretations of equilibrium phenomena
Facilitated by cognitive load, historical superstructure of beliefs
See Glenn Loury’s Du Bois and Tanner lectures
O’Flaherty & Sethi
Crime in Cities
Columbia University
55 / 55