Illegal Labor Markets
Lent Term
Ec 423: Labour Economics
Lecture 9
Legal vs. Illegal Sectors

So far have consider two roles of occupation
selection: High vs. Low skills
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human capital/ signaling model
Gender/Race segregation
Can be other types of markets
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Informal: Legal jobs not reported/measured in
standard activity
Black Market: Trade/activity often in illegal or
restricted goods
Criminal Activity: Illegal actions performed for
gain but not necessarily for the purpose of trade
What’s Important about Illegal Markets?

Important alternative way to allocate time
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May have different returns to human capital
May have distinct career paths/specific
capital/OTJ training
Externalities
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High potential social costs to criminal activities
themselves
May generate costs for local areas (similar to
agglomeration issues for urban growth)
Link to Legal Sector
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Wages
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Decision to enter or exit may be based on
expected wages
Strong interaction here with:
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Returns to Education
Education Production/Credit Constraints
Discrimination
Occupation Mobility
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May be long-term costs to entry into illegal
sector
Much more difficult to exit once detected
This Lecture
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Model of Criminal Participation
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Focus on interaction with legal sector
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Wages in Legal vs. Illegal Sector
Penalty for Participation
Racial Composition
Next time: Focus on the Illegal Sector
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Taxing Participation
Deterrence vs. Incapcitation
Extreme Test for Economics
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Inherently risky: attitudes toward risk are
critical in decision-making.
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Criminal behavior is subject to strategic
gaming by the police, criminals, and the
public, per the Prisoner's Dilemma.
Psychology of Criminality
Basic Model

Individual will choose to commit crimes in
a given time period rather than do legal
work when:
(1 - p)U(Wc) - pU(S) > u(w)
Wc is the gain from successful crime
 p the probability of being apprehended
 S the extent of punishment,
 W is earnings from legitimate work
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Implications for legal wages
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Crime must pay a higher wage than
legitimate activities.
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If p= 0, U(Wc) > U(W) only if Wc > W
As p rises the gap between Wc and W must
increase to maintain the advantage of crime.
Successful crime must pay off more the
greater the chance of being apprehended
May be non-pecuniary gains to crime (we’ll
sidebar this for now but come back to it)
Risk Aversion

that attitudes toward risk are measured by
the curvature of U
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Differences in responses to costs of crime
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changes in the chances of being apprehended
changes in the extent of punishment
Heterogeneity
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Clearly not likely to be the same as average RA
in population
May be lots of heterogeneity within those in the
illegal sector
Costs and Opportunity Costs

If we accept that sentences “deter” crime,
must suggest that some individuals on the
margin respond to costs
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the major factors that affect the decisions to
commit crime - criminal versus legitimate
earnings, the chance of being caught, and the
extent of sentencing - are intrinsically related.
If tougher sentences can theoretically reduce
crime then so may improvements in the
legitimate opportunities of criminals
Crime Supply

To get the supply of crimes and criminal
participation equations for the population,
aggregate to obtain the supply curves of
crime:
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CPP = f(Wc,p, S, W)
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CPR = g(Wo,p, S, W)
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CPP =f(1 - p)W c - pS - W),p)
CPR = g(1 - p)Wc - pS - W,p)
where the first term represents the
expected value of crime versus legal work,
and p measures risk.
Crime “Demand”

For Informal and Black Market, ‘Crime’ demand is
just the demand for the products supplied
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Easy to imagine in the case of drugs or prostitution
Generally, issue is elasticity of demand
Victims' crime more complicated to think about
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Should be negatively related to Wc or to the expected
reward to crime ((1 - p)Wc - pS - W) in a demand type
relation.
Intuition 1: Additional crimes are likely to induce society
to increase p or S, cutting the rewards to crime.
Intuition 2: As criminals commit more crimes, they will
move from more lucrative crimes to less lucrative
crimes.
Market Equilibrium
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An upward sloping supply curve to crime
and downward sloping "demand" relation
produce a market clearing level of crime
and rewards to crime
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comparable to the market clearing wages and
employment for other occupations or industries
Important implication for the efficacy of mass
incarceration in reducing crimes.
Simple demand-supply framework fails to
explain some important phenomenon
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concentration of crime in geographic areas or over
time
Adverse effect of crime on legitimate earnings
Returns to Incarceration

A major benefit of incarceration is that it removes
criminals from civil society so that they cannot
commit additional offenses
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Given the wide variation in crimes committed by
criminals, incarceration of chronic offenders should have
a particularly large effect in reducing crime.
Inelastic Supply: if you lock up someone who commits,
l0 muggings a year, no one replaces that criminal in the
alley, the number of muggings should drop by 10
Perfectly Elastic Supply: if you lock someone who
commit, instant replacement and no decline in crime
Supply and Demand with Incarceration
Supply
WC
w2
w1
Demand
L2
L1
LC
Theory and Evidence Based on Model
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Effect of Legal Employment/wages on
criminal participation
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Does increased unemployment increase crime?
Do increases in wages in certain sectors reduce
crime?
Does inequality affect crime?
Exclusivity of Illegal and Legal Sectors

Typically for ease, we think of
crime/legitimate work decision a
dichotomous one,
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The border between illegal and legal work is
porous,
persons commit crimes while employed doubling up their legal and illegal work.
Some persons use their legal jobs to succeed
in crime
Some criminals shift between crime and work
over time, depending on opportunities.
Maximization Problem

chooses time at market work (tm) and time
committing crime (tv)
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Individual then
subject to a budget constraint
and a time constraint
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For simplicity set nonlabor income A=0 and
define the marginal rate of substitution
Participation conditions
The individual’s reservation wage = u0.
 participation in the two sectors requires
that
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w > u0
p'(0) > u0
the returns to the first hour of work in
either sector is greater than the
reservation utility of an individual
Participation in the Insurgency

An individual working in both the legal and
illegal sectors will choose their optimal
time allocation to satisfy: p'(tV) = w
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to participate in both sectors: p'(0) > w
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Three groups
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Only Legal: p'(0) ≤ 0 : tv=0, tm>0
Both: p'(0) > 0: tv >0, tm > 0
Only Illegal: p'(0) >> 0: tv >0, tm = 0
Extensive vs. Intensive Margin
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In theory, can change crime labor supply
both by changing number participating
and/or number of hours available
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Can put this together to estimate
Unobservability of participation
Difficult to observe true participation
 Use production function of crime in an
area j at time t as Ajt = f (Ljt, Kjt)
 Can Observe total number of crimes (i.e.
output)
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Can now return this to our standard labor
economics framework
For many types of crime, extremely labor
intensive, don’t need to worry about K
Labor:
Evidence: Unemployment and Crime
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Large sociology/criminology literature
doesn’t find much
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Depends heavily on macroeconomic and time
series variation
Unclear what underlying forces drive market
activity and crime—typically left out of analysis
Not much “natural experiment” evidence on
this
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Control for a bunch of stuff
Structural model
Mechanisms linking Crime and
Economic Conditions
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Lots of things happen when the economy
is worse
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Worse legitimate employment opportunities,
More criminal opportunities
Increased consumption of criminogenic
commodities (alcohol, drugs, guns)
Changes in the response of the criminal justice
system.
Rafael and Winters use this breakdown
and then use military contracts as an
instrument for employment opportunities
First Stage
Bottom line: Not much Evidence
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Controlling for other factors, almost all of these
studies report a statistically significant but
substantively small relationship between
unemployment rates and property crime
(consistent across lots of evidence)
Can explain an estimated 2 percent decline in
property crime (out of an observed drop of almost
30 percent)
Violent crime does not change
May operate in indirect channels of state and local
government budgets.
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increased spending on police
prisons
Issues with Estimation
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Most criminals have limited education and labor
market skills, poor employment records, and low
legitimate earnings.
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For instance, the 1991 Survey of State Prison Inmates
reports that two-thirds had not graduated high school,
though many had obtained a general equivalency degree
Among 25-34 year olds, approximately 12% of all male
high school dropouts were incarcerated in 1993.
The average AFQT score of criminals is below that of noncriminals.
A disproportionate number of criminals report that they
were jobless in the period prior to their arrest.
Issues with Existing Evidence
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Those business cycles may not
significantly affect the outcomes of the
worst off
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Changes in unemployment not operating on
correct margin
Not observing same set of people affected by
jobs/wages/etc.
Crimes that may be most affected may be
least observable
Incarceration in the US
Long-term Labor Market Consequences
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Crime rates not just negative externality,
but huge costs for individuals in terms of
lost earnings
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Why?
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Signal of quality
Depreciation of human capital
Loss of experience
Identification issue
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Prison is not independent of other things
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Worse offenders in prison longer
Least able in prison (?)
Can try to separate out 3 effects
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Type of person who would be in prison (if
prison itself is unobservable)
Ever in prison
Duration in Prison
Evidence on Incarceration - 1
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Freeman's studies of the effects of criminal activity on the
labor market outcomes for youth finds incarceration was
significantly linked to lower future employment and weeks
worked,
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Cannot say whether the link is due to the sentencing or to the
fact that only youths deeply involved in crime are incarcerated.
In the NLSY young men who were incarcerated worked around 12
weeks less per year as other young men over an ensuing seven
year period, giving a 25% lower rate of work activity.
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One reason for the huge incarceration effect in the NLSY is that
persons incarcerated have a high probability of engaging in crime
again and being re-incarcerated and thus not able to work even if
they wanted to do so.
even among non-institutionalized young men, those who have been
to jail/prison have lower employment rates than others and a lower
rate of employment than they had before going to jail or prison (
Nagin and Waldfogel (1995) find a positive effect of conviction
on employment in a sample of British youths.
Evidence on Incarceration - 2

Bushway's (1996) analysis of the National Youth Survey 32 found adverse
effects from being arrested on both weeks worked and weekly earnings.
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Grogger (1995) merged longitudinal arrest records from the California
correctional system with unemployment insurance earnings records to
examine the effects of arrests and sanctions on male employment and
earnings.
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Within three yem's of an arrest, respondents who were arrested worked seven
weeks less, and earned $92 per week less, than would otherwise be expected
without an arrest
Men who were arrested, convicted, or sent to jail or prison had lower earnings and
employment than others, but more in the short-term than in the long run.
Workers who went to prison had about a 20% lower earnings than others, while
those who went to jail experienced about a 15% lower earnings
Attributed about one-third of black-white differences in non-employment to the
effect of arrests on future employment. Waldfogel (1992) finds a large effect of
incarceration on earnings and employment
The negative earnings effect is more pronounced among white collar
criminals,
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10-30% less 5-8 years after release than those convicted but not incarcerated.
Conviction for embezzlement and larceny reduces the future legitimate incomes by
about 40%,
Lott (1993a) shows even greater drops in legitimate income, presumably due to
reduced time in legitimate work, for persons convicted of drug dealing.
Effect of Duration on Earnings
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What is the effect of longer incarceration
rates on earnings? (Kling AER paper):
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Variation in judges generates variation in
sentencing
Judges are randomly assigned
Look at earnings and recidivism rates (we’ll
focus on the first one)
Earnings by Duration in Prison
Source: Kling, AER (1999)
Identification
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Begin with simple OLS specification of earnings
(Y) on sentence length (S) controlling for
individual characteristics, X,
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Data used in the paper has very small sample
size of observations on both pre- and post-spell
outcomes for the same individuals
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to estimate the extent of any pre-existing differences,
he imposes a modeling assumption that the association
between incarceration length and pre-spell outcomes is
stable over time.
How he estimates—OLS
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Fixed effects model:
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Assume things are the same for individuals
and any deviation due to incarcerations so look
at same individuals, pre and post incarceration
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Control for actual pre-existing differences and
then compare changes over time
Source: Kling, AER (1999)
How he estimates—IV
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Model exogenous variation in sentence
length itself as function of judge (Z)
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Identifying assumption
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Judge Assignment is random
Some judges have ‘preference’ for longer
sentences
Preference independent of underlying case
characteristics (or at least conditionally
independent)
Source: Kling, AER (1999)
Bottom line on Duration
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no substantial evidence of a negative effect of
incarceration length on employment or earnings.
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In the medium term, seven to nine years after
incarceration spells began, the effect of incarceration
length on labor market outcomes is negligible.
In the short term, one to two years after release, longer
incarceration spells are associated with higher
employment and earnings -- a finding which is largely
explained by differences in offender characteristics and
by incarceration conditions, such as participation inwork
release programs.
Bottom-line on Incarceration
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Involvement with the criminal justice system affects future labor
market outcomes.
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Incarceration is negatively correlated with future outcomes while the
correlation between arrest and conviction and ensuing work activity is
generally more moderate.
The question remains open, however, about the causal mechanisms, if
any, that underlie the links.
Moreover, the effects probably vary among groups and over time and
across prison experiences.
As more and more men are sent to jail or prison
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Any stigma attached to incarceration in the job market may fall (it is
less of signal)
The adverse relation between incarceration and labor outcomes may
also have a strong age component, being larger among younger men
and smaller among older men in the declining part of the age-crime
curve.
Some evidence that prisoners who receive job training or who work in
prison have better employment experiences after release than others.
Is there a ‘stigma’ to incarceration?
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The labor market prospects of ex-offenders
are likely to be impacted by whether
employers have access to their criminal
history records.
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Employers may be reluctant to hire job applicants
with criminal histories for fear that such
applicants may harm a customer or be more likely
to steal.
If employers can and do review criminal history
records, individuals with past convictions are
likely to be excluded from consideration.
Given the high proportion of blacks who have
served time, one might argue that such exclusion
should have particularly adverse consequences
for African-Americans.
Holzer, Rafael, Stoller (2006)
The effect of Criminal Background Checks
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Use variation in legality of employment
checks to measure likelihood of
background checks
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Look at employment rates of blacks
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Follow-up studies in sociology using ‘names’
approach on resume finds more mixed results
Hard to know how much is really due to
incarceration statistical discrimination vs. other
stuff
Costs for Non-Criminals
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Employer review criminal history records may
also impact the labor market prospects of
individuals without criminal records.
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If accessibility to criminal history information is limited
(due to cost, state prohibitions, or the incompleteness of
state and federal records), employers may infer the
likelihood of past criminal activity from race
Such statistical discrimination would adversely affect the
employment outcomes of individuals with clean histories
that belong to demographic groups with high conviction
rates.
How big is the effect?
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about 30% of employers do not want to hire exoffenders but do not check criminal records.
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For these employers, there is a total employment
reduction of 1.0-1.3 percentage points on a base of
roughly 10 percent (Table 2).
These data imply that statistical discrimination of this
type reduces the demand for labor among black men by
10-13 percent, which can be regarded as a lower bound
to the true effect.
The extent to which this reduced demand translates into
wage and employment reductions then depend, of
course, on the relevant labor demand and supply
elasticities for this group
The Illegal Sector
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Slightly outside the bounds of labor
economics
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Prostitution/drugs/ etc. typically performed by
organized crime
Markets for illegal activity linked with markets
for informal activity
Negative externalities:
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Increased crime in neighborhoods
Reduced property values  worse public goods, etc.
Big Issue: Observability
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Very hard to observe
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Prices
Quantities
Labor Supply/Demand
Not clear how well defined market is
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Extortion/risk/costs of business
Inelastic demand
Growing work
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Economics of Organized Crime
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Mostly Theoretical on networks or organization
Increasing Empirical focus, largely due to
international terrorism issues
Economics of Drugs/Drug Markets
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Addiction
Penalties
Rehabilitation
Innovation in Illegal Markets: Crack
Crack cocaine is a smoked version of
cocaine that provides a short, but
extremely intense, high.
 The invention of crack represented a
technological innovation that dramatically
widened the availability and use of cocaine
in inner cities.
 Sold in small quantities in relatively
 anonymous street markets, crack provided
a lucrative market for drug sellers and
street gangs
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Observing Crack
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Really hard to do
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Previous literature has mixed up outcomes
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At the time, didn’t really know what was happening so
not much data collection
Now, hard to observe ex-post
Outcomes and correlates the same thing—hard to test
what the causal effect was
Homicides
Foster care
Birthweight
Hard to know what the true contribution of crack
might be
Outcomes vs. Proxies
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Inputs into the index
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cocaine arrests and cocaine-related ER visits
frequency of crack cocaine mentions in
newspapers,
Cocaine-related drug deaths
the number of DEA drug seizures and undercover
drug buys that involve cocaine.
Outcomes - 1
Outcomes - 2
Drugs and Gangs
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An important aspect of illegal markets is
that the finance illegal activities
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Most frequent concern is the role of drugs
in financing gangs and thus encouraging
violence
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Akerloff and Yellen (1994) model: need 3
parties: the gang, the police and the populace
Concerns over negative externalities here are
very large
Gang Organization
Data
Really not many sources
 Levitt and Venkatesh collect data in
Chicago gangs
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We don’t know how externally valid these are
Provide important insight into gangs
Average Financing of Gangs
How much money per sale?
‘‘back-of-the envelope’’ suggest these
estimates are reasonable. Using
 these revenue figures and average dollars
per sale of $10
 the number of sales per hour by a drugselling team ranges from five to twelve
over the sample.
 That frequency of sale is consistent with
self-reports of the participants as well as
other observational data
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Expenditures
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nonwage costs:
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costs of drugs sold
payments to higher levels of the gang
Weapons
payments to mercenary fighters
funeral costs/ payments to families of the deceased
The greatest nonwage expenditure of the gang
was the regular tribute payment to higher
levels of the gang.
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almost 20 percent of total revenues.
Returns to Gang Membership
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the gang leader retains between $4,200 and
$10,900 a month as profit, for
an annual wage of $50,000–130,000
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This value is well above what leaders could hope to
earn in the legitimate sector given their education
and work experience. otherwise would have been,
The officers each earn roughly $1000 per
month.
These tasks are generally full-time jobs (in
the sense that the people who perform them
would be unlikely to be concurrently
employed in the legitimate sector)
Return to gang-membership - 2
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Relatively low wages in the first few years

In year four: wages shoot up. Why?
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On the job training?
Increased promotion/weeding out
Tournament
Evidence of Gang Tournament
Next Time
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Economics of Crime: The Costs side

What happens if we increase the cost of crime?
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Increased Sentence Length
Increase Probability of Detection

Does response depend on type of crime?
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Does response depend on type of criminal?