Illegal Labor Markets

1
Illegal Labor Markets

• Lent Term
• Ec 423 Labour Economics
• Lecture 9

2
Legal vs. Illegal Sectors

• So far have consider two roles of occupation
  selection High vs. Low skills
• human capital/ signaling model
• Gender/Race segregation
• Can be other types of markets
• 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

3
Whats Important about Illegal Markets?

• Important alternative way to allocate time
• May have different returns to human capital
• May have distinct career paths/specific
  capital/OTJ training
• Externalities
• High potential social costs to criminal
  activities themselves
• May generate costs for local areas (similar to
  agglomeration issues for urban growth)

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Link to Legal Sector

• Wages
• Decision to enter or exit may be based on
  expected wages
• Strong interaction here with
• Returns to Education
• Education Production/Credit Constraints
• Discrimination
• Occupation Mobility
• May be long-term costs to entry into illegal
  sector
• Much more difficult to exit once detected

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This Lecture

• Model of Criminal Participation
• Focus on interaction with legal sector
• Wages in Legal vs. Illegal Sector
• Penalty for Participation
• Racial Composition
• Next time Focus on the Illegal Sector
• Taxing Participation
• Deterrence vs. Incapcitation

6
Extreme Test for Economics

• Inherently risky attitudes toward risk are
  critical in decision-making.
• Criminal behavior is subject to strategic gaming
  by the police, criminals, and the public, per the
  Prisoner's Dilemma.
• Psychology of Criminality

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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) gt 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

• Crime must pay a higher wage than legitimate
  activities.
• If p 0, U(Wc) gt U(W) only if Wc gt 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 (well
  sidebar this for now but come back to it)

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Risk Aversion

• that attitudes toward risk are measured by the
  curvature of U
• Differences in responses to costs of crime
• changes in the chances of being apprehended
• changes in the extent of punishment
• Heterogeneity
• Clearly not likely to be the same as average RA
  in population
• May be lots of heterogeneity within those in the
  illegal sector

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Costs and Opportunity Costs

• If we accept that sentences deter crime, must
  suggest that some individuals on the margin
  respond to costs
• 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

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Crime Supply

• To get the supply of crimes and criminal
  participation equations for the population,
  aggregate to obtain the supply curves of crime
• CPP f(Wc,p, S, W)
• CPP f(1 - p)W c - pS - W),p)
• CPR g(Wo,p, S, W)
• 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.

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Crime Demand

• For Informal and Black Market, Crime demand is
  just the demand for the products supplied
• Easy to imagine in the case of drugs or
  prostitution
• Generally, issue is elasticity of demand
• Victims' crime more complicated to think about
• 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.

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Market Equilibrium

• An upward sloping supply curve to crime and
  downward sloping "demand" relation produce a
  market clearing level of crime and rewards to
  crime
• 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
• concentration of crime in geographic areas or
  over time
• Adverse effect of crime on legitimate earnings

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Returns to Incarceration

• A major benefit of incarceration is that it
  removes criminals from civil society so that they
  cannot commit additional offenses
• 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

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Supply and Demand with Incarceration
Supply
WC
w2
w1
Demand
L1
L2
LC
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Theory and Evidence Based on Model

• Effect of Legal Employment/wages on criminal
  participation
• Does increased unemployment increase crime?
• Do increases in wages in certain sectors reduce
  crime?
• Does inequality affect crime?

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Exclusivity of Illegal and Legal Sectors

• Typically for ease, we think of crime/legitimate
  work decision a dichotomous one,
• 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.

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Maximization Problem

• chooses time at market work (tm) and time
  committing crime (tv)
• Individual then
• subject to a budget constraint
• and a time constraint
• For simplicity set nonlabor income A0 and define
  the marginal rate of substitution

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Participation conditions

• The individuals reservation wage u0.
• participation in the two sectors requires that
• w gt u0
• p'(0) gt u0
• the returns to the first hour of work in either
  sector is greater than the reservation utility of
  an individual

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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
• to participate in both sectors p'(0) gt w
• Three groups
• Only Legal p'(0) 0 tv0, tmgt0
• Both p'(0) gt 0 tv gt0, tm gt 0
• Only Illegal p'(0) gtgt 0 tv gt0, tm 0

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Extensive vs. Intensive Margin

• In theory, can change crime labor supply both by
  changing number participating and/or number of
  hours available
• Can put this together to estimate

22
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)
• Can now return this to our standard labor
  economics framework
• For many types of crime, extremely labor
  intensive, dont need to worry about K
• Labor

23
Evidence Unemployment and Crime

• Large sociology/criminology literature doesnt
  find much
• Depends heavily on macroeconomic and time series
  variation
• Unclear what underlying forces drive market
  activity and crimetypically left out of analysis
• Not much natural experiment evidence on this
• Control for a bunch of stuff
• Structural model

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Mechanisms linking Crime and Economic Conditions

• Lots of things happen when the economy is worse
• 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

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First Stage
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Bottom line Not much Evidence

• 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.
• increased spending on police
• prisons

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Issues with Estimation

• Most criminals have limited education and labor
  market skills, poor employment records, and low
  legitimate earnings.
• 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 non-criminals.
• A disproportionate number of criminals report
  that they were jobless in the period prior to
  their arrest.

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Issues with Existing Evidence

• Those business cycles may not significantly
  affect the outcomes of the worst off
• 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

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Incarceration in the US
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Long-term Labor Market Consequences

• Crime rates not just negative externality, but
  huge costs for individuals in terms of lost
  earnings
• Why?
• Signal of quality
• Depreciation of human capital
• Loss of experience

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Identification issue

• Prison is not independent of other things
• Worse offenders in prison longer
• Least able in prison (?)
• Can try to separate out 3 effects
• Type of person who would be in prison (if prison
  itself is unobservable)
• Ever in prison
• Duration in Prison

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Evidence on Incarceration - 1

• 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,
• 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.
• 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.

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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.
• 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
• 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.
• 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,
• 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.

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Effect of Duration on Earnings

• What is the effect of longer incarceration rates
  on earnings? (Kling AER paper)
• Variation in judges generates variation in
  sentencing
• Judges are randomly assigned
• Look at earnings and recidivism rates (well
  focus on the first one)

36
Earnings by Duration in Prison
Source Kling, AER (1999)
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Identification

• Begin with simple OLS specification of earnings
  (Y) on sentence length (S) controlling for
  individual characteristics, X,
• Data used in the paper has very small sample size
  of observations on both pre- and post-spell
  outcomes for the same individuals
• 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.

38
How he estimatesOLS

• Fixed effects model
• Assume things are the same for individuals and
  any deviation due to incarcerations so look at
  same individuals, pre and post incarceration
• Control for actual pre-existing differences and
  then compare changes over time

39
Source Kling, AER (1999)
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How he estimatesIV

• Model exogenous variation in sentence length
  itself as function of judge (Z)
• Identifying assumption
• Judge Assignment is random
• Some judges have preference for longer
  sentences
• Preference independent of underlying case
  characteristics (or at least conditionally
  independent)

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Source Kling, AER (1999)
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Bottom line on Duration

• no substantial evidence of a negative effect of
  incarceration length on employment or earnings.
• 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.

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Bottom-line on Incarceration

• Involvement with the criminal justice system
  affects future labor market outcomes.
• 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
• 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.

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Is there a stigma to incarceration?

• The labor market prospects of ex-offenders are
  likely to be impacted by whether employers have
  access to their criminal history records.
• 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.

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Holzer, Rafael, Stoller (2006)
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The effect of Criminal Background Checks

• Use variation in legality of employment checks to
  measure likelihood of background checks
• Look at employment rates of blacks
• 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

48
Costs for Non-Criminals

• Employer review criminal history records may also
  impact the labor market prospects of individuals
  without criminal records.
• 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.

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How big is the effect?

• about 30 of employers do not want to hire
  ex-offenders but do not check criminal records.
• 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

52
The Illegal Sector

• Slightly outside the bounds of labor economics
• Prostitution/drugs/ etc. typically performed by
  organized crime
• Markets for illegal activity linked with markets
  for informal activity
• Negative externalities
• Increased crime in neighborhoods
• Reduced property values ? worse public goods, etc.

53
Big Issue Observability

• Very hard to observe
• Prices
• Quantities
• Labor Supply/Demand
• Not clear how well defined market is
• Extortion/risk/costs of business
• Inelastic demand

54
Growing work

• Economics of Organized Crime
• Mostly Theoretical on networks or organization
• Increasing Empirical focus, largely due to
  international terrorism issues
• Economics of Drugs/Drug Markets
• Addiction
• Penalties
• Rehabilitation

55
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

56
Observing Crack

• Really hard to do
• At the time, didnt really know what was
  happening so not much data collection
• Now, hard to observe ex-post
• Outcomes and correlates the same thinghard to
  test what the causal effect was
• Previous literature has mixed up outcomes
• Homicides
• Foster care
• Birthweight
• Hard to know what the true contribution of crack
  might be

57
Outcomes vs. Proxies

• Inputs into the index
• 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.

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Outcomes - 1
60
Outcomes - 2
61
Drugs and Gangs

• An important aspect of illegal markets is that
  the finance illegal activities
• Most frequent concern is the role of drugs in
  financing gangs and thus encouraging violence
• Akerloff and Yellen (1994) model need 3 parties
  the gang, the police and the populace
• Concerns over negative externalities here are
  very large

62
Gang Organization
63
Data

• Really not many sources
• Levitt and Venkatesh collect data in Chicago
  gangs
• We dont know how externally valid these are
• Provide important insight into gangs

64
Average Financing of Gangs
65
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 drug-selling
  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

66
Expenditures

• nonwage costs
• 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.
• almost 20 percent of total revenues.

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Returns to Gang Membership

• the gang leader retains between 4,200 and
  10,900 a month as profit, for
• an annual wage of 50,000130,000
• 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)

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Return to gang-membership - 2

• Relatively low wages in the first few years
• In year four wages shoot up. Why?
• On the job training?
• Increased promotion/weeding out
• Tournament

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Evidence of Gang Tournament
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Next Time

• Economics of Crime The Costs side
• What happens if we increase the cost of crime?
• Increased Sentence Length
• Increase Probability of Detection
• Does response depend on type of crime?
• Does response depend on type of criminal?