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Redemption in an Era of Widespread Background Checking Alfred Blumstein, Kiminori Nakamura Heinz College - Carnegie Mel

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Redemption in an Era of Widespread Background Checking Alfred Blumstein, Kiminori Nakamura Heinz College - Carnegie Mellon Univ. March 27, 2009 Some Discussion at an ASC Meeting in about 1970

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Redemption in an Era of Widespread Background CheckingAlfred Blumstein, Kiminori NakamuraHeinz College - Carnegie Mellon Univ.March 27, 2009
some discussion at an asc meeting in about 1970
Some Discussion at an ASC Meeting in about 1970
  • Old Fogy: “We shouldn’t computerize criminal-history records because computers don’t understand the Judeo-Christian concept of redemption”
  • Rejoinder: “Paper records certainly don’t understands that concept, but computers can certainly be taught”
  • This paper is developing information on what to teach the computers
the motivation
The Motivation
  • Technology has made background checking easy - and so very ubiquitous
    • Most large companies now do background checks (~80%)
    • Statutes require background checks for many jobs
  • Criminal records are also ubiquitous
      • Lifetime probability of arrest > 0.5
      • 14 million arrests a year
      • 71 million criminal records in state repositories
      • 90% of the records are computerized
  • Criminal records have long memories
      • Many people are handicapped because of an arrest or conviction that happened long ago, and so is “stale”
the problem
The Problem
  • We know from much research that recidivism probability declines with time “clean”
  • At some point in time, a person with a criminal record who remained crime-free is of sufficiently low risk that the “stale” record no longer contains useful information
  • Need a basis for establishing when redemption from the prior mark of crime occurs
  • We still have no measures of redemption time
    • Also, we want to know how it varies with age and crime type at the prior arrest
need empirical approach and estimates
Need empirical approach and estimates
  • Lack of empirical evidence leaves employers to set arbitrary cut-off points
    • 5 or 10 years (nice round numbers)
    • 7 years (Biblical origins?)
    • 15 years (conservative)
    • Forever (usually unreasonable)
  • Employers vary in level of concern
    • In dealing with vulnerable populations (elderly, children)
    • Bank teller
    • National security
    • Construction worker
research approaches
Research Approaches
  • Recidivism studies (e.g., BJS, 1997, 2002)
    • Usually involve short observation period -
    • Most recidivism occurs in 3-5 years
  • Birth Cohort studies (e.g., Kurlychek, Brame, & Bushway, 2006, 2007)
    • Limited sample size and short follow-up
  • Rap sheets:
    • Criminal records from state-level repositories
    • Samples ~100,000
    • Permits rich disaggregation, long-term follow-up
    • But no information about the never-arrested
our data
Our Data
  • Arrest-history records from NY state repository
  • Population of individuals who were arrested for the first time as adults (≥ 16) in 1980 (≈ 88,000)
  • Follow-up time > 25 years
  • We will report on redemption estimates for:
    • Age at first arrest: A1
      • = 16, 18, 20
    • Crime type of first arrest: C1
      • = Robbery, Burglary, Aggravated Assault
analytic issues survival probability
Analytic Issues: Survival Probability
  • Survival probability – S(t)
    • Survive without a subsequent arrest
    • Eventually saturates – only a few have more arrests after a sufficiently long time
    • Provides an estimate of fraction still clean at any t
analytic issues hazard
Analytic Issues: Hazard
  • Conditional probability of a new arrest
    • Conditional on surviving to t
    • Pr{arrest at t|survive to t} = Hazard - h(t)
    • New arrest (C2) here could be for any crime
    • Will later consider concern about specific subsequent crime types (C2s)
two comparison groups
Two Comparison Groups

General Population

  • The employer has a single preferred applicant
  • Turn to some general measure of how common arrest is for people of the same age
    • Redemption occurs when hazard crosses age-crime curve
  • We denote the time to redemption as T*

The Never-Arrested

  • The employer has a pool of job applicants
  • Comparison would be between the risk for those with a prior vs. those without
    • We don’t expect these two hazards to cross
    • Redemption occurs when hazard is “close enough” to those without
  • We denote the time to redemption as T**
the age crime curve
The Age-Crime Curve
  • Very commonly used in criminology
  • Probability of arrest as a function of age
  • For our population, arrested for the first time in NY in 1980, we created a “progressive” age-crime curve for each value of A1
    • For A1 =18, arrests of 19s in 1981, 20s in 1982, etc
t comparison to general pop n of the same age by the age crime curve
T*: Comparison to General Pop’n of the Same Age by the Age-Crime Curve
  • Benchmark: The age-crime curve = risk of arrest for any crime in the general population of the same age
  • T* is at the intersection of h(t) and A-C curve

T* = 7.7 years

h(T*) = .096

values of t by c 1 and a 1 arrest probability at t
Values of T* by C1 and A1(Arrest Probability at T*)
  • Age effect: Younger starters need to remain crime-free longer to achieve redemption
  • Crime type effect: Robbery > AA ~ Burg
using the survival function we estimate fraction reaching t
Using the Survival Function, we estimate fraction reaching T*
  • Age effect: The fraction increases with age
  • Crime type effect: Lowest for young robbers
t comparison to the never arrested
T**: Comparison to the Never-Arrested
  • Benchmark: The risk of arrest for those who have never been arrested
  • The risk of arrest for those with a prior is likely to stay higher than that of those without
  • Estimate T** when h(t) and hna(t) are “close enough”
  • Data to directly estimate hna(t) for the never-arrested is not available from repositories, so must be modeled
approximating the hazard of the never arrested
Approximating the Hazard of the Never-Arrested
  • Population of the never-arrested at age A (Nna(A)):

Nna(A) = Population of New York of age A in 1980

– Σ(First-time arrestees in 1980 for all A1 < A)

  • Hazard of the never-arrested at age A (hna(A)) is calculated as:

# of first-time arrestees for A1 = A

hna(A) =

Nna(A)

determining close enough
Determining “Close Enough”
  • Estimate T** as the time when h(t) becomes “close enough” to hna(t)
    • Simple Intersection method used for T* won’t work if h(t) > hna(t) for all t
    • Introduce risk tolerance, δ
accounting for uncertainty in h t
Accounting for uncertainty in h(t)
  • Use confidence interval (CI)
  • We use bootstrap for the CI instead of
  • We use upper CI to be conservative: T** is the time when the upper CI of h(t) intersects (hna(t)+δ)

±zα/2

p·q/n

T** = 18.3 years

h(T**) = .025

future work
Future Work
  • Robustness test across states
      • Replicate with similar data from other states’ repositories
    • Robustness across sampling years
      • Add 1985, 1990
    • Concern over C2 – the next crime
  • Convictions vs Arrests
    • Anticipate fewer in number
    • Anticipate higher hazards
      • Weeded out the innocent
  • Test for arrests outside New York
    • Need national data from FBI – in process
policy uses of the results
Policy Uses of the Results

Users of Criminal Records:

  • Employers
    • Inform employers of the low relevance of records older than T* or T**
    • Enact statutes to protect employers from “due-diligence liability” claims if last arrest is older than T* or T**
  • Pardon Boards
    • Length of law-abiding period is an important factor in pardons
      • Information about T* and T** provides guidance on how long a law-abiding period is long enough
policy uses of the results cont
Policy Uses of the Results – cont.

Distributors of Criminal Records:

  • Repositories
    • State repositories could choose not to disseminate records older than T* or T**
    • Could seal (or expunge) records older than T* or T**
  • Commercial Vendors
    • If states seal or expunge records older than T* or T** years, commercial vendors should do similarly
contributions
Contributions
  • First use of official state repository records to produce redemption times
  • Strong estimates of redemption times, T* and T**
  • Provides a basis for responsiveness to user criteria in assessing redemption
  • T* or T**can be generated based on the specifications (A1, C1, δ, C2, etc.) set by the users
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