Redemption in an Era of Widespread Background Checking
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Redemption in an Era of Widespread Background Checking Alfred Blumstein, Kiminori Nakamura Heinz College - Carnegie Mellon Univ. March 27, 2009 - PowerPoint PPT Presentation

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 Checking Alfred Blumstein, Kiminori Nakamura Heinz College - Carnegie Mellon Univ. March 27, 2009

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Redemption in an Era of Widespread Background CheckingAlfred Blumstein, Kiminori NakamuraHeinz College - Carnegie Mellon Univ.March 27, 2009


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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


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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”


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    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


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    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


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    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


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    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


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    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


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    Survival Prob. by A1

    16

    18

    20


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    Survival Prob. by C1


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    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)


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    Hazard h(t) = Cond. Prob. of a New Arrest(C1 = Burglary, 3 A1s)


    Hazard h t a 1 18 3 c 1 s l.jpg

    Hazard h(t) (A1 =18, 3 C1s)


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    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**


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    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


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    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


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    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


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    Using the Survival Function, we estimate fraction reaching T*

    • Age effect: The fraction increases with age

    • Crime type effect: Lowest for young robbers


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    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


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    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)


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    Hazard of the never-arrested: hna(t)

    hna(t)


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    Compare h(t) to hna(t)


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    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


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    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


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    Tradeoff of Risk Tolerance (δ)and T**


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    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


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    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


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    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


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    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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    Questions, Challenges, Suggestions?


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