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

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

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

  2. 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

  3. 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”

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. Survival Prob. by A1 16 18 20

  10. Survival Prob. by C1

  11. 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)

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

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

  14. 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**

  15. 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

  16. 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

  17. 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

  18. Using the Survival Function, we estimate fraction reaching T* • Age effect: The fraction increases with age • Crime type effect: Lowest for young robbers

  19. 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

  20. 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)

  21. Hazard of the never-arrested: hna(t) hna(t)

  22. Compare h(t) to hna(t)

  23. 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

  24. 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

  25. Tradeoff of Risk Tolerance (δ)and T**

  26. 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

  27. 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

  28. 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

  29. 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

  30. Questions, Challenges, Suggestions?