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

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

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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

- 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

- 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

- Many people are handicapped because of an arrest or conviction that happened long ago, and so is “stale”

- 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

- 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

- 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

- 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

- Age at first arrest: A1

- 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

Survival Prob. by A1

16

18

20

- 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)

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**

- 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

- 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

- Age effect: Younger starters need to remain crime-free longer to achieve redemption
- Crime type effect: Robbery > AA ~ Burg

- Age effect: The fraction increases with age
- Crime type effect: Lowest for young robbers

- 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

- 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)

hna(t)

- 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

- 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

- 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

- Anticipate fewer in number
- Anticipate higher hazards
- Weeded out the innocent

- Need national data from FBI – in process

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

- Length of law-abiding period is an important factor in pardons

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

- 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