Study led by long-time SEARCH Member asks: Can someone with a criminal record be considered 'redeemed' by a potential employer?

Dr. Al Blumstein

A study led by award-winning criminologist and long-time SEARCH Member Dr. Al Blumstein delves into one of the most persistent and perplexing questions connected to employment-related criminal record checks: Can someone with a criminal record who refrains from committing further crimes reduce his or her hiring risk to that associated with individuals who were never arrested?

The question is particularly relevant in modern U.S. society when more than 80 percent of employers conduct some sort of criminal record background check on potential employees, according to the Society for Human Resource Management.

The study, which is still in progress, is based on a statistical concept called the "hazard rate," which is the probability that someone who has stayed clean will be arrested.

Findings reached so far indicate that the hazard rate of someone with a criminal record is never the same as for those who were never arrested, but the longer an individual stays clean, it is reasonable to expect that the hazard rate will become acceptable enough for employers when hiring for certain professions.

The June 2009 issue of the NIJ Journal, published by the U.S. Department of Justice's National Institute of Justice (NIJ), reported on the study in an article

co-authored by Blumstein, the J. Erik Jonsson University Professor of Urban Systems and Operations Research at the Heinz College of Carnegie Mellon University, and Kiminori Nakamura, a doctoral student at the university.

The Journal article notes that the NIJ funded the study to determine an "actuarial" estimate as to when an individual with a criminal record is at no greater risk of committing another crime than is another individual of the same age.

The study, which utilizes the criminal history records of 88,000 New York state offenders arrested for the first time in 1980, finds that the hazard rate declines the longer an offender stays clean.

A more in-depth discussion of the study findings and research methods appeared in the May 2009 issue of Criminology. For more information on that article, see http://www3.interscience.wiley.com/journal/117996443/home.

The study was featured in a USA Today news story on July 1, 2009.

Blumstein, one of the nation's preeminent criminologists, was awarded the Stockholm Prize in Criminology - considered the field's Nobel Prize - in 2006. His work has covered many aspects of criminal justice, including crime measurement, criminal careers, sentencing, deterrence and incapacitation, prison populations, demographic trends, juvenile violence and drug-enforcement policy.

Blumstein has been at At-Large Member of the SEARCH Membership Group since 1991, and is also a past governor-appointed State Member representing Pennsylvania. He was also a member of the ground-breaking Bureau of Justice Statistics/SEARCH National Task Force on the Criminal Record Backgrounding of America, whose report

was one of the first to address relevancy criteria for criminal records used for background checks.

What is NIEM?
NIEM, the National Information Exchange Model, is a partnership of the U.S. Department of Justice and the Department of Homeland Security. It is designed to develop, disseminate and support enterprise-wide information exchange standards and processes that can enable jurisdictions to effectively share critical information in emergency situations, as well as support the day-to-day operations of agencies throughout the nation.

NIEM enables information sharing, focusing on information exchanged among organizations as part of their current or intended business practices. The NIEM exchange development methodology results in a common semantic understanding among participating organizations and data formatted in a semantically consistent manner. NIEM will standardize content (actual data exchange standards), provide tools, and managed processes.

NIEM builds on the demonstrated success of the Global Justice XML Data Model. Stakeholders from relevant communities work together to define critical exchanges, leveraging the successful work of the GJXDM.