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Record W7027871107

Do Occupational Licenses Exacerbate the Prison-to-Poverty Pipeline?

2017· article· en· W7027871107 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicOccupational and Professional Licensing Regulation
Canadian institutionsnot available
Fundersnot available
KeywordsLicensurePrisonQuarter (Canadian coin)Occupational licensingUnemploymentState (computer science)LegislationPension
DOInot available

Abstract

fetched live from OpenAlex

After she had served a year in prison, returned to school, and earned her nursing degree, the state of Illinois denied Lisa Creason the chance to become a licensed nurse because of her prior conviction. In August 2016, however, Creason stood by Illinois Governor Bruce Rauner as he signed a new law that would allow her, and other former inmates, a second chance to earn a living as a nurse. Nevertheless, despite Illinois’ progress in this area, workers around the country continue to face barriers to occupational licensure due to similar regulatory barriers. Specific regulations vary from state to state, but more than a quarter of workers in the United States—in occupations ranging from florist to entrepreneurs—must obtain occupational licenses to do their jobs. But many of the estimated 650,000 ex-offenders released from prisons in the United States each year do not have the opportunity to become licensed at all. In a recent report, Emily Fetsch, a research assistant at the Ewing Marion Kauffman Foundation, explores how occupational licensing requirements diminish the economic potential of the formerly incarcerated. Removing barriers in licensing would reduce unemployment by former inmates, curb recidivism, and boost the economy, Fetsch argues. After summarizing research on the correlation between employment and recidivism, she recommends policy reforms for state regulators to improve the disproportionate effects of occupational licensing on the formerly incarcerated. Festch explains that the highest rate of recidivism typically occurs within the first three years after release. Almost 50 percent of ex-offenders return to prison within those three years. Yet, the rate of returning to prison for an individual who finds work within the first year of release drops to 16 percent. In other words, for the recently released ex-offender, a job can make the difference between a successful re-entry into society or landing back behind bars. Fetsch also posits that the very licenses meant to protect the public might actually lead to an increase in crime—all while disenfranchising thousands of people who could contribute to the national and state economies. Indeed, a study cited by Festch found that, between 1997 and 2007, the states with the heaviest occupational licensing burdens had an average increase of over nine percent in the rate at which prisoners would commit a new crime within three years of release. In the same time period, the states that had the lowest barriers for former inmates to gain their occupational licensing saw the average rate of recidivism decline by nearly 2.5 percent. Given these findings, Fetsch offers several reform proposals that might curb recidivism while preserving occupational licensing’s core functions. First, Fetsch proposes that occupational licensing should only exclude people with criminal records from jobs when their convictions are recent, relevant to the occupation, and pose a public safety threat. Currently, 29 states do not consider the relevance of the conviction records for license applicants. Twenty-one states do require a “direct” relationship between the occupational license sought and the applicant’s criminal history in order to justify the denial of license. However, the lack of uniformity between state regulations only adds to the stresses and challenges individuals reentering society must overcome. Additionally, Fetsch stresses that labor regulations should offer former inmates more opportunities to secure certificates of restoration or rehabilitation that would later lead to qualifying for occupational licenses. Only nine states provide pathways for ex-offenders to have a second chance of obtaining a license. Because those leaving prison typically have lower levels of education and workplace skills, the lack of opportunities to gain new work experience or a license for those experiences greatly hinders those who hope to redeem or enhance their economic potential. Fetsch further notes that, currently, 37 states allow occupational licensing agencies to prevent individuals who have been arrested but never convicted from obtaining a license. Only 10 states prohibit inquiries and considerations of arrests that do not lead to convictions to be relevant to the job. Consequently, she recommends that states should ensure that only individuals who have actually been convicted of a crime should be considered for disqualification of an occupational license. Ultimately, Fetsch urges regulators to consider broader reform in occupational licensing policy altogether, including the consideration of whether there is a justifiable need for the licenses at all. She argues that when public safety is not threatened, certification or another, less stringent form of regulation could replace licensing. An earlier Obama Administration White House report asserts that occupational licensing regulation has led to the loss of millions of jobs without accomplishing meaningful public health and safety goals. Previous papers have posited that these occupational licenses contribute to broader income inequality and reduce social mobility. Now, with research showing the importance of employment as a means to counter recidivism, Fetsch concludes that policymakers need to ensure that occupational licenses enable productivity and opportunity, rather than impose ongoing punishment for those seeking to re-enter the workforce after serving time. Occupational licensing policy may now serve as one of the major keys to economic and criminal justice reform in the United States. The Kauffman Foundation published Fetsch’s report online in November 2016.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.722
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.045
GPT teacher head0.283
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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