MétaCan
Menu
Retour à la cohorte
Enregistrement W7027871107

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

2017· article· en· W7027871107 sur OpenAlexaboutno aff

Notice bibliographique

RevueeYLS (Yale Law School) · 2017
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueOccupational and Professional Licensing Regulation
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésLicensurePrisonQuarter (Canadian coin)Occupational licensingUnemploymentState (computer science)LegislationPension
DOInon disponible

Résumé

récupéré en direct d'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.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,722
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0010,000
Communication savante0,0000,001
Science ouverte0,0010,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0010,005

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,045
Tête enseignante GPT0,283
Écart entre enseignants0,238 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2017
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueeYLS (Yale Law School)Même sujetOccupational and Professional Licensing RegulationTravaux en français237 207