Notice bibliographique
Résumé
Guns.Civilian ownership of firearms is a contentious political issue.We use national measures of firearm licenses and the total number of registered and restricted firearms collected by the Royal Canadian Mounted Police (RCMP) to revisit the relationships guns may have with homicides, suicides, and crime.Using fixed effects models at different geographic levels, we estimate the impacts of guns on deaths, suicides, and firearms-related crimes for urban Canada between 2013 and 2019.We find that increasing other-restricted guns (neither rifles nor handguns) by about 50 (per 100, 000) increases firearms-related deaths by about 0.1 (per 100, 000).Other-restricted guns are also increasing firearms-related deaths whose intent are classified as assaults or self-harms.Licenses are generally unrelated to the different firearms-related deaths.Effects of different firearms types and different licenses on firearms-related crimes are heterogeneous based on the different crimes considered.Many of our coefficient estimates are small in magnitude, suggesting large changes in guns or licenses in a heavily-related, Canadian context, would be necessary to reduce firearms-related deaths. COVID-19 and Labour Markets.In this paper, we study the effect of COVID-19 on the labour market and reported mental health of Canadians.To better understand the effect of the pandemic on the labour market, we build indexes for whether workers: (i) are relatively more exposed to disease, (ii) work in proximity to co-workers, (iii) are essential workers and (iv) can easily work remotely.Our estimates suggest that the impact of COVID-19 was significantly more severe for workers that work in proximity to co-workers and those more exposed to disease who are not in the health sector, while the effects are less severe for essential workers and workers that can work remotely.Last, using the Canadian Perspective Survey Series, we observe that reported mental health is significantly lower among some of the most affected workers such as women and less-educated workers.We also document that those who were absent from work because of COVID-19 are more concerned with meeting their financial obligations and with losing their job than those who continue working outside their home.College Graduates.Despite the rapid increase in the returns to higher education witnessed in the labor market over the past few decades, there has also been a marked increase in the share of individuals who drop out of college or university.iii Several Canadian provincial governments introduced graduate retention tax credits available to students after their graduation.Credit availability was tied to students successfully completing their education with the aim of increasing the local stock of human capital by discouraging cross-province migration.We analyze the efficacy of the graduate retention tax credits within a difference-in-difference framework using confidential data from both administrative tax records and longitudinal surveys.Graduate retention credits were unable to decrease internal migration but were able to reduce the interest graduates paid on their loans.Supervised Consumption Sites.Opioid related deaths are a major issue facing policy makers due to their dramatic increase over the past two decades in both Canada and the United States.While supervised consumption sites (SCSs) are a policy tool for harm reduction in Canada, they may also negatively affect communities via increased crime.Moreover, their relatively infrequent use as a policy instrument leaves them largely unexplored by economists.This study uses variation in site openings between 2014 and 2019 in Toronto to estimate their impact on reported crime.My two-way fixed effects models show quarterly crimes increases in the total number of reported crimes for those neighbourhoods where a site opens.The increases are mainly due to assaults and break and enters which show quarterly increases in levels by 68 and 75 reported crimes (per 100, 000), respectively.My event studies, however, show no changes in reported crimes in the three quarters following site openings.Clear effects of the impacts of SCSs on reported crime are difficult to determine due to various limitations.Additional quantitative and qualitative research would better guide policymakers about the benefits and costs of SCSs.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,022 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,006 | 0,013 |
| Études des sciences et des technologies | 0,002 | 0,005 |
| Communication savante | 0,008 | 0,008 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,004 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,067 | 0,014 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».