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Essays in the Application of Quasi-Experimental Methods to Linked Administrative Data

2024· dissertation· en· W7131748018 sur OpenAlex
Lisa Ann Meehan

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Notice bibliographique

RevueTuwhera (Auckland University of Technology) · 2024
Typedissertation
Langueen
DomaineEconomics, Econometrics and Finance
ThématiquePolitics, Economics, and Education Policy
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésImmigrationCitizenshipGovernment (linguistics)Order (exchange)WorkforceNatural experimentRelevance (law)Public policy
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Each of the three papers in this thesis applies quasi-experimental methods to administrative data in order to make causal inferences about the effects of a particular event or policy. Paper 1: Does citizenship improve migrant parents’ integration and children’s outcomes?: Evidence from a natural experiment Does obtaining citizenship improve the integration of immigrants into the host country’s society? Does it improve the outcomes of immigrants’ children? Attempts to answer these questions are thwarted by potential self-selection: those who are more motivated to integrate are also more likely to naturalise. In order to make causal inferences, this paper exploits a natural experiment of New Zealand’s removal of birthright citizenship. It finds that this policy change had no effect on family outmigration behaviour, parents’ fertility and labour market outcomes, nor on children’s health outcomes. These results contrast to existing evidence on Germany’s introduction of birthright citizenship, which was found to have positive effects on parents’ and children’s integration outcomes. These different findings may reflect differences in the two countries’ broader immigration policy settings, and the resulting differences in the characteristics of the countries’ migrants. These differences also highlight that the New Zealand case is of potentially more relevance to Canada and the US, the only two western countries that retain unrestricted birthright citizenship, given greater similarities in these countries’ immigration policy settings. Paper 2: Workforce vaccine mandates: The effect on vaccine uptake and healthcare workers’ labour market outcomes As part of its COVID-19 policy response, the New Zealand government implemented vaccination mandates as a condition of ongoing employment for certain workers. This paper examines the effect of these mandates on vaccination uptake among mandated healthcare, education and corrections workers and on healthcare workers’ labour market outcomes. This is enabled by New Zealand’s linked population-wide administrative data, which includes a comprehensive national vaccination register linked to tax records to identify employment outcomes. Overall, the results suggest that in the context of already-high vaccination rates, workforce vaccine mandates provided limited benefit in terms of increasing vaccination rates among mandated workers. Moreover, they negatively impacted healthcare workers’ labour market outcomes, which may have had wider consequences in terms of exacerbating existing health workforce skills shortages. Paper 3: The effect of a minor health shock on labour market outcomes: The case of concussions The literature on health shocks finds that minor injuries have only short-term labour market impacts. However, mild traumatic brain injuries (mTBIs, commonly referred to as concussions) may be different as the medical literature highlights that they can have longer-term health and cognitive effects. Moreover, TBIs are one of the most common causes of disability globally, with the vast majority being mild. Thus, it is important to understand the impact of mTBIs on labour market outcomes. We use administrative data on all medically-diagnosed mTBIs in New Zealand linked to monthly tax records to examine the labour market effects of a mTBI. We use a comparison group of those who suffer a mTBI at a later date to overcome potential endogeneity issues, and employ a doubly-robust difference-in-differences method. We find that suffering a mTBI has negative labour market effects. Rather than dissipating over time, these negative effects grow, representing a decrease in employment of 20 percentage points and earning losses of about a third after 48 months. Our results highlight the need for timely diagnosis and treatment to mitigate the effect of mTBIs to reduce economic and social costs.

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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)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
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 enseignants0,646
Score d'incertitude au seuil0,878

Scores Codex et Gemma par catégorie

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

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 Opus0,070
Tête enseignante GPT0,351
Écart entre enseignants0,281 · 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