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Enregistrement W6926080596 · doi:10.20381/ruor-28753

Essays in Environmental Economics and Human Capital

2023· article· en· W6926080596 sur OpenAlexaboutno aff

Notice bibliographique

RevueuO Research (University of Ottawa) · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueMaternal Mental Health During Pregnancy and Postpartum
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésHuman capitalClimate changeNatural experimentEmpirical evidenceShock (circulatory)Consumption (sociology)CognitionPanel dataInstrumental variableLife course approachPregnancy

Résumé

récupéré en direct d'OpenAlex

Chapter 1: This paper offers new causal evidence on how the timing of prenatal temperature shocks affects fetal health, sex ratio at birth, and early-age human capital. Analyzing data on nearly 2 million live births from sub-Saharan African countries and exploiting exogenous spatial and temporal variation in monthly temperature, we uncover three findings. First, we find that a cold temperature shock decreases the likelihood of a male birth. This effect is non-linear, being larger in the first and third trimesters of pregnancy. It is also highly heterogeneous, being larger for older women, higher parity births, and rural areas. Second, combining our empirical estimates with a climate model, we find that the number of fetal deaths caused by climate change will rise from 200 to 400 per 100,000 live births by 2050 throughout sub-Saharan Africa. Third, in contrast to their differential effect on fetal mortality, prenatal temperature shocks increase infant mortality more for females than for males, suggesting that only healthier male fetuses survive to adverse in utero conditions. Our analysis implies that the design of policies to avert the negative impacts of climate change on children should account for stages of fetal development. Chapter 2: Despite its enormous individual and social costs; the fundamental and long- run causes of cognitive aging remain understudied. We study the causal effect of in-utero temperature exposure on cognition during old age. Combining unique data on South African adults between 40 and 99 years of age with geospatial information on historical temperatures, our identification strategy exploits exogenous, within-municipality-of-birth, month-to-month variations in temperature, and controls for contemporaneous weather and location at the time of survey administration. We find that temperature in the first trimester of pregnancy negatively affects the cognitive function score later in life, but temperature in the second and third trimesters has a positive effect on adults cognitive function score. These differing effects result in an overall U-shaped relationship between prenatal exposure to temperature and cognition. This non-linear relationship is robust across measures of memory, reasoning, and information processing speed. Our findings are consistent with the fetal programming theory, which holds that the first trimester of pregnancy is the most crucial window of brain formation. In accordance with this theory, brain development occurring in the first trimester of pregnancy would therefore have the highest vulnerability to external shocks. Heterogeneity analysis reveals that the effect of prenatal temperature on cognition is larger for men, individuals over 75 years of age, and individuals with low social capital. Analyzing causal mechanisms, we find that prenatal temperature affects key determinants of individuals' cognitive reserve. We also find that exposure to drought during the first trimester of pregnancy and reduced sleep during adulthood are other potential channels through which the effects of prenatal exposure to temperature operate. Chapter 3: A large literature seeking to understand the labor market impacts associated with the clean energy transitions broadly finds opposite effects. On the one hand, a net positive impact on the workforce i.e. the new green jobs created in renewable energy sectors will compensate for the jobs lost in fossil-fuel sectors, while on the other hand, the so-called regulated dirty energy sector will reduce the fraction of workers hired. However, empirical and simulation models typically ignore transitional impacts associated with environmental regulations on labour. These relate to how workers adjust over time to environmental regulations, not just the steady state impact that is the focus of prior studies. We evaluate an environmental regulation (Ontario coal-fired electricity generating plants phase-out) regarding its transitional and long-term impacts on employee's outcomes including (i) wages; (ii) unemployment insurance; (iii) sector mobility; and (iv) geographic location. Using the Longitudinal Worker File (LWF) and Postal Codes Conversion File (PCCF) maintained by Statistics Canada, we estimate the labor market impacts of clean energy policy by comparing employees from affected coal plants to a comparable group of employees from non-affected plants. We find that, workers exposed to Ontario phase-out coal policy have earned on average 7000 $ CAD yearly less compared to those who weren't exposed. Our findings are consistent across a set of alternative specifications and robustness checks. Moreover, results from the event study approach suggest that the regulation leads to labor costs with the de- cline of wages just in transition. We provide supportive evidence on large labor costs due to environmental regulation policy and shed lights on the importance of reforms and training programs to support workers during the transition.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: Théorique ou conceptuel
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,047

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,002
Études des sciences et des technologies0,0010,003
Communication savante0,0020,003
Science ouverte0,0010,001
Intégrité de la recherche0,0020,002
Charge utile insuffisante (le modèle a refusé de juger)0,0140,001

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 Opus0,046
Tête enseignante GPT0,312
Écart entre enseignants0,266 · 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; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
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
Publié2023
Routes d'admission1
Résumé présentoui

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