Essays on Labor, Immigrants, and Inputs
Notice bibliographique
Résumé
In my dissertation, I concentrate on the broad topic of trade and its effects on domestic markets, employing both empirical analysis and theoretical frameworks. This study delves into two primary facets of trade's influence: labor and production. While the impact of trade with China on labor markets on manufacturing employment is well studied, I contribute to existing literature by investigating its effects on STEM (Science, Technology, Engineering, and Math) occupations. Despite considerable attention to STEM education, there is little research on these specific occupations. I extend this analysis in my second chapter by examining how immigrants are affected differently from U.S.-native workers within these sectors. Additionally, I analyze the repercussions of tariff adjustments on input goods and their impact on final goods production within a country. By highlighting these indirect effects, my research advances our understanding of trade dynamics, by examining often overlooked segments of the economy. My first chapter studies the effect of Chinese import penetration on U.S. labor. Recently, several papers have established that imports from China have been detrimental to manufacturing employment. I extend the analysis to non-manufacturing occupations, paying special attention to STEM and non-STEM designations. Following the literature, I exploit regional variation of local labor markets and measure the varying levels of import exposure. Results from this paper show positive labor outcomes from imports from China: while we do see that manufacturing employment drops, we find significant positive effects on the non-manufacturing sectors. Chinese imports increased STEM wages, which are mostly non-manufacturing. These effects are long-lasting, still present 13 years after China's accession to the WTO. Non-STEM, non-manufacturing wages also increase. This suggests that Chinese imports produced a reallocation of resources away from manufacturing sectors and into non-manufacturing ones. In my second chapter my co-author, Dr. Bikramjit Saha, and I investigate how increased trade with China impacted immigrant and native-born workers in the US. Our analysis covers both STEM and non-STEM occupations across manufacturing and nonmanufacturing industries by building on the work done in my first chapter. We examine responses among both immigrant and US-born workers to import penetration at the local labor market level. We uncover the trade-induced gains and losses for US-born STEM workers resulting from changes in the movement of STEM immigrant workers across sectors and regions impacted by imports from China. Furthermore, the shift of non-STEM native-born workers from manufacturing to nonmanufacturing industries could potentially boost the wages of foreign-born workers. Understanding these dynamics has implications for how to shape immigration policy in response to trade agreements and broader sectoral shifts in labor markets. My last chapter starts to examine how changes in the tariff rate on Canadian input goods impact Canadian final goods. To conduct this analysis, I utilize data from the Canada–United States Free Trade Agreement. In order to address this research question, I adopt an empirical approach and I construct a measure of an input tariff rate. This measure quantifies the changes in the tariff rate of the inputs used by the final good. The dataset used in this study consists of data on Canadian industries and the changes tariff rate from 1991 to 2001. The findings indicate that a reduction in the tariff rate of inputs for Canadian industries has a negative impact on the sales growth rate of final good producers, regardless of the firm's size.
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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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,003 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,003 |
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 ».