Understanding the Flows of Goods and Workers: General Equilibrium Implications of Migration Policy
Notice bibliographique
Résumé
This dissertation uses a combination of methods from International Trade and Labor Economics to measure the general equilibrium effects of immigration policy. In Chapter II, I quantify the effects of high-skill migration on the location of highskill industries and multinational activity. To establish empirically the link between multinational enterprises (MNEs) and migration, I assemble a novel firm-level dataset on high-skill visa applications and show that there is a large home-bias effect, demonstrating that foreign MNEs in the US tend to hire more migrant workers from their home countries compared to US firms. To quantify the general equilibrium implications for production and welfare, I build a quantitative model that includes trade, MNE production, and the migration decisions of high-skill workers. The estimated model is used to run two main counterfactual exercises. The first one evaluates the implications of a more restrictive immigration policy in the US in line with recent proposals whose aim is to reduce high-skill immigration. I find that a restriction on immigration to the US that decreases its total workforce by 2.1% would decrease by 3%-4% the US share of production in industries that rely heavily on high-skill migrants, such as IT and High-Tech manufacturing. This decline in US production would coincidently fuel IT sector growth predominantly in India (4.4%) and Canada (1.2%) and would decrease welfare for US workers by 0.98%. In the second counterfactual exercise I increase the barriers to MNE production to calculate the welfare gains generated by MNEs. I show that a model not incorporating migration would overestimate the MNE welfare gains for high-skill workers by 34% and underestimate welfare gains for low-skill workers by 7%. Chapter III studies how US immigration policy and the Internet boom affected not just the US economy, but also led to a tech boom in India. Indian students enrolled in engineering schools to join the rapidly growing US IT industry, via the H-1B visa program. As visas are capped, many could not join the US workforce and remained in India, enabling the growth of an Indian IT sector. The increase in IT productivity allowed India to eventually surpass the US in software exports. Our general equilibrium model captures firm-hiring across various occupations, innovation and technology diffusion, and dynamic worker decisions to choose occupations and fields of major in both countries. Using the estimated model, we find that high-skill migration raised the average welfare of workers in each country. The H-1B program induced Indians to switch to computer science (CS) occupations, increasing the CS workforce and overall IT production in India by 15%, and induced US workers to switch to non-CS occupations, reducing the US native CS workforce by 4.7%. Finally, chapter IV quantifies the gains of high-skill immigration for the US leading to the dot-com boom experienced in the late 1990s. We construct a general equilibrium model of the US economy and calibrate it using data from 1994 to 2001. Built into the model are positive effects high skilled immigrants have on innovation. Counterfactual simulations based on our model suggest that immigration increased the overall welfare of US natives, and raised workers’ incomes by 0.2% to 0.3%. High-skill immigration did, however, have significant distributional consequences.
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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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,003 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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.
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 ».