EVIDENCE FROM MEXICO: HEALTHY MIGRANT HYPOTHESIS, WEIGHT AND WAGE DYNAMICS, A BAYESIAN APPROACH TO QUANTILE REGRESSION

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dc.contributor.advisorHalliday, Timothy
dc.contributor.authorHotchandani, Hazel
dc.contributor.departmentEconomics
dc.date.accessioned2025-02-20T22:37:02Z
dc.date.available2025-02-20T22:37:02Z
dc.date.issued2024
dc.description.degreePh.D.
dc.identifier.urihttps://hdl.handle.net/10125/110233
dc.subjectEconomics
dc.titleEVIDENCE FROM MEXICO: HEALTHY MIGRANT HYPOTHESIS, WEIGHT AND WAGE DYNAMICS, A BAYESIAN APPROACH TO QUANTILE REGRESSION
dc.typeThesis
dcterms.abstractChapter one utilizes data from the Mexican Family Life Survey (MxFLS) to conduct an empirical analysis within the framework of the Roy Model, examining whether Mexican emigrants to the United States are positively selected in terms of health. Understanding whether emigrants are favorably self-selected or represent a random sample of Mexico’s population can inform policymakers in shaping labor market and immigration policies that impact both native and non-native workers. By comparing the pre-migration Body Mass Index (BMI) of individuals who migrated to the United States with those who remained in Mexico, this study directly tests for selection. The results indicate that younger, single, and non-overweight individuals are more likely to migrate, as they are better positioned to succeed in the United States labor market and benefit from greater wage differentials. Conversely, the effect of health on migration diminishes for married individuals, with higher capital and social investments in Mexico, coupled with greater migration costs, reducing their incentives to migrate. Chapter two explores the dynamics of weight and wages within Mexico using all three waves of MxFLS data, analyzing how the weight-wage relationship varies across gender, community size, and occupation type. The findings reveal a wage premium for male workers in rural areas and blue-collar jobs, while the weight-wage relationship for females is more complex and mixed. Chapter three introduces a parametric modeling approach based on the asymmetric Laplace distribution of errors and demonstrates the application of Bayesian inference to quantile regression. Using non-informative improper priors with a uniform distribution, values are simulated from the posterior distributions via a random-walk Metropolis-Hastings (MH) algorithm. Applying this methodology to pooled MxFLS data uncovers important features of the wage returns to BMI, particularly for male workers in Mexico, that would otherwise be hidden by traditional mean regression techniques.
dcterms.extent103 pages
dcterms.languageen
dcterms.publisherUniversity of Hawai'i at Manoa
dcterms.rightsAll UHM dissertations and theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission from the copyright owner.
dcterms.typeText
local.identifier.alturihttp://dissertations.umi.com/hawii:12421

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