2000
DOI: 10.2139/ssrn.252019
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Mobility in the Urban Labor Market: A Panel Data Analysis for Mexico

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Cited by 6 publications
(7 citation statements)
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“…f Such cross-sectional wage regressions are biased when workers' unobserved characteristics affect both their occupational choice and wages. For example, several studies find workers transitioning from the formal sector into the informal sector after spending several years accumulating experience and knowledge in the formal sector (Gong, van Soest, and Villagomez 2004;). Hence, studies that rely on panel data to control for time-invariant unobserved worker characteristics find smaller informal-formal wage differentials.…”
Section: Box 41 Informality and Wage Inequality (Continued)mentioning
confidence: 99%
“…f Such cross-sectional wage regressions are biased when workers' unobserved characteristics affect both their occupational choice and wages. For example, several studies find workers transitioning from the formal sector into the informal sector after spending several years accumulating experience and knowledge in the formal sector (Gong, van Soest, and Villagomez 2004;). Hence, studies that rely on panel data to control for time-invariant unobserved worker characteristics find smaller informal-formal wage differentials.…”
Section: Box 41 Informality and Wage Inequality (Continued)mentioning
confidence: 99%
“…This study attempts to analyze how the mobility between labor markets varies between genders through analysis of the entire South Korean labor market including the unemployed and non-economically active populations. The study also investigates the gender gaps in employment and working conditions under the stratified labor market (Several attempts have been made to model labor mobility using transition matrices constructed from probabilities of actual movements by the same individuals across distinct labor market states [12][13][14][15][16]). Prior studies have focused on the analysis of gender gaps in working conditions and employment or gaps in the working conditions of workers, rather than gender gaps under the stratified structure of the labor market.…”
Section: Introductionmentioning
confidence: 99%
“…Many methods exist for fitting a multinomial logit model with random effects, such as simulated maximum likelihood (Gong et al, 2004), quadrature (Hartzel et al, 2001;Hedeker, 2003), multinomial-Poisson transformation (Lee et al, 2017), and moment-based estimation (Perry, 2017), among others. Computational strategies for the MVP model, on the other hand, are less well studied.…”
Section: Discussionmentioning
confidence: 99%