Immigrants and Ethnic Differences

Merce and mathematicsphysics the low earnings elds

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Unformatted text preview: the analysis. merce, and Mathematics/Physics. The low earnings fields are (from the bottom up) Arts and Humanities, Agricultural/Biological Sciences, and the (Other) Social Sciences. Education and Economics have more mixed records, but generally lie in the middle of the earning distribution. The consistency of these patterns is interesting— although it should at the same time be recognised that the estimated effects do in fact vary to a significant degree across surveys. For example, the coefficient for Engineering and Computer Science graduates ranges from a low of 0.09 (1988) to a high of 0.21 (1984 and 1992). The patterns for female graduates are relatively similar to those of male graduates, but show some moderate differences in the details. Thus, while the high earnings fields are the same four as for men, the relative standings of these is somewhat different, with Engineering and Computer Science graduates generally performing even more strongly, Mathematics/Physics graduates also typically doing a little better, and Other Health graduates not doing as well as in the male case. (In this context, recall, from Table 1, the very different distributions across these fields between men and women.) In short, these findings indicate that cross-field earnings patterns are relatively similar for men and women, rather than varying with ‘femaleness’ of the discipline. 3.3. Adjusted earnings patterns 3.3.1. The standard models Table 4 presents the discipline coefficients generated by the regression models which include the control variables representing labour market experience, personal characteristics, and so on, described above.8 The effects shown here are, therefore, net of any influences which operate through those other variables (e.g. certain fields lead to greater full-time employment opportunities). The general ordering of the field effects is the same as for unadjusted earnings: once again, the high earnings fields are Other Health, Engineering and Computer Science, Mathematics/Physics, and Commerce; the low earnings fields are Arts and Humanities, Other Social 8 The full model results are reported in Finnie (1998). See Daymont and Andrisani (1984) for a discussion of the potential endogeneity of certain regressors in such models. Fortin – Econ 560 Lecture 4B o Black et al. uses the 1993 NGS and finds that among men and women matched on observables, who speak English at home, between 44 and 73 percent of the gender wage gaps are accounted for by such pre-market factors as highest degree and major. o When they restrict attention further to women who have “high labor force attachment” (i.e., work experience that is similar to male comparables) we account for 54 to 99 percent of gender wage gaps. After education, the accumulation of work experience is the most important human capital factor that explains the distribution of earnings across workers. The Mincer-Polachek hypothesis argues that the discontinuity of women’s labour fo...
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This document was uploaded on 02/26/2014 for the course ECON 560 at UBC.

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