I am conducting a study on social mobility in Brazil using a survey of 2014. The study uses for social origin a typology of social class based on employment. The social destination is analyzed in terms of income from all sources. An original question of the research is: is the effect of social origin lower among the more educated individuals? Would higher education equalize or neutralize the effect of social origin, whether these advantages or disadvantages of origin? To answer this question I have estimated interactive effects between the origin and education of the children from 27 to 66 years old.
I performed semi-elasticity estimates using class as a categorical variable and income as a continuous variable. However, I question whether make sense to rephrase the terms of this original research question for the purpose of using elasticity. I take into account that the effects of interactions are symmetrical, that is, the effect of education by class origin is equivalent to the effect of the class origin by education. In view of this symmetry of interactive effects, the original question could be formulated in this equivalent way: Is the effect of education lower in all class origins (advantageous and disadvantageous) among more educated individuals?
The results with interactive effects were estimated using the margins (option: eyex) in the form of elasticity between education and children's income conditional on social origin (i.class##c.education ). The results were stratified by 8, 10 and 15 year of complete education. This was the estimated model and the margins command:
Code:
svy, subpop(id66): glm income i.class##c.education i.cohort i.state [where he lived at 15 years] i.sex i.collor, family(gamma) link(log) margins tipbr5_d, eyex (education) at(education = (8 11 15))
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Expression : Predicted mean income, predict() ey/ex w.r.t. : education 1._at : education = 8 2._at : education = 11 3._at : education = 15 ----------------------------------------------------------------------------------- | Delta-method | ey/ex Std. Err. z P>|z| [95% Conf. Interval] ------------------+---------------------------------------------------------------- education | _at#class | 1#1. top | 1.304128 .0498687 26.15 0.000 1.206387 1.401869 1#2. skilled | 1.158187 .0617412 18.76 0.000 1.037177 1.279198 1#3. few_assets | .8176008 .0213492 38.30 0.000 .7757571 .8594446 1#4. worker | .866426 .0257348 33.67 0.000 .8159866 .9168653 1#5. destitute | .7345084 .0242293 30.31 0.000 .6870198 .781997 2#1. top | 1.793176 .0685695 26.15 0.000 1.658783 1.92757 2#2. skilled | 1.592508 .0848941 18.76 0.000 1.426118 1.758897 2#3. few_assets | 1.124201 .0293552 38.30 0.000 1.066666 1.181736 2#4. worker | 1.191336 .0353854 33.67 0.000 1.121982 1.26069 2#5. destitute | 1.009949 .0333153 30.31 0.000 .9446522 1.075246 3#1. top | 2.44524 .0935038 26.15 0.000 2.261976 2.628504 3#2. skilled | 2.171601 .1157647 18.76 0.000 1.944706 2.398496 3#3. few_assets | 1.533002 .0400298 38.30 0.000 1.454545 1.611459 3#4. worker | 1.624549 .0482528 33.67 0.000 1.529975 1.719122 3#5. destitute | 1.377203 .04543 30.31 0.000 1.288162 1.466244 -----------------------------------------------------------------------------------
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