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Hello Economists, I have some questions about the gender pay gap: Some economists (1) state that occupation accounts for ~30% of the gender wage gap and industry accounts for another ~20% of the wage...

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Hello Economists, I have some questions about the gender pay gap:

Some economists (1) state that occupation accounts for ~30% of the gender wage gap and industry accounts for another ~20% of the wage gap. What are the ingredients necessary for calculating the impact of occupation and industry variables on the gender wage gap? For example, is the amount of segregation needed? Or reference years? 2) Would you please explain the logic behind the occupational and industry variables; are occupation and industry endogenous variables? 3) Would you please explain the steps and how to calculate these estimates with a concrete example? 4) Since occupations are subsets of industries, is there double counting? How do you parse occupations and industry? Thank you very much for your reply, Charles

(1) See attached files

Answered Same Day Dec 25, 2021

Solution

Robert answered on Dec 25 2021
114 Votes
1) What are the ingredients necessary for calculating the impact of occupation and industry variables
on the gender wage gap? For example, is the amount of segregation needed? Or reference years?
We need a panel data that provides data for the following variables –
a. Male and female hourly wage data
. Control variables – This includes data on Race – Black and White, Education – School, College and
Advanced degree, Experience (in years). Some of these variables (specifically education) constitute the
human capital (Kahn and Blau, 2006). These control variables are required to segregate the effect of
confounding variables.
c. Sectoral employment data – The data set should also provide information on the hiring of the workers
in particular sectors. This data would capture the occupation and different industrial sectors where
workers are hired.
Some degree of segregation is needed to account for variables that could confound our results. For e.g.
The wages are a direct function of experience. It is expected that higher experience should result in
higher wage. So, the data set should be able to indicate whether the number of...
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