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st: Fwd: Questions

From   Jorge Eduardo Pérez Pérez <[email protected]>
To   <[email protected]>
Subject   st: Fwd: Questions
Date   Sun, 4 Sep 2011 11:52:32 -0400

I think this email was intended to go to Statalist.

Why not add a dummy variable for individuals in rural areas?
Jorge Eduardo Pérez Pérez

---------- Forwarded message ----------
From: Husaina Banu Kenayathulla <[email protected]>
Date: Sun, Sep 4, 2011 at 9:08 AM
Subject: Questions
To: Perez Perez Jorge Eduardo <[email protected]>


I am Husaina. I have some issues with my third analysis for my dissertation:

I run the OLS regression separately for male and female labor force.
My dependent variable is natural log of earnings (which includes
earnings from self-employed and wage work).  My independent variable
is experience, exprience squared, years of scho0ling, ethnicity,
urban/rural, and region.

When I run the model and test for hetereskedasticity,my model violates
the constant variance assumption for residuals. Thus, I use robust std
erros. But, when I check for normality of residuals (using skewness
and kurtosis test in STATA), it also violates the normality
assumption. The q-q plot and p-plot is attached. If my normality
assumption is violated, my estimates is unbiased but all my testing is
invalid.  I think the problem arise because I have large negative
outliers but these cases are valid (individuals in rural areas whose
income is very low). So, I can't drop them. They are also influential.
I am not sure what to do.  Do you have suggestions? Can I write the
results when the normality assumption is violated?

Any advice will be great!


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