Good afternoon,
I'm trying to generate average predicted probabilities of employment for those who are work-limiting disabled and the non-disabled to compare.
Whilst I have looked at numerous resources online and understood them, a paper's methodology I am trying to replicate uses the term 'average predicted probabilities'. I want to ensure the method I have found online is applicable to generate average predicted probabilities.
My dependent variable is: 'emp' (=1 if wages >0, =0 if otherwise) and my variable of interest is 'DISTYPE' ( 1 = work-limiting disabled; 2 = daily activity- limiting disabled; 4= non-disabled).
I was wondering whether any clarity could be provided regarding whether the following approach is valid:
Any help would be greatly appreciated.
I'm trying to generate average predicted probabilities of employment for those who are work-limiting disabled and the non-disabled to compare.
Whilst I have looked at numerous resources online and understood them, a paper's methodology I am trying to replicate uses the term 'average predicted probabilities'. I want to ensure the method I have found online is applicable to generate average predicted probabilities.
My dependent variable is: 'emp' (=1 if wages >0, =0 if otherwise) and my variable of interest is 'DISTYPE' ( 1 = work-limiting disabled; 2 = daily activity- limiting disabled; 4= non-disabled).
I was wondering whether any clarity could be provided regarding whether the following approach is valid:
Code:
probit emp DISTYPE SEX ETH AGES1 URESMC1 HDPCH191 IND1 MARSTA1 HIQUAL81 REGWKR1 SKSBN911 FTPTWK1, nolog
HTML Code:
margins, at(DISTYPE=(1 4)) atmeans vsquish Adjusted predictions Number of obs = 4,830 Model VCE : OIM Expression : Pr(emp), predict() 1._at : DISTYPE = 1 SEX = 1.538302 (mean) ETH = 1.146791 (mean) AGES1 = 10.33685 (mean) URESMC1 = 10.54244 (mean) HDPCH191 = .6463768 (mean) IND1 = 3.478054 (mean) MARSTA1 = 2.133747 (mean) HIQUAL81 = 26.86522 (mean) REGWKR1 = 6.403313 (mean) SKSBN911 = .3763975 (mean) FTPTWK1 = -.9333333 (mean) 2._at : DISTYPE = 4 SEX = 1.538302 (mean) ETH = 1.146791 (mean) AGES1 = 10.33685 (mean) URESMC1 = 10.54244 (mean) HDPCH191 = .6463768 (mean) IND1 = 3.478054 (mean) MARSTA1 = 2.133747 (mean) HIQUAL81 = 26.86522 (mean) REGWKR1 = 6.403313 (mean) SKSBN911 = .3763975 (mean) FTPTWK1 = -.9333333 (mean) ------------------------------------------------------------------------------ | Delta-method | Margin Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- _at | 1 | .1761419 .0237056 7.43 0.000 .1296797 .222604 2 | .2090076 .0206316 10.13 0.000 .1685705 .2494448 ------------------------------------------------------------------------------
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