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Re: st: RE: Mincer wage function as a log function - how can stata calculate the percent values


From   bumbuminc <bumbuminc@googlemail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: RE: Mincer wage function as a log function - how can stata calculate the percent values
Date   Wed, 29 Oct 2008 19:32:08 +0100

Thanks you very much for your help.
And yes you are right, i'm only used to -regress- in this context. I
will try to get more about the glm command.
Boris

2008/10/29, Maarten buis <maartenbuis@yahoo.co.uk>:
> --- Maarten buis <maartenbuis@yahoo.co.uk> wrote:
>> Another way of thinking about the difference between these two models
>> is that you are modeling the geometric mean of wage, while I am
>> modelling the arithmatic mean of wage.
>
> Below you can see that the two models give largely similar results. So,
> which one should you choose? There is a trade-off here:
>
> On the one hand people in your discipline are apperently used to
> -regress- in this context, but it is a pain to explain that the
> coefficients you report are ratios of geometric means rather than
> ratios of means. There is nothing wrong with geometric means and their
> ratios other than that a lot of people aren't used to them, and start
> getting worried about your analysis when there is no need to be
> worried.
>
> On the other hand, using -glm- will allow you to present your results
> in terms of ratios of means, but apperently people in your discipline
> aren't used to using -glm- in this contex. There is nothing wrong with
> using -glm- in this context other than that a lot of people aren't used
> to it, and start getting worried about your analysis when there is no
> need to be worried.
>
> Maybe the easiest way out is to add a paragraph to your methods section
> where you discuss the difference between the two models. Say that you
> choose one of these and say that you have also used the other model but
> that the results where very similar.
>
> *------------------------- begin example --------------------
> sysuse nlsw88, clear
> gen ttl_exp2 = ttl_exp^2
> gen wage_log = ln(wage)
> gen cons = 1
> reg wage_log ttl_exp ttl_exp2 grade cons, eform("exp(b)") nocons
> glm wage ttl_exp ttl_exp2 grade cons, eform link(log) nocons
> *-------------------------- end example ----------------------
>
> -- Maarten
>
> -----------------------------------------
> Maarten L. Buis
> Department of Social Research Methodology
> Vrije Universiteit Amsterdam
> Boelelaan 1081
> 1081 HV Amsterdam
> The Netherlands
>
> visiting address:
> Buitenveldertselaan 3 (Metropolitan), room N515
>
> +31 20 5986715
>
> http://home.fsw.vu.nl/m.buis/
> -----------------------------------------
>
>
>
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