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Re: st: Interpreting Coefficient of a count independent variable


From   Muhammad Anees <anees@aneconomist.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Interpreting Coefficient of a count independent variable
Date   Mon, 16 Apr 2012 15:03:43 +0500

Thanks Maarten,

You have given me the right direction. It works. Thanks again

Anees

On Mon, Apr 16, 2012 at 1:18 PM, Maarten Buis <maartenlbuis@gmail.com> wrote:
> On Mon, Apr 16, 2012 at 7:24 AM, Muhammad Anees wrote:
>> I am trying to figure out how smoking affects earnings contradictory
>> to the normal routine of studying the affects of earnings on smoking
>> in my previous studies.
>> I have run the following result and I want to know the possible
>> interpretational issues with a count independent variables.
>
> None, other than the usual caveat that the effects may not be linear.
> I typically start with  a -glm- with the log link for a variable like
> income. I would than try adding an extra indicator variable for
> non-smokers as these are in all likelihood very different the rest of
> the curve. After that I would try various linear splines to capture
> any additional non-linearity. Consider the example below:
>
> *----------------- begin example -------------------
> sysuse nlsw88, clear
>
> gen c_hours = hours - 40
> mkspline t40 0 mt40 = c_hours
> gen byte normal = hours == 40 if hours < .
>
> gen marst = !never_married + 2*married
> label var marst "marital status"
> label define marst 0 "never married"    ///
>                   1 "widowed/divorced" ///
>                   2 "married"
> label value marst marst
>
> gen c_grade = grade - 12
>
> glm wage t40 mt40 normal c_grade i.race i.marst ///
>    c.ttl_exp##c.ttl_exp c.tenure##c.tenure     ///
>    , link(log) eform
>
> preserve
> replace c_grade = 0
> replace race = 1
> replace marst = 0
> replace ttl_exp = 0
> replace tenure = 0
>
> bys hours : keep if _n == 1
>
> predict yhat, mu
>
> twoway line yhat hours
> restore
> *------------------ end example --------------------
>
> I would interpret these results as follows: If someone works less than
> 40 hours a week her hourly wage will increase by (1-1.02)*100%=2% for
> every hour extra worked. There is a sudden (but non-significant) drop
> in this curve at 40 hours of (1-.98)*100%= -2%. After 40 hours a week
> the hourly wage will drop a non-significant 1% per hour extra worked.
>
> This story is also shown in the graph. I guess that it should also be
> possible to also do this with -margins- and -marginsplot-, but I was
> unsuccessful, so I fell back to old and trusted -predict- solution.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
> *
> *   For searches and help try:
> *   http://www.stata.com/help.cgi?search
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/



-- 

Best
---------------------------
Muhammad Anees
Assistant Professor/Programme Coordinator
COMSATS Institute of Information Technology
Attock 43600, Pakistan
http://www.aneconomist.com

*
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