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From |
Maarten Buis <maartenlbuis@gmail.com> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
Re: st: Interpreting Coefficient of a count independent variable |

Date |
Mon, 16 Apr 2012 10:18:05 +0200 |

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/

**Follow-Ups**:**Re: st: Interpreting Coefficient of a count independent variable***From:*Muhammad Anees <anees@aneconomist.com>

**References**:**st: Interpreting Coefficient of a count independent variable***From:*Muhammad Anees <anees@aneconomist.com>

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