RE: st: Interpreting Coefficient of a count independent variable

[Cameron McIntosh <cnm100@hotmail.com>]

A few additional observations from the peanut gallery... I might also suggest thinking about instrumenting the smoking predictor, as it's certainly endogenous:

Lochner, L., & Moretti, E. (May 2011). Estimating and Testing Non-Linear Models Using Instrumental Variables. NBER Working Paper No. 17039.  http://emlab.berkeley.edu/~moretti/nonlinearities.pdf

Hausman, J.A. (1983). Specification and estimation of simultaneous equation models. In Z. Griliches & Intriligator, M.D. (Ed.), Handbook  of  Econometrics (vol I, pp. 391-448). North-Holland Publishing Company.http://pria.uran.ru/ebooks/Unsorted/07%20Hausman%20-%20Specification%20and%20Estimation%20of%20Simultaneous%20Equation%20Models.pdf

Abrevaya, J., Hausman, J.A., & Khan, S. (2010). Testing for Causal Effects in a Generalized Regression Model With Endogenous Regressors. Econometrica, 78(6), 2043–2061.

Baum, C.F., Schaffer, M.E., & Stillman, S. (2007). Enhanced routines for instrumental variables/generalized method of moments estimation and testing. The Stata Journal, 7(4), 465-506.http://www.stata-journal.com/sjpdf.html?articlenum=st0030_3

Bascle, G. (2008). Controlling for endogeneity with instrumental variables in strategic management research. Strategic Organization, 6(3), 285-327. 

Gennetian, L. A., Magnuson, K., & Morris, P. A. (2008). From statistical associations to causation: What developmentalists can learn from instrumental variables techniques coupled with experimental data. Developmental Psychology, 44(2), 381-394. 

Larcker, D. F., & Rusticus, T. O. (2010). On the use of instrumental variables in accounting research. Journal of Accounting and Economics, 49(3), 186-205. 

Sovey, A.J., & Green, D.P. (2011). Instrumental Variables Estimation in Political Science: A Readers' Guide. American Journal of Political Science, 55(1), 188-200.

Also consider that the smoking-income relations may be reciprocal, as you seem to note (in which case I think you would need to instrument both smoking and income):

Paxton, P., Hipp, J.R., & Marquart-Pyatt, S. (2011). Nonrecursive Models: Endogeneity, Reciprocal Relationships, and Feedback Loops. Quantitative Applications in the Social Sciences, Volume 168. Thousand Oaks, CA: Sage.

Wright, S. (1960). The treatment of reciprocal interactions, with or without lag. Biometrics, 16, 423-445.  http://www.ssc.wisc.edu/soc/class/soc952/Wright/Wright_The%20Treatment%20of%20Reciprocal%20Interaction,%20with%20or%20without%20Lag,%20in%20Path%20Analysis.pdf	 Wong, C-S., & Law, 
K.S. (1999). Testing reciprocal relations by nonrecursive structural equation models using cross-sectional data. Organizational Research Methods, 2(1), 69-87.

Cam

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