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From |
Maarten buis <maartenbuis@yahoo.co.uk> |

To |
statalist@hsphsun2.harvard.edu |

Subject |
RE: st: automatically adjusting for bias in antilog transformation? |

Date |
Sun, 3 Oct 2010 14:03:03 +0100 (BST) |

--- On Fri, 1/10/10, Tatyana Deryugina wrote: > the effect on Y (not its log) of x_1 and x_2 > (both x_1 and x_2 are dummy variables). When there is a log involved, you are best of thinking of effects in terms of ratios rather than differences. That is after all what that log does, it tells the model that you think that the expected value of y changes by a fixed ratio for a unit change in x, regardles of how high that initial value of y is. So to translate this to your problem, consider the example below: *------------ begin example -------------- sysuse nlsw88, clear gen byte baseline = 1 glm wage union collgrad ttl_exp baseline, /// link(log) eform nocons *--------------- end example ------------- (For more on examples I sent to the Statalist see: http://www.maartenbuis.nl/example_faq ) The parameter of baseline is the expected wage when all covariates are 0, so someone with no experience no union membership and no college degree is expected to earn $5.49 per hour. This trick is discussed in Newson, R. (2003) "Stata tip 1: The eform() option of regress" The Stata Journal, 3(4): 445. <http://www.stata-journal.com/article.html?article=st0054> Union membership leads to an increase in wage with a factor 1.11 (i.e. 11%), a college degree leads to an increase in wage with a factor 2.98 (i.e. 198%), and a year extra experience leads to an increase in wage by a factor 1.04 (i.e. 4%). 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/

**References**:**RE: st: automatically adjusting for bias in antilog transformation?***From:*Tatyana Deryugina <tatyanad@mit.edu>

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