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From: uunet!mitel!Software!hans@ncar.UCAR.EDU (Hans Johnsen)
Newsgroups: soc.feminism
Subject: Re: equal pay interesting statistic
Message-ID: <5667@peplinsk>
Date: 7 Dec 90 14:35:09 GMT
References: <93640@aerospace.AERO.ORG>
Sender: nadel@aerospace.aero.org
Reply-To: uunet!mitel!uunet.uu.net!mitel!spock!hans@ncar.UCAR.EDU (Hans Johnsen)
Organization: Mitel. Kanata (Ontario). Canada.
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Approved: nadel@aerospace.aero.org
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In article <93640@aerospace.AERO.ORG> baranski@meridn.enet.dec.com writes:

>While women in general make ~$.69 for every ~$1.00 that men make, on the same
>scale, married men make ~$1.30, and single women make ~$1.10...

>So, it looks like the disadvantaged are married women and single men...

Be careful about the conclusions you draw. There are *no* statistics
given here for married women or single men. You are drawing
conclusions which may not be as valid as you think. Even assuming 50%
of each gender is married reveals: single men $.70 (ie: still higher
than female average) and married women $.28!!!!!!

>Jim Baranski

These statistics (or similar ones) are often used, although I have
never seen a reference for them. Assuming they are legitimate, they
are still basically meaningless without taking into account the
following: 

1) What is single? Are single parents considered in the 'single' stats?

2) Are the unemployed counted in these figures? What about those on
welfare? These two factors could make an enormous difference in the
stats, as a large percentage of unemployed are young (probably single)
men, whereas a large percentage of welfare recipients are single
mothers. 

3) What percentage of males are married? females? (see above).

4) What is the breakdown by amount of training? That is, women are
making inroads in professional fields, but low paying clerical jobs
are still mostly female, whereas higher paying labour jobs are male.
Also note that it may be that a female professional is more likely to
be single, whereas a male profeesional is more likely to be married.



In short, there are three kinds of liars: liars, damn liars, and
statisticians. 1/2 :-)

hans