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RayW
Obul; n
=
20
Oken; n
7. (C hull)
Opor; n = 10
=
300
1
10
45
2
40
VesD
400
12
3
140
35
1000
RayH
1200
4
1400
120
450
1600
1800
30
14
NumVes
10
FibL
15
100
5
400
Figure 4.13 CVA biplot of the full Ocotea data set with the two newly constructed
variables, VLDratio and RHWratio , added as additional calibrated axes.
gender remuneration differentials to factors such as publication record (Ward, 2001), time
allocation (Toutkoushian, 1999), rank and seniority (McNabb and Wass, 1997), institution
type (Barbezat and Hughes, 2005), age and education (Bayer and Astin, 1975; Gordon
et al. , 1974), an unexplained portion was generally found (see, for example, Toutkoushian,
1998; Barbezat and Hughes, 2005). This unexplained differential is commonly called the
gender remuneration pay gap.
As far as the South African situation is concerned, South African higher education
institutions were faced after 1994 with the reality of transforming to an organizational
culture appropriate for the 'new' South Africa. In the case study presented here, we
focus on the gender remuneration differentials for academic staff at Stellenbosch Uni-
versity for the years 2002 - 2005. Our data concern the permanent full-time academic
staff at Stellenbosch University for 2002 and 2005. These data are available in the R
dataframe Remuneration.data and are described in detail in Walters and Le Roux
(2008). Remuneration.data consists of the following columns:
ID
a coded identification number;
Remun
the total cost of employment before deductions for December 2002 and
2005 (in units of R10 000) - inflation was not taken into account
because it affected all staff equally during the study period;
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