APPENDIX F |
||
Regression of jobcode, tenure, & agency against wages |
Summary
of Regression Results |
|
|
|
|
|
|
|
|
|
Regression Statistics |
|
|
|
|
Multiple
R |
0.902637794 |
|
|
|
R Square |
0.814754987 |
|
|
|
Adjusted
R Square |
0.804067775 |
|
|
|
Standard
Error |
271.7997975 |
|
|
|
Observations |
221 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Coefficients |
Standard Error |
t Stat |
P-value |
Intercept |
1814.241446 |
63.52608133 |
28.55900141 |
0.0000000000 |
Years
Tenure |
17.52089276 |
2.383335582 |
7.351416602 |
0.0000000000 |
Administration
& Information |
-29.13475375 |
51.63514375 |
-0.564242716 |
0.5731968746 |
Dept. of
Revenue |
75.32121953 |
97.48364809 |
0.772654912 |
0.4406037226 |
Dept. of
Employment |
7.588965523 |
70.91123005 |
0.107020644 |
0.9148757543 |
Game
& Fish |
25.6220526 |
111.4128949 |
0.229973852 |
0.8183380944 |
WYDOT |
153.3310593 |
66.76538331 |
2.296565252 |
0.0226392677 |
Dept. of
Health |
-132.0783036 |
85.79561564 |
-1.539452833 |
0.1252136727 |
Dept. of
Family Services |
-133.4730332 |
92.76554115 |
-1.438821264 |
0.1517042281 |
IT01 |
1652.548006 |
74.48481311 |
22.18637514 |
0.0000000000 |
IT02 |
1203.906942 |
59.91491801 |
20.0936091 |
0.0000000000 |
IT03 |
738.7833016 |
64.40123457 |
11.47157048 |
0.0000000000 |
IT04 |
440.3478206 |
64.99258485 |
6.775354782 |
0.0000000001 |
Source: LSO analysis
of SAO payroll data.
To make some determination as
to whether there is internal equity or inequity among the salaries of
technology staff across state agencies, we constructed a regression to show the
relationship between salary, jobcode, tenure, and agency. If there is internal equity within the
state, the matter of which agency is the employer should not be statistically
significant. We found that tenure and
jobcode are most significant in explaining wage. We also found working for WYDOT had a probability of increasing
wages, and working for Department of Health or Department of Family Services
had a small probability of decreasing wages.
The dependent variable we
used was monthly pay for IT01-IT05 for December 1999. We used twelve independent variables: years of tenure, four dummy variables indicating jobcodes
IT01-IT04 (the default being IT05), and seven dummy variables indicating one of
seven agencies having more than 5 technology staff. Agencies having more than 5 technology staff are Administration
& Information, Dept. of Revenue, Dept. of Employment, Game & Fish,
WYDOT, Dept. of Health, and Dept. of Family Services.
Regression results show 81.5
percent of wages are explained by tenure, jobcode, or agency. Regression results also show moving up the
IT job ladder adds to salary.
Regression results show tenure and jobcode to be more
significant than agency in explaining IT wages. There is some indication that working for WYDOT increases the
wage of a technology worker, and lesser indications that working for Dept. of
Health or Dept. of Family Services decreases the wage of a technology
worker.