TABLE OF CONTENTS
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. 


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