Compensation Survey

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Compensation Survey

   External Analysis
    + Select Competitors Based on
       - Industry
       - Employee Number
       - Union Status
Select BenchMark Jobs for Each

   Select 2 (and better) 3 from Each Structure
   Typically will select to match lowest, highest
    and middle positions
BenchMark Jobs

1.   Contents are well known, stable, and
     agreed upon by employees
2.   Common across a number of employers
3.   Represent the entire range of jobs being
     evaluated within the company
4.   Generally accepted for the purposes of
     setting pay levels
Select Benchmark Jobs

   CompAnalysis and Appendix B
   Admin Asst        Admin 2
   Executive Asst    Admin 4
BenchMark Comparison Sheet

   In a Perfect World……

   Typical Reconciliation Form p. 257
   Rate each position Independently and
    resolve differences per discussion
Survey       Characteristics

   They contain a lot of data
   Outdated data due to lag time
   Statistical analysis needed to
    integrate internal job
    structures with external market
Appropriate Statistics to Summarize
Survey Data

Descriptive Properties
       Central tendency
    –    Represents the fact that a set of
         data center around a central point

       Variation
    –    Represents the amount of spread
        or dispersion in a data set
    Central Tendency

                Two Type of Measures

   Arithmetic mean (mean, average)
    –   Sum of salaries / number of salaries
    –   Represents typical market salaries

   Median (midpoint)
    –   Middle value of sequential numerical data

Help Compensation Pro Understand Dispersion
 Standard deviation
    –    Refer to the mean distance of each
        figure from the mean
   Quartile
    –   % of figures below a point
    –   Based on 4 groupings

   Percentile
    –   % of figures below a point
Update Salary Survey

   Using Past Wage Levels
   Need to Estimate Wage Levels for Future
   Most Up To Date Data We Have Is:
    + CPI Data
    Consumer Price Index

   Indexes monthly price changes in
     goods & services

   Published for 2 groups
    –   All urban consumers (CPI-U)
    –   Urban wage & clerical earners (CPI-W)

    Choose between Depending on Population of Workers

    (CPI-U more representative of white collar)
CPI Calculation – Known Data

Current CPI – Previous CPI       X 100
 Previous CPI

Start With Date of Salary Data and Estimate
  Increase from There
CPI – Future Estimates

   CPI Data Not Yet Available
   Implementation Period In the Future
   Determine Time Period since Salary Data
   Determine CPI for 4 Previous Time Periods
    and Average
    Regression Analysis

   Statistical procedure designed to
     find the best - fitting line between
     two variables
    Formula: Y = a + bX
       Y = Predicted salary
       X = Job evaluation points
       a = Y intercept (X = 0)
       b = the slope ($ per Evaluation Point)
Data You Need

Predicted Salary
 Salary Survey + CPI Update (*)
Job Evaluation Points (*)
 Internal Consistency Report
Pay Ranges (Horizontal Distance) Chap 9
Pay Grades (Vertical Distance) Chap 9
Your Report - CompAnalysis

For Each Job Structure
 Graph 1 – Market Pay Graph
 Graph 2 – Pay Policy Lines
 Graph 3 – Pay Grades and Ranges
 Statistics – Summary for Structure
Pay Level Policies

   Market lead
    –   Levels above market pay lines
    –   Best for differentiation strategies
    Market lag
    –   Levels below market pay lines
    –   Best for Lowest - Cost Strategies
    Market match
    –   Pay according to market pay line
    –   Appropriate with differentiation strategy