Here is the methodology used in my pay analysis calculations:
“Full time employee” definition
All employees – those making more than the equivalent of a full year’s pay at the then-current minimum wage in California.
- 2012 – $8.00/hour * 2080 hours = $16,640/year
- 2018 – $11.00/hour * 2080 hours = $22,880/year
Certificated employees – those making equal to or more than the contracted minimum starting rate for such employees, per OTA contracts for the years specified.
- 2012 – $40,888 Using 2013 number, 2012 does not appear easily available. Per OTA Contract July 2013 Appendix B-1.
- 2018 – $44,575. Using average of $43,912 (per OTA Certificated Salary Schedule effective 7/1/15) and $45,238 (per OTA Certificated Salary Schedule effective 7/1/18)
Employee group classification:
Employee position classifications (e.g. “Administrative”, “Certificated”, etc) are defined by job titles. For example, a job with “Teacher” in the title is assumed to be “Certificated”, while a job with “Principal” in the title is assumed to be “Administrative”.
This can be somewhat unclear at times. A best effort has been made to connect job titles to the appropriate classification, however this effort is likely to be incorrect in some cases. Any feedback on mis-classified jobs is welcome.
Longitudinal analysis is used
Longitudinal analysis is necessary to find the rate of pay increase within the district.
One might think that it would simply be a matter of looking at the state’s “J90” reporting (“Certificated Salaries & Benefits”) and tracking the average rate of pay for a district to see how, for example, teacher pay has changed. It’s not that simple…
Why? Because the mix of employees changes all the time, and having more senior employees leave and more junior take their place can affect the overall average in ways that do not actually reflect the real rates of change.
For example, let’s say in one year we have 100 employees, all making exactly $100,000/year. The total payroll for that year would then be $10,000,000, and the average would be $100,000/year.
The next year, 50 of those retire and are replaced with employees making a starting rate of $50,000/year. The remaining 50 are all given 10% raises and are now making $110,000/year.
The total payroll is now (50 * $50,000 = $2,500,000) + (50 * $110,000 = $5,500,000) = $8,000,000. That means the average is now $8,000,000/100 = $80,000.
If we are looking at averages, it appears the district has CUT people’s pay – they have gone from an average pay of $100,000/year to $80,000/year.
In reality, those who stayed received what most would consider a very generous raise – 10%, while the new hires have gone from making nothing (presumably they just graduated from college) to making something – an infinite raise.
Longitudinal analysis avoids this false use of statistics by looking strictly at individuals who have been with the district from the beginning to the end of the period and examining actual raise rates for those people.
Longitudinal analysis must be done by matching employee names since no other more specific data (employee ID) is available. This means all such analysis is likely partial given that names are sometimes changed and can be reported differently in different data sets (“Bob” instead of “Robert”, or “Robert” instead of “Robert T.”) however as you can see below the matched sample sizes are large enough to be significant as an indicator of total numbers.
It is, for instance, very unlikely if the organization is giving 50% or more of it’s employees raises at a certain rate that they would not apply that same rate to most – if not all – of the other employees of the company.
Cases of involuntary pay reductions or demotions are exceedingly rare, accordingly employees who recorded base pay declines between the beginning and latest data periods were excluded from the longitudinal analysis on the assumption that any decline was likely due to termination, retirement, family leave, a shift from full to part time, or some other voluntary change in job circumstances.