Wages by education
in Washington state

Connecting education with expected wages is a subject of interest at national, state and local levels. Since educational assignments are available only at the national level, as normal, the merging of education with expected wages is done by uniting occupational employment estimations with local wages and with national educational categories by occupation.


BLS educational assignment

The following Bureau of Labor Statistics (BLS) educational assignments are presented in the Excel workbook, occupations.xlsx. This file contains two tables containing educational assignments. The two tables are Table 1.11 Educational attainment for workers 25 years and older by detailed occupation, 2015-16 and Table 1.12 Education and training assignments by detailed occupation, 2016.

Table 1.11, is the table that contains estimated percentages of employee distributions between seven educational levels for each occupation, based on American Community Survey responses.

Table 1.12 contains typical education needed for entry into an occupation. These educational assignments are mainly based on expert opinions from within the BLS. Each occupation is assigned a single educational category. The single educational categories do not represent and are not intended to represent educational levels of employees working in occupations.


Educational assignment differences

There is a significant difference between these two types of educational assignments and their intended use. For instance, according to Table 1.11 Computer systems analysts have some employment in each of the seven educational categories. The largest employment in this occupation falls within the Bachelor’s degree category at 47.4%. This occupation also has a significant portion of employment in the Master’s degree category at 23.4%. Table 1.12’s single assignment of a typical education needed for entry into Computer systems analysts is a Bachelor’s degree.

Attempts to connect wages to educational levels, based only on entry level educational categories, create a questionable methodology. Such a methodology creates misleading messages about the relationship between education and wages because employers actually request various levels of education (per ACS survey). These misleading messages can be seen in the following examples. When single entry level educational assignments are applied to wage estimations and total employment, workers with Master’s degrees experience significant decreases in wages compared to Bachelor’s degrees. For state level estimations, the decrease results in a decrease of more than 18.3 thousand dollars per year. Post-secondary non-degree awards also lead to decreases in wages compare with High school diploma or equivalent. Some college, no degree would cost a worker more than 8.5 thousand per year in lost wages compared with just High school diploma or equivalent. The single educational category approach makes a case against education.


Multi-educational distributions

In contrast to the single educational category approach, we took the BLS educational distributions and applied them to our unsuppressed alternative1 occupational employment estimations for 2017Q2. We then joined the employment-educational data to our alternative wage estimations. As a result, we were able to estimate average wages by educational levels for the state and Workforce Development Areas (WDA). See the Hourly average wage table and the Annual average wage table below.


Main assumption

The main assumption behind the use of the BLS educational distributions for employment estimations is: The distribution of employment in each occupation, between educational levels, is the same for the state and WDAs as it is for the nation.


Hourly and annual average wage tables

As can be seen in the Hourly average wage table and the Annual average wage table, results from the BLS educational distributions look reasonable. For the state and all WDAs, increases in educational levels correspond with increases in wages. The method behind these tables acknowledges the fact that each occupation employs workers from various educational levels. Wages are adjusted to 2018Q1.


Hourly average wage table




Annual average wage table



Multi-educational method

We produced the Hourly average wage table and the Annual average wage table results by following the following four steps:

  1. Employment distributions for each occupation were normalized to totals of 1.
  2. Normalized results were merged with alternative employment estimations for 2017Q2 and alternative wage estimations for 2018Q2. See educational column titles in the wage tables above.
  3. Employment estimations for each occupation were dispersed between each educational category according to shares from step 1.
  4. Wages for each educational category for each area were estimated as averages of initial wages weighted by employment.


Formal description

  1. Let \(a_{ij}\) as a percentage of occupation \(i~(i~ \epsilon \ I )\) in educational category \(j~(j=1,...,7)\).
    In Table 1.11 we have  \(\sum_{j=1}^7a_{ij}\)   is very close to 100, but slight differences could be due to rounding. We normalized \(a_{ij}\) as: \[\overline{a}_{ij}= {a_{ij}}/{\sum_{j=1}^7a_{ij}}~~~for~each~occupation~~~i~\epsilon \ I.\]
  2. Let define matching occupational employment for each occupation \(i\) and area \(v~(v=0,1,...,12)\) as \(e_i^v\)   and hourly wages as \(w_i^v\).
  3. Employment for each area in each occupation \(i\) is defined as \[e_{ij}^v = \overline{a}_{ij}~ *~ e_i^v \] and since \(\sum_{j}\overline{a}_{ij}~=~1~for~each~i~(i~ \epsilon \ I )\) we have \[\sum_{j}e_{ij}^v~=~e_i^v\]
  4. Weighted hourly wages for each educational group in each area are calculated as \[h_j^v~=~\sum_{i~ \epsilon \ I}(w_i^v*e_{ij}^v)/\sum_{i~ \epsilon \ I}e_{ij}^v~.\] Annual wages are simply \[p_j^v~=~2080~*~h_j^v~.\]




  1. See https://esd.wa.gov/labormarketinfo/projections for more details about occupational classification differences for 2018 only.↩