The EMSI Average Standard Wage (ASW)

Editor’s note: For the last few months, EMSI has been working on a way to look at relative wage levels across metros areas and occupations. The following serves as a preview of a soon-to-be-released addition to our dataset, including the methodology behind it and initial metro-level analysis.

The average hourly wage for salaried workers in the Boston metro area is $26.23, compared to $20.75 for the nation, or 126% of the national average. What does this tell us? What does this mean? Without context, not much.

Electrical engineers in the same economy make 122% of the national average for that occupation. Does that mean they are oversupplied or undersupplied? Are they getting paid what they are worth or what would be expected?

To help answer these questions, EMSI is working on the development of an addition to our dataset, “Expected Wage.” Once fully released, it will provide context to our occupational wage data and help our customers understand the relative cost of labor in a general economy.

Along with observed salaries, we can help you understand expected salaries and the ratio of actual-to-expected salaries for any Metropolitan Statistical Area — and any occupation.

Boston’s economy, like many others in the US, is complex. It has a diverse set of specialized workers in biotechnology and finance and other fields that may be skewing the overall wages higher. Giving an average wage for Boston, or most metro areas, isn’t that helpful.

EMSI’s expected wages can tell you if labor is indeed expensive in Boston compared to the national average. And it call tell you if salaries for a particular occupation — say petroleum engineers — are higher than one should expect in Houston or any other MSA.

Higher-than-expected salaries may indicate a relative occupation labor shortage, making it a less-than-ideal place for a business to launch a recruiting effort, but perhaps a good place for the jobseeker to focus effort. If a region has lower-than-expected salaries in a particular occupation, the opposite may be true.

Note: At this time, EMSI’s expected wages are only available for MSAs.

How It Works

The engine for estimating expected wages is what we term the EMSI “Average Standard Wage,” or ASW. The idea behind the ASW is similar to the standard market basket that underlies the consumer price index (CPI). In the case of the ASW, we examined our national dataset and identified a representative set of occupations that:

  1. Exist in all MSAs;
  2. Exhibit relative wage stability over time; and
  3. Are spread across all broad (2-digit) occupation sectors.

Of the around 750 occupations tracked in the Standard Occupation Classification (SOC) system, we identified 46 that meet these three criteria. These are ubiquitous, stable, and diverse occupations.

We summed the wages across all 46 occupations in the national economy and divided this by the corresponding sum of all jobs. This constitutes our “national ASW.” We then followed the same procedure for each of the nation’s 351 MSAs and labeled these “regional ASWs.”

Estimating Expected Wages

We view the ASW as a fundamental measure of general labor costs and use it as value to express specific occupation wage rates at the national level. For example, on average retail salespersons nationally make 58% of the national ASW, so we might express their wages as 0.58. Alternatively, on average petroleum engineers nationally make 281% of the national ASW, so we might express their wages as 2.81.

Focusing on a particular MSA, we expect the wages of retail clerks to be 58% of the ASW for that MSA, and petroleum engineers to be 281% of the ASW for that MSA. Comparing expected wages to observed wages provides an indicator of the high-cost/low-cost character of the MSA’s labor market for those occupations.

High Labor-Cost/Low Labor-Cost MSAs

In a later blog post we will display expected versus observed wages across MSAs for each of the 23 2-digit SOC occupations.  Finer detail, down to the some 750 5-digit SOC occupations, will be introduced into EMSI’s labor market research tools this year. For this post, we’ll focus on the raw ASWs, and concentrate on a select group of high- and low-cost MSAs.

The attached table divides MSAs into three broad groups according to size measured in number of jobs:

  • “Large MSAs” have greater than 190,000 jobs.
  • “Mid-sized MSAs” have between 190,000 and 69,000 jobs.
  • “Small MSAs” have less than 69,000 jobs.

We formed the ratio of MSA ASWs to the national ASW and sorted MSAs accordingly. For each of the three MSA groups, we collected the six largest and the six smallest ratios and displayed these in the table. The high-end ratios designate a “high-cost” (i.e., high labor cost) MSA, while the low-end ratios designate a low-cost MSA. (Note: The data below can be found in the above graphic; click on it for a larger image.)

Among the large MSAs, San Francisco-San Mateo-Redwood City, California, is seen as the highest-cost large MSA, exhibiting a standard average wage that is 131% of the national ASW. At the other end, McAllen-Edinburg-Mission, Texas, is seen as the lowest-cost large MSA, exhibiting an ASW that is 82% of the national ASW.

As for mid-sized MSAs, Anchorage, Alaska, is the highest-cost MSA, exhibiting a ASW that is 114% of the national ASW, while Joplin, Missouri, is the lowest-cost medium MSA, with an ASW that is 79% of the national ASW.

Finally, Napa, California, is the highest-cost small MSA, with an ASW that is 117% of the national ASW, while Wheeling, West Virginia-Ohio, is the lowest-cost small MSA, with an ASW that is 76% of the national ASW.

In our next post we apply the technique offered by regional and national ASWs to arrive at highlight regions and occupations with higher- and lower-than-expected salaries. We are confident Analyst users will find this new information useful in making recruitment, site selection, and job search decisions.

Please contact Rob Sentz (rob@economicmodeling.com) if you have questions on EMSI’s Average Standard Wage. You can find out more about EMSI here. Follow us @desktopecon.

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