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If you’ve read some of these blogs, you know that I’m always on the lookout for interesting new ways to use data to create occupational information. This week I’m gearing up to write a second edition of 250 Best-Paying Jobs (JIST), and I’m experimenting with ways to use the earnings data collected by the Occupational Employment Statistics survey of the Bureau of Labor Statistics.

One of the tables published by OES is the earnings of detailed occupations in specific metropolitan areas. Are you considering entering an occupation? Would you consider moving to a location where the pay is very good? Then, with the data from this table, I can tell you the metro areas where the pay is best. And I have done so in a few books in which, for each occupation, I list the metro areas with the highest median earnings.

This method has a limitation, however: Some metro areas that turn up again and again on these lists have high costs of living, and the high wages there simply represent the general inflation of all prices there: labor, housing, food, gasoline, and so forth. If you move to one of these areas, then, your higher pay is not likely to buy you a more affluent lifestyle. In addition, you’ll be in a higher tax bracket.

What I wanted to identify for each occupation was not the best-paying metro areas, but rather the metro areas where wages are higher for some reason other than a general inflation of all wages.

So here’s the approach I used.

First I divided the median earnings for all occupations in the metro area by the median earnings for all occupations in the United States as a whole. This gave me a general wage-inflation figure for each metro. You may not be surprised to find that San Jose–Sunnyvale–Santa Clara, CA, the area that includes the Silicon Valley, had the highest percentage: 53.8% above the national average. The area where I live, Trenton-Ewing, NJ, came in fourth, at 34.9% above average. But keep in mind that this figure can actually represent deflation in low-wage communities. In all of the 50 states, the metro areas with the lowest percentages were three border towns in southern Texas, where the cost of living is kept down by cross-border shopping and probably by the presence of many undocumented workers. The lowest of these, McAllen-Edinburg-Mission, TX, has average pay that is only 63.4% of the national average.

Next, for each occupation for which OES publishes annual earnings information, I multiplied the national median earnings by the wage-inflation factor for each metro area. This told me what the workers in this occupation and metro area would be earning if their pay varied only by the same amount as all other workers in their area. I think of this figure as the expected inflated wage for the occupation and area. Again, it could also be deflated below the national average.

Finally, I divided the actual earnings figure for the occupation and area by the expected inflated wage. This told me how much (in percentage terms) the actual earnings varied from what you’d expect simply on the basis of the local labor market as a whole.

The results I got were interesting, and I’ll discuss them in detail next week. (How's that for a teaser?)