As discussed in Chapter 4, to estimate the low-skill employment for each region and year we converted industry employment to occupational employment by using the National Industry Staffing Patterns (NISP), a data file that includes occupational employment within each industry. This file is based on a national sample only. We took the following steps:
- Calculated the percent of employment within each of the 801 occupations for each two-digit industry from the NISP. For example, we estimated the percent of all workers in the Legal Services industry who are file clerks (2 percent in 1998).
- Applied the percent distribution calculated in (1) to the industry employment for each region (from ES-202) to estimate number employed by occupation.
- Classified employment by skill level (low-skill, medium-skill, and high-skill) depending on the education and training requirements of the occupation.
By applying this national percent distribution to our regional industry employment data, we were assuming that there were no regional differences between the occupational distribution for each industry. To test the validity of this approach, we compared the occupation estimates generated from the ES-202 and NISP with estimates from the Occupational Employment Survey (OES). The OES data are only available for 1998, which precluded us from using the data for the analysis. Also, the OES excludes agricultural, fishing and forestry and private household industries from the survey.
Exhibit C.1 shows a comparison of the percentage of 1998 employment by skill level from the ES-202 merged with the NISP and the OES for selected regions (MSAs only). As this exhibit shows, the percentage of employment that was low-skill was slightly lower from ES-202/NISP than from OES for all regions except Vermont.
|Region||Skill Level||ES-202/NISP (%)||OES (%)|
|Decatur and Florence, Alabama||Low-Skill||41||47|
|Jamestown, New York||Low-Skill||43||50|
|Florence, South Carolina||Low-Skill||41||50|
|Eau Claire, Wisconsin||Low-Skill||43||50|
|Source: Lewin calculations using ES-202, NISP, OES, and BLS education and training requirements data.|