How Well Have Rural and Small Metropolitan Labor Markets Absorbed Welfare Recipients?. Calculating Employment and Wages by Occupation


To estimate employment and wages by occupation, we obtained employment and wage data by industry from the BLS Covered Employment and Wages (ES-202) system. We distributed employment and wages in each industry across occupations using the National Industry Staffing Patterns (NISP).

It would have been preferable to use employment and wage data by occupation instead of by industry, obviating the need for an industry to occupation conversion. The Occupational Employment Statistics (OES) survey, on which the NISP is based, tabulates employment and wage data by occupation. However, wage data are only available for 1998. In addition, the OES did not survey all agricultural, forestry, fishing, and private household industries. Therefore, we used the ES-202 for employment and wages.

1.Covered Employment and Wages (ES-202)

The ES-202 program provides comprehensive employment and wage information by industry for workers covered by state Unemployment Insurance laws and federal workers covered by the Unemployment Compensation for Federal Employees program. Employment is reported monthly for covered workers who were working or who received pay during the pay period including the 12th of the month. Wages, from payroll records, are reported quarterly for all covered workers who received pay during the quarter.

The ES-202 data are coded according to the Standard Industrial Classification (SIC) system, which classifies establishments by industry activity. While a four-digit SIC level is assigned to all reporting establishments, employment and wages are often aggregated to the two-digit or one-digit level. Examples of industries in these categories include the following:

Four-Digit Two-Digit One-Digit

Wheat Grains

Agricultural Production Crops Agriculture, Forestry, Fishing

Creamery Butter

Food and Kindred Products Manufacturing

Womens Clothing

Stores Apparel & Accessory Stores Retail Trade

Junior Colleges

Educational Services Services

Because of confidentiality concerns, ES-202 data are not disclosed for any level in which the universe has three or fewer employers or is dominated by a single employer that represents more than 80 percent of employment. We obtained ES-202 data at the two-digit SIC level rather than four-digit SIC to reduce the number of undisclosed employment records.(34) When we encountered undisclosed cells at the two-digit SIC level, we allocated the remaining employment and payroll at the one-digit SIC level (total minus all disclosed employment within an industry level) equally across all undisclosed cells.

2.National Industry Staffing Patterns (NISP)

For this study, we use the NISPs occupational estimates by industry, which are produced from the national OES, to convert employment and payroll (total wages) by industry for each region to employment and payroll by occupation. Prior to 1996, the OES surveyed only one-third of the industries in the sample each year, taking three years to fully complete the survey. This was done to reduce respondent burden. In addition, it only collected employment data. In 1996, OES began to survey all industries in each year, but recommended using three years of data to reduce sampling error. As discussed above, it began collecting wage information in 1996.

We obtained the NISP at the two-digit level from BLS for 1990 through 1998. We combined 1990 to 1992, 1993 to 1995, and 1996 to 1998 NISPs to obtain the industry occupation matrix for 1993, 1996, and 1998. Because NISP is only produced at the national level, we made the implicit assumption that there were no significant regional differences in the occupational distribution for each industry. We tested this assumption using the Current Population Survey (CPS), and found a consistent pattern of occupational employment in each industry across four regions (Northeast, Midwest, South, West) and in urban versus rural areas. These results are presented in Appendix A.

The 1998 NISP was used to distribute payroll for 1993, 1996, and 1998. We assumed there were no significant differences in the occupational distribution within an industry over time. We tested this assumption using the CPS. The CPS shows a declining percentage of payroll in low-skill occupations over time, while we assumed no decline in the proportion of total payroll allocated to low-skill occupations (see Appendix B). We are not too concerned about the latter assumption because there is no decline in the share of employment in low-skill occupations from the CPS, while there is an increase over time from the NISP (i.e., we found employment in low-skill occupations grew at a faster rate than employment in high-skill occupations). Thus, in both cases, wages per low-skill worker grew at a slower rate than wages per high-skill worker.