How Well Have Rural and Small Metropolitan Labor Markets
Absorbed Welfare Recipients?

Appendices

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Contents

• Appendix A: Comparison of NISP Employment Across Regions Using the CPS
• Appendix B: Comparison of NISP Payroll Across Regions and Time using the CPS
• Appendix C: Comparison of Low-Skill Employment Estimates Calculated using ES-202/NISP and OES
• Appendix D: Sensitivity Of Changes In Wages And Employment To Alternative Elasticity Assumptions
• Appendix E: Employment and Wages By Skill LeveL
• Appendix F: Results for Jackson, Tennessee
• Endnotes

Appendix A:
Comparison of NISP Employment Across Regions Using the CPS

We used the National Industry Staffing Patterns (NISP) to convert employment by industry to employment by occupation in our study regions. By using a national distribution of occupations by industry for 12 different regions, we implicitly made the assumption that the distribution of employment by occupations in an industry did not change by region. We used the CPS Outgoing Rotation Groups for 1994, 1996, and 1998 to test the plausibility of this assumption.⁽¹⁾ We compared the distribution of occupations by skill level (low, medium, and high) by one-digit industry for urban (MSA) versus rural (non-MSA) areas and for four geographic regions (Northeast, Midwest, South, and West).

The CPS had a higher percentage of medium-skill workers and a lower percentage of low-skill workers compared to the NISP. This difference between the two data sources stems from the nature of the two surveys. The NISP (derived from the Occupational Employment Statistics (OES)) is an employer survey, in which employers answer questions about the occupations of their employees. The CPS is a household survey, in which individuals themselves answer questions about their occupation. We believe that there is “occupation creep” in the CPS, which means that individuals are likely to report being in higher-skill occupations than they actually are. For example, a food preparation worker (low-skill) might report himself as a restaurant cook (medium-skill). We do not think that this issue presents a problem for our analysis, because occupation-creep is likely to be consistent across regions.

We found that the distribution of skill levels across one-digit industries was consistent across four regions and urban versus rural areas for most industries. Agriculture and mining were the two industries that varied across rural versus urban areas and across regions. We concluded that we had a reasonable degree of confidence in the NISP, with the caveat that we might have introduced some measurement error in the agriculture and mining industries. It is important to note that we selected areas that did not have a large share of agricultural or mining employment.

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Appendix B:
Comparison of NISP Payroll Across Regions and Time using the CPS

We used the NISP to convert payroll by industry to payroll by occupation in our study regions. This conversion methodology is similar to the one we employed to convert employment by industry to employment by occupation, with one exception: we only had the 1998 NISP for payroll, whereas we had the NISP for employment for all years. Therefore, by using the 1998 NISP to convert payroll by industry to payroll by occupation for 1993, 1996, and 1998, we were implicitly making the assumption that the distribution of payroll by occupation in an industry did not change by region or by year. We used the CPS March Supplement for 1994, 1996, and 1998 to test the plausibility of this assumption. We compared the distribution of payroll by occupation by skill level in an industry for urban (MSA) versus rural (non-MSA) areas and for four geographic regions (Northeast, Midwest, South, and West) across the three years. We found that the distribution of payroll by skill level in one-digit industries was consistent across four regions and urban versus rural areas for most industries.

Examining the CPS employment and payroll shares over time, we found that the share of low-skill employment was relatively constant over time (about 28 percent of employment was low-skill), while the percent of payroll declined over time (the share of low-skill payroll declined from 18 to 16 percent). This implied that the gap between low-skill and high-skill wages increased over time. Examining the NISP, we found a slight increase in low-skill employment over time (from 41 to 42 percent). Therefore, by applying the 1998 NISP wage distribution to the earlier years, and not allocating a smaller share of payroll in the earlier years (because the share of low-skill employment was smaller), we were implicitly assuming that the gap between low-skill and high-skill wages increased over time, similar to the CPS analysis.

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Appendix C:
Comparison of Low-Skill Employment Estimates Calculated using ES-202/NISP and OES

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:

1. 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).
2. Applied the percent distribution calculated in (1) to the industry employment for each region (from ES-202) to estimate number employed by occupation.
3. 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.

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Appendix D:
Sensitivity of Changes in Wages and Employment to Alternative Elasticity Assumptions

The elasticity assumptions were instrumental in determining the size of the demand and supply shifts from the employment and wage data that we collected. Therefore, we used alternative labor demand and labor supply elasticities to test the sensitivity of our results to the elasticity assumptions. In the report, we used a supply elasticity of 0.3 and a demand elasticity of 0.4. We used three sets of alternative elasticity assumptions to conduct the sensitivity analysis. We incremented the assumed elasticities by 0.1, we decremented the assumed elasticities by 0.1, and we used a supply elasticity of zero.

Exhibits D.1 and D.2 present the decomposition of the change in employment into the change due to the demand shift and the change due to the supply shift under the three different elasticity assumptions. Change in employment due to the shift in demand and shift in supply was not sensitive to small changes in the elasticity assumptions where elasticities were incremented or decremented by 0.1, but was sensitive to large changes in the elasticity assumptions. With a supply elasticity of zero, all the change in employment was due to the shift in supply; the shift in demand had no effect on employment. The average percent change in employment attributable to supply increased from 2.1 percent to 5.6 percent in the 1996 to 1998 period. However, we did not believe that a supply elasticity of zero was plausible. Therefore, we felt confident that the employment findings were robust to the elasticity assumptions.

Exhibits D.3 and D.4 present the decomposition of the change in wages into the change due to the demand shift and the change due to the supply shift under the three different elasticity assumptions. We found that the decomposition of the change in wages was more sensitive to the elasticity assumptions than the decomposition of the change in employment. The decomposition depended on the sum of the demand and supply elasticities. A higher sum decreased the percentage change in wages attributable to the shift in demand (or supply). The change in wages became smaller, because the demand and supply curves were more elastic; i.e., they were more responsive to changes in wages. A smaller change in wages was needed to bring about a change in employment. Hence, when we incremented the elasticity assumptions by 0.1, the percentage change in wages attributable to demand decreased from 9 to 7 percent. The reverse was true for a lower sum of the demand and supply elasticities. When we decremented the elasticity assumptions by 0.1, the percentage change in wages attributable to demand increased from 9 to 12 percent. A supply elasticity of zero increased the percentage change in wages attributable to demand to 16 percent; however, as discussed above, we do not believe this elasticity assumption was plausible.

Based on the sensitivity analysis, we concluded that our employment findings were robust to the alternative elasticity assumptions, but our wage findings were not. However, our basic findings are not affected much by reasonable changes in the elasticities as a result of the small size of the increase in employment due to welfare reform relative to the low-skill labor market.

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Appendix E:
Employment and Wages By Skill LeveL

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Appendix F:
Results for Jackson, Tennessee

Initially, we planned on including Jackson, Tennessee as another region in our study. However, data quality issues precluded our use of Jackson as a region. In examining ES-202 data for Jackson, we found a sharp increase in total employment, especially between 1996 and 1998 (19.4 percent). Other sources (Census Bureau and the Tennessee labor department) reported smaller employment increases during these years. For example, the state reported an increase of 4.3 percent for this region. Given this discrepancy, we excluded the region from our analysis.

Exhibits F.1 and F.2 present the results of our economic model using BLS data for Jackson. Between 1993 and 1996, according to the data, both employment and wages increased in Jackson. Employment increased by 10.4 percent, compared to an average of 9.2 percent for the 12 regions. Wages increased by 1.5 percent compared to an average decrease of 0.4 percent for the 12 regions and 0.1 percent for the U.S. The maximum impact of welfare reform on employment was negative in this period, because Jackson experienced an increase in welfare caseloads during this time.

Between 1996 and 1998, analysis of the BLS data found that both employment and wages increased in Jackson, Tennessee and the increases were much higher than the average increase in the 12 regions and in the U.S. Employment increased 19.4 percent compared to an average of 5.6 percent in the 12 regions. Wages increased 6.6 percent compared to an average of 2.1 percent in the 12 regions. The magnitude of the supply and demand shifts are substantially larger than in the other regions and the U.S. We believe these findings are unreliable for the 1996 to 1998 period given the unreliability of the employment data. Therefore, we decided not to report the findings in the full report.

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Endnotes

(1) We use the year 1994 because CPS made significant changes in design starting in 1994 and we do not feel estimates from 1993 are comparable to estimates from later years. [ Back to text ]

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