1. Performance Webinar #3 Focusing on Average Earnings

2. 2 Webinar Layout Slide Area Attendee List Chat Room Notes Connection Status

3. 3  To chat, type text into the text box. When asking a questions, be sure to identify your State.  Select whom you wish to chat with by using the To: drop-down menu.  Click the arrow button Chat Feature

4. 4 Background  Three Performance Management Conferences held in February/March, 2006 focused on revised performance and reporting policies  Follow-up from conferences included requests for performance-related webinars around specific topic areas  Today’s webinar is the third in a series of 6 webinars hosted by ETA Performance Specialists

5. 5 Future planned webinars  September, 2006 – VETS Performance/Reporting Issues  October, 2006 – Certificates and Training for Adults and Dislocated Workers  November, 2006 – Innovative Practices to Improve State Performance  December, 2006 – Open for suggestions (send to ETAperforms@dol.gov)ETAperforms@dol.gov

6. 6 Webinar Outline  Brief History of Earnings Outcomes  Training and Employment Guidance Letters (TEGL):  7-99  15-03  28-04  17-05  Data Sources  National Results  Analyzing Outcomes  Lower Living Standard Income Level (LLSIL)  Subject Matter Experts:  BLS Quarterly Census of Employment and Wages (QCEW)  New York State (Average Earnings’ Forecasting Model)

7. 7 Our Speakers... Ron Fionte  Branch Chief, Bureau of Labor and Statistics (BLS) Bill Meehan  Principal Economist, Division of Research and Statistics, New York State

8. 8 History of Earnings Outcomes  TEGL 7-99  Average Earnings Change (Adult, Older Youth)  Earnings Replacement Rate (DW, TAA, NEG)  Effective 7/1/2000. Rescinded by TEGL 17-05  TEGL 15-03  Earnings Increase 1 & 2 (Adult, Older Youth, DW, TAA, VETS)  Never fully implemented. Rescinded by TEGL 28-04  TEGL 28-04  Six Months Earnings Increase (Adult, DW, TAA, Wagner- Peyser, VETS)  Effective 7/1/2005. Rescinded by TEGL 17-05

9. 9 TEGL 17-05 Average Earnings (Adult, DW, NEG, TAA, Wagner-Peyser, VETS) Of those who are employed in the 1 st, 2 nd and 3 rd quarters after the exit quarter: Total earnings in the second quarter plus total earnings in the third quarter after the exit quarter divided by the number of participants who exit during the quarter. Effective Date: 07/01/2006

10. 10 TEGL 17-05 Older Youth Earnings Change: Older Youth Of those who are employed in the 1 st quarter after the exit quarter and who are either not enrolled in post-secondary education or advanced training / advanced training-occupational skills training in the 3 rd quarter after the exit quarter or are employed in the 3 rd quarter after the exit quarter: [Total post-program earnings [earnings in quarter 2 + quarter 3 after exit] minus pre-program earnings [earnings in quarter 2 + quarter 3 prior to participation] divided by the number of older youth participants who exit during the quarter. Effective Date: 07/01/2006

11. 11 Data Sources  Wage Records  UI Wage Records  Additional Wage Record Data Sources:  Automated Record Matching / Data Sharing Systems (WRIS and FEDES)  OPM, USPS, US DoD, Railroad Retirement System, State New Hires Registry and State Department of Revenue or Tax  Supplemental Sources (only for grantees that do not have access to wage records, e.g. NFJP, SCSEP, INAP)

12. 12 UI Wage Records  Primary data source  Includes private sector and non-profit sector  Also includes government employer wage reports:  State  Local  Judicial, and  Public School

13. 13 Additional Wage Record Data Source: WRIS & FEDES WRIS (Wage Record Interchange System)  Created to facilitate the interstate exchange of UI wage data  50 states participating FEDES (Federal Employment Data Exchange System)  Focused on providing access to employment records maintained by the following agencies:  Office of Personnel Management (OPM)  Department of Defense (DOD) and  United States Postal Service (USPS)  29 states participating

14. 14 From Data Sources to Benchmarks  Wage data are collected, compiled and compared to established benchmark standards for purposes of data analysis  Two primary data sets used for establishing benchmark standards for purposes of analysis are:  Lower Living Standard Income Level (LLSIL)  Quarterly Census of Employment & Wages (QCEW)

15. 15  Data and methodology:  Based on the 1981 lower living family budget (BLS)  BLS still provides data to ETA which publishes the LLSIL  Uses the “Poverty Guidelines” issued by HHS  Annual updates based partially on the Consumer Price Index for All Urban Consumers (CPI-U)  Data are presented by geographic region and for 23 selected Metropolitan Statistical Areas (MSA) NOTE: This data should not be used for statistical purposes due to the nature of the base calculation which has not been updated since 1981. Lower Living Standard Income Level (LLSIL)

16. 16  Program uses:  70% LLSIL used by WIA to define:  low income individual  disadvantaged youth  disadvantaged adult  Used in eligibility determinations under Work Opportunity Tax Credit (WOTC)  Since the 70% LLSIL is used as an eligibility gateway to services under WIA Adult, the average earnings outcome should approach or exceed the one-half the 70% LLSIL rate Lower Living Standard Income Level (LLSIL)

17. 17 National Results

18. 18 National Results

19. 19 National Results

20. 20 Quarterly Census of Employment and Wages (QCEW) Ron Fionte Branch Chief Bureau of Labor Statistics (BLS) (617) 565-2335 fionte.ronald@bls.gov

21. 21 The Quarterly Census of Employment and Wages Program: What is it?  A quarterly census of employers covered under Unemployment Insurance Tax laws, and Federal employers covered under Unemployment Compensation for Federal Employees.  not a sample

22. 22 QCEW Output: Macrodata & Microdata Macrodata Output:  Published data summed by location, industry and ownership  Number of establishments, monthly employment, and quarterly wages  Summed by geographical area, industry (NAICS) code and ownership

23. 23 QCEW Output: Macrodata & Microdata Microdata Output:  Confidential establishment level data; generally for internal use only  Sample frame for establishment surveys  Geocode-able, providing a detailed mapping reference

24. 24 Macrodata Output: Employment  All workers covered by UI laws and on the payroll as of the pay period including the 12th of the month.  Includes full and part time and those on paid leave. Does not include those on unpaid leave.  Published 3 ways: Monthly per quarter, quarterly averages, annual averages.

25. 25 Macrodata Output: Total Quarterly Wages  Total amount paid to covered workers during the quarter, regardless of when the services were performed.  Bonuses, overtime, and severance pay are included.  Possible that wages are counted for workers not included in employment total (if they never worked in a pay period including the 12th)

26. 26 QCEW Program Macrodata: What’s it used for? Provides detailed industry employment and wages data down to the county level*. As a benchmark for other BLS programs. Input to Bureau of Economic Analysis’ (BEA) Personal Income and Gross Domestic Product statistics. Input to other BLS programs: LAUS, MLS. *subject to confidentiality restrictions

27. 27 UI Tax Rate & Actuarial Analysis UI-Covered Employment Local Area Unemployment Personal Income (BEA) Gross Domestic Product (BEA) Economic Forecasting Current Employment Statistics Occupational Employment Statistics Job Creation/Destruction Size Class Dynamics Business Survival Rates Geocoded Establishments Occupational Employment Statistics Occupational Safety and Health Statistics Current Employment Statistics National Compensation Survey Industrial Price Program Occupational Safety and Health Statistics Programmatic Uses Benchmarking (Employment Base) General Economic Uses QCEW Data Analytical Uses Sampling Mass Layoff Statistics State Revenue Projections Jobs Openings & Labor Turnover Survey Job Openings & Labor Turnover Survey Quarterly Press Releases, Annual Employment and Wages Local Economic Development Indicators Clusters Analysis Shift Share Industry Diversity Indexes Location Quotients Federal Funds Allocation $175 Billion (HUD, USDA, HCFA/CHIP) Minimum Wage Studies Uses of QCEW, Quarterly Census of Employment and Wages Data Local Economic Impact Response Planning Local Government Services Planning Interagency Data Uses Improve CPS After 2000 Census LEHD Industry Code Sharing Local Transportation Planning

28. 28 Pre- vs. Post-Program Earnings’ Analysis Bill Meehan Principal Economist Division of Research and Statistics, New York State Department of Labor (518) 457-1300 us0bpm@labor.state.ny.us

29. 29 How Pre-Program Earnings Relate to Post-Program Earnings  Pre-program earnings can be a predictor of post-program earnings. In general:  The higher the pre-program earnings of a group of participants, the higher the post- program earnings  The lower the pre-program earnings of a group of participants, the lower the post- program earnings

30. 30 How Much Higher; How Much Lower?  In the Adult program in PY 2005 in New York State:  A 1 dollar change in pre-program earnings resulted in:  a 50 cent change in post-program earnings  In the Dislocated Worker program in PY 2005 in New York State:  A 1 dollar change in pre-program earnings resulted in:  a 30 cent change in post-program earnings

31. 31 How Was the Relationship Determined?  Simple observation of an apparent relationship between pre- and post- program earnings  Relationship was recognized under JTPA  The 30 cent and 50 cent relationships were determined using a regression analysis with pre-program earnings as the independent variable and post-program earnings as the dependent variable

32. 32 How strong is the relationship between pre- and post-program earnings?  The magnitude of the relationship (50 cents for Adults and 30 cents for DWs) was strongest in the middle earnings range of pre-program earnings  Not as strong in the low end  individuals with no pre-program earnings had much higher post-program earnings  Or in the high end  an increase in earnings in the higher range of the pre- program earnings ($15,000+) leads to an increase in post-program earnings, but not as much of an increase as in the lower pre-program earnings range

33. 33 Pre- and Post-program Earnings in Recent Years in New York State -- Adults AVERAGE PRE AND POST PROGRAM EARNINGS PY2005 Qtr 3 1 PY 2004PY 2003 Pre-Program Earnings RangeIndividuals Average Pre Program Earnings Average Post Program EarningsIndividuals Average Pre Program Earnings Average Post Program EarningsIndividuals Average Pre Program Earnings Average Post Program Earnings TOTAL18,028 $ 6,905 $ 11,46219,812 $ 7,125 $ 11,49327,087 $ 6,367 $ 10,812 $04,841 $ - $ 9,4285,183 $ - $ 9,8857,738 $ - $ 9,057 $1-$2,5002,710 $ 1,140 $ 8,3432,786 $ 1,152 $ 7,9754,128 $ 1,135 $ 7,766 $2,501-$5,0002,151 $ 3,707 $ 8,5942,261 $ 3,739 $ 8,7133,361 $ 3,708 $ 8,294 $5,001-$7,5001,761 $ 6,231 $ 9,8912,087 $ 6,214 $ 9,6582,747 $ 6,191 $ 9,343 $7,500-$10,0001,561 $ 8,720 $ 10,9751,856 $ 8,726 $ 10,9122,327 $ 8,711 $ 10,626 $10,001-$15,0002,296 $ 12,326 $ 13,3202,615 $ 12,348 $ 12,9693,238 $ 12,306 $ 13,023 > $15,0002,708 $ 22,352 $ 20,2263,024 $ 22,505 $ 19,9193,548 $ 22,036 $ 19,810 1 Source: PY 2005 Qtr 3 WIASRD. Includes exiters from 4/1/2004 to 3/31/2005

34. 34 General Performance Issues QUESTIONS? ETAPerforms@dol.gov