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Performance Webinar #3. Focusing on Average Earnings. Connection Status. Attendee List. Slide Area. Chat Room. Notes. Webinar Layout. Chat Feature. To chat, type text into the text box. When asking a questions, be sure to identify your State.

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Performance Webinar #3

Focusing on Average Earnings


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Connection Status

Attendee

List

Slide

Area

Chat

Room

Notes

Webinar Layout


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Chat Feature

  • 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


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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


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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 [email protected])


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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)


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Our Speakers . . .

Ron Fionte

  • Branch Chief,Bureau of Labor and Statistics (BLS)

    Bill Meehan

  • Principal Economist, Division of Research and Statistics, New York State


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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


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TEGL 17-05

Average Earnings (Adult, DW, NEG, TAA, Wagner-Peyser, VETS)

Of those who are employed in the 1st, 2ndand 3rd 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


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TEGL 17-05 Older Youth

Earnings Change:

Older Youth

Of those who are employed in the 1st 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 3rd quarter after the exit quarter or are employed in the 3rd 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


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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)


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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


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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


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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)


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Lower Living Standard Income Level (LLSIL)

  • 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.


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Lower Living Standard Income Level (LLSIL)

  • 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





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Quarterly Census of Employment and Wages (QCEW)

Ron Fionte

Branch Chief

Bureau of Labor Statistics (BLS)

(617) 565-2335

[email protected]


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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


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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


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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


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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.


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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)


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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


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Uses of QCEW, Quarterly Census of Employment and Wages Data

Quarterly Press Releases, Annual Employment and Wages

  • Job Creation/Destruction

    • Size Class Dynamics

    • Business Survival Rates

Geocoded Establishments

Industrial Price Program

National Compensation Survey

Current Employment Statistics

Occupational Employment Statistics

Occupational Safety and Health Statistics

Job Openings & Labor Turnover Survey

  • Local Economic Development Indicators

  • Clusters Analysis

  • Shift Share

  • Industry Diversity Indexes

  • Location Quotients

Current Employment Statistics

Gross Domestic Product (BEA)

Occupational Employment Statistics

Personal Income (BEA)

Minimum Wage Studies

State Revenue Projections

Occupational Safety and Health Statistics

Economic Forecasting

Jobs Openings & Labor Turnover Survey

General Economic Uses

Benchmarking

(Employment Base)

QCEW Data

Analytical Uses

Sampling

  • Interagency Data Uses

  • Improve CPS After 2000 Census

  • LEHD

  • Industry Code Sharing

Programmatic Uses

Local Government Services Planning

UI Tax Rate & Actuarial Analysis

Local Economic Impact Response Planning

UI-Covered Employment

Local Area Unemployment

Local Transportation Planning

Mass Layoff Statistics

Federal Funds Allocation

$175 Billion

(HUD, USDA, HCFA/CHIP)


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Pre- vs. Post-Program Earnings’ Analysis

Bill Meehan

Principal Economist

Division of Research and Statistics,

New York State Department of Labor

(518) 457-1300

[email protected]


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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


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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


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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


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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




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