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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

Performance Webinar #3

Focusing on Average Earnings

webinar layout

Connection Status

Attendee

List

Slide

Area

Chat

Room

Notes

Webinar Layout
chat feature
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
background
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
future planned webinars
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)
webinar outline
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)
our speakers
Our Speakers . . .

Ron Fionte

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

Bill Meehan

  • Principal Economist, Division of Research and Statistics, New York State
history of earnings outcomes
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 Rescinded by TEGL 17-05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 Rescinded by TEGL 28-04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
tegl 17 05
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

tegl 17 05 older youth
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

data sources
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)
ui wage records
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
additional wage record data source wris fedes
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
from data sources to benchmarks
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)
slide15

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.

slide16

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
quarterly census of employment and wages qcew
Quarterly Census of Employment and Wages (QCEW)

Ron Fionte

Branch Chief

Bureau of Labor Statistics (BLS)

(617) 565-2335

fionte.ronald@bls.gov

the quarterly census of employment and wages program what is it
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
qcew output macrodata microdata
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
qcew output macrodata microdata23
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
macrodata output employment
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.
macrodata output total quarterly wages
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)
qcew program macrodata what s it used for
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
slide27

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)

pre vs post program earnings analysis
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

how pre program earnings relate to post program earnings
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
how much higher how much lower
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
how was the relationship determined
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
how strong is the relationship between pre and post program earnings
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
general performance issues
General Performance Issues

QUESTIONS?

ETAPerforms@dol.gov

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