Using the BLS Template (slide hidden from show; may be ...

Using the BLS Template (slide hidden from show; may be ...

Sampling and Collection in the Occupational Employment Statistics (OES) Program Dixie Sommers and Laurie Salmon Occupational Information Development Advisory Panel May 4, 2011 Overview Data available from OES Uses and users of OES data Standard classifications used OES sample design

OES survey operations OES estimation methods Special OES tabulations for O*NET 2 Data available from OES 3 Occupational Employment Statistics Employment and wages for over 800 occupations Cross-industry estimates for

The Nation States, District of Columbia, and selected territories Over 580 metropolitan and nonmetropolitan areas National estimates by specific industries Estimates by ownership Published annually with May reference date May 2010 data to be published May 17, 2011 Data items available Employment Hourly

and annual mean wages Hourly and annual wages by percentile 10th, 25th, median, 75th, 90th percentiles Measure of sampling error Employment and mean wage percent relative standard errors (PRSEs) Uses and users Employers and Human Resources

professionals Compare pay to data for their industry or area Understanding occupational employment and wages in making location and expansion decisions Academic researchers Understanding the structure of the labor market Understanding wages Media and general public

Uses and users Career and job search information Students and job seekers Guidance and career counselors Policy and program uses E.g., wages for Foreign Labor Certification Staffing patterns uses Preparing employment projections

O*NET sampling design to identify industries with concentrations of employment in occupations being surveyed Standard classifications used 8 Industry classification North American Industrial Classification System (NAICS)

Establishments are classified according to the goods or services the establishment produces Issued by Office of Management and Budget Jointly developed by U.S., Canada, and Mexico U.S. Economic Classification Policy Committee chaired by Census Bureau 9 Revised every five years (2002, 2007, 2012) Industry classification NAICS example 21 Mining, Quarrying, and Oil and Gas Extraction 211 Oil and Gas Extraction 2111 Oil and Gas Extraction

21111 Oil and Gas Extraction 211111 Crude Petroleum and Natural Gas Extraction 211112 Natural Gas Liquid Extraction 10 Occupational classification Standard Occupational Classification (SOC) Workers and jobs are classified into occupations based on the work performed

Issued by Office of Management and Budget Standard Occupational Classification Policy Committee chaired by BLS Established SOC Classification Principles and Coding Guidelines Revised 2000 and 2010 Occupational classification 2010 SOC structure 23 Major groups 97 Minor groups 461 Broad occupations 840 Detailed occupations 12

Occupational classification SOC Example Major group Minor group Broad occupation Detailed occupations 41-0000 Sales and related occupations 41-2000 Retail sales workers 41-2020 Counter and rental clerks and parts salespersons 41-2021 Counter and rental clerks Receive orders, generally in person, for repairs, rentals,

and services. May describe available options, compute cost, and accept payment. 41-2022 Parts salespersons Sell spare and replacement parts and equipment in repair shop or parts store. 13 Occupational classification SOC Manual provides approved modifications to the structure Delineation below the detailed occupation level permitted Add digits to the code

11-3031 Financial Managers 11-3031.01 Treasurers and Controllers OMB recommends that those needing extra detail use the O*NET structure Occupational classification All Federal agencies publishing occupational data for statistical purposes required to use SOC Increases data comparability across Federal programs

SOC developed for statistical purposes only Non-statistical purposes play no role in SOC development OMB will not modify the SOC to meet requirements of non-statistical programs Using industries and occupations together The combination of industry and occupation can further define the work E.g., retail salesperson may work selling cars and may need to drive. Others may work in

stores and need to stand. OES provides these data Distribution of an occupations employment by industry Distribution of an industrys employment by occupation (staffing pattern) 16 OES methodology Sample design Data collection cycle Estimation

17 OES sample design Sampling frame Unemployment insurance list of employers Covers 98 percent of wage and salary jobs Industry, county and employment level for each establishment Supplemented by other sources for industries not covered by state unemployment insurance Mainly Federal government and railroads

Universe and sample sizes Universe size of about 8 million establishments 1.2 million establishments in OES sample OES sample design Sample stratification By metropolitan and non-metropolitan area By industry strata Generally 4-digit NAICS, some 5-digit

NAICS By ownership for certain sectors Education and hospitals by state government, local government, and private ownership OES sample design Sample allocation for each stratum Include all large establishments Certainty units Improves sample efficiency For all other units

Based on expected variability and stratum size Minimum number of sample units Data collection cycle Full sample collected over 3-year cycle Two collection panels per year Reference dates of May and November 21 OES Survey Operations OMB

clearance Operational structure Data collection and processing 22 OMB clearance OMB clearance to conduct the survey Requires Description of purpose and uses No duplication of other federal data sources Detailed sample description

Description of respondent burden hours and cost Response rate targets Use of standard classification systems Description of collection methods 23 Operational structure Federal-State Cooperative Program

BLS National and Regional offices State Workforce Agencies BLS responsibilities Concepts and procedures Sample design and selection Survey form design, printing and mailing

Data capture and estimation systems Produce and publish estimates Data quality assurance Training and technical assistance Confidentiality policy and procedures Funding Operational structure State workforce agency responsibilities Address refinement of sample units Data collection, including nonresponse follow-up Data processing and editing Occupational coding Estimates review and publication

Protect data confidentiality OES survey forms Developed through cognitive and field testing For all types of establishments Verify known information about the establishment: employment, industry Request contact information for follow-up 26 OES survey forms Structured

forms For medium size and larger establishments Specific to individual industries or groups of industries Lists occupations commonly found in the industry Includes occupation definitions Employer determines how SOC codes relate to establishments job categories OES survey forms

Unstructured forms For smaller establishments For all non-responding establishments in the third follow-up mailing Open-ended format No occupations listed on form Employer reports by own job categories Data coded to SOC by state or regional office staff OES survey forms All

forms Request employment in the occupation by wage intervals Wage intervals used to estimate wage means, medians, and percentiles Data collection Mailing Includes form, letter, information sheet Second and third mailings to nonrespondents Response

mode options Complete paper form and mail back Complete form online Phone response Fax response Provide electronic payroll file (mail or email) Provide paper payroll listing Data collection

Improving response rates Pre-notification postcards Telephone follow-up Flexibility in reporting mode Web site for respondents Why respondents data are important Provide publications Confidentiality pledge Training data collectors on reluctance aversion

31 Data collection Response mode varies by establishment size Response panel rates for most recent 77.7 percent of establishments 69.5 percent of employment

OES estimation methods Use three years of data (six panels) May 2010 data based on these panels: May 2010 November 2009 May 2009 November 2008 May 2008November 2007 Employment estimation Sample weight adjustment Benchmarked to industry employment level

from external source 33 OES estimation methods Wage estimation using wage interval data BLS National Compensation Survey data used to estimate mean wages in each interval BLS Employment Cost Index used to age wages collected in earlier panels

Wages estimation using wage rate data Direct computation of means, medians, and percentiles Wage rate data for in certain sectors Federal government, U.S. Postal Service Special tabulations for O*NET Distribution of occupational employment by 6-digit NAICS More detailed than published OES data Shows

industries and areas with most employment in the occupation Useful for targeting sample selection on industries where occupation known to exist 35 Contact Information Dixie Sommers Assistant Commissioner, Office of Occupational Statistics and Employment Projections 202-691-5701 [email protected] Laurie Salmon

Supervisory Economist, Division of Occupational Employment Statistics 202-691-5701 [email protected]

Recently Viewed Presentations

  • Nursing Theorist Betty Neuman - Rebekkah McConnell, RN

    Nursing Theorist Betty Neuman - Rebekkah McConnell, RN

    Mrs. Loder is a 42 year old woman currently hospitalized for new onset of seizure disorder after experiencing a headache for three days. She has stable, long term full time employment in a managerial position. She takes no current medications,...
  • Outline - Home | ACCA Global

    Outline - Home | ACCA Global

    Eurostat Commission Report - COM(2013) 114 Although IPSAS, as it stands currently, could not easily be implemented in EU Member States, the IPSAS standards would be suitable as a reference framework for the possible future development of EPSAS
  • The Past, Present and Future

    The Past, Present and Future

    head-to-toe assessment of the patient . ... Security features are usually built in, and integrated prompts encourage accurate and comprehensive charting by "forcing" certain entries to be made before the user can progress further through the system.
  • M S A E C Rules for Team

    M S A E C Rules for Team

    You now have 30 seconds left 10 9 8 7 6 5 4 3 2 1 STOP 2. A census-taker knocks on a door and asks the woman inside how many children she has and how old they are. "I...
  • International Language Centers Procdure CHINE visa Ce document,

    International Language Centers Procdure CHINE visa Ce document,

    Après le règlement, nous vous remettrons un récépissé (coupon du retrait) et un reçu de paiement. (5) Conservez bien votre récépissé original et récupérerez le passeport à la date prévue au centre de visa. Dois-je . payer quand je dépose...
  • 'Standards' in Trading Standards - BSI Group

    'Standards' in Trading Standards - BSI Group

    The 14-part BS EN 71 series covers every possible aspect of toy safety, from flammability to toxicity of materials to their potential to trap clothing or injure a child's body. This set of standards underpins the EU Directive on Toy...
  • Mole Conversions - Lake Stevens School District

    Mole Conversions - Lake Stevens School District

    Review: Molar Mass of Compounds Ex. Molar mass of CaCl2 Avg. Atomic mass of Calcium = 40.08g Avg. Atomic mass of Chlorine = 35.45g Molar Mass of calcium chloride =
  • Today's Webinar Focuses on ARR Hospital Intervention Plans

    Today's Webinar Focuses on ARR Hospital Intervention Plans

    Today's Webinar Focuses on ARR Hospital Intervention Plans. Introduction to ARR. Implementation efforts. Intervention plans and budgets. Interventions. Metrics. Next steps and timeline. Update on HSCRC case mix to CRISP data merge tests. ARR: Intervention Plans and Budgets 11/17/2011