A Demonstration of OES 

Web-Based Chart and Map 

Visualization Tools 

Tanner Beam 

Economic Analyst 

Occupational Employment Statistics (OES) 

FCSM Conference 

November 2, 2013 
Outline 

 Overview of OES survey 

 Challenges of creating visuals 

 Demonstration of products 

 Research into future developments 

2 
Occupational Employment 

Statistics 

 Employment and wage estimates for 

over 800 occupations 

 Estimates published annually with May 

reference date 

 Cooperative effort between BLS and 

50+ state workforce agencies 

3 
Example of Three-Year 

Methodology 

 Estimates with May 2012 reference date 

produced with the following panels: 

May 2012

November 2011 

May 2011

November 2010

May 2010

November 2009

 Each year, two oldest panels drop off and two 

new ones are added 

Approximately 200,000 

units each; total 1.2 

million 

4 
Coverage and Classification 

 SOC – Occupations : 

 NAICS – Industries 

2,3,4, and some 5 and 6-digit levels 

(Broadest  narrowest scope) 

5 

23 Major occupation groups 

97 Minor occupation groups 

461 Broad occupations 

840 Detailed occupations 
2010 SOC 

Occupational Hierarchy 

6 

Major group 15-0000 Computer and Mathematical Occupations

Minor group 15-2000 Mathematical Science Occupations

Broad 

occupation

15-2040 Statisticians

This broad occupation is the same as the detailed occupation 15-2041 

Statisticians.

Detailed 

occupation

15-2041 Statisticians

Engage in the development of mathematical theory or apply statistical 

theory and methods to collect, organize, interpret, and summarize 

numerical data to provide usable information. May specialize in fields, 

such as bio-statistics, agricultural statistics, business statistics, 

economic statistics, or other fields. Include mathematical statisticians.
Estimates Produced 

 OES Data 1997 – 2012 

 Cross-industry by geographic area 

 U.S. 

 State 

 MSA / Non-MSA 

 Industry-specific 

 U.S. 

 States (Research Data) 

 By ownership (public/private): 

 U.S. only 7 
Visualization Challenges 

 Large amount of data 

 Survey limitations 

 Limited resources 

 Diverse user base 

8 
Data Available in Charts

 For a particular area: 

Occupations with highest employment 

Occupations with highest location quotients (LQs) 

 For a particular industry: 

Occupations with highest employment 

 For a particular occupation: 

Areas with highest employment 

Areas with highest LQs 

Industries with highest employment 9 
Data Available in Maps

 Employment estimates 

 Annual mean wage estimates 

 Location Quotients 

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Distribution – detailed occ

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Distribution – minor occ

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Distribution – major occ

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Software Defaults 

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 Number of levels (categories): 

FLOOR(1+3.3 log(n)), where n is # of response 

variables. 

– n= 52 (states)  6 levels 

– n=575 (MSAs)  10 levels 

Alternative: user specify # of levels 

 Ranges of levels determined by: 

“Equal distribution (quantizing) algorithm” 

Alternative: Nelder Algorithm (Applied Statistics 

25:94-7, 1976) 
Default # of Levels 

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Nelder Equalizing Algorithm 

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May 2011 – Employment 

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May 2012 - Employment 

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Location Quotient 

 Ratio of: 

( area occupational emp / area total emp ) to 

( US occupational emp / US total emp ) 

Example 

– Bakers in Breadtown= 5 

– Workers in Breadtown = 50 

– Bakers in Breadland = 2500 

– Workers in Breadland = 200,000 

– LQ = (5/50) / (2500/200,000) = 8 

 Popularized by BLS (?) 

QCEW LQ Calculator 

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May 2011 – LQ

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LQ values, States 

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May 2012 - LQ

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Future Research 

 Possible changes to existing products 

New algorithms for levels 

Levels by size classes 

Use polys instead of exact areas 

 Possible creation of new visual products 

Unused variables (e.g. wages in bar charts) 

New spatial concentration indices (e.g. Gini) 

Other data transformations 

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Contact Information 

Tanner Beam 

Economic Analyst 

Occupational Employment Statistics 

www.bls.gov/oes 

202-691-6592

beam.tanner@bls.gov