Download presentation
Presentation is loading. Please wait.
1
MODULE 3: REAL-TIME LABOR MARKET INFORMATION
Emsi Certification MODULE 3: REAL-TIME LABOR MARKET INFORMATION
2
Analyst & Data Two Types of Data Traditional LMI Occupations
Industries Programs Real-Time LMI Job Postings Alumni Data Analyst & Data OUTLINE
3
Two Types of LMI TRADITIONAL LMI REAL-TIME LMI Government Sources
Private On-line Sources
4
Core Data Points – Traditional LMI
Job Counts: Number of computer programmers working in a region Earnings: Amount of money those computer programmers are making Job Change: Growth/Decline of computer programmers in the region Program Completions: Number of graduates to fill future openings
5
Traditional LMI Weaknesses
Traditional LMI Strengths Highly Structured Projections & Trends Total Coverage Lack of Detail No Localized Skills Information Outdated (maybe) No Employer Data
6
Core Data Points – Real-Time LMI
Skills sought after by potential employers Employers who are advertising for the position Specific job titles requested by the employer Total Postings: Count of all postings Unique Postings: Unduplicated count of job postings
7
Real-Time LMI Weaknesses
Real-Time LMI Strengths Provides Details on Job Descriptions Connects Jobs to Specific Businesses Reflects Current Outlook & Demand Small Portion of Economy Represented Non-Standardized Structure Unable to Produce Projections
8
Traditional LMI Weaknesses Real-Time LMI Strengths
Provides Details on Job Descriptions Connects Jobs to Specific Businesses Reflects Current Outlook & Demand Lack of Detail No Localized Skills Information Outdated (maybe) No Employer Data
9
Types of geographical regions
Emsi believes that the use of both of these datasets together creates the best overall LMI dataset to help you make choices and help provide students avenues to those careers that are doing well.
10
Regions WE CREATE NATIONAL LMI Nation State MSA County ZIP
11
Regions WE CREATE NATIONAL LMI Nation State MSA County ZIP
12
MSA MSAs µSAs Population 50K+ 374 MSAs Population 10-50K+ 581 µSAs
Metropolitan Statistical Area and Micropolitan Statistical Area MSAs µSAs Population 50K+ 374 MSAs Population 10-50K+ 581 µSAs
13
MSAs Larger, multi-county regions Describe major economies well.
Shouldn’t be combined to create larger regions. Shouldn’t be broken up into individual counties. Powerful for national comparisons.
14
MSAs
15
Counties Can simply be a single county.
Many users focus on one or two counties. Easy to break out into component counties and analyze. Combine to build larger regions
16
ZIPs Regions within counties
Describe regions that cross county boundaries Can be combined to build larger regions Best when combined Weak when used one at a time
17
Projections
18
Projections
19
Historic Projected
20
Projections don’t take unexpected things into account – because they’re unexpected
21
How do we calculate the Emsi 10 year Projection?
Emsi’s 10-Year Projection
22
Emsi’s Projection Accuracy
23
Example of Projection
24
Industries
25
Businesses NAICS (North American Industry Classification System)
1001 Codes 5 Levels Example: 31-Manufacturing 312-Beverage & Tobacco Product mfg. 3121-Beverage mfg. 31211-Soft Drink & Ice mfg. Ice mfg.
26
(Quarterly Census of Employment and Wages)
QCEW (Quarterly Census of Employment and Wages) Crucial dataset County level data Includes: Job Counts Annual Earnings # of Establishments Strength: Basis of our employee industry data due to job counts and annual earnings data Weaknesses: Lots of suppressions Doesn’t cover the self-employed
27
(County and ZIP Business Patterns)
CBP & ZBP (County and ZIP Business Patterns) Strength: Ranges provide a starting point in dealing with suppressions in other data Weaknesses: Not entirely consistent with QCEW Numbers are ranges CBP publishes employment ranges for industries in counties ZBP publishes employment ranges for industries in ZIPs
28
(American Community Survey)
ACS (American Community Survey) Strength: Publishes industry and occupation demographics Weakness: Sample-size survey Not a complete picture of the nation Huge data set with many interesting data points
29
(Quarterly Workforce Indicators)
QWI (Quarterly Workforce Indicators) Strengths: Very accurate count of hirings & separations by industry Demographic breakouts of industries Weaknesses: Significant lag time Suppressions Publishes industry: Demographics Hires Separations
30
Industry Demographic Examples
31
Emsi Analyst Application (scenario)
32
Occupations
33
Occupations O*NET SOC SOC (Standard Occupation Classification)
(from Department of Labor) 800 Codes 4 Levels Example: – Computer Occupations – Computer Occupations – Software Developers – Web Developer >900 Codes 5 Levels Example: Computer Network Architects – Computer Network Architects – Telecommunications Engineering Specialist
34
(Occupational Information Network)
O*NET (Occupational Information Network) Strengths: Allows us to find “similar” occupations. More detailed job titles helpful for using job posting analytics Weakness: Survey data Publishes knowledge, skill and ability characteristics of occupations
35
CONNECTION BETWEEN BUSINESSES & WORKERS
Staffing Pattern CONNECTION BETWEEN BUSINESSES & WORKERS
36
Staffing Pattern
37
Staffing Pattern Example
38
(Occupational Employment Statistics)
OES (Occupational Employment Statistics) Strengths: Helps regionalize staffing patterns Publishes wages Weaknesses: MSA level or Group of Counties Doesn’t match industry employment Weak time-series Publishes estimates for occupations at the metropolitan level Job counts Hourly earnings figures [Additional information for presenters: Our data scientists put the data through a lot of pre-processing (unsuppression, cleaning, classification changes, etc.) and testing to make sure the estimates are reasonable and match our industry data. Also this data source is complied by surveys which make it less reliable than industry datasets.
39
Emsi Analyst Application (scenario)
40
Programs
41
NCES / IPEDS Annual counts of completions by program for each college
(National Center for Education Statistics / Integrated Postsecondary Education Data System) Strengths: Only source for graduates by program Great starting point for connecting programs to occupations Weaknesses: Self-reported by institution Schools not participating in federal financial aid program not required to report Some mappings are too general Annual counts of completions by program for each college Crosswalk of programs and the occupations they typically train for Full-time equivalent enrollment Demographic data of enrolled students
42
CIP to SOC Crosswalk Programs (CIP) Occupations (SOC)
Emsi Data Experts have updated the NCES matrix and added ~1000 new links to improve mapping!
43
General Program Connections
Business Degree Cost Estimators Sales Management CEOs Managers Management Analysis
44
Crosswalk Example
45
Release Schedules & Suppressions
46
Suppressions Data not disclosed by the government
When one business in a region makes up the majority of employment in that industry When there are not many businesses in an industry How does Emsi account for these? CBP & ZBP ranges create a starting point Data Processing to fill in the rest
47
Is our method reliable?
48
Compiling the Sources
49
Release Schedules: Traditional LMI
50
Lag Time: Traditional LMI
51
Emsi Traditional LMI Data Updates
To account for release differences, we update Emsi Traditional LMI Data quarterly!
52
JOB POSTING ANALYTICS & PROFILE DATA
Real-Time LMI JOB POSTING ANALYTICS & PROFILE DATA
53
Job Postings
54
Job Postings Used to get information about businesses and the potential employees Comprised of 300 million unique job postings collected Job boards, recruiting temp agencies & individual company’s career pages
55
Job Postings 15,000 sources/month OR
6-8 million unique active posts/month ~400,000 unique businesses over the last 5 years, with >90,000 unique companies/month
56
Job Posting Weaknesses
Job Posting Strengths Job Posting Weaknesses Data on skills & certificates Companies hiring Posting Intensity Gives context to Traditional LMI Voluntary Not all occupations covered Professional, high education types greater representation E.g.: RNs = >80,000 postings Welders = 901 postings
57
Deduplication One of the most critical processes Unique Job Postings
April 2016: 90% of postings were duplicates 2 Step process: Exact Matching Step Fuzzy Matching Step
58
Job Postings: Updates Updated Monthly Lag time of ~1.5 months
59
Profile / Alumni Data
60
Profiles / Alumni Data Used to get information about an institution’s Alumni Comprised of million unique profiles collected from publicly sourced online professional profiles and resumes.
61
Alumni Data Weaknesses
Alumni Data Strengths Alumni Data Weaknesses Data on skills & certificates for alumni Where alumni are working Self Reported Profiles have different level of “completeness” Not all occupations covered Professional, high education types greater representation
62
Emsi Analyst Application (scenario)
63
For more information, please visit our website: economicmodeling.com
Thank You For more information, please visit our website: economicmodeling.com Thank you so much for attending. Please don’t forget to return to LearnUpon and complete the quiz associated with this course.
Similar presentations
© 2025 SlidePlayer.com. Inc.
All rights reserved.