Module 2 Emsi Data DATA BEHIND THE SCENES

Module 2: Emsi Data DATA BEHIND THE SCENES

Who is Emsi? Module 1: Why Career Coach? Module 2: Emsi Data Module 3: Using Career Coach Module 4: Employer Portal Bonus Module Knowledge Checks Advanced Training CHECKPOINTS

Why study data sources? § Understanding our data is crucial to understanding and trusting Career Coach § Provides deeper understanding so you can answer tough questions from students and other staff “The most helpful part [of the training] was learning about the data sources and weaknesses. ” -Gail Nguyen MONTGOMERY COLLEGE

§ Why Data? § Emsi LMI Origins § Industry Data § Occupation Data § Program Data § Release Schedules & Updates § Summary Emsi Data MODULE CHECKPOINTS

Emsi LMI HOW WE BUILD OUR DATA

How Emsi LMI is Built § Start with Industry Data § More and Better Quality Sources § Link Industry and Occupation § Occupational Sources Added § Link Occupation and Program § Program Sources Added

§ Why Data? § Emsi Data Origins § Industry Data § Occupation Data - Traditional § Real-Time Occupation Data § Program Data § Release Schedules & Updates § Summary Emsi Data MODULE CHECKPOINTS

Industry Data Sources

Compiling Industry Data Quarterly Census of Employment and Wages (QCEW) Non-employer Statistics (NES) American Communities Survey (ACS) Local Area Personal Income (LAPI) County & Zip Business Patterns (CBP & ZBP)

All Industry Data Sources Use NAICS Codes (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. 312112 -Ice mfg.

QCEW (Quarterly Census of Employment and Wages) § Crucial Dataset § County Level § Quarterly Release § Includes § Job Counts § Earnings § 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

CBP & ZBP (County and ZIP Business Patterns) § CBP publishes employment ranges for industries in counties § ZBP publishes employment ranges for industries in ZIPs Strength § Ranges provide a starting point in dealing with suppressions in other data Weakness § Ranges (200 -300), not hard numbers (256)

Combined Data Sources QCE W CBP/ZB P USPS NCES

Is our method reliable?

§ Why Data? § Emsi Data Origins § Industry Data § Occupation Data § Program Data § Release Schedules & Updates § Summary Emsi Data MODULE CHECKPOINTS

How Emsi Data is Built § Start with Industry Data § Core Datasets § Link Industries and Occupations § Occupational Sources Added

Occupation Data Sources

Occupation Sources Use O*NET SOC (Standard Occupation Classification) • More than 900 codes at 5 different levels of specificity • Example: • 15 -1143. 01 Telecommunications Engineering Specialist

Linking Industry to Occupations: Industries All hospitals in your region Occupations Surgeons, nurses, lab techs, receptionists

Staffing Pattern

NIOEM (National Industry-Occupatio n Empl oyment Matrix) § National table of what industries employ which occupations Strength § Starting point for translating industry figures into occupations Weakness § National level

OES (O ccupationa l Employment St at isti cs) § Publishes estimates for occupations at the regional level § Job counts § Hourly earnings figures Strengths: § Helps regionalize staffing patterns § Publishes wages Weaknesses: § MSA or County groups § Doesn’t match industry employment

Careers: Wages

O*NET (Occ upational Information Ne tw ork) § Knowledge, skill and ability characteristics of occupations § Level of Education Attainment for each occupation § “What would I do in this job? ” Strengths: § Provides information on individual occupations § Provides National Education Attainment Weakness: § Not tied to any particular employer/job posting

Career Pages Resume Builder

Growth Projections

Historic Projected

Projections don’t take unexpected things into account – because they’re unexpected

How do we calculate Growth Projections? § We start by creating three different 10 -year projections based on different timeframes (5, 10 and 15 years of historical data). § We combine these into one 10 -year projection § We add Federal & State projections if they’re current and consistent with our figures

Emsi’s Projection Accuracy

Job Projections in Career Coach

Live Job Postings § Live job postings come from indeed. com § Each career shows students job postings and a link to connect with potential employers.

Live Job Postings

Job Postings § Used to get information about businesses and the potential employees § Comprised of 86. 36 million unique job postings collected and curated § Job boards, recruiting temp agencies & individual company’s career pages

Job Posting Strengths Job Posting Weaknesses § Data on skills & qualifications § Voluntary § Companies hiring § Not all occupations covered § Professional, high education types greater representation § E. g. : RNs = >80, 000 postings Welders = 901 postings § Posting Intensity § Gives context to Traditional LMI

JPA - Activity

JPACompanies

What is a “Skill”? Common Skills: Broad, basic competencies § Character traits: Ethics, assertiveness, etc. § General cognitive or physical abilities: Critical thinking, Creativity, etc. § Basic interpersonal skills: Leadership, Cooperation, etc.

What is a “Skill”? Hard Skills: Technical, subject matter specific § Sales techniques, online marketing, foreign languages, etc.

What is a “Skill”? – Final Notes § Career Coach displays Hard Skills as Relevant Skills § Nationalizing skills to make them more useful

JPA Skills

§ Why Data? § Emsi Data Origins § Industry Data § Occupation Data § Program Data § Release Schedules & Updates § Summary Emsi Data MODULE CHECKPOINTS

How Emsi Data is Built § Start with Industry Data § Core Datasets § Link Industries and Occupations § Occupational Sources Added § Link Occupation and Program § Program Sources Added

Career Coach A CUSTOM MATCH BETWEEN YOUR PROGRAMS AND THE CAREERS THEY TRAIN FOR

Each (Credit) Program has a CIP Code (Classification of Instructional Programs) • 1, 700 C ode s; rep o rte d to IPED S • Three Le vels of De ta i l • Example : 26 – Bi olo g ical & Bi o medi ca l Scien ce s 26. 0 1 – Bi olo g y, G en e ral 26. 010 2 – Biome d ical Sci e nce s For non-credit programs, we manually assign them to the closest CIP

IPEDS Data (From the National Center for Education Statistics) § Crosswalk of programs and the occupations they typically train for § “CIP-to-SOC Crosswalk” if you like rhyming Strengths: § Great starting point for connecting programs to occupations Weaknesses: § Self-reported by institution § Some mappings are too general § Not unique to a specific college

CIP to O*NET SOC Crosswalk Programs (CIP) Occupations (SOC) Emsi data scientists have personalized and updated the IPEDS matrix and added ~1000 new links to improve mapping!

Specific Program Connections

General Program Connections

General Program Connections Business Degree Cost Estimators Sales Management CE O ers g a s n Ma Management Analysis

Program Connections

1. Initial Program Research 2. Emsi Mapping 3. Feedback & Review 4. Custom Program/Career Mapping

§ Why Data? § Emsi Data Origins § Industry Data § Occupation Data § Program Data § Release Schedules & Updates § Summary Emsi Data MODULE CHECKPOINTS

Release Schedules: Traditional LMI IPEDS ACS OES CBP & ZBP QCE W Q 1 Q 2 Q 3 Q 4

Lag Time: Traditional LMI QCEW ACS, OES, IPEDS CBP & ZBP 1 Year 2 Years Collection Date 6 Months Release Date

Emsi Traditional LMI Data Updates To account for release differences, we update Emsi Traditional LMI Data quarterly!

Emsi Real-Time LMI Release Schedule § Live job postings updating constantly § Job Posting Analytics are updated monthly

§ Why Data? § Emsi Data Origins § Industry Data § Occupation Data § Program Data § Release Schedules & Updates § Summary Emsi Data MODULE CHECKPOINTS

Creating Good Data: Summary § Emsi creates good industry data using the great sources available. § We use staffing patterns and additional sources to get occupational data. § We connect your programs to the occupations they train for based on our experience and feedback from your staff. § We take all this complex data, built for and primarily used by economists, and wrap it into a clean and easy-to-use tool so that students can make decisions using powerful economic data.

Who is Emsi? Module 1: Why Career Coach? Module 2: Emsi Data Module 3: Using Career Coach Module 4: Employer Portal Bonus Module Knowledge Checks Advanced Training CHECKPOINTS

Thank You Presenter Info Alys Lease(alys. Nafsinger@economicmodeling. com) Moses Bratrud(moses. Bratrud@economicmodeling. com)