Projections Managing Partnership Summit · Projections Managing Partnership Update Coretta Pettway,...

Projections Managing Partnership Summit

June 6-7, 2016

Minneapolis, MN

Day 1 Agenda

Time Session

8:00-9:00 a.m. Registration, Breakfast, and Office Hours

9:00-10:00 a.m. Welcome, Introductions, and Strategic Plan Update

10:00-10:15 a.m. Break

10:15-11:15 a.m. National Perspectives: ETA

11:15 a.m.-12:00 p.m. BLS Separations

12:00 p.m. – 1:00 p.m. Lunch (on your own)

1:00-2:30 p.m. Skills Projections

2:30-2:45 p.m. Break

2:45-4:00 p.m. Report Manager

4:00-4:30 p.m. Step-Ahead Adjustment

4:30 p.m. Reception (Atrium)

Strategic Plan Update

Coretta Pettway

Projections Managing Partnership Update

Coretta Pettway, Chair

PMP Summit

Minneapolis, MN

June 6, 2016

PMP Vision

To maximize the efficiency of public investments by facilitating talent development with high quality industry and occupational projections.

PMP Mission

To enable/support states as they develop and deliver high quality state and local employment projections.

• Coretta Pettway, Chair – Ohio Department of Job & Family Services

• Paul Shannon, Vice Chair – Arizona Department of Administration

• Alexandra Hall – Colorado Department of Labor & Employment

• Jim Henry – Alabama Department of Labor

• Jacqueline Keener – North Carolina Department of Commerce

• Carolyn Mitchell – Maryland Department of Labor, Licensing & Regulation

• Jason Palmer – Michigan Department of Technology, Management, & Budget

• Graham Slater – Oregon Employment Department

• Vacant

PMP Board of Directors

PMP Board Strategic Plan Priorities

• Develop formal process for subject matter expert succession planning

• Bolster and build relationships with key national partners

• Explore ways to enhance financial stability and sustainability

PMP Organizational Structure

PMP Board of Directors

Communications & MarketingCommittee

Training Committee

Product & Process InnovationCommittee

GovernanceCommittee

PMP Committees

• Communications & Marketing – Chair, Alexandra Hall (CO)

• Training – Chair, Jacqueline Keener (NC)

• Product & Process Innovation – Chair, Graham Slater (OR)

Committee Purpose and Objectives

Communications & Marketing:

Purpose

o Provide clear, informative communications about PMP and its activities to the PMP’s internal network, as well as to projections users.

Objectives

o Translate technical, projections-related material into layman’s terms.

o Develop content for PMP’s support and public-facing websites.

o Inform projections analysts, LMI Directors, and projections users of important PMP activities and events.

o Foster greater interaction between projections analysts and projections users.

Select Strategic Plan Items: Communications & MarketingItem Priority

RankCommittee Time Horizon Action Dashboard

8 High Board 2 Year Articulate the value of the PMP in the

national LMI infrastructure to NASWA,

etc.

9 High Board 2 Year Engage with BLS on their strategic

planning efforts related to projections

and OES

10 High Board 2 Year Develop marketing strategy (including

effective infographics)

21 Medium Communications 2 Year Enhance resources available on public-

facing website

22 Medium Communications 2 Year Engage federal partners and associations

in communicating with customers and

understanding their needs

Dashboard Key: Not Initiated On Schedule Requires Attention At Risk Complete

Communications & Marketing:

• Held 3 focus group calls with projections users from Community Colleges, Economic and Workforce Development, and Vocational Rehabilitation Services.

• Initiated a Marketing Plan to help foster and expand the PMP network.

• Gathered input on PMP’s public and analyst support websites.

Committee Accomplishments & Ongoing Activities


Training:

Purpose

o Develop training content to meet the professional development needs of the PMP network.

Objectives

o Identify high-priority sessions for the annual PMP Summit to support professional development among projections analysts.

o Develop and organize opportunities for e-learning.

o Recruit subject matter experts for training efforts.

Select Strategic Plan Items: Training

Item Priority


14 High Training 2 Year Build staff capabilities to utilize Report Manager

module

15 High Training 2 Year Provide opportunities for analysts to contribute more

actively on committees

16 High Training 2 Year Formalize process to provide appropriate successors for

relevant subject matter experts

23 Medium Training 2 Year Supplement developed e-learning with in-person

training and networking opportunities for analysts

24 Medium Training 2 Year Develop framework to provide support to “onboard”

new analysts in the states

25 Medium Training 2 Year Create curriculum to allow analysts to better

communicate the value and applicability of projections

to users

26 Medium Training 2 Year Build on existing curriculum to provide periodic training

for users


Training:

• Developed a 1-day Projections Suite Training to be held after the 2016 PMP Summit.

• Revamped the PMP Summit Agenda to include more interactive sessions.

• Prioritized “beginner” and “intermediate” topics for onboarding new analysts.

• Initiated recruitment for new training instructors.



Product & Process Innovation:

Purpose

o To provide guidance for states to create the highest quality and most relevant projections data for customers.

Objectives

o Perform research and technical innovations related to producing employment projections and improving the process by which employment projections are developed, in accordance to Board recommendations.

o Better projections analysts’ understanding of the BLS Separations methodology and its implications.

Select Strategic Plan Items: Product & Process InnovationItem Priority


11 High PPI 2 Year Complete step-ahead methodology project

12 High PPI 2 Year Complete separations methodology project

13 High PPI 2 Year Complete state comparisons project

6 Medium PPI 5 Year Validate the “gold standard” quality by evaluating state

projections/methods against actual outcomes, private

data products, and other benchmarks

4 Low PPI 5 Year Explore methodology and feasibility of “data fuzzing” to

combat data suppression concerns from users

5 Low PPI 5 Year Prioritize internal R&D efforts to enhance projections

data (e.g., skills, labor supply)


Product & Process Innovation:

• Facilitated launch of BLS Separations rate methodology, and launched BLS Separations/Replacements methodology comparison.

• Launched Step-Ahead Adjustment Pilot-Test.

• Created guidance for reviewing occupational estimates prior to self-publication.

• Designed state projections comparison module for the Projections Suite software.


• PMP Summit 2016: Minneapolis, MN; June 6-8; co-hosted by the LMI Institute.

• Continue collaboration with the BLS to implement new Separations methodology.

• Analyze results of BLS Separations/Replacement methodology comparison and Step-Ahead Adjustment Pilot-Test.

• Maintain and improve the Projections Suite software.

• Upgrade Projections Training website.

Future Initiatives

Break

:-D

National Perspectives: ETA

Sam Wright

Employment and Training Administration

June 6, 2016

Sam Wright

The Goal of WIOA

The purpose of the Workforce Innovation and Opportunity Act

(WIOA) is to provide workforce investment activities through

statewide and local workforce investment systems that increase the

employment, retention, and earnings of participants. WIOA

programs are intended to increase the occupational skill attainment

by participants and the quality of the workforce, thereby reducing

welfare dependency and enhancing the productivity and

competitiveness of the Nation.

24

Major Difference in WIOA and WIA

Major difference between Workforce Innovation Act (WIA) and WIOA as it relates to the PMP:

WIOA puts emphasis on the collaboration between ETA and the Department of Education.

25

Why Does ETA Provide Funding for State

Projections?

26

1)All Data is Local: While National Projections are the key input of the State Projections estimation model, there are major differences.

2)Clear Objectives: Projections give a state’s LMI and education shops clear goals to reach full employment.

3)Source of Information: A valuable source of information for workforce and economic development policies and investment decisions made by the governor and state and local workforce investment boards.

Projections Four Primary Customer Groups

1) The public (including job seekers and employers);

2) Labor market intermediaries who help individuals find a job or make career decisions (such as employment and school counselors, case managers at American Job Centers, and community-based organizations);

3) Policymakers, employment and economic program planners and operators (such a Governors, State Legislators, etc.)

4) Miscellaneous other customers, including researchers, commercial data providers, and the news media.

27

PMP and the Department of Education

Process Map

28

State

Projections

PMP

PMP and the Department of Education

Process Map (cont’d.)…

29

Employers

Curriculums/

Training Programs

Education / Apprenticeship

The Role of Projections

Projections are the economic data that determines the curriculums and strategies that insure current and future workforces reach their full employment potential.

30

Where Do We Go From Here?

31

1. State Projections data used to identify skill gaps.

Increased Employer Involvement

2. Increased Visibility of the PMP

Public Access to the Total State Projections file

Recommend a separate webpage

Employment and Training Administration

Thank You

32

Questions?

BLS Separations

Michael Wolf

34 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Occupational Separations

Michael WolfEmployment Projections Program

PMP SummitJune 6, 2016

35 — U.S. BUREAU OF LABOR STATISTICS • bls.gov35 — U.S. BUREAU OF LABOR STATISTICS • bls.gov

Overview

Job Openings Concepts

Results of the Separations Method

Detailed Findings

Next Steps


Job Openings Concepts


Job Openings Usage


What We’re Measuring

Job Openings measure opportunities to enter an occupation for individuals not currently employed in that occupation

Opportunities arise because of

1. growth in the occupation

2. workers permanently leaving and needing to be replaced


Transfer Entrants

Other Occupations

Occupational Flows

Labor Force Exits

Outside the Labor Force

OccupationalTransfers

Other Occupations

Same Occupation

Same Occupation

Outside the Labor Force

Labor Force Entrants


Separations Method Results


Separations Method

Longitudinal/retrospective survey data estimate labor force exits and occupational transfers

Regression models estimate projected rates of separations for each occupation

Projections of separations are combined with employment projections to produce job openings


Job Openings

Separations method gives much higher results than replacements method

17.7 million vs. 4.7 million openings annually

Without an independent data set to test accuracy, how do we stress test the method?


Job Openings Context

What does 17.7 million openings mean?

Equivalent to every current worker either leaving the labor force or changing occupations once every 10 years

JOLTS data average 55.8 million hires, 54.9 million separations annually over the past 10 years

BLS projects 3.6 million labor force entrants annually from 2014-2024


Example Rates, 2014-24

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

Replacement Rate

Occupational Transfer Rate

Labor Force Exit Rate

Total, All Occupations

ActuariesWaiters and Waitresses Machinists


Rates Context

Inverse of rate is proxy for average tenure in occupation

45

Occupation Annual Separation Rate

Inverse Average Tenure

Surgeons 3.2% 1/.032=31.3 31.3 Years

Actuaries 6.7% 1/.067=14.9 14.9 Years

Waiters & waitresses

18.7% 1/.187=5.3 5.3 Years


Detailed Findings


Occupational transfers model

47

Binary dependent variable: does an individual leave their current occupation and find employment in a new occupation?

Independent variables Age Sex Occupation Education Race Ethnicity Citizenship status Full time/part time status Class of Worker Industry Year


Occupational transfers results

48

0

0.2

0.4

0.6

0.8

1

1.2

Age Independent Variable Coefficients



49

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Occupation Independent Variable Coefficients



50

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

lths hs scnd ad ba ma doc

Education Independent Variable Coefficients


Labor force exits model

Binary dependent variable: does an individual leave the labor force?

Independent variables Age Sex Age*Sex interaction Occupation Education Race Ethnicity Citizenship status Full time/part time status Class of Worker Industry Year


Labor force exits results

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Age, Sex Coefficients

Female Male



0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

Occupation Independent Variable Coefficients


0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0.45

0.5

lths hs scnd ad ba ma doc

Education Independent Variable Coefficients



Findings from 2014-24 results

Separation rates are more consistent over time

640 and 656 occupations had rate changes of 1 percentage point or less from 2012-22

Average absolute change is 0.62 and 0.71


Next Steps


2014-24 projections

2014-24 Projections use the old replacement rates method

BLS has calculated 2014-24 projections using both methods for internal analysis and review

Will be used in state review starting in July


Next Steps

BLS will work with the PMP and states to identify common approaches to communicating and presenting data from the new method

Both BLS and states will publish 2016-26 projections using the new separations method

Contact Information


Michael WolfDivision Chief

Employment Projections Programwww.bls.gov/emp

[email protected]

Lunch

:-D

Skills Projections

Paul LaForge, Brian Pulliam, George Putnam

PMP SummitMinneapolis, MN

June 2016

Top 15 Knowledge Domains

Top 15 Content Skills

Top 15 Generalized Work Activities

Use Report Manager◦ Projections Method

◦ Reports Method

O*NET – Skills, Knowledge, Generalized Work Activities

Projections – Estimation Suite Occupational Projections

Geography – select area you want to look at

Timeframe – select timeframe you want to look at

Occupations – No specific selection

Projections – Under Estimation Suite Occupational Projections – Base Year Employment, Projected Year Employment, Openings Due to Growth, Replacements

O*NET – Select one: Skills, Knowledge, Generalized Work Activities

Geography – select area(s) you want to look at

Timeframe – select timeframe(s) you want to look at

O*NET Itself SOC Codes vs O*NET Codes

Importance and Level must both meet threshhold

Annual Openings vs Total Openings

Adding Other Criteria such as Education or Wages is time consuming

Brian Pulliam

Arkansas Department of Workforce Services

[email protected]

Phone: (501) 682-3123

mailto:[email protected]

George Putnam (IL)2016 Projections Summit

Minneapolis, MNJune 2016

Why are we discussing skills-based product development?

Analyst polling results◦ Polling webinar on April 26, 2016◦ 27 participants representing 19 states

(21 respondents to polling questions)◦ Kudos to Randall Arthur and LMI Institute

Implications of polling results for skills-based product development

Report Manager O*NET Assignments◦ Worker Attributes- skills and knowledge

◦ Job Requirements- general work activities

PA O*NET Assignments (Tim McElhinny)◦ Job Requirements- knowledge, detailed work

activities, and tools/technologies

Occupation-specific skills that require only moderate-term training

Determining career pathways◦ Share job skills (knowledge, dwa, t/t)

◦ Rank

Education requirements

Wages

Skills Gap

Transferable job skills to related occupations

Who are we?

How do we respond to customer requests …?

What skills-related information is in high-demand by customers?

What support exists among us for combinations of skills-related information?

How familiar are we with the characteristics of the O*NET database?

Who are we?

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10 11Do you work on ... ? Do you handle customers? How often?

Ind

Both

Occ

Yes

No

DNA

2-3/wk

2-3/mnth

1/qtr

How do we handle customer requests?

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11Electronic file? Internet search and download? Canned report?

Often

Never

Sometimes

Often

Often

Sometimes

Sometimes

Never

Never


0

2

4

6

8

10

12

14

16

18

1 2 3How often do you receive requests for skills-based information?

Often

Never

Sometimes


0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Skills-related projections? Emerging skills? Skills gap?

Often

Never

Sometimes

Often

Often

Sometimes

Sometimes

Never

Never

Often

Sometimes Never

CIPS (Instructional)?

What support exists for combinations of skills-related information?

0

2

4

6

8

10

12

14

16

18

1 2 3Do you support the development of skills-related information in RM?

Strong Support

No Support

Some Support


0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Skills-related info on occs? Skills-related info on inds? Skills-related info on emp proj?

Strong

No

Some

Strong

Strong

Some

Some

No

No

Strong

Some

No

Skills-related info on edu ?


0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Skills-related info on earnings? Skills-related info on emerging? Skills-related info on skills gaps?

Strong

No

Some

Strong

Strong

Some Some

No

No

Strong

Some

No

Skills-related info on CIPS?

How familiar with the O*NET database?

0

2

4

6

8

10

12

14

16

18

1 2 3 4 5 6 7 8 9 10 11Familiar O*Net components? Familiar O*Net GWA,K, S? Familiar O*Net DWA and T/T?

Very

Not

Somewhat

Very

Very

Somewhat

Somewhat

Not

Not

How familiar with the O*NET database?

0

2

4

6

8

10

12

14

16

18

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Familiar O*Net importance/level? Importance/level essential? Importance/level weighting?

Very

Not

Somewhat

Equal

Very

Somewhat

Thresholds

Neither

Not

SME

Analyst

SME + Analyst

Who determine weight?

Who are we?◦ Many analysts have responsibility for both industry and

occupation projections AND handle customer requests◦ Implication: product development should facilitate current

practices not create new requirements

How do we respond to customer requests …?◦ Analysts respond to customer requests with electronic files,

Internet searches and canned reports◦ Implication: product development should maximize flexibility in

output format

What skills-related information is in high-demand by customers?◦ Analysts identify skills projections and skills gap as high-demand

information, less so emerging skills and CIPS◦ Implication: product development should focus on phased

implementation with skills projections and skills gap as the highest priority

What support exists among us for combinations of skills-related information?◦ Analysts support highest for employment projections,

occupations, education and earnings as critical dimensions to skills-related products

◦ Implication: product development should reflect a multi-dimensional perspective on skills

How familiar are we with the characteristics of the O*NET database? ◦ Analysts are somewhat familiar with components of the

O*NET database and products should utilize thresholds as filters on skills

◦ Implication: product development should enhance analyst familiarity with O*NET and permit adjustment of level and importance thresholds for state-specific requirements

Skills-Based ProjectionsPaul LaForge

Goals

• Avoid Misleading with Perceived Precision

• Keep the Narrative Consistent with Methodology

• Provide Actionable Information to Customers

Expanding Projections

• Descriptor-Based Projections

• Index-Based Projections

• Index-Descriptor-Based Projections

• Skills-Based Projections

• Skills-Based Industry Projections (& More)

Descriptor-Based Projections

• Categorization of occupation- or industry-coded projections

• Examples: “Growth”, “Size”, “Wage”, “Green”

• Common Challenges:

▫ Various data series types

▫ Distribution analysis (Range of Observations, Range of Values, Confidence Intervals)

▫ More than one data series as basis

Index-Based Projections

• Index value assigned to each occupation or industry code based on multiple data series

• Examples: “25.6”, “12553”, “0.00034”


▫ Heterogeneous data series

▫ Implementing a Principal Components Analysis approach

▫ Interpreting the index values

Index-Descriptor-Based Projections

• Categorization of index values assigned to occupation or industry codes

• Examples: “Hot Jobs”, “5-Star Jobs”


▫ Methodologically sound underlying index values

▫ Methodologically sound categorization(e.g. distribution analysis)

Skills-Based Projections

• Linking O*NET elements to occupation-coded projections

• Examples: “Skills”, “Knowledge”, “Tools & Technologies”

• Common Challenges:▫ Skills-based employment substantially higher▫ One to many vs. one to one occupations▫ Sometimes, many to one occupations▫ Sometimes, many to many occupations▫ Sometimes, “many to maybe” linkages


“Many to Maybe”

May want to filter O*NET elementsmatching to occupations:

• Level (Typically “3” or More)

• Importance (Typically “4” or More)


“Many to Maybe”

O*NET Reference

http://www.onetcenter.org/ombclearance.html


“Many to Maybe” Challenges• Level (typically “3”)• Importance (typically “4”)• Sometimes need to understand observation and

employment distribution across Level and Importance (or similar measures)

• Sometimes there’s no quantitative adjustment mechanism▫ Example: Tools & Technology

(Use United Nations Standard Products and Services Code instead with disclaimers)


“Many to Maybe” ChallengesO*NET-SOC

Code Title T2 Type T2 Example

Commodity

Code Commodity Title

11-2011.00 Advertising and

Promotions Managers

Technology Actuate software 43231602 Enterprise resource

planning ERP software


Promotions Managers

Technology Adobe Systems Adobe

Acrobat

43232202 Document management

software


Promotions Managers


AfterEffects

43232103 Video creation and editing

software


Promotions Managers


Creative Suite software

43232402 Development

environment software


Promotions Managers


Dreamweaver

43232107 Web page creation and

editing software

Skills-Based Industry Projections

(& More)• Using I/O matrix to crosswalk industries to

skills

• Example #1: “Ranked Knowledge Needs in Services”

• Example #2: “Ranked Knowledge Needs for Five-Star Jobs in Services”

• Example #3: “Ranked Knowledge Needs for Five-Star Jobs in Services by Education Level& Geography”

Questions or Comments?

Paul LaForge

[email protected]

Break

:-D

Report Manager

Steve Brock, Paul LaForge

Steve Brock

Utah Department of Technology Services

June 6, 2016

Review: What is Report Manager? Web-based system for creating, displaying and

exporting a variety of projections-related data

Indexes

Descriptors

Skills projections (crosswalking SOC data to O*Net)

Can handle industry and occupation data

Can handle any geography

RM Architecture Web program and databases hosted on Utah DTS

servers

States upload data to a Microsoft SQL Server database

Database also contains “directories” (NAICS, SOC, O*Net)

Individual state data is stored in “warehouses”, which are separate databases linked to the main database

How does RM manipulate data? SQL Server Analysis Services

Online Analytical Processing (OLAP) tool

Data is organized into “cubes”, defined by “dimensions”

The cubes have to be built, or “processed”, from the state data and applicable directories

Each time data is changed (new state data or a new directory), the cube must be re-processed

That way, data is immediately present at reporting time without extra processing

How did that work out for us? At the time, Analysis

Services was the best option available….

Analysis Services Issues Analysis Services was designed more for an overnight-

processing operation rather than real-time use

RM needs to reprocess a cube each time that cube’s state uploads new data or changes existing data

This causes massive drag on the database server, especially if multiple states are uploading data simultaneously

Analysis Services Issues As we move forward with

plans to incorporate detailed O*Net and CIP-based data, it’s clear that our current database servers won’t be able to handle the load.

In fact, they experience some issues now, as we know.

Other RM Issues System still has some bugs odd features related to:

Data import

Report generation

Release of June 2 fixed many of these problems

We’re continuing to resolve issues as we find them

The Future of RM New easy-to-use report functionality, including some

pre-defined reports

Replacing the Analysis Services portion

New database provider: MongoDB

Different from any other provider we’ve used

More about Mongo coming up!

Current timeline End of September/beginning of October 2016: First

version with new reporting capability and Mongo analysis

Later in fall: Additional upgrades/enhancements

Paul LaForge

Mongo!

MongoDB

Expedia

Forbes

Bosch

AstraZeneca

MetLife

facebook

craigslist

Who Uses MongoDB?

Takes advantage of technological advancesin Big Data

Better supports greater O*NETdetail linking to projections

Facilitates simpler, more intuitive user interface

Decreases time it takes to generate reports

Why MongoDB?

Manage Multiple SOC/NAICS Directories

Built with occupation and industry classification systems in mind

Enables user to link data based on different occupation or industry directories

Existing Features

Pre-loaded with Key Data Elements

O*NET versions

BLS Green Data Elements

ETA Green Data Elements

Existing Features

Develop Indexes & Descriptors (Occupations or Industries)

Handles various data types

Performs distribution analysis

Informs as to the most statistically appropriate categorization scheme

Handles multiple, heterogeneous data series (indexes)

Implements principal component analysis approach

Existing Features

Develop Skills-Based Projections

Manages one-to-many, many-to-many challenges of linking employment projections to O*NET SOC

Filters out by key measures (e.g., Level >= 3, Importance >= 4)

Incorporates best practices established by PMP research

Existing Features

Generate Custom Pivot Table Reports

Provides access to an n-dimensional cube of your data

Facilitates complex calculations across geography, time, occupation, O*NET elements

Enables basic exporting to Excel or CSV

Existing Features

Geography

Timeframe

Various Employment Measures

O*NET Elements

Occupations

User-Developed Skills-Based Projections

User-Developed Descriptors

Custom Reports

Simplified User Interface

Better Implementation of Detailed O*NET

“Canned” Reports

Upcoming Features

Simplified selection of data from current report generation screen

Driven by research efforts in PA and a recent survey of analysts

Should facilitate current practices and not create new requirements

Should maximize flexibility in output format

Should focus on phased implementation with skills projections and skills gap as the highest priority

Should enhance analyst familiarity with O*NET

“Canned” Reports

Timelines

Currently developing a MongoDB-based version in parallel

Will need early adopters who could begin testing incremental feature enhancements as soon as July, 2016

Anticipate transition complete by Fall 2016

Interested in testing? Email [email protected]

Questions / Comments?

Paul LaForge

[email protected]

Long-Term Step-Ahead Projections (Pilot Project):

Leveraging Short-Term Projections

Robert Richards, NM

George Putnam, IL

Long-Term Step-Ahead Projections: Enhancing Timeliness

• ETA deliverables for FPY 2013 (Jul13 to Jun14)– 2012 – 2022 Long-Term Projections (statewide)– 2013 – 2015 Short-Term Projections (statewide)

• Proposed Strategy (two options)– 2013 – 2022 Long-Term Projections (statewide)– 2014 – 2022 Long-Term Projections (statewide)

• Pilot Test Objective– Adjust 2012-2022 LTIP

• Long-Term Industry Projections (base 2012 and projection 2022)

– Adjustment source data 2013-2015 STIP• Short-Term Industry Projections

(base 2013Q1 and projection 2015Q1)

• Step-Ahead Adjustment is not a required ETA TEGL deliverable

Step-Ahead Adjustment Methodology: Validation Steps

• LTIP 2013-2022 (9-year horizon)

– Validate the 2013 “projected base” employment in LTIP 2013-2022 (9-year horizon) based on actual 2013 employment reported in the 2015-2017 STIP

• LTIP 2014-2022 (8-year horizon)

– Validate the 2014 “projected base” employment in LTIP 2014-2022 (8-year horizon) based on actual 2014 employment reported in the 2015-2017 STIP

Pilot: Industries Per State

Common Industries: 2013

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%

(Actual/Proj) < abs1% abs1% > (Actual/Proj) < abs3% (Actual/Proj) > abs3%

LTIP Naïve 2013 STIP 2013

Preliminary Results Prepared for 2016 PMP Summit

Detailed Industries: 2013


0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

40.0%

45.0%



Common Industries: 2014


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%



Detailed Industries: 2014


0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%



Preliminary Results: Observations• LTIP 2013-2022 (9-year horizon)

– 2013-2015 STIP approach outperforms 2012-2022 LTIP Naïve approach

– (Actual/Proj)< abs1% • Common industries: 42% vs 28%• Detailed industries: 35% vs 24%

– abs1% > (Actual/Proj) < abs3% • Common industries: 34% vs 37%• Detailed industries: 36% vs 37%

• LTIP 2014-2022 (8-year horizon) – 2013-2015 STIP approach slightly outperforms 2012-2022 LTIP Naïve

approach– (Actual/Proj)< abs1%

• Common industries: 17% vs 14%• Detailed industries: 14% vs 14%

– abs1% > (Actual/Proj) < abs3% • Common industries: 32% vs 28%• Detailed industries: 24% vs 24%

• Consistent industry structure across pilot states– Re-estimate results for CA and WI to approximate

the combination of 3-digit and 4-digit industries reported by other states

• Expand analysis to include evaluation of 2012 base-year employment

• Produce analysis by industry sector

Long-Term Step-Ahead Methodology:Next Steps

• Preserves the coherence and integration of the PMP Projections Suite software/products– Sustains national projections infrastructure– State analyst priority

• Requires only a single data preparation process – Generates identical historical industry employment series

for the 2-year, 10-year and step-ahead industry projections– Realizes significant data processing efficiency

• Maintains data integrity/output consistency– 2-year, 10-year and step-ahead industry projections AND

OCCUPATION PROJECTIONS– Customer priority

Long-Term Step-Ahead Methodology:Advantages

Reception

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