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Using Data to Influence Policy Change -Manny Lamarre, Executive Director Governor’s Office of Workforce Innovation (OWINN)

Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

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Page 1: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Using Data to Influence Policy Change

-Manny Lamarre, Executive Director Governor’s Office of Workforce Innovation (OWINN)

Page 2: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

How do you feel about data?

Page 3: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

“In God [I] trust, all others must bring data.”

-unknown

Page 4: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

OUTCOMES:

1. Prepare all K-12 Students for College & Career Success

2. Increase Nevadans with postsecondary degrees &

credentials

3. Increase Employment Outcomes in Training and

Credentialing programs

OWINN helps drive a skilled, diverse, and aligned workforce in the state of Nevada by promoting cooperation and collaboration among key entities focused on workforce development.

Page 5: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

OWINN’s Priorities 2018-2019

Aligning workforce training with labor market data through policies & practices

Scaling work-based learning and Registered Apprenticeships

Outreach and awareness – workforce opportunities & partnerships

Page 6: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Once Upon A Time……

Page 7: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

But then he hit rock bottom……

Page 8: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Nevada’s record high unemployment rate……

Page 9: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

But after finding a special person that helped him

focus on his priorities………

Page 10: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Jobs Growth & Diversification Efforts

0

200,000

400,000

600,000

800,000

1,000,000

1,200,000

1,400,000

1,600,000

1,800,000

2014 2024

tota

l e

mp

loym

en

t

321,000 replacements

Page 11: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

14%

46%

32%

8%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

No Formal Educational Credentials

High School Diploma and Less than a

Bachelor's

Bachelor's Degree

Master's Degree and Higher

Typical Entry Level Education for Top 100 In-Demand

Jobs

None, 25%

Work Based

Learning, 75%

Typical On-The-Job Training for In-Demand,

Middle Skill Jobs

None Work Based Learning

Page 12: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Even in a royal marriage, things are not perfect (In-laws)

Similar problems are Lurking in Nevada’s Workforce Ecosystem

Page 13: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Middle Skills Jobs Gap

323,413

601,245

308,142

323,210

629,140

280,450

- 100,000 200,000 300,000 400,000 500,000 600,000 700,000

Low

Middle

High

Skill Level of Jobs Skill Level of Workers

A gap of roughly ~28,000 workers exist in middle skills jobs in the state

Percent Alignment with Skill LevelHigh: 23/25

Middle: 49/51Low: 26/26

Page 14: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Labor Force Participation Rate

50.0%

52.0%

54.0%

56.0%

58.0%

60.0%

62.0%

64.0%

66.0%

68.0%

2002 2004 2006 2008 2010 2012 2014 2016 2018

State of Nevada Labor Force Participation Rate by Age (Annual Average)

age 16-24 age 55-64

Page 15: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Non-institution Population

0

50,000

100,000

150,000

200,000

250,000

300,000

350,000

400,000

450,000

500,000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

State of Nevada civilian non-institutional population 16+ (annual average)

age 16-24 age 55-64 age 65+

Page 16: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Gaps in College & Career Readiness

~10,260 not classified as high school

graduates

2014

34,201

23,941

13,610

10,260

6,974

0 10,000 20,000 30,000 40,000

Students Start High School

Students Reach Graduation

Enroll in College

Do Do Not33.67%

~6,974 not enrolled

in post-secondary

% that completes

the pipeline

Page 17: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Going to the source…listening to student perspectives

Page 18: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Response Distribution & Age of Respondents

16-18,

56%, 19-22,

22%,

23+,

22%,

Age: Percentage of

Respondents

16-18

19-22

23+

CountyResponse

s

Distributi

onCarson City 63 8.1%Churchill 18 2.3%

Clark 411 52.6%Douglas 24 3.1%

Elko 91 11.6%Esmeralda 0 0.0%

Eureka 1 0.1%Humboldt 10 1.3%

Lander 6 0.8%Lincoln 3 0.4%Lyon 33 4.2%

Mineral 2 0.3%Nye 26 3.3%

Pershing 1 0.1%Storey 0 0.0%

Washoe 88 11.3%White Pine 5 0.6%Seven participants did not correctly provide their zi

p code.

782 statewide

Page 19: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Perception About Employment Barriers

9.1%

28.3%

7.9%

10.2%

9.7%

8.6%

5.9%

12.6%

7.5%

0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0%

ACCESS TO JOB INFORMATION

EDUCATION

I'M MEANINFULLY EMPLOYED

I'M NOT SURE

JOB FUNCTION SKILLS

SOFT SKILLS

SOMETHING ELSE

TRANSPORTATION

OTHER

What do you believe is keeping you from gaining meaningful

employment?

Page 20: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Perspectives of Young Adults

✓ Regardless of age, young adults believed that education (four-year college degree) is their greatest barrier to employment.

✓ Young adults were divided amongst transportation, soft skills, job functioning skills, and not being sure about the additional barriers they faced to employment (depending on the age group).

✓ Young adults expressed concerns that the lack of job function skills was strongly attributed to their inability to access training or gain relevant work experience.

✓ Young adults perceptions of industries were limited to occupations of parents or close adults or industries with strong regional presence.

✓ Young adults believe that schools (K-12 and postsecondary) are the place to support them in removing barriers to employment by equipping them with career skills.

Page 21: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Perspectives of Young Adults

“HOW CAN WE GET EXPERIENCE

IF EVERY JOB WE APPLY TO

REQUIRES EXPERIENCE?”

– Nevada Student

Page 22: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

We are using these data points to influence policy and craft solutions

Page 23: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Work-Based Learning Helps Students Overcome Obstacles

VS.

Page 24: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

55,000!

NEVADA’S BIG GOAL

Page 25: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Defining Work-Based Learning

High-quality work-based learning incorporate: Academic alignment, employer engagement,

pursuit of industry-recognized credentials, & connection to employment

EducationOn-the-job experience

Work-Based Learning

Work-based learning provides students with authentic work experiences where they apply and

develop employability and technical skills that support success in careers and post-secondary

education.” –National Governor’s Association

Page 26: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Initial successes & outcomes Nevada can cheer for…

Page 27: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Strategic Changes to Expand Apprenticeships & Work-Based Learning

State Policy Changes

• SB 516 – statewide coordination

between agencies, aligning with

economic development, changes

to Registered Apprenticeships

• SB66 – K-12 WBL bill

• SB19 – Dual enrollment

• AB7 – college & career readiness

diplomas

DOL, NGA & NSFY

• Expansion & Accelerator Grants

• NGA Work-Based Learning Policy

Academy

• New Skills For Youth

GWDB Industry Sector Councils

• LEAP

• GWDB Industry Sector Councils,

GOED, DETR, State Board, &

OWINN

• State & regional in-demand

occupations for the

• Skills and education requirements

Registered Apprenticeships & Work-Based Learning

Page 28: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

NGA Work-Based Learning Policy Academy

Page 29: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Commitment Letters

Governor Brian Sandoval

“This initiative will allow Nevada to further integrate work-based learning in education and workforce development. As my tenure as Governor, I worked to bring together stakeholders from across the state to work collaboratively to strengthen work-based learning opportunities for young adults. Nevada is committed to scaling work-based learning to help answer the call of opportunities to foster stronger connections between education, the workforce, and economic development.”

• Don Soderberg, Director, DETR

• Steve Canavero, Superintendent of Public Instruction, NDE

• Frank Woodbeck, Vice Chancellor of Workforce Development

• Cory Hunt, Deputy Director, GOED

• Brian Mitchell, Director, OSIT

Page 30: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%

Construction

Manufacturing

Professional & Business Services

Trade, Transportation, Utilities

Information

Financial Activities

Healthcare & Social Assistance

Other Services

Government

RA Apprenticeship Industry-Occupation Analysis

Post-Transfer Pre-Transfer

Page 31: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Nevada’s Apprenticeship Outcomes Data

141

2833

1315

715

279

137

152

4045

2059

1314

330

173

8%

43%

57%

84%

18%

26%

0 1000 2000 3000 4000 5000 6000 7000 8000

Total Occupations

Active Apprentice

Minorities

Aged 16-25

Veterans

Women

Jun-16 May-18 Change

Page 32: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

Lessons in Using Data to Influence Policy Change

1. Don’t assume people know the data or how to read it • (data visualization matters: charts & graphs for non-

nerds)

2. Use stories for people with big hearts

3. Use numbers for people with big brains• What are the questions you are trying to answer?

(not data for data sake)

4. Create a sense of urgency – articulate the loss and gains

5. Have a specific Ask or Call to Action & repeat the same data over and over

Page 33: Using Data to Influence Policy Change · Lessons in Using Data to Influence Policy Change 1. Don’t assume people know the data or how to read it • (data visualization matters:

How do you feel about data?

THE END….