Academy Overview: Strengthening State Data Systems to Improve Outcomes for Low-Income Adults

1. Academy Overview: Strengthening State Data Systems to Improve Outcomes for Low-Income Adults Laura Dresser, COWSJennifer Phillips, JLP ConsultingJune 30, 2010WPFP Conference, Joyce Foundation, Chicago

2. But we already use data! • One out of four working families with children—a total of 42 million people—are low-income • Adults in low-income working families worked the equivalent of nearly one and a quarter full-time workers per family Source: Working Poor Families Project

3. Sharp Increases in Unemployment for Least Educated Adults Source: Bureau of Labor Statistics 2007-2009

4. Economic success hinges on educated adult workforce • Nearly half of all job openings will require more than a high school education • Nearly 90 million adult workers are not prepared for these positions (no HS diploma or college or low English language skills) • Almost half of our workforce in 2030 will be comprised of today’s working adultsSources: BLS, NCAL, NSC

5. What today is about • Better understanding state data systems and how to strengthen and link them (which systems? adult education, postsecondary, ‘workforce’) • Improving analysis and use of state data systems to answer the questions that motivate our work

6. Begin with the end in mind • Types of data and purposes • Data that is collected to point out a problem or to analyze the situation • Data that is collected to evaluate past performance and guide future program, policy or budget decisions • State data systems must begin with the end in mind and zero in on PURPOSE

7. Workforce-related State Data Systems: What systems are we talking about? • Workforce development • Postsecondary education programs • Adult basic education • K-12 (in some places) AND • UI wage data (to track employment and earnings outcomes)

8. Why should we integrate data from multiple systems? • Do we really know how these systems are performing? • Questions that we can’t answer: • To what extent do high school dropouts who earn a GED go on to obtain a postsecondary credential? • What are the educational and labor market outcomes for unemployed workers who use federal and state resources to obtain training at community colleges? • What value do noncredit community college certificates have in the workplace?

9. What can a statewide data system do? • Follow the educational progress and labor market outcomes of all adult students and workers • Track and measure the educational and skills development progress, completions, and outcomes • Track and measure the labor market outcomes Source: Recommendations for Incorporating Postsecondary and Workforce Data into Statewide Longitudinal Data Systems

10. Washington’s Tipping Point Example • Applying data not as a supporting tool but as a strategic weapon • Collecting and analyzing data to change policy, make investment decisions, evaluate performance, and improve customer service • Snowball effect from data findings – a powerful motivator for change

11. Why isn’t this more commonplace? • Leadership and management • Privacy laws and data sharing agreements • Missing data on low-skilled, low-income populations • Linkages to wage record data • Creating a culture of using data for continuous improvement

12. Imperative for Change • President Obama’s 2020 goal for 5 million more degree and certificate holders • Administration’s strong interest in using data to determine performance outcomes • USDOL funding to support these systems • Other privately funded multi-state initiatives • Growing interest in creating “a culture of evidence”

13. How to get involved: What role can policy advocates play? • Know the state of play in your state • Help state leaders define the data sets needed • Find innovative ways to tackle privacy and data sharing issues drawing upon other state’s success • Find ways to become a valued partner in the process or spearhead a coalition to help initiate and guide state leaders

14. How to get involved (Con’t.) • Find ways to foster and cultivate state leadership that supports data collection • Highlight the big research questions that can’t be answered to policy makers and demonstrate how data collection can provide answers • Identify performance goals and create a storyline and compelling message about what will happen if XX people succeed

15. Using Data to Make Policy Change: WHY? • General statistics on their own don’t often make change (e.g. 42 million low-income) • To know how these systems are performing • Not enough resources or time to guess at how we are doing or what to do

16. The Mighty Statistic • If you want to change people’s minds with data, the data will need drama and depth by being put into a real-life context. • That’s the fundamental strategy needed to make numbers stick: To drag them within the grasp of our intuition. Source: Switch: Making Change When Change is Hard, Dan and Chip Heath, http://www.fastcompany.com/video/made-to-stick-the-mighty-statistic?partner=rss

17. A State Data System is Only as Strong as the Questions it Seeks to Answer • What are the employment and earnings outcomes for various education and training paths? • Where are low-income adults falling out of the educational pipeline? • Which workforce programs are most effectively channeling adults towards further education and higher earnings?

18. Answers to Questions Should Inform State Policy Change • Should more ABE funds be re-directed towards transitions to postsecondary? • Are enough WIA funds going towards training, and is this training effective? • How are community colleges working for working adults? What changes could help improve success for this group?

19. Data Analysis is Only Effective if It’s Accessible • Focus must be on how to tell an accessible story with the data that moves policy • Keep it simple, focused, and precise, but also keep it flexible (and don’t spend too much time on it)

20. Today’s Data Day • Infrastructure • Analysis and engagement • Roles for advocates

21. We Hope You’ll Come Away With… • A better understanding of state data systems and how they can be strengthened to improve outcomes for low-income adults • Action steps to help ensure that the development and use of state data systems be geared towards state policy and systems change

22. Laura DresserCenter on Wisconsin Strategyldresser@cows.org • Jennifer PhillipsJLP Consultingjennifer-phillips@comcast.net