Multi-State Longitudinal Data Exchange: An Update WICHE’s 4-State Pilot

1. Multi-State Longitudinal Data Exchange: An UpdateWICHE’s 4-State Pilot 2012 National Forum – PPI Committee Hans Peter L’Orange State Higher Education Executive Officers

2. Review Background and Goals • Bill and Melinda Gates Foundation funded; Western Interstate Commission for Higher Education (WICHE) managed • Improving policy and practice related to human capital development; provide information about workforce outcomes • “Where did they go? What did they do when they got there?” • K-12, postsecondary and labor representatives from WA, OR, ID, HI • Focus on the necessary architecture, governance structures, and standard reporting while complying with applicable privacy laws

3. Addressing Issues That Matter • K-12: Information on college performance and workforce outcomes can inform development of K-12 curriculum and college/career readiness • Postsecondary: Information on job placement and earnings can inform effectiveness of programs and instruction and their alignment to current and future workforce needs • Workforce: Understanding prior education and prior training can help identify skill gaps and equity gaps. Also helps to understand how incumbent workers access education to advance their careers.

4. Issues for Policy Makers • Skills development through education and training for various populations in a state • Gaps in education for various demographic and socio-economic groups • Insight into education and employment patterns across state lines given high mobility • Additional knowledge about outcomes of students in a regional context

5. Five Types of Policy Questions • 2. What proportion of students completing high school in Hawaii enroll in college in that state within a year? 1. What proportion of students beginning college in Oregon earn a bachelor’s degree in six years?

6. Five Types of Policy Questions • 4. What proportion of students who were enrolled in college in Idaho in a given year are enrolled in Washington, Oregon, or Hawaii the next year? 3. What proportion of high school graduates in Washington complete college within 10 years and are earning $35,000 or more per year?

7. Five Types of Policy Questions 5. What proportion of students who complete high school in Washington also complete at least an associate’s degree and are employed in the aeronautics industry in the state or in Idaho, Oregon, or Hawaii?

8. Issues in the multistate exchange project • Governance • Confidentiality (FERPA) • Common data element definitions • Effective use • Transactional vs. research purposes • Balancing “good enough” and “perfect” information • Accountability vs. formative evaluation • Short-term vs. long-term analytical frames and the availability of data going back in time • Limitations of workforce information • And then, adding multiple states to the mix: • Unequal sophistication among state data systems • Participation is altogether voluntary = governance challenges

9. Governance Challenges and Lessons • Education and labor don’t have a shared history of working together within any of the states • Necessary to bring stakeholders together within each state • Every entity needs to have a voice and to recognize the value they add to the process • Building trust and common understanding takes time

10. Memorandum of Understanding : Scope Establishes “a data-sharing relationship among several states and WICHE in order to: • coordinate with state agencies in Washington, Oregon, Hawaii, and Idaho to compile longitudinally linked education and workforce data; • provide an enhanced data file to [state agency] that contains additional information obtained through the exchange process on only the individuals originally supplied to the exchange by [state agency] , in order to support [state agency’s] efforts to conduct evaluation, as allowed under FERPA; • Examine, from the regional perspective, the mobility of students across state borders and among educational providers and their educational preparation for success in the workforce, in order to conduct evaluation of educational programs, as allowed under FERPA.”

11. MOU: Justification “In accordance with the Federal Family Educational Rights and Privacy Act (FERPA), and in particular 34 CFR 99.31 (a)(3)(iv) and 99.35, [state agency] is a state educational authority that, for the limited purposes of this Agreement, designates WICHE as its authorized representative for the purpose of assembling data to conduct evaluations of publically-funded education and training programs. Procedures used in this agreement will be governed by FERPA and all applicable state laws.” MOU: Description of Data “The data to be exchanged under this agreement are housed within state or institutional data systems and pertain to individual students’ educational records and to information about individuals’ employment captured by state Unemployment Insurance systems. These data include personally identifiable information, including student names, personal identifiers such as student numbers and social security numbers, any combination of information that together would make it possible to easily identify individuals.”

12. MOU: Process for Exchanging Data • WICHE will supply specifications for a dataset to be prepared by [state education agency] to include identifying information and individual demographic characteristics • [State education agency] will produce a data file with the requested data elements and provide it to WICHE. Other participating states/agencies will deliver similar files to WICHE. • WICHE will merge data from all participating states/agencies, creating a merged cohort file. • WICHE will assign a randomly generated “Exchange ID” to each unique individual in the merged cohort file. • WICHE will send this cohort file to participating states/agencies along with specs. for adding additional enrollment and award data.

13. MOU: Process for Exchanging Data • [State agency] will append requested data elements to the cohort file for only those students in cohort file and return data to WICHE. • WICHE will match data to the data files submitted by other states and to the contents of the National Student Clearinghouse, creating a “core exchange dataset.” • WICHE will seek to obtain workforce data to append to the core exchange dataset: • Data file containing only individual SSN’s and Exchange IDs will be sent to each of the participating labor agencies • Labor agencies will append workforce info from their UI wage records • Labor agencies will strip SSN but not Exchange ID • Using Exchange IDs, WICHE will append the workforce data to matched records in the core exchange dataset.

14. MOU: Process for Exchanging Data • For each participating state education agency, WICHE will return records containing data only on the individual students originally supplied to the exchange by that agency. • [State agency] will review, validate, and convey concerns to WICHE • WICHE will strip all identifying information other than the Exchange ID from the core exchange dataset and use the resulting data to calculate statistics on student enrollment, graduation and workforce participation; cells with fewer than 10 cases will not be reported. • WICHE shall maintain the data file in a secure environment until the conclusion of the pilot. All data will be destroyed within 6 months of the conclusion.

15. Cohort Files • Cohort A (high school graduates): • Graduated from a public high school in [state] during the 2004-5 academic year (including trailing summer 2005) • High school graduates defined as consistent with definition used for CCD submission • Students dually enrolled in postsecondary institutions included only if they received a high school award as defined above.

16. Cohort Files • Cohort B (first-time postsecondary students): • Identified as first-time postsecondary enrollees during the 2005-06 academic year (including leading summer 2005) • Undergraduate only • Exclude students earning dual credit if they are current high school students • Include students enrolled in credit-bearing and remedial/developmental courses

17. Data Matching Files Cohort Social Security Number State/sector student ID First name Middle name Last name/Surname Suffix Birth date Sex Race/ethnicity Enrollment Institutional IPEDS UnitID Academic term start & end CIP code (first major) Credits attempted & earned Total cumulative credits Student level Pell Recipient Degree seeking status Awards High School graduation date High School diploma type CEEB code Academic award level Institutional IPEDS UnitID Academic award date CIP for academic award

18. Data Definitions/Derivations Discussion • Mapping to the Common Education Data Standards (CEDS) where possible • Develop other definitions and code sets where necessary • Derived variables developed by working group • e.g. full-time status, enrollment status, employment

19. Lessons So Far • Real value comes from incorporating workforce data • But linking is hard due to SSNs and privacy rules (cultures) • Concern over too much focus on initial employment • Genuine appetite for linked data • But some fear… (how are data going to be used?) • At what level of detail? (some want identifiable, some not) • Balance the expectations of partnering state agencies (i.e. what data for what purposes) (operational vs. research) • A big contribution can be made just by helping reduce the gaps in what is knowable • How big is the loss of individual data across sectors really?

20. Next Steps • Sign-offs on the Memorandum(s) of Understanding • Complete data derivation decisions • Refine proposed governance structure • Confirm NSC participation or develop alternative mechanism for data matching • Start sharing data