Linking P-20 Education Data to
Workforce Data
Brian Jacob
Gerald R. Ford School of Public Policy University of Michigan
March 6, 2014
Presentation to the Michigan P-20 Advisory Council
Agenda
• Benefits of data linkage
– What other states are doing
• An example from Michigan
– CAPSEE Project: Linking wage records with CC
transcript data in Michigan
• Some specific opportunities in Michigan
• Questions/discussion
Benefits of data linkage
• Labor market information (LMI) is important as an outcome
as well as a contextual factor that predicts many early
school outcomes
• LMI as an outcome
–
–
–
–
Impact of CTE programs (high school or CC)
Long-term impact of elementary or secondary initiatives
Economic returns across different types of degrees
Link btw/ content and workforce prep
• LMI as an independent variable
– Importance of family income & parental employment in early
schooling outcomes
– Relationship between LMI and participation/success in early
childhood education
Data Linking in Other States
• Many states linking education and labor force data
– TX and FL: long-standing P-20 -> Workforce (UI) links
– WA recently added K-12 to PostSec-UI link
– NC, VA, CA: long-standing links btw/ PostSec & UI
• Analyses that utilize linked data
– Do students’ college major choice respond to changes in
wages? (Washington State)
– What are the economic returns to CC degrees and
certificates? (Virginia, North Carolina, Florida, California)
– What are the long-run economic benefits of starting at a 4yr vs. 2-yr college? (Texas)
CAPSEE Michigan
• CAPSEE (Center for Analysis of Postsecondary Education and
Employment)
– IES funded research center to explore the association between various
postsecondary pathways and employment and earnings
• Collaboration btw/ UM and 5 CCs in Michigan
– Macomb, Oakland, Jackson, Washtenaw and Alpena
• Motivating research questions
– What are the economic returns to CC in Michigan?
• Does this vary by award type (e.g., AA, cert.), field (e.g., allied health,
advanced manufacturing), student demographics (e.g., age, gender)?
– Quick turnaround analyses tailored to college-specific needs
CAPSEE Michigan: Data
• Detailed student transcript data from 5 CCs from 20012011
– Includes info on course-taking, award receipt, student
demographics, course of study, financial aid and remediation
– Personal identifiers (including SSN) provided for matching
purposes only
– Matched to NSC records from 1995-2012
• Linked to UI wage records from 1998q2 – 2011q2
– We conducted the match ourselves on a non-networked
computer in LARA’s office in Detroit
– We just take the non-identified data back with us (no SSN’s out
in the open)
– Maintain data on secure servers at UM with access limited to a
select set of researchers (who have all signed confidentiality
agreements)
CAPSEE Michigan: Analysis
• Multivariate regression to control for differences
between individuals with and without CC degrees
• Fixed effect regression models that compare an
individual’s employment and earnings after she gets a
degree with her own prior labor market outcomes
• Matching estimators that compare individuals who
receive a degree with others that look like them in terms
of demographics and prior employment/earnings
CAPSEE Michigan: Preliminary Findings
Earnings and employment gains realized by MI community college students
Women
Men
Short Cert Certificate Associates Short Cert Certificate Associates
Quarterly
$0
$620
$2,346
$1,345
$ 918
$1,441
earnings gains
Increased prob.
of employment
4%
5%
14%
4%
8%
9%
Log quarterly
earnings gains
0%
13%
32%
0-8%
0-4%
12%
Distribution of awards by field
Returns by field of study
Earnings gains by field of study
Liberal arts
STEM
Business
Health
Vocational, tech
Vocational, non-technical
* Indicates significance level of p>.1 or greater.
Certificate
$-218
$411
$257
$1,698*
$976
$116
Associates
$-488*
$84
$41
$3,894*
$926*
$-71
Responding to CC Needs: Example 1
1. Online course taking at MCC
• Between 2003 and 2010, share of students taking an online course in their
1st year rose from 6.5% to 15% (w/in 3 years from 13.4% to 23%)
– Gap for women over men rose from 3% to 8%
– Gap for 25+ year-olds vs. under 25 rose from 3% to 7%
– Students online in their first year take ½ of first-year credits online.
• Pass rates are 5-10% lower for students in online courses than for students
in face-to-face courses
• Online courses play an important role in allowing students to remain
connected to the labor force – i.e., reduces the “opportunity cost” of
college
– Conditional on the total number of credits taken, students taking a higher fraction
of courses online realize a lower drop in earnings while enrolled (relative to preenrollment)
Responding to CC Needs: Example 2
2. School-specific reports
• Provided school specific measures of employment and earnings by
degree and field of study
• Hosted conference to help senior staff interpret results
• We have continued to respond to follow-up requests
Responding to CC Needs: Example 2
2. School-specific reports
TABLE 6. Earnings and Employment for award recipients in Quarter 2 of 2011,
by program
Earnings >
Quarterly
Quarterly
Employed half time at
earnings if
earnings
employed
min. wage
Associate in Arts (AA)
0.64
0.57
3,922
6,101
Associate in General Studies
0.69
0.65
4,622
6,670
(AGS)
Business Administration (AAS)
0.71
0.67
5,025
7,123
Nursing (AAS)
0.83
0.82
10,625
12,775
Associate in Science (AS)
0.72
0.63
6,226
8,688
Practical Nursing Certificate
0.83
0.81
8,660
10,392
Medical Assistant (AAS)
0.87
0.87
6,719
7,753
Accounting (AAS)
0.68
0.66
4,269
6,239
Medical Assistant Certificate
0.8
0.77
4,374
5,468
Law Enforcement (AAS)
0.65
0.62
4,552
6,954
Notes: Sample is students who first enrolled at JCC in or after Fall 2001, and who were not enrolled in any school in quarter 2 of 2011. Employed
is defined as having any earnings reported in Unemployment Insurance records. Earnings>half time at min. wage equals 1 if quarterly earnings are
Future Plans:
Automating links between CC and UI
• Community colleges have reporting requirements
• Gainful employment, WIA, Pell, etc.
• They’re forced to use their own surveys to get these numbers.
• LARA/DTMB has wage records for any of these students working in MI
• The idea: automate the process and provide aggregate data
• Colleges send lists of SSN’s to LARA/DTMB for selected student groups
• LARA/DTMB sends back the share employed and median/mean
earnings for that group of students
• No individual records are ever sent
• The program is automated so colleges just upload the list of SSN’s
• The resulting data can be used by schools/researchers/gov’t
Automating links between CC and UI
California community colleges have something like this already:
http://salarysurfer.cccco.edu/SalarySurfer.aspx
And an even more detailed version at the college level:
http://datamart.cccco.edu/Outcomes/College_Wage_Tracker.aspx
From this site:
• Students can look up pre- and post-award earnings, by degree.
• Government researchers and policy makers can use this information to
evaluate the effectiveness of programs in matching students to jobs.
• Schools can use this the access to data for performance requirements,
without their own surveys or ever having individual student outcomes.
Is this really possible in Michigan?
• With the help of Tom Howell and others at CEPI & MDE, we have
already made tremendous strides in linking K-20 data
• CAPSEE Michigan has demonstrated the feasibility of linking CC-UI
in a secure setting
• While we are waiting for the Holy Grail, an independent, non-state
entity (e.g., the Ed Policy Initiative at UM) can develop and host
something like CA’s wage tracker
– Michigan public colleges provide SSNs for data that already exists in
STAR
– Match to workforce data provided by LARA, following protocols
established by CAPSEE
– Develop website that will allow the public access to aggregated data
with a minimum cell size
Questions?
For more information on CAPSEE Michigan and
related projects, see our website:
Education Policy Initiative
Ford School of Public Policy
University of Michigan
http://www.edpolicy.umich.edu/