Institutional researchers are experts at examining analytical patterns related to preparedness for college, access to admissions, enrollment, progress and retention, the learning experience, financial aid, graduation patterns, and other related data stories. However, analysis of the value of higher education requires reaching beyond institutional data sources into the data sources on post-graduation outcomes that help us tell the story of employment, earnings, geographic movement, career progression, and more. These sources often require investments of time, analytical expertise, and institutional resources for full and effective use. What follows are short descriptions of some data sources that highlight best uses and value, along with some limitations associated with these data sources.
First Destination Survey
First Destination Survey (FDS) is standardized by the National Association of Colleges and Employers (NACE) as a vehicle for the collection of information about plans at the time of graduation—including plans for continuing education or seeking employment. For those seeking employment, the data source includes whether graduates obtained a job as well as information about earnings, employers, employment sector, job titles, and geographic location. NACE prepares annual FDS reports based on voluntary data contributions; for the 2019 report, 349 institutions contributed FDS data. Institutions can link FDS responses to student records, including demographic data. It is one of the few sources that provides information on graduates who leave the U.S. for work or schooling, if they respond. The data can be useful in triangulating with other sources of graduate outcome data.
FDS data are limited to information at the time of graduation, limited by response rates, and also carry other cautions and considerations associated with survey data.
State-level Labor Market Information (LMI)
In many states, public colleges and universities partner with state-level Labor Market Information (LMI) offices to obtain employment information on graduates by matching with unemployment insurance (UI) records. Examples of institutions with robust relationships with their LMI include University of Texas System, Pennsylvania State University, and University of Minnesota. These state-level partnerships generate data that have a powerful potential for understanding the movement of graduates into the labor market and may allow for matching of earnings and employer data with student records so that data can be disaggregated by program or socio-demographic variables.
Matches are limited to graduates with UI earnings records within the given state. Graduates who go out of state for employment or who are employed in sectors that do not generate UI records (e.g., self-employment and gig-workers, military) will not give a match. Effective matching relies on the use of a Social Security Number or another common identifier. These data are complex, so effective and accurate use requires that institutions have skilled staff trained for this role.
U.S. Census Bureau’s Post-Secondary Educational Outcomes Project (PSEO)
Starting in 2018, the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics (LEHD) program made the first release of graduate outcomes data under the PSEO project. The LEHD has relationships with LMI offices that are the basis for a source of jobs data that comprise a longitudinal database covering over 95% of U.S. private sector jobs. PSEO is a partnership with universities and university systems in which the jobs data are matched to individual records of college graduates through a process designed to meet legal standards for protecting individual privacy. PSEO now provides data for more than 400 institutions in 11 states (as of July 2021) on earnings, employment outcomes, industry, and state of employment by degree-level and field of study (based on CIP code aggregation). This is currently the most complete publicly available source for participating institutions and is a model for a national system of graduate employment outcomes records. Matches are made across all states, which improves match rates for institutions that have a high fraction of graduates working all over the country.
Like state-level LMI, graduates not covered by UI are not matched. Limitations include the lack of disaggregation by socio-demographic variables, at least as of August 2021.
College Scorecard, created in 2013, makes data about colleges more accessible to consumers in a centralized, interactive tool. Revised in 2019, new metrics help potential students find and compare colleges by field of study, average annual costs, admissions, retention and graduation rates, lowest and highest median salaries two (2) years after graduating, and average annual cost. The average annual cost is quite useful because it takes into account family income and how that will affect the cost of schooling. Other factors that are taken into account include percent of students receiving federal loans, median total debt after graduation and typical monthly loan payments, and repayment rates, as well as socio-economic diversity of the student body. College Scorecard makes median earnings available for field of study (four-digit CIP codes) for every institution. In addition to median earnings, College Scorecard also makes available the median total debt after graduation and the monthly loan payment amount.
The data available in the College Scorecard provide transparency for students and accountability for colleges. The Scorecard allows students and their family to see a college education as a four-year investment and make projections regarding the return on investment (ROI). However, the drawbacks are that the Scorecard offers incomplete program-specific earnings data for many institutions, uses median data and ranges on a variety of different metrics, and should be used in conjunction with other resources including the Department of Labor career tool. Thereby, using the College Scorecard could prove to be time consuming.
Steppingblocks sources data from a range of public sources, including online profiles, posted resumes, National Center for Education Statistics (NCES) data, university websites, public databases, and institutional partnerships. They apply a robust set of analytical and data science techniques to normalize, classify, and produce standardized output for every institution. Institutional profiles include degree level and major, graduation years, and a range of attributes related to employment, including earnings, job title, industry, company type, location of employment, career progression information, demographic data, and information on the skills employers are seeking. There is no requirement to provide a data file of graduates, so there are no privacy concerns or data sharing issues for the institution. The institution can give access to a wide range of stakeholders for a range of purposes, including career advising, program review, and alumni development. Researchers at the University of Michigan have partnered with Steppingblocks as a source of earnings data, given the reliability of the analytics engine.
Emsi Labor Market Analytics (Emsi)
Emsi’s data and analytics are designed to help higher education institutions optimize program offerings, connect students to programs and careers, and communicate outcomes and impact. The Emsi alumni outcomes data answer the following questions:
- What are their job titles and where do they work?
- Which employers are hiring an institution’s grads?
- Are alumni employed in fields related to their program of study?
- What are alumni estimated earnings?
- What skills do alumni have
- Are alumni staying in state or migrating out of state
- Which other institutions have your alumni attended?
Emsi’s data products include comprehensive, local labor market information (LMI), regional economic models (input-output), human capital analytics, demographics, among others. The only downside is that is zip code specific datasets are lacking.
Because of the complexity and limitations of the data, and the associated costs, data sharing arrangements tend to be focused on research efforts and relationships rather than on regular reporting of graduate outcomes.
National Student Clearinghouse
The National Student Clearinghouse serves the education and workforce communities and all learners, providing educational reporting, data exchange, verification, and research services.
Leveraged by nearly all institutions, the Clearinghouse is a data source for post-graduate enrollment in other institutions and programs using the Student Tracker service. Student Tracker allows IR professionals to submit files of students or graduates and match to records on subsequent enrollment in more education. This complements the workforce and employment data and completes the accounting for a more complete picture of post-graduate outcomes.
Uses of Post-Graduate Outcomes Data Sources
Post-graduate outcomes data help inform students how attending an institution will assist them after graduating. For institutions, post-graduate outcomes data are a basis for assessing how well-prepared graduates are for the world of work and understanding how employment and earnings connect with ROI. Post-graduate outcomes serve as a barometer for the value of specific programs and a signal for what to open or expand and what to revise or close. Furthermore, accrediting agencies now check that institutions are meeting federal requirements to present data on post-graduate outcomes, including employment and earnings patterns. Post-graduate outcomes data will be even more important in the coming years as federal and state policymakers seek to control student debt.
Gary J. Aguayo, is Assistant Director and Academic Program Manager for the Percy E. Sutton Search for Education Elevation and Knowledge (SEEK) Program at Queens College (Hispanic Serving Institution - HSI) of the City University of New York (CUNY) since 2018. Prior to being at Queens College he oversaw Student Academic Support at Boricua College (HSI), for sixteen years, where he also wrote and received grant funding from federal, state entities and private foundations. He is keenly aware of how to use data to inform decision-making and has served on two Middle States Commission on Higher Education accreditation visits. His research interests focus upon opportunity programs, low-income, first-generation students and issues regarding diversity, equity and inclusion as it pertains to student admissions and hiring and retaining faculty and administration.
Jocelyn Milner, Ph.D., is vice provost for academic affairs and director of academic planning and institutional research at University of Wisconsin-Madison. She has a wide portfolio with interests in data analytics and data governance, student success and equity in graduate outcomes, program quality, institutional efficiency, and institutional accreditation. Milner holds a PhD in Biochemistry from the University of Guelph and has numerous scientific publications, grants, and patents to her credit.