Longitudinal Analysis of the Career Path Outcomes of University Graduates

1. Longitudinal Analysis of the Career Path Outcomes of University Graduates Bamby Fields, Eastern Washington University Fran Hermanson, Washington State University Nevena Lalic, University of Washington Melissa Beard, Education Research & Data Center (Moderator)

2. Presentation Outline • Data Governance • Eastern Washington University • Washington State University • University of Washington • Concluding thoughts about using employment data

3. Data Governance • PCHEES data linked with employment data • Data sharing agreement between ERDC and the institution for de-identified data

4. Eastern Washington university

5. Data Elements • 5 cohorts of graduates • ’05-06, ’06-07, ‘07-08, ’08-09, ‘09-10 • 6 Employment years • Year0- the year of the award • Year1 – year5, relating to 1 year past the award year to 5 years past the award year • 2005-06 cohort had full 6 years of data • For each employment year: • Main employer, number of employers, NAICS code, wage, time status, student origin and more

6. Data Preparation • Getting to know the data • Importing the data, assigning field names • Adding values for clear output & analysis NAICS codes, age, race, wage range etc. • Recoding data • Text to numeric • Missing data • Hunting for outliers and deciding what data to explore • Plan on devoting a hefty share of effort on this

7. Research Questions • Wages over time, wages by CIPs, NAICS codes, gender, race • Statistically significant differences? • CIPS related to NAICS codes • Career tracks- NAICS changes • Full time/part time employment • Number of employers by CIP or NAICS • How does your data relate to state and national data? • And many more…

8. About EWU’s data • EWU is a regional comprehensive with > 50% First Gen and a high percentage of Pell recipients- 63% of undergrads eligible for need-based financial aid • 88% of EWU first-time freshman are from WA State and 40% are from Spokane County • Most popular degrees: • Business Administration, Psychology, Biology, Dental Hygiene and Communication Studies. • Of the 8,786 graduates in the merged file, 87% are from Washington State. • 44% of the 2005-06 employment cohort had an employer for each of the 6 years. • Not necessarily unemployed (graduate education, military, self employment, non-profit, out-of-state employment etc.)

9. Decreases in NAICS sector % % of Employment in lower wage NAICS sectors decreased over time- 2005-06 cohort data • Accommodation and Food Services • 15%in Year04%inYear5 • Retail Trade • 19%inYear06%inYear5 • Arts, Entertainment, and Recreation • 4%in Year02%inYear5

10. Increases in NAICS Sector % % of employment in higher wage NAICS sectors increased over time- 2005-06 cohort • Educational Services • 8% in Year020% in Year5 • Finance and Insurance • 5% in Year09% in Year5 • Public Administration • 7% in Year013% in Year5 • Health Care and Social Services • 13% in year018% in year5

11. Degrees with a High Number of Employers • Dental Hygiene • 18, 12, 7, 6 and 5 employers • Education • 9, 6, and 5 employers • Other CIPs associated with 4 employers in the 6 years of data • Engineering Tech, Psychology, Film/Cinema, Community Health Services, Business Admin, Marketing, Speech Communication, Spanish Language/Literature, Interdisciplinary

12. CIPs with highest percentages of wages $60,000 or more • Dental Hygiene- 15% • Computer Science- 11% • Interdisciplinary- 9% • Accounting- 7% • Finance- 6% • Marketing- 6% • Mechanical Engineering- 5% • Criminology- 5% • Business Administration- 3% • Biology- 3% • Engineering Tech- 3%

13. Full-time and Part-time Employment and Wage Increases Over Time • In Year1 after graduation, % of PT and FT was the same (30%) 40% not reported • The percentage of PT decreased over time • Year5- 15% • Mean wage rose steadily across the 6 years (2005-06 cohort) • From the $10,000 - $19,000 wage band in Year1 • To $40,000 - $49,000 wage band in year5

14. CIP wages that increased the most ($20,000 or more) in the 5 years after graduation) • City/Urban, Community and Regional Planning • Speech-Language Pathology • Physics • Mechanical Engineering • Community Health Services • Geology • Chemistry • Kinesiology and Exercise Science • Computer and Information Science • Biology • Finance • Teaching (Business, Social Studies, Mathematics, Spanish) Science Math & Technology

15. CIP wages that increased the least (less than $10,000) in the 5 years after graduation • Fine Arts • Anthropology • Humanities • History • Art History • English • Operations Management

16. Gender Gap • A higher % of females worked part-time than males for each year in the file (4% - 10% difference) • The mean wage was significantly higher for males than for females in each of the 6 yeas of data (P=.00 for years 0–4 and P=.01 for year5) • When filtered for FT only, the mean wage was also significantly higher for males (P=.04 to P=.000)

17. Changes in NAICS Sectors- Degree Career Tracks • Health Care- mostly health care, some education and public administration • Visual and Performing Arts – only 4% in an arts and 5% in education • Social Sciences- public administration, health care, administrative, professional/scientific/enterprises, finance and retail • Psychology- health care, education • Physical Sciences- professional/scientific/enterprises and health care • Parks, Recreation and Leisure- health care, education, public administration

18. Changes in NAICS Sectors- Degree Career Tracks • Foreign Languages- health care, education • Engineering- manufacturing, professional/scientific/enterprises • Teaching- education, health care, public administration • Computer Science- professional/scientific/enterprises, manufacturing • Biology- health care, professional/scientific/enterprises, manufacturing, education, public administration • Communication, Journalism- retail, wholesale, information, finance, education, health care

19. Putting the Data into Context • Spokane County has the 15th highest unemployment of the 57 Washington Counties (Sept. 2012 preliminary) • Spokane County= 8.2% • Seattle-Bellevue-Everett MD = 7.0% • Average Annual Wage of Spokane County is Comparatively low • Spokane County = $39,931 • Washington State = $50,257 • King County = $63,268

20. The Value of Employment Studies • Highlight Career Paths • Specific 4 year college degrees lead to a variety of professions • Some degrees have a wider path than others • Communication Studies & History- miles wide • Health Sciences- narrow path • Provide data to: • help build a case that a 4 year education is “worth it” (increasing salary and varied employment opportunities) • tell the story of who we are and how we contribute to our regional economy and services • Support enrollment management strategic planning, curriculum planning, university/program accreditation, student advising and state/federal outcomes assessment

21. Washington state university

22. About WSU • Founded in 1890 in Pullman, it is Washington’s original land-grant university, with a mission of improving quality of life • In addition to the Pullman campus, WSU has campuses in Spokane, the Tri-Cities, and Vancouver, extension offices in every county, and a Global Campus with online degree programs accessible worldwide

23. WSU Growth and Demographics • Substantial growth in the percentage of Multicultural and First Gen students. • Currently 61% of undergrads are eligible for need-based financial aid • 87% of WSU first-time freshman are WA residents, 74% Pullman freshmen are from West of the mountains while Tri-Cities and Vancouver freshmen are primarily (+90%) from their immediate region • Business Administration, Social Sciences, Health Sciences, Engineering, and Communication degrees are most popular

24. Degree Recipients • Of the 22,028 graduates in the merged file, 89% are from Washington State • Baccalaureate degree production up 4% • Steady growth in STEM degrees (3%) • Racial/Ethnic Minorities up 1% and growth in STEM was 4% • Women, down1% overall and growth in STEM was 1%

25. Why the Focus on STEM • According to Change the Equation (CTEq) in Washington over the past three years, prospects for the unemployed have been grim • For unemployed people with STEM skills, however, the odds have been much better. • Overall, jobseekers outnumbered online job postings by 3.7 to one • In STEM, job postings outnumbered unemployed people by 2.1 to one CTEq is a non-profit, non-partisan CEO-led initiative aimed at stepping up STEM – science, technology, engineering and mathematics – education in the United States). Source: changetheequation.org

26. Employed in WashingtonSTEM v. Non-STEM • After graduation, 70% of the graduates were employed in Washington • 15% with STEM degrees • Four years later the percentage of graduates employed dropped to 50% • Four years later the percentage of those in STEM disciplines was 10%

27. Earnings Over Time • STEM earning power increased over time • 87% STEM earned <20K year 1; 48% earned 50K+ in year 4 • 85% non-STEM earned < 20K year 1; 30% earned 50K+ in year 4 • Earning power for Women increased but at much lower rates than Men • 93% Women earned <20K year 1; 27% earned 50K+ in year 4 • 87% Men earned < 20K year 1; 58% earned 50K+ in year 4 • Earning power for Minority similar to Non-minority by year 4 • 91% Minority earned <20K year 1; 47% earned 50K+ in year 4 • 88% Non-minority earned < 20K year 1; 46% earned 50K+ in year 4

28. Still More to Explore • Very rich set of data – tell more of the story • Deeper dive into the data to look at career path changes and how it affects earnings • Compare alumni survey results with results of wage data

29. University of washington

30. Discussion Points • Data limitations: whose wage data is available? • Advantages of a longitudinal perspective • Ideas for analysis and specific applications

31. Data Limitations • Generalizations about career outcomes of particular degrees – even if only in-state – require representative samples • Need to explore correlations between characteristics of interest and likelihood of being included in the wage study

32. Which Students’ Data Do We Have? • A preliminary study for UW data revealed that the following is associated with a lower likelihood of inclusion: • Graduating in 2008/09 or 2009/10 • Taking longer to graduate • Earning degrees in many STEM fields (e.g. bioengineering, chemistry, physics, mathematics, astronomy) as well as some humanities fields (classics, linguistics, Asian languages)

33. Which Students’ Data Do We Have? • A preliminary study for UW data revealed that the following is associated with a higher likelihood of inclusion: • Pell eligibility • More major changes before graduation • Graduating from nursing programs, social work, construction management or the Tacoma business program

34. Which Students’ Data Do We Have? • A preliminary study for UW data revealed that the following is not strongly associated with likelihood of inclusion: • Number of credits earned at UW • Degree GPA • Type of entry (as a freshman or transfer student) • Full-time status

35. Advantages of Longitudinal Perspective • Allows for moving beyond volatile first year • Average wages may behave differently over time depending on • Degree earned • Graduation year • Industry

36. Example: Full-time average wages

37. Example: Percent employed full-time

38. Political science graduates per industry, over time

39. Biology graduates per industry, over time

40. Possibilities for future study • Testing effectiveness of programs aimed to improve workforce placement • Impact of timing of market entry • Within a program, impact of: • multiple degrees, • multiple majors, • more credits, or • higher GPA

41. Remaining limitations • Not capturing work location • Inability to distinguish “jobs” from careers • Inability to take into account work experience gained out of state

42. Contact Information • Bamby Fields (EWU)-bfields@ewu.edu • Fran Hermanson (WSU)-fran.hermanson@wsu.edu • Nevena Lalic (UW)-nlalic@uw.edu • Melissa Beard (ERDC)-melissa.beard@ofm.wa.gov