Outcomes Associated with Higher Education: Occupational, Social, and Economic Attainment

1. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Outcomes Associated with Higher Education: Occupational, Social, and Economic Attainment Nicholas J. Beutell, Ph.D. Hagan School of Business Iona College

2. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Models guiding this study • Starting point was mobility research—examined all major models mostly informed by Sociology • Many of these studies are highly quantitative using older longitudinal data sets • Examined higher education literature for studies on outcomes and the impact of education • Higher education literature is quite active on this subject • Reviewed careers literature that attempts to explain why certain people are upwardly mobile

3. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Intragenerational vs Intergenerational Mobility • Two major paradigms in mobility research • Intragenerational mobility examines what people accomplish over the span of their lives • Intergenerational mobility studies the impact attainments made by a previous generation on the present generation • This study assesses intragenerational mobility (also called career advancement and occupational attainment) with respect to education level in relation to occupation, income, social involvement, health, and well-being

4. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Intragenerational Mobility Status • Mobility patterns in the US have not changed much over the last decade • Looking at income we find that the lowest quintile has seen income erode whilst the highest quintile is doing quite well • Growing income inequality poses challenges for educational and health care access for less well off segments of the population • Americans feel that it is hard to keep up let alone get ahead economically

5. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Higher Education as a Key to Mobility • Despite hard economic times college is seen as the way to get ahead • Enrollments and costs are escalating but graduation rates are down • Questions have also been raised about the quality of the graduates suggesting that even those with degrees may not be adequately prepared for work or further education • This study looks at the impact of education on a person’s life using the National Study for the Changing Workforce

6. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Higher Education Outcomes and Issues • Completing a bachelor’s degree is a milestone of educational and professional advancement • Many benefits are associated with degree completion: financial, occupational, adjustment (health), civic engagement, among others • Degree attainment has lagged behind the large number of entrants to bachelor’s programs • Gender, ethnicity/race, and socioeconomic factors appear to affect graduation rates and subsequent career attainment

7. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Higher Education Outcomes: The Data • We examine the relationships between educational level and various outcomes for working Americans • Specifically, we look at income, health, autonomy, engagement, work-life fit, fitness and lifestyle choices, schedule control, turnover intentions, job satisfaction, life satisfaction

8. Income by Education

9. Mental health symptoms by Education

10. Work autonomy by Education

11. Economic security by Education

12. Work-life fit by Education

13. Work schedule control by Education (1=complete, 6=none)

14. Level of engagement with employer by Education (standardized, mean=0, sd=1)

15. Turnover intentions by Education (1=not likely, 3=very likely)

16. Job satisfaction by Education

17. Life satisfaction with Education

18. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England Higher Education Outcomes: The Data • Looking at bivariate relationships seems to offer impressive evidence that one should stay in school since all of these outcomes will accrue with more years of education • The picture is more complicated by adding gender to the equations as our second IV (three following graphs depict statistically significant results)

19. Est. 2008 Earnings (Education by Gender Interaction)

20. Schedule control (Education by Gender Interaction) 1=complete control and 6=none

21. Economic security (Education by Gender Interaction) 1=low and 4=high

22. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England HE Outcomes: Predicting Income • Next, we add some covariates to the equations • Take the example of income—what is the impact of controlling for things like ethnicity, gender, age, years in the workforce, occupation, spouse’s income and education, family financial situation when growing up, and, of course, respondent’s education

23. Business & Educational Roundtable Conference October 16-18, 2012 Oxford, England HE Outcomes: Predicting Income (contd.) • Variables were entered in blocks: • Block 1: Gender, race, marital status, age, presence of a child 18 years or younger • Block 2: Occupation and number of years in the labor force since 18th birthday • Block 3: Spouse education and spouse income for 2008 • Block 4: Mother’s education, father’s education, and financials when growing up • Block 5: Respondent’s education

24. Total model predicting income

25. MANCOVA Results for Significant Variables Predicting Income • No interaction effects were observed • However, the following main effects were significant: occupation, years in the workforce, spouse’s income, gender, education, and race/ethnicity • Many factors influence income, and, there are probably others as well

26. Education and Income • Income is multi-determined and education plays a significant role • Income of spouse/significant other is also important (influence of couples on each partner’s mobility is an emerging research area) • This analysis suggests that mobility factors are complex and multifaceted

27. Results: Education and Occupation • Highly significant association between education and occupation (see table below) • Education and occupation interacted significantly to predict: • level of engagement with employer • job challenge and learning • work-life fit • autonomy • spouse’s income • mental health symptoms • work interfering with family and work-family synergy

28. Association of Education and Occupation

29. Education X occupation interaction for work-family synergy

30. Results: Education and Gender • Gender and education interacted to predict: • job satisfaction (see graph below) • hours of volunteer work per week • treatment for blood pressure • treatment for mental health • weight in pounds • having a credit card • availability of personal health insurance through job • current overall state of health

31. Education X Gender Interaction for Job Satisfaction

32. Education X Gender Interaction predicting hours of volunteer work per week

33. Education X gender for health insurance at main job (1=yes , 2=no)

34. Education X Gender for Overall Health

35. Job satisfaction-life satisfaction relationship by level of education • Strongest association between job satisfaction and life satisfaction were found at the two extremes of the education scale: those with less than a HS diploma (R2 = .28) and those with a postgraduate or professional degree (R2 = .25) • Controlled for ethnicity/race, gender, age, marital status, and income

36. Additional analyses (not reported here) • Impact of spouse/significant other on career advancement, occupational attainment, and income. Topic has not received much attention but is beginning to emerge • Examined couples based on relative importance of family/self and work in the life space: ++Family/self, +Family/self, Dual-centric, +Job, and ++Job

37. Limitations • Use of cross-sectional database instead of longitudinal sample • Some of the measures are based on single items • All participants were employed so we do not know about the unemployed or people who had given up looking for work • Income was an estimate from respondents and may be over- or under-stated • Data from this national probability sample were collected just before the full force of the recession. Subsequent sampling may reveal a somewhat different view of the results • The sample may be biased since it was based on the presence of a land-line telephone (anyone without a landline telephone was excluded)

38. Future research • Validate results against longitudinal findings • Focus on a single well-defined cohort that can be sampled over fixed intervals • Conduct meta-analytic study to estimate effect sizes for variables • Use more exact measures of income • Examine variables such as motivation and ability as determinants of career advancement and mobility • Include environmental variables to understand how these interact with individual level variables • Formulate theoretical mobility models that can be tested using empirical research

39. Suggested new title Career Advancement and Education: Income, Occupation, and Gender

40. Thank you!