SRC Summer Internship Program 5th Annual Symposium

1. SRC Summer Internship Program5th Annual Symposium • Tuesday • July 29, 2008 • Noon-2:00 p.m. • ISR Building, Room 6050 The Survey Research Center is an equal opportunity employer that values diversity in the workplace.

2. Agenda • Welcome • Coordinators • Background • Overall Purpose of Symposium • 10 Minute Presentations (wide spectrum of topics) • Symposium Format • General Q/A http://www.isr.umich.edu/sip

3. Acknowledgements • Sponsors: • Health and Retirement Study • Life Course Development Program • Economic Behavior Program (2) • Family and Demography Program • Social Environment and Health Program • Partners: • Senior Staff Advisory Committee • SRC Diversity Committee • Summer Institute • Survey Research Operations • Inter-university Consortium for Political and Social Research • ISR and SRC Human Resources • SRC Computing • SRC Director’s Office

4. Factors Related to Role and Emotional Functioning of Air Force Personnel Social Environment and Health Penny Pierce, PhD, Col., USAFR Amiram Vinokur, PhD Douglas Roehler University of Michigan

5. Overview • Background • Objective • Population • Role and Emotional Functioning • Results • Key Findings • Similarities • Differences • Future Research

6. Background • Work, Family, and Stress Study assesses readiness and deployability of Air Force servicemen and women • Models of stress, coping, resource conservation (gains/losses), and retention tested • Interactive effects of job and family, deployment-related, and organizational stressors were all studied

7. Project Description Objective • Gains, losses, and social support were investigated to see if they are predictive of role and emotional functioning in Air Force personnel. • Relationships were explored for Air Force personnel that are highly committed to the service and for those reporting a low commitment to the service. (photo courtesy of the Spring City Chronicle, October 14, 2006)

8. Sample

9. Role and Emotional Functioning RE Functioning determines the capacity for daily life management. Questions included… (Caplan et al., 1984)

10. Control Variables Sex Deployment Status Dependent Child Status Rank Theater of War Variables Predictor Variables • Social Support • Gains • Losses

11. Results

12. Similarities For both high and low commitment groups… • increased social support related to increased role and emotional functioning

13. Results

14. Similarities For both high and low commitment groups… • increased social support related to increased role and emotional functioning • increased reports of losses related to decreased role and emotional functioning

15. Results

16. For the high commitment group, greater reports of gains were related to increased role and emotional functioning.

17. Results

18. Questions for Future Research • Why are gains unrelated to role and emotional functioning in the low commitment group? • What factors influence Air Force personnel to develop and/or sustain commitment to the service and what erodes commitment?

19. Acknowledgements • Special thanks to… • Col. Penny Pierce • Dr. Amiram Vinokur • Dr. Lisa Lewandowski-Romps • Mrs. Lillian Berlin • Mrs. Susan Clemmer • Mrs. Elli Georgal • George Myers & Anita Johnson This research is supported by the Tri-Service Nursing Research Program

20. July 29, 2008 Money Resource Allocation & Child Quality Xuanzhong Wang Frank Stafford, PhD Panel Study for Income Dynamics

21. Overview • Project Description • Research Questions • Primary Results • Methodology/Procedure • Results • Conclusion

22. Research Questions • What has affected the amount of money resource allocated to children? • How parents allocate money resource when there are more than one child in the FU? • Age, Sex, Ability? Primary Result • Strong association between child’s ability and school related expenditure

23. Analytic Sample

24. Data • Child data: CDS-I & CDS-II • Standardized Woodcock Johnson Test (WJR) Score (CDS-I & CDS-II) • School related expenditure: sum of school cost, private lessons, school supplies (CDS-I & CDS-II) • Total expenditure: sum of school related cost and all other cost (food, clothes, vacation and etc.) (CDS-II) • Family Income data: PSID Core • Family income in 2002 • Variation in family income over the past 10-15 years

25. Methodology/Procedure • Child data were merged with Family Income data using FIMS • Sibling information gathered for the subsample • Complex Survey features incorporated with the STATA svy command • Standard error estimation using linearization

26. Assumptions • Ability of a child can be measured • Standardized WJR score is an estimate of ability • No imputation for missing data • i.e. case wise deletion in analysis

27. Four Ability Brackets(Based on 2002 WJR Score Distribution) 1: Least Capable (Standardized WJR Score in the 1st Quartile) 4: Most Capable (Standardized WJR Score in the 4th Quartile)

28. Mean Expenditure Comparison Most Capable Moderately Capable Entire Population LessCapable Least Capable

29. Regression Results

30. Results • Strong association between child’s ability and school related expenditure • Hard to conclude causation either way - use WJR score in 1997 as a predictor (No significant effect) - use of instrumental variables (No significant effect) - regress WJR score(02) on expenditure(97) (No significant effect) • Other factors might have an effect too

31. Consider Sibling’s Ability

32. Consider Sibling’s Ability

33. Regression Results

34. Conclusion & Further Issues • Parents prefer more equal development among children - Ability of children in the same family is usually correlated - Choice of having less children • Expenditure by family is only part of the money resource allocated to children • Cost of Living

35. Many Thanks to:

36. Many Thanks to: Professor Frank P. Stafford Steven Heeringa, Patricia Berglund & Brady West George and Anita Fellow Interns All of You =)

37. Questions ? If you have any further questions, please feel free to E-mail me: wangxz@umich.edu

38. Presenter: Fan Fei Sponsor: Professor Charles C. Brown Health and Retirement Study (HRS), Economic Behavior Program Retirement Timing and Factors Leading to Premature Retirement

39. Outline • Background • Financial loss due to premature departure • Factors leading to premature departure • Size of their pensions: worth the wait? • Lack of knowledge about their own pensions • Health status: unwilling departures? • “Early out windows”?

40. Health and Retirement Study(HRS) • Begun in 1992, a nationally representative study of over 22,000 individuals age 50 and older and their spouses • Longitudinal design, conducted every 2 years, tracked the respondents until their refusals or deaths • Current director: Prof. David Weir • http://hrsonline.isr.umich.edu/

41. Defined-Benefit Pension Plans • Employer-provided “social security” • Pension benefit = f (salary, years of service) • Normal Retirement Age (NR) • Early Retirement Age (ER) • Pension benefits greatly increase at ER, creating strong incentive for people to leave at or past ER, and not before!

42. Data and Sample • HRS (Health and Retirement Study) core data • Identify those with pension on their jobs in 1992 HRS cohort and track them until they leave their employers • 1992 HRS Pension Present Value Database • Based on employer-provided pension descriptions and respondents’ reports of salary and years with employer • Contain calculations of present value of pensions at certain ages, workers’ early retirement age (ER) age and normal retirement (NR) age • ONLY defined-benefit plan owners, with non-missing values for key measures

43. Financial Loss from Leaving Before ER • N = 249 • Approximate losses as we don’t have PV at every age

44. Why do people leave before ER?Explanations of Premature Departures • Are their pensions smaller than those workers who leave at/after ER? • Do they understand their pensions? • Pension type • Early retirement age (ER) • Did they have health problems that might lead to involuntary premature departure? • Did they leave because of the “early out windows” provided by employers?

45. Factor #1: Size of Pension • Size of the pensions are the magnitude of incentives.

46. Factor #2: Knowledge of Pension • Compare the survey answers and employer-provided pension information. • Mismatching is a strong indicator of one’s lack of basic knowledge of his/her own pension.

47. Factor #2: Knowledge of Pension • Not knowing ER correctly can lead to unwise retirement timing . • Compare 1) Workers’ reports of their ERs in survey AND 2) More reliable ER calculated from employers’ pension descriptions • The early-leaving group showed • Significantly-lower percentage in “exact matching” 20.46% vs 29.80% • Significantly-higher percentage in group [-5, -1] (worker-reported ERs were one to five years too low than “actual” ERs) 32.83% vs 14.07% • Caveats

48. Factor #3: Health Status • Health problems might force people out prematurely. • Focus on the health status and health change in the wave people left their 1992 employers. • Controlling for age, we found those left before ER reported significantly poorer health status. • Few significant differences found in “health change (in past two years)” measures.

49. Factor #4: “Early Out Window” • EOW: Special financial reward packages to stimulate retirement. Offered when firms want to downsize. • From the sample of 1433 individuals, we found many EOW takers were already past their ERs. So we lack evidence to claim “early out windows” stimulate premature departures.

50. Factor #4: “Early Out Window” Percentage of retirees accepting “EOW” among total retirees in each wave