1. Factors Influencing RNs’ Decisions to Work Carol S. Brewer, Ph.D.* Chris T. Kovner, Ph.D.** William Greene, Ph.D.** Yow Wu-Yu, Ph.D.* Liu Yu, Ph.D. (cand.)* This work was supported by a grant from AHRQ R01 HS011320 Presented at AcademyHealth, June 6, 2004 *University at Buffalo ** New York University

2. Participation PT FT Why important: Why important: –If only 10% of the PT RN population worked FT, it would add 31,000 RNs to supply Part of larger analysis also looking at work/notwork Part of larger analysis also looking at work/notwork –If the RN works, how much (PT or FT)  FT defined as >35 hrs per week for all jobs

3. Research questions What factors are associated with the work decision (WK/NW) and amount of work (FT/PT)? What factors are associated with the work decision (WK/NW) and amount of work (FT/PT)? Are the WK/NW and PT/FT decisions made together or separately? Are the WK/NW and PT/FT decisions made together or separately?

4. Data sources The National Sample Survey of Registered Nurses March 2000 (Spratley et al., 2001) The National Sample Survey of Registered Nurses March 2000 (Spratley et al., 2001) –County level data (some restrictions) –Female RNs in 300 MSAs represented MSA/County level variables MSA/County level variables –InterStudy Competitive Edge Part III Regional Market Analysis (2002) –Area Resource File (2002)

5. Sample 35,358 registered nurses 35,358 registered nurses Exclusions: Exclusions: –Did not live or work in the USA –Missing MSA codes for job and living location –Did not work (or live) in an MSA Analytic sample was 21,123 females Analytic sample was 21,123 females  Married 14, 898  Single 6,225.

6. Economic Environment Variables Induced demand HYP. Means Induced demand HYP. Means –Medical/surgical specialists per 1000 pop + 1.74 –Primary care practitioners per 1000 pop + 0.24 –% of HMO services paid FFS + 17.4% Managed care/demand Managed care/demand –Index of competition -.68 –Penetration rate of managed care - 29.6% Poverty/demand Poverty/demand –% non-HMO Medicaid as % of total MSA pop + 7.4% –% uninsured pop ? 13.6% –% families living in poverty ? 8.1% Unemployment rate + 1.8% Unemployment rate + 1.8%

7. Demographics Characteristics Working Non working Modal Age 40-4450-54 Modal Tot Inc $50-75,000$50-75,000 Non-white16.1%12.8% Marital status 69.8%74.4% Any kids < 6 18.1%15.7% Student7.4%2.9%

8. Working RNs Characteristics Dominant function direct care 51.6% Staff/general duty nurses 50.9% Work in hospitals 60.5% Satisfaction (mean) 1= extremely satisfied 1= extremely satisfied married 2.31 married 2.31 single 2.42 single 2.42

9. Analysis Analytic method: bivariate probit regression Analytic method: bivariate probit regression –Tested hypothesis that WKNW / FTPT decisions are related  Single RNs Rho= -0.45, p= 0.02  Married RNs, Rho=-0.51, p= 0.00

10. Results Interpretation of marginal effects Probability of working or working FT changes (+ or -) by amount of marginal effect at mean of variable changes (+ or -) by amount of marginal effect at mean of variable Ex: The probability of a 25-30 yr old RN working FT decreases by 0.12 compared to a RN < 25

11. Significant marginal effects PT/FT regression: Economic variables Significant marginal effects PT/FT regression: Economic variables Probability of FT decreases MarriedSingle Primary care physician Primary care physician ratio ratio-0.18-0.23 Other sig var (very small effects): Unemployment rate, penetration rate for both % non HMO M’caid, Specialist ratio for single only

12. Probability of FT increases MarriedSingle Index of competition Index of competition0.120.11 Significant marginal effects PT/FT regression: Economic variables Other sig var (very small effects): sig for both % families in poverty Size of MSA (small, medium, compare to large)

13. Significant Demographic variables in Part-time / Full-time regression Probability of FT decreases Probability of FT decreases –All age categories: Stronger effect for married, >60 –if any children < 6  Stronger for married (-0.30 vs -0.17) –Baccalaureate RN vs. AD Probability of FT increases Probability of FT increases –Minorities married, ME=0.16 –Total family income, (non linear) NS for married  0.30 to 0.19 for single –Student status NS for married  PT student or not a student

14. Significant organizational variables in PT/FT regression Probability of FT decreases Probability of FT decreases – small ME= - 0.01 married, ONLY –Satisfaction: small ME= - 0.01 married, ONLY –Settings: Educators, student health, ambulatory care SIG vs. hospital RNs Probability of FT increases Probability of FT increases –Function: Supervisors, teachers, administrators vs. direct care RNs: ME=0.09- 0.21 married, ONLY –Positions: ALL other (NP, CNS, administrator, etc) vs. staff RNs, Stronger for married

15. Conclusions MSA level economic variables MSA level economic variables –Influential on PT/FT decision, but not decision WK/NW Influence of demographic variables Influence of demographic variables –Age, children, minority, income and student status  more effect on FT work decision than WK –Education (BSN-married, Master’s single)  weak but negative = concern Organization variables Organization variables –satisfaction significant, neg, if married –Hospital, direct care and staff RNs most likely to be PT –Functions and positions indicating career path more likely to be sig

16. Implications Need to target single vs married RNs Need to target single vs married RNs What organizations can change: What organizations can change: –Career orientation may influence PT/FT  chicken or egg ? Develop career paths early –Age related work conditions, esp after age 55 –Improve satisfaction –Recruit minorities Work decision different from how much to work Work decision different from how much to work

17. Implications Government policy Government policy –Clarify education: rewards need to be clear –Economic variables-need to understand  What can Govt manipulate?  May help in predicting regional variability in shortages.  Job market or health of population? –For ex: IOC- perhaps hospitals are competing for nurses and end up with more full-time workers due to higher wages