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
Participation PT FT

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
– If the RN works, how much (PT or FT)
 FT defined as >35 hrs per week for all jobs
Research questions
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?

Data sources

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
– InterStudy Competitive Edge Part III Regional
Market Analysis (2002)
– Area Resource File (2002)
Sample
35,358 registered nurses
 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
 Married 14, 898
 Single 6,225.
Economic Environment Variables

Induced demand
HYP.
– Medical/surgical specialists per 1000 pop +
– Primary care practitioners per 1000 pop +
– % of HMO services paid FFS
+

1.74
0 .24
17.4%
Managed care/demand
– Index of competition
– Penetration rate of managed care

-
.68
29.6%
Poverty/demand
– % non-HMO Medicaid as % of total MSA pop +
– % uninsured pop
?
– % families living in poverty
?

Means
Unemployment rate
+
7.4%
13.6%
8.1%
1.8%
Demographics Characteristics
Working
Non working
Modal Age
40-44
50-54
Modal Tot Inc
$50-75,000
$50-75,000
Non-white
16.1%
12.8%
Marital status
69.8%
74.4%
Any kids < 6
18.1%
15.7%
Student
7.4%
2.9%
Working RNs Characteristics
Dominant function direct care
Staff/general duty nurses
Work in hospitals
Satisfaction (mean)
1= extremely satisfied
married
single
51.6%
50.9%
60.5%
2.31
2.42
Analysis

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
Results
Interpretation of marginal effects
Probability of working or working FT
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
Significant marginal effects
PT/FT regression: Economic variables
Probability of FT
Married
decreases
Primary care physician -0.18
ratio
Single
-0.23
Other sig var (very small effects):
Unemployment rate, penetration rate for both
% non HMO M’caid, Specialist ratio for single only
Significant marginal effects
PT/FT regression: Economic variables
Probability of FT
increases
Index of competition
Married
Single
0.12
0.11
Other sig var (very small effects): sig for both
% families in poverty
Size of MSA (small, medium, compare to large)
Significant Demographic variables in
Part-time / Full-time regression

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
– 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
Significant organizational variables in
PT/FT regression

Probability of FT decreases
– Satisfaction: small ME= - 0.01 married, ONLY
– Settings: Educators, student health,
ambulatory care SIG vs. hospital RNs

Probability of FT increases
– Function: Supervisors, teachers,
administrators vs. direct care RNs: ME=0.090.21 married, ONLY
– Positions: ALL other (NP, CNS, administrator,
etc) vs. staff RNs, Stronger for married
Conclusions

MSA level economic variables
– Influential on PT/FT decision, but not decision
WK/NW

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
– 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
Implications
Need to target single vs married RNs
 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
Implications

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