Future intentions of registered nurses employed in the western new york labor market: relationships among demographic, economic, and attitudinal factors

Future Intentions of Registered Nurses Employed in the Western New York Labor Market: Relationships Among Demographic, Economic, and Attitudinal Factors Carol S. Brewer and Eric Nauenberg

Demographic, economic, and attitudinal factors may affect the work participation behavior of full and part-time RNs in hospital and non-hospital settings. The sample (N ⫽ 776) included randomly selected RNs from the 1997 registration lists of the New York State Department of Professional Licensing. Classical t-tests and chi-square tests were used to test for differences between hospital, non-hospital, full-time and part-time RNs. Only RNs employed in hospital settings were significantly less satisfied and less committed to their organization than were non-hospital based nurses; however these attitudes, frequently shown to be related to turnover behavior, did not result in intentions to leave. Differences in satisfaction and commitment across job settings begin to explain work participation behavior of nurses, as distinct from organizational behavior. © 2003 Elsevier Inc. All rights reserved.

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ECURRENT shortages have occurred frequently in nursing labor markets for more than 40 years. Recent shortages are influenced heavily by the demands of a growing managed care sector, drops in hospital admissions and length of stay, and declining nursing student enrollments (Brewer & Kovner, 2001; Buerhaus & Staiger, 1999). Expansion of the supply of nurses can be achieved through the training of new nurses as well as by encouraging more participation and re-entry into the profession. However, the former solution is normally a long-term process poorly suited to meeting changes in short-term demand. However, a change in the national workforce participation rate from 81.7% (Spratley, Johnson, Sochalski, Fritz, & Spencer, 2001) to 82.7% (the 1996 rate; Moses, 1997) would add 28,000 RNs to the total supply. If only 10% of approximately 625,139 PT

Carol S. Brewer, PhD, RN, Associate Professor, School of Nursing, University at Buffalo, Buffalo, NY, USA; Eric Nauenberg, PhD, Associate Professor, Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada. Supported by a grant from the Agency for Health Care Policy Research (R03 HS09353-02), with additional research development funds from the SUNY Buffalo School of Nursing. Address reprint requests to Carol S. Brewer, PhD, RN, School of Nursing, University at Buffalo, 918 Kimball Tower Buffalo, NY. E-mail: [email protected] © 2003 Elsevier Inc. All rights reserved. 0897-1897/03/1603-0003$30.00/0 doi:10.1016/S0897-1897(03)00046-6 144

RNs worked FT, the equivalent of 31,000 FT RNs would join the workforce (calculated from data contained in Spratley et al., 2001). This study is important because it is one of the first to connect the concept of job satisfaction and organizational commitment across job settings within the workforce, as opposed to simply within organizations, connecting the conceptual frameworks of turnover literature with labor economics. Two bodies of research are relevant to this problem. In the body of turnover research, arising from the study of organizational behavior, job satisfaction refers to the general attitude toward the job or specific dimensions of the job. Organizational commitment refers to the relative strength of an individual’s identification with and involvement in an organization (Price, 1997). Nursing research in the fields of organizational sociology and psychology, which includes these constructs, is voluminous and focuses on samples from a single institution or group of institutions rather than a geographic sample. In these studies of hospital RNs, job satisfaction, organizational commitment and intentions to leave/turnover are consistently significantly correlated. (Blegen, 1993; Irvine & Evans, 1995). Also, economic factors (wages and benefits) heavily influence satisfaction and organizational commitment (Lum, Kervin, Clark, Reid, & Sirola, 1998). Most of the economic labor supply studies of nursing have been conducted primarily on national

Applied Nursing Research, Vol. 16, No. 3 ( August), 2003: pp 144-155

FACTORS AFFECTING RN WORK AND INTENTIONS

or state level socio-demographic data focusing on the impact on wages and annual hours of work (Brewer, 1998). Typically researchers developed labor models that use secondary data collected for other purposes, such as the National Sample Survey of Registered Nurses (NSSRN) or the Current Population Survey (Brewer, 1996; Link, 1992). The results generally indicate that older age, the presence of children under age 6, a married marital status, and higher spousal income have dampened the likelihood of labor force participation. However, the measured effects of added education and higher wages have varied. Union representation positively affects wages (Hirsch & Schumacher, 1995). Employment benefits are not measurable in the data sources used in these studies. One well-known study (Mueller & Price, 1990) linked the frameworks of economics and turnover research but used a sample of RNs from one hospital. The researchers found that an oversupply of RNs in the local labor market depressed turnover of nurses in the hospital studied. Both satisfaction and commitment were significant and negatively related to intention to leave. The purpose of this study is to examine demographic, economic (wages, benefits, collective bargaining), and attitudinal factors (satisfaction, organizational commitment, and job perceptions) that may influence intentions to leave and workforce participation in a local labor market.

The researchers found that an oversupply of RNs in the local labor market depressed turnover of nurses in the hospital studied.

METHODS

Sample The population consisted of 18,792 RNs (residing in New York State and age ⬍65) registered with the State Department of Professional Licensing as working in the eight counties comprising Western New York. A power analysis that considered planned multivariate regression, potentially undeliverable surveys, and assuming a response rate of 50% determined that an adequate sample to

145

survey was approximately 1,500 and this number of surveys was originally mailed. Undeliverables (n ⫽ 107) from the first mailing were replaced once from the list of remaining in-state RNs on the registered list, resulting in a final sample of 1,482 eligible RNs under the age of 65. The response rate was 54.1% (802), and the final sample size after eliminating unusable surveys was 776. Instrument The survey was modeled after the National Sample Survey of Registered Nurses (NSSRN; Moses, 1997) with a number of augmented areas such as spousal work and education, benefits, and union representation. Our survey also included sections on family and personal demographics, economic data (wages, income, benefits), and if unemployed, reasons for not working. To assess organizational commitment, the validated 9-item short version of the 15 item Organizational Commitment Scale (OCS) developed by Mowday et al. (1979) was used. Responses to the nine items on the OCS ranged from 1 to 7 (7 ⫽ strongly agree), and an overall average score was calculated. Cronbach’s alpha for this OCS was 0.92 and the Guttman Split Half was 0.90, indicating excellent reliability in this sample of RNs and consistent with other research on RNs. Work intentions in the next year were assessed according to the following single question with five possible responses: (1) stay with same employer and same position; (2) stay with same employer and change position; (3) change employer; (4) temporarily leave nursing; and (5) permanently leave nursing. To address work attitudes, which could affect participation in the labor force, two single-item assessments of satisfaction were used. One item was a global satisfaction question by using a 5-point Likert-type scale (1 ⫽ extremely satisfied; during the analysis the item was reverse scored to make interpretation more intuitive), and the second item was a 3-point Likert-type scale comparing satisfaction this year to last year (more, less, or about the same). Single-item measures were necessary because of space limitations in the survey and were modeled on others used in the literature (Editors of Nursing88, 1988). It is not possible to compute an alpha co-efficient for reliability on single-item measures (Price, 1997).

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RN job perceptions were measured by two questions. The first asked if the RN’s job description had been redefined (redesigned or restructured). The second asked if the RN’s work unit reduced the number of RNs providing direct patient care, and if so, was it with or without replacement? Sample Stratification and Statistical Methods Stratification of the sample into five groups: hospital and non-hospital work location, full-time and part-time work status, and unemployed was performed ex-poste (at the point of data analysis rather than when the sample was selected). The hospital RN group was defined as employed by a hospital in an inpatient/acute care unit, including the emergency room, a nursing home unit in a hospital, or a hospital-based outpatient clinic, surgery center, or ambulatory care unit. All others were classified as non-hospital RNs. Full-time work status was defined as at least 35 hours per week of employment as an RN with a person’s primary source of employment. Those working as an RN for less than 35 hours per week were classified as part-time. Those RNs not working in nursing or completely unemployed are considered as unemployed in this analysis. Data Analysis RN labor market perceptions, demographic, economic, attitudinal factors, and work intentions across both the work location and the work status strata were analyzed for the Western New York (WNY) labor market. Differences were tested using two-factor level means and proportions with classical t tests, and across multiple-factor levels using ␹2 tests to compare hospital and non-hospital full-time RNs, hospital and non-hospital part-time RNs, hospital versus non-hospital RNs, and parttime versus full-time RNs. Adjustments were made in the variance used in the tests to include variation among unemployed nurses and for ex-poste stratification. (Neter et al., 1996; Mendenhall, Ott, & Scheaffer, 1990). Ex-poste stratification adjustments to the variances were based on populationbased weights in the NSSRN (1996) corresponding to the 125 respondents from Western New York. A regionally defined labor market allowed for control of factors that can be expected to influence RNs’ labor supply participation such as cost of living, employment opportunities, and local wage standards.

BREWER AND NAUENBERG

RESULTS

There were 345 RNs working in hospitals, 318 RNs working for non-hospital employers, 442 fulltime RNs, and 221 working part-time. There were also 113 non-working RNs. Demographics of the sample are described in Table 1. WNY RNs are similar to the NSSRN (Spratley, Johnson, Sochalski, Fritz, & Spence, 2001) in age (46.2 v 45.2 years), percent of women (95.5% v 94.6%), and marital status (75.4% v 75.2%). This study’s sample is more white (97.3% v 86.6%), are more likely to have an associate degree (AD) (41.7% v 32.1%), and have more children at home under 6 years old (12.9% v 8.0% children) than the NSSRN would suggest. Hospital and part-time RNs were younger than non-hospital/full-time RNs and had 2 to 3 years less experience than their counterparts (Table 1). Part-time hospital RNs were more likely to be married. The data also indicated that the likelihood that a spouse works full-time was inversely related to whether the nurse was engaged in full-time employment. Full-time hospital RNs are more likely to be diploma or (AD) RNs than hospital part-time RNs. Women are significantly more likely to work part-time than are men, and hospitalbased workers were more likely to be unionized than were non-hospital workers. There were no significant differences by race, multiple jobs, or children’s age.

Women are significantly more likely to work part-time than are men, and hospital-based workers were more likely to be unionized than were nonhospital workers.

Job Perceptions, Job Satisfaction, Organizational Commitment, and Intention to Leave RNs’ perceptions of considerable change in the nursing labor market are shown in Table 2. Almost two fifths of all nurses had their positions redefined, restructured or reengineered. Hospital RNs were significantly (p ⬍ 0.05) more likely to have their current positions redefined than non- hospital

FACTORS AFFECTING RN WORK AND INTENTIONS

147

Table 1. Significant Demographic Factors: Age, Work Experience, Weeks Worked, Gender, Children, Union Representation, Martial Status, Spousal Work Status FT/PT Status by Work Venue Grand Mean (SD) n ⫽ 776

Age

Work experience (years)

Total Sample Statistic

Weeks worked per year

Gender (female)(%)

Hospital FT (SD) n ⫽ 232

Nonhospital FT (SD) n ⫽ 208

46.2 (8.3)

44.7 (7.7)

19.5 (8.4)

19.2 (8.1)

49.7 (6.1)

95.5

50.8 (4.0)

92.2

Work Venue

Hospital PT (SD) n ⫽ 113

Nonhospital PT (SD) n ⫽ 108

47.8 (7.8)

42.1 (6.7)

21.5 (8.0)

17.1 (6.7)

49.0 (6.5)

95.2

49.4 (7.8)

98.2

FT/PT Status

Hospital (SD) n ⫽ 345

NonHospital (SD) n ⫽ 318

FT (SD) n ⫽ 442

PT (SD) n ⫽ 221

Not Working (SD) n ⫽ 113

46.5 (8.2)

43.8 (7.5)

47.4 (8.0)

46.2 (7.6)

44.3 (7.8)

50.3 (9.6)

19.4 (8.7)

18. (7.7)

20.7 (8.3)

20.3 (8.1)

18.2 (7.8)

18.8 (9.8)

*t(552)⫽ 4.05¶ †t(333) ⫽ 3.93*¶ ‡t(775) ⫽ 5.67*¶ §t(775) ⫽ 2.86*¶ *t (552) ⫽ 2.82*¶

50.0 (7.5)

†t(333) ‡t(775) §t(775) *t(552)

⫽ ⫽ ⫽ ⫽

2.05*㛳 3.51*¶ 3.01*¶ 4.00*¶

95.6

†t(333) ‡t(775) §t(775) *t(552)

⫽ ⫽ ⫽ ⫽

1.57 3.84*¶ 1.07 1.00

48.4 (9.0)

100.0

50.4 (5.6)

94.5

48.8 (7.4)

96.9

49.9 (5.4)

93.9

49.0 (8.4)

99.1

†t(333) ⫽ 0.78 ‡t(775) ⫽ 0.96 §t(775) ⫽ 2.23㛳 Education (%) Diploma AD BA/BS MA or higher Union representation (%)

Marital status (%) (married)

26.9 41.7 29.8 1.7 41.5

75.4

28.0 45.3 24.6 0.4 45.7

70.7

22.6 38.9 32.7 4.8 38.9

69.2

23.9 38.9 36.3 0.0 56.6

86.7

30.6 37.0 32.4 1.9 21.3

82.4

27.1 43.8 28.8 0.3 49.3

75.9

25.6 42.2 30.0 3.2 33.0

73.6

25.7 42.9 28.9 2.5 42.5

69.9

27.6 41.9 30.4 0.0 39.4

84.6

30.0 36.4 31.8 1.8 5.6

*␹2 (3) ⫽ 13.50¶㛳 †␹2 (3) ⫽ 3.24 ‡␹2 (3) ⫽ 4.97 §␹2 (3) ⫽ 3.89 *t(552) ⫽ 1.45

78.8

†t(333) ‡t(775) §t(775) *t(552)

⫽ ⫽ ⫽ ⫽

5.78¶㛳 4.33¶㛳 0.77 0.26

†t(333) ⫽ 0.69 ‡t(775) ⫽ 0.45 §t(775) ⫽ 2.91㛳 Sample size for spousal work status Spousal work status: Spouse works FT (%) Spouse works PT (%)

n ⫽ 585

n ⫽ 164

n ⫽ 144

n ⫽ 98

n ⫽ 89

n ⫽ 262

n ⫽ 234

n ⫽ 309

n ⫽ 187

n ⫽ 89

82.2

82.3

83.3

91.8

85.4

85.9

84.2

82.8

88.8

66.3

7.2

8.5

10.4

3.1

4.5

6.5

8.1

9.4

3.7

6.7

Spouse unemployed (%)

10.6

9.1

6.3

5.1

10.1

7.6

7.7

7.8

7.5

27.0

*␹2 (2) ⫽ 1.08 †␹2 (2) ⫽ 2.02 ‡␹2 (2) ⫽ 0.48 §␹2 (2) ⫽ 5.73㛳

A chi-square test was used to test for differences across the multiple work intention categories. *Hospital full-time/non-hospital full-time comparison. †Hospital part-time/non-hospital part-time. ‡Hospital/non-hospital comparison. §Full-time/non-hospital full-time comparison. 㛳 p ⬎ 0.05. ¶ p ⬍ 0.01.

RNs; however, there were no significant differences between full-time and part-time RNs. Non– hospital-based nurses were less likely than hospital RNs regardless of full- or part-time status to have

experienced reductions in RN staff size and were less likely to experience RN reductions and/or displacement by other less-educated staff than were their hospital counterparts. There were no differ-

148

BREWER AND NAUENBERG

Table 2. RN Job Perception Factors: JobRedefinition, and RN Reductions With and Without Replacement FT/PT Status by Work Venue

Work Venue

FT/PT Status

NonNonNonGrand Hospital hospital Hospital hospital Hospital hospital Mean (SD) FT (SD) PT (SD) (SD) FT (SD) FT (SD) PT (SD) PT (SD) (SD) n ⫽ 776 n ⫽ 232 n ⫽ 208 n ⫽ 113 n ⫽ 108 n ⫽ 345 n ⫽ 318 n ⫽ 442 n ⫽ 221

Job redefined (%)

39.4

45.2

36.5

39.3

33.0

43.2

35.4

41.1

36.3

*t(439) †t(220) ‡t(662) §t(662)

⫽ ⫽ ⫽ ⫽

1.86 0.98 2.06㛳 1.20

RNs reduced No replacement (%)

27.9

36.9

15.5

38.3

19.6

37.3

16.8

27.0

29.2

With replacement (%)

14.4

16.6

9.9

19.1

12.7

17.4

10.9

13.5

16.0

*␹2 †␹2 ‡␹2 §␹2

⫽ ⫽ ⫽ ⫽

33.05¶ 13.67¶ 46.72¶ 1.40

Not reduced (%)

57.7

46.5

74.6

42.7

67.6

45.3

72.3

59.5

54.7

(2) (2) (2) (2)

There were 113 subjects classified as not working not included in the table above. T tests are used to test for differences in two categories (e.g., yes/no questions) and chi-square tests are used to test for differences across multiple categories (e.g., questions on RN reductions) *Hospital full-time/non-hospital full-time comparison. †Hospital part-time/non-hospital part-time. ‡Hospital/non-hospital comparison. §Full-time/part-time comparison. 㛳 p ⬍ 0.05. ¶ p ⬍ 0.01.

ences in reduction or replacement by full-time and part-time.

higher mean number of benefits than non-hospital/ part-time RNs.

Economic Factors Median incomes and wages were reported because these variables are typically skewed to the right (Table 3). Mean RN annual income was significantly higher for full-time versus part-time RNs, but the hourly wage was not significantly different suggesting that the difference in incomes was largely due to the difference in the number of hours worked. However, across work venues both incomes and hourly wages were significantly higher for hospital versus non-hospital RNs. Spousal income was significantly higher for part-time versus full-time RNs. The percent of RNs attaching importance to the benefits package in choosing current employment was significantly higher (72.9%) for full-time RNs compared with part-time RNs (43.9%); yet, the difference between hospital and non–hospitalbased RNs was not as large (58.4% v 72.8%) and was driven by differences between part-time workers in both venues. This may be because hospitalbased and full-time RNs also had a significantly

The percent of RNs attaching importance to the benefits package in choosing current employment was significantly higher (72.9%) for fulltime RNs compared with part-time RNs (43.9%); yet, the difference between hospital and non–hospitalbased RNs was not as large (58.4% v 72.8%) and was driven by differences between part-time workers in both venues.

Attitudes Most nurses were extremely or moderately satisfied. Full-time hospital-based RNs had the largest proportion of extremely or moderately dissatisfied

FACTORS AFFECTING RN WORK AND INTENTIONS

149

Table 3. Economic Factors: RN Income, Hourly Wages, Benefits, and Spousal Income FT/PT Status by Work Venue

Work Venue

FT/PT Status

Not NonNonNonGrand Mean Hospital hospital Hospital hospital Hospital Hospital Working (SD) FT (SD) PT (SD) (SD) (SD) FT (SD) FT (SD) PT (SD) PT (SD) (SD) n ⫽ 776 n ⫽ 232 n ⫽ 208 n ⫽ 113 n ⫽ 108 n ⫽ 345 n ⫽ 318 n ⫽ 113 n ⫽ 442 n ⫽ 221

Mean RN annual 35,063 42,712 income ($) (14,049) (8,497)

39,314 26,804 19,099 (13,787) (11,797) (7,778)

37,501 32,417 41,075 23,039 NA (12,234) (15,371) (11,409) (10,732)

Median RN 37,000 annual income ($) Mean hourly 19.11 wage ($) (3.21)

41,750

38,000

26,000

20,000

40,000

33,860

40,000

22,500

NA

20.91 (2.38)

17.15 (1.80)

21.04 (3.21)

16.94 (3.35)

20.95 (2.67)

17.07 (2.44)

19.14 (2.83)

19.03 (3.86)

NA

21.00

17.00

21.00

17.00

21.00

17.00

19.00

19.00

NA

6.19 (2.51)

5.60 (2.47)

4.58 (2.91)

2.41 (2.23)

5.7 (2.75)

4.5 (2.82)

5.9 (2.50)

3.5 (2.81)

NA

Median hourly 19.00 wage ($) Mean number of 5.11 benefits (2.84)

Benefits package 64.1 important (%)

Sample size for spousal income (i.e., among married RNs) Mean spousal income ($)

Median spousal income ($)

75.6

69.4

54.3

n ⫽ 458 n ⫽ 164 n ⫽ 144 n ⫽ 98

31.2

68.9

58.4

n ⫽ 89

n ⫽ 242 n ⫽ 216

72.8

Not Working (SD) n ⫽ 113

*t(439) †t(220) ‡t(662) §t(662)

⫽ ⫽ ⫽ ⫽

3.14¶ 5.69¶ 4.73¶ 19.55¶

*t(439) †t(220) ‡t(662) §t(662)

⫽ ⫽ ⫽ ⫽

17.76¶ 7.99¶ 17.93¶ 0.32

*t(439) ⫽ 2.49㛳

†t(220) ‡t(662) §t(662) 43.9 NA *t(439) †t(220) ‡t(662) §t(662) n ⫽ 285 n ⫽ 173 n ⫽ 65

⫽ ⫽ ⫽ ⫽ ⫽ ⫽ ⫽

49,558 39,838 42,744 63,440 60,100 49,248 50,449 42,550 61,781 52,410 *t(370) ⫽ (43,868) (17,836) (21,561) (76,031) (40,388) (54,453) (34,669) (23,463) (67,213) (31,387) †t(251) ⫽ ‡t(522) ⫽ §t(522) ⫽ 41,894 40,000 41,894 49,500 50,000 40,500 44,400 40,000 49,500 50,048

6.20¶ 5.34¶ 11.13¶ 1.45 3.57¶ 2.82㛳 7.31¶

0.46 0.57 0.74 3.48¶

Note. A large part of the difference between mean/median spousal income levels between categories of nurses can be explained by differences across these categories in the tendency of the spouse to work full or part time see Table 1. *Abbreviation: NA, not applicable. hospital full-time/non-hospital full-time comparison. *Hospital full-time/non-hospital full-time comparison. †Hospital part-time/non-hospital part-time. ‡Hospital/non-hospital comparison. §Full-time/part-time comparison. 㛳p ⬍ 0.05. ¶p ⬍ 0.01.

responses (15.3%), which occurred almost twice as frequently as for other nurses in the sample. In addition, a higher proportion of hospital-based RNs (28.6%) were less satisfied than the previousyear, compared with non-hospital nurses (33.3%). Current mean satisfaction, as well as satisfaction compared with the previous year, was lower for

hospital RNs than for non-hospital RNs (Table 4). In terms of commitment to their current employer, there was no difference among full-time and parttime RNs, but hospital-based RN scores averaged significantly lower than non-hospital RN scores. Relatively few WNY RNs intended to change or leave their current positions, and there was no

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Table 4. Attitudinal Factors: Organizational Commitment, Satisfaction, and Satisfaction During the Previous Year FT/PT Status by Work Venue

Work Venue

FT/PT Status

Grand Mean n ⫽ 776

Hospital FT (SD) n ⫽ 232

Nonhospital FT (SD) n ⫽ 208

Hospital PT (SD) n ⫽ 113

Nonhospital PT (SD) n ⫽ 108

Hospital (SD) n ⫽ 345

Nonhospital (SD) n ⫽ 318

FT (SD) n ⫽ 442

PT (SD) n ⫽ 221

Organizational 5.02 (1.29)

4.90 (1.39)

5.18 (1.27)

4.81 (1.08)

5.15 (1.29)

4.88 (1.30)

5.17 (1.27)

5.04 (1.34)

4.98 (1.19)

*t(439) ⫽ 2.17㛳

commitment

†t(220) ⫽ 2.09㛳

(range 1

‡t(662) ⫽ 2.95¶

⫽ low to 7

§t(662) ⫽ 0.58

⫽ high) Work

2.71 (0.87)

2.74 (0.83)

2.63 (0.90)

2.85 (0.77)

2.65 (0.96)

2.78 (0.81)

2.64 (0.92)

2.69 (0.87)

2.75 (0.87)

*t(439) ⫽ 0.85

satisfaction

†t(220) ⫽ 1.85

(range 1

‡t(662) ⫽ 2.33㛳

⫽ high to

§t(662) ⫽ 0.59

5 ⫽ low) Comparative ⫺0.19 (0.70) ⫺0.31 (0.70) ⫺0.06 (0.68) ⫺0.27 (0.72) ⫺0.08 (0.70) ⫺0.30 (0.70) ⫺0.07 (0.69) ⫺0.19 (0.70) ⫺0.18 (0.71)

*t(439) ⫽ 2.89¶

satisfaction

†t(220) ⫽ 1.02

1 yr

‡t(662) ⫽ 2.39㛳

before

§t(662) ⫽ 0.03

survey (range ⫺1 ⫽ less to 1 ⫽ more)

Note. There were 113 subjects classified as not working not included in the table. *Hospital full-time/non-hospital full-time comparison. †Hospital part-time/non-hospital part-time. ‡Hospital/non-hospital comparison. §Full-time/part-time comparison. 㛳p ⬍ 0.05. p ⬍ 0.01.

significant difference in this regard between either hospital-based and non-hospital or full-time and part-time RNs (Table 5). The majority of nurses planned to stay in the same position. Intentions were examined by years of experience and age. Those nurses who were planning to leave permanently (n ⫽ 24) had the greatest mean years of experience (26.8 years), and those RNs planning to change employers (n ⫽ 37) or leave temporarily (n ⫽ 4) had the least mean years of experience (16.1 years and 12.0 years, respectively). The same trend is apparent in the mean age of those planning to leave permanently, to change employers, and to leave temporarily (52.0, 44.2, and 40.7 years, respectively). The oldest, most experienced nurses were those planning to leave permanently. DISCUSSION

The major findings of this study reveal differences between RNs primarily in hospital and non-

hospital settings in economic incentives to work, job perceptions, job satisfaction, and organizational commitment. Part-time/full-time status played a role with some factors. It was surprising to note that these differences did not translate into markedly different intentions. This study adds to the literature in two ways. Turnover research has largely failed to connect the effects of attitudinal factors and intent to leave to the potential effects on the nursing labor market, and nursing labor economics research has largely focused on demographic and wage factors to the exclusion of attitudinal variables. A recent paper from the United Kingdom, which examined both areas simultaneously, found that certain sources of dissatisfaction were more influential on intentions to quit than others. Specifically, dissatisfaction stemming from limited opportunities for promotion and further training had a bigger impact on intentions than did either dissatisfaction stemming

FACTORS AFFECTING RN WORK AND INTENTIONS

151

Table 5. RN Intentions to Work in the Next Year FT/PT Status by Work Venue

Stay, same position (%) Stay change position (%) Change employer (%) Permanently leave (%) Temporarily leave (%)

Work Venue

FT/PT Status

Hospital PT (SD) n ⫽ 113

Nonhospital PT (SD) n ⫽ 108

Hospital (SD) n ⫽ 331

Non Hospital n ⫽ 304

FT n ⫽ 427

PT n ⫽ 208

Grand Mean n ⫽ 776

Hospital FT (SD) n ⫽ 232

Nonhospital FT (SD) n ⫽ 208

81.3

81.9

77.9

77.0

71.3

83.7

78.6

82.4

78.8

*␹2 (4) ⫽ 5.63

8.5

5.2

10.6

11.5

5.6

7.6

9.5

8.2

9.1

†␹2 (4) ⫽ 5.94

5.8

5.2

4.3

6.2

8.3

5.7

5.9

4.9

7.7

‡␹2 (4) ⫽ 3.28

3.8

3.0

4.3

1.8

5.6

2.7

4.9

3.7

3.8

§␹2 (4) ⫽ 2.24

0

0.0

1.4

0.9

0.0

0

0

0

0

Note. There were 113 subjects classified as not working not included in the table above. A chi-square test was used to test for differences across the multiple work intention categories. *Hospital full-time/non-hospital full-time comparison. †Hospital part-time/non-hospital part-time. ‡Hospital/non-hospital comparison. §Full-time/part-time comparison. 㛳p ⬍ 0.05. p ⬍ 0.01.

from excessive workload or inadequate wages (Shields & Ward, 2001). Local labor market conditions may help explain the findings that dissatisfaction did not translate into intentions to leave or change positions. During the 1990s, hospitals were experiencing downsizing and merger activity at an unprecedented level, which is consistent with WNY data. Shindul-Rothschild et al. (1996) found that RN perceptions of hospital reorganization adversely influenced attitudes of RNs in ways similar to what were found in this current study. Although many WNY RNs had experienced job redefinition or RN reductions on their units, it was clear that this was more likely to have happened to hospital RNs, especially fulltime hospital RNs. RNs seem to have felt the impact of the restructuring efforts described earlier; however, there is no known normal level of reorganization, and hospital RNs may experience more job reorganizations as a function of the size and nature of the hospital organization. The relatively younger age of RNs in hospitals could also produce greater rates of intentions to leave in this group of nurses; however, no differences were found between hospital and non-hospital nurses with regard to their intention to leave.

The Relationship Between Commitment, Satisfaction, and Intentions Both satisfaction and commitment are identified as precursors to intention to leave and turnover (Irvine & Evans, 1995). Labor supply participation is important when a nurse decides to leave a job or reduce hours. Intention to leave is the most direct precursor in these turnover models to actual turnover and is also an indicator of future labor behavior. In the aggregate, external turnover—leaving the employer rather than changing the position— depresses the supply of RN labor, particularly if this group of nurses tends to stay out of the labor force for an extended time, retire early, or reduce their hours. Older, more experienced, and nonhospital RNs were more likely to express intention to leave, but no significant differences were found in intentions between hospital and non-hospital RNs. Shindul-Rothschild et al. (1996) reported over twice the percentage (12.4%) of RNs were unlikely or very unlikely to stay in nursing than in our sample. The difference between this study and others may be because of differences in the age profile of the RN population studied because nurses tend to migrate out of hospitals as they age,

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and others’ findings of greater intent to leave permanently among non-hospital RNs may be capturing an intention to retire based on age. In the current study, more nurses were over age 50 in the non-hospital group (36.7%) than in the hospital group (18.8%); yet, differences were not observed. Some reasons for this lack of difference may have been (1) the limited prospects for alternative employment in the local market may have created “job lock,” (2) differences in the phrasing of the intentions question compared with other surveys, (3) RNs in the local area may have been fearful that their responses would not be held confidential, and (4) response bias. The editors of Nursing88 (1988) used a global job satisfaction item similar to the one in the current study with a 5-point Likert scale (5 ⫽ very satisfied). The job satisfaction average in their study during the 1988 shortage was 3.1 compared with 2.7 in this study, and a smaller proportion of that study’s RNs was dissatisfied compared with current findings. More RNs in the current study appeared to be satisfied, although hospital RNs were less so. This finding has been supported by findings from the NSSRN 2000 report (Spratley et al., 2001). RNs who work in hospitals and nursing homes or in staff nurse positions are the least satisfied. Single-item measures tend to be less sensitive than multi-item measures, which argues that, if anything, lack of satisfaction is understated in this study. Organizational commitment is another important variable considered as a factor in intentions to leave or stay. The scores in this study (Table 5) fell within the range reported for nine different samples by Mowday et al. (1982), and slightly above Price and Mueller (1986) and Gurney et al. (1997). Satisfaction and commitment are intimately related. Studies suggest the relationships are complex, either causal (Gurney, Mueller, & Price, 1997; Mueller & Price, 1990) or each attitude represents a separate construct contributing independently to intention (Lum, Kervin, Clark, Reid & Sirola, 1998; Irvine & Evans, 1995). Both models suggest that if job perceptions or market events affect RN satisfaction and organizational commitment, one would expect to see changes in intentions and ultimately turnover (Borda & Norman, 1997; Irvine & Evans, 1995). The meta-analysis by Irvine and Evans (1995) categorized satisfaction and commitment as the primary variables having an impact on intentions,

BREWER AND NAUENBERG

but these variables in turn are influenced by pay, job market conditions, individual differences in expectations, stress adaptation, and work conditions. Mueller and Price’s (1990) model of turnover included economic (pay and attitude toward pay), psychologic, and sociologic independent variables. The contributions of variables from each category were significant in their model; however, they did not consider the influence of the overall market except for perceptions of difficulty in finding employment. A recent article by Shields and Ward (2001) found that the source of dissatisfaction was more important than the overall level of dissatisfaction in terms of having an impact on intentions; therefore, the lack of association in our study between dissatisfaction and intentions might be because of the source of dissatisfaction, which was not investigated. Another recent article about RNs in the southeastern United States mentioned group cohesion as a possible mitigating factor possibly affecting the relationship between work satisfaction and intentions (Shader, Broome, Broome, West, & Nash, 2001) All of these studies suggest that the need to be financially secure might provide motivation to remain with the hospital employer, even under various adverse conditions (e.g., low wages). The standard deviations of the spousal income were very large, which indicates that the median income is a more reliable indicator of the income level. Nurses who were unemployed had the highest median spousal income, and full-time and hospital nurses had the lowest, which is consistent with the “income effect” (Ehrenberg & Smith, 2000). Also, the RNs in this study were more likely to have associate degrees than RNs nationally (Moses, 1997) and to be locally educated; these factors may be interpreted as family and cultural ties that bind them to this region. Conditions of the local health care market present an intriguing potential explanation for the lack of differences in intent. There are important economic pressures driving the reorganization of institutions and reduction of overhead in NYS (Center for Health Policy Studies, 1999). WNY experienced downward shifts in hospital employer demand, increased managed care penetration, and an increase in hospital merger activity from 1992 to 1998. The local health care systems, newly formed in 1997, reported downsizing (Kaleida

FACTORS AFFECTING RN WORK AND INTENTIONS

Health, 1999). Hospital employment growth was 5.0% from 1988 to 1991 and ⫺5.9% from 1992 to 1998 (Brewer & Kovner, 2001). During this period, hospital and nursing home inpatient days in New York State decreased 13.8% and outpatient visits increased 9.5% per 1000 population; hospital inpatient days decreased 16.4% in total. In New York State in 1996, an estimated 50.7% of nurses were working in hospitals (Brewer & Kovner, 2001). From 1994 to 1997, the full-time equivalent (FTE) RNs per bed (combined hospitals and nursing homes) stayed constant at 0.99 FTEs per bed, but the total RN FTEs employed decreased 7.3% (American Hospital Association, 2000). From 1988 to 1991, in New York State average inflation adjusted RN hospital wage growth was 8.9%, whereas from 1992 to 1997 it was only 0.71% (Brewer & Kovner, 2001). Within this economic climate, RNs who had jobs were unlikely to leave them even though the associated increases in job redefinition and RN reductions would be likely to influence RN attitudes toward work. Also, unemployed RNs (not otherwise analyzed for this article) responded that they did perceive difficulties in getting a job. Although there is no base line with which to compare these responses, there was at least some perception of a difficult labor market, which is consistent with intentions to stay in one’s current position. This may also indicate a pent-up demand for job changes, which may be contributing to the current shortages (Brewer & Kovner, 2001). Explanations of Wage Differentials This study found higher wages and benefits in hospitals, and, in particular, hospital-based RNs reported the current benefits package was considered important to remaining in their current positions. Average wages in this study were within the norms for the mid-Atlantic region reported in a recent nationwide survey (Mee & Carey, 2000). Four alternative explanations exist for the better wages and benefits for hospital RNs. The economic theory of compensating differentials states that the higher wage offered by hospitals is inducement to compensate for the less desirable working conditions. Without longitudinal data comparing attitudes and career movements of hospital and nonhospital RNs over time, this is difficult to assess. A second explanation is that hospitals may pay higher wages and benefits to attract and keep RNs

153

with the best skills (Ehrenberg & Smith, 2000). Schumacher and Hirsch (1997) found that higher wages in hospital environments for RNs were at least partially because of the unmeasured higher ability and talents of the hospital RN. A third possibility is that unions explain the wage and benefits differential. Nurses may have been more highly paid because the larger hospital units they work in are more likely to be unionized. In this study, hospital RNs were more likely to be unionized. A fourth, and related explanation, is that hospitals are larger employers than non-hospital nursing employment units. Larger employers pay more than smaller employers (Ehrenberg & Smith, 2000). This is partly related to the combined greater likelihood of unionization, monopoly power, greater capital investment, and more skilled workers. Thus, higher wages and benefits may have outweighed lower job satisfaction and commitment and helped to retain RNs. Clearly, the relative influence of work attitudes and economic factors may vary depending on the local market conditions. More research is needed examining the very powerful retentive force of higher wages and benefits for RNs in hospitals, especially if their spouses earn less. Of particular concern to nursing administrators should be whether this differential will erode in large hospital systems, particularly if unionized systems equalize wages across settings. If that wage differential were to erode, or if economic conditions created job openings as have occurred recently, lack of attention from nursing administrators to the factors that influence work participation may contribute to an exodus of nurses from hospitals. This is particularly likely if the growth in non-hospital jobs continues. LIMITATIONS

There were several limitations to this research. Regional studies, because of unique aspects of the sample as well as any response bias that might be present, are not necessarily applicable to other regions. This region is highly penetrated by managed care and was coalescing into two major hospital systems at the time of this study. The RN sample was representative of WNY except for possible under-representation of minority and younger (age ⬍30) RNs, although given the aging of the RN workforce, this age skew can be seen positively. In the state of New York, more new gradu-

154

BREWER AND NAUENBERG

ates receive associate degrees compared with nationally (73.3% v 59.7%; National League for Nursing, 1997). Associate degree RNs tend to be older (Moses, 1997), and because the New York State registration list moved to a rolling renewal in 1996, new graduates (most likely to be younger) are more likely to be omitted from the sample. Only 4.5% of New York State RNs are under 30 (Brewer & Kovner, 2001). These factors may help explain the very small proportion of RNs (0.8%) under age 30 in this sample and the somewhat higher average age of our sample. Associate degree nurses are twice as likely (43.4%) to remain in the same state in which they received their degree as baccalaureate RNs (23.6%; Moses, 1997). The sample in this study had both a high proportion of nurses with associate degrees and a high proportion of locally trained RNs in the area, indicating socio-economic or cultural ties that held nurses captive to local economic events. Another limitation is the response rate in the survey. The rate of 54.1%, although within the norms of survey research, is still low enough to produce some undetectable sample bias; however, the sample in this study is reasonably representative of non-minority RNs over 30 years old when compared with the NSSRN sample.

istrators to increase wages to retain RNs through the growing demand for non-hospital RNs or to attract more highly qualified RNs to hospitals. The extent of this pressure will be affected by whether there is an increase in the rate of unionization in the non-hospital sector. Also, if non-hospital RNs are increasingly employed by integrated systems whose wages and benefits become more uniform across the system, the hospital/non-hospital wage differential may decrease, causing problems in retaining RNs in the hospital sector. Additional research, particularly longitudinal studies of hospital and non-hospital RNs within a labor market, is needed to explore these possibilities.

IMPLICATIONS

Current market status in NYS appears to again have tilted toward a shortage (Health Resources and Services Administration, 2002). Increasing registered nurse participation in the workforce will take concentrated effort based on understanding the dynamics of nurse workforce participation. Under improved economic conditions, as seemed to occur in 1998 (Robinson, 1999), or when hospital downsizing stabilizes long enough for demand to again put pressure on hospital utilization, RN intentions to leave may increase as soon as employment opportunities expand.

Improving the supply of nurses requires understanding why nurses participate in the workforce. Further study is needed with market level samples of RNs from all employment settings to examine the role of market changes on perceived nursing job opportunities, the work environment, and RN attitudes toward work, particularly in hospitals. Hospital RNs may be somewhat less satisfied, but that satisfaction apparently was not sufficiently low, given the local market conditions and other factors, to offset differences in earnings between hospital-based and non-hospital employment, and lack of employment opportunities in WNY. If nonhospital settings paid higher wage rates and offered more hours, the hospital RNs might be more inclined to leave. Further research to investigate these relationships is needed. Another question raised by this research is whether there will be pressure on nursing admin-

Another question raised by this research is whether there will be pressure on nursing administrators to increase wages to retain RNs through the growing demand for non-hospital RNs or to attract more highly qualified RNs to hospitals.

ACKNOWLEDGMENTS The authors would like to thank Dr Noah Meltz, Professor Emeritus of Economics and Industrial Relations, University of Toronto, for his guidance and critique of this article, and Jason Osborne, PhD, the original project director and analyst who is now at the University of Oklahoma, Chao Ru, MA, Chaitali Ghosh, MA, and Jan Grzankowski, DNS, for their help in completing the data analysis.

FACTORS AFFECTING RN WORK AND INTENTIONS

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