Projected Registered Nurse Workforce
in Hawai’i 2005 - 2020January 2007 (download pdf file)

Summary & Introduction | I. Nursing Supply Model | II. Nursing Demand Model

III. Projecting RN Shortage | IV. Limitations | V. Recommendations

VI. Conclusions | VII. References

I. The Nursing Supply Model

The Bureau of Health Professions (BHPr), Health Resources and Services Administration in the U.S. Department of Health and Human Services, created the Nursing Supply Model (NSM) ¹ to project the national supply of registered nurses (RN) in the United States.

Three predominant measures are used by the NSM to predict the annual supply of registered nurses through to the year 2020. These measures are:

1) RN population, defined as the estimated number of licensed RNs;

2) Active RN supply, defined as the estimated number of licensed RNs participating in the nurse workforce (i.e., employed in nursing); and

3) Full time equivalent (FTE) RN supply, defined as the estimated number of FTE RNs employed in nursing (i.e., RNs employed full time during the entire year are counted as one FTE, while RNs employed part time or for part of the year are counted as some portion of an FTE).

The NSM projects the size of the RN population and then estimates the other two supply measures (i.e., active RNs and FTE RNs) based on projected workforce participation rates.

Other elements of the NSM include:

• State-level estimates. The NSM produces independent projections for each of the 50 states and the District of Columbia and aggregates these state-level projections to produce national projections.

• Inter-state migration. The NSM tracks the net flow of RNs across states. Some states are consistently net exporters of RNs, while other states are consistently net importers of RNs.

• Age distribution. The NSM tracks and reports the RN population by age.

• Education level. The NSM tracks and reports RNs by highest education attained using three levels: (1) associate degree or diploma, (2) baccalaureate degree, and (3) RNs upgrading; master’s degree or higher.

The potential RN population, the participation rate, and the FTE equivalent rate are themselves dependent on age and education level. Hence, the NSM tracks the nursing population by age and highest level of education.

Factors considered in the Nursing Supply Model

A. Estimating the Registered Nurse Population

The NSM begins by estimating the nursing population in each year. To do this, it starts with the population from the previous year. It then uses pre-estimated probabilities to determine the net migration of nurses in to or out of the state, changes in education, attrition, foreign immigration and new graduates into the nursing labor pool.

i. The Starting Population

The default starting population is the number of registered nurses in the state in the base year 2000. This information is disaggregated by age and education. In the NSM the default base year may be changed.

ii. Migration

To determine net migration, the model uses pre-estimated probabilities of immigration into or emigration out of each state. The probabilities depend on an RN’s age and education level. Older or more educated nurses tend to be more stable than younger or less educated nurses. The actual number of immigrants into or emigrants out of a state in a particular year is the nursing population times the probability of immigration or emigration. Net migration is measured as the number of nurses entering the state minus the number of nurses leaving the state.

Figure 1.1 Proportion of RNs who Emigrate from Hawai’i

Figure 1.2 Proportion of RNs who Immigrate to Hawai’i

iii. Education

The model predicts many changes in labor market supply based on education. Hence, it has to predict changes in education among the nursing pool. The modal assumes that all new graduates earning associate or baccalaureate degrees are new entrants into the market. Licensed RNs earning baccalaureate degrees, master’s degrees or higher are assumed to be current labor market participants upgrading their education. The model uses a pre-estimated default number of nurses upgrading and applies those to estimate the number of nurses that earn advanced degrees.

iv. Attrition

Attrition is a permanent departure from the labor market. To estimate attrition, the model uses pre-estimated probabilities that a nurse with a specified education and age will leave the labor market. It applies the probabilities to the nursing labor pool to estimate the number of nurses that leave the profession each year.

Figure 1.3 Percent Participation Rate by Age

v. Foreign Immigration

The model’s statistics show that there is little foreign immigration into Hawai’i’s RN labor market. This potential source of RN supply is insignificant.

vi. State Population and Potential Pool of Applicants to Nursing Programs

The default NSM uses US Census data to determine state population projections and the population of women aged 20 to 44 as the potential pool of applicants to nursing programs and leaves men out of the market. However, the model uses only relative changes in the population of women in this age group to predict changes in nursing program enrollments. As long as the male population increases at the same relative rate as the female population, it is assumed there is no inconsistency in estimating the nurse population.

The NSM assumes that every one percent change in the pool of potential applicants for nursing programs as compared to the pool that existed in the year 2000 results in a one percent change in the number of nursing school graduates as compared to the number of graduates in the year 2000. The pool of potential applicants to nursing schools is the number of women age 20 to 44. For example, if the proportion of women age 20 to 44 that make up Hawai’i’s population were to fall by two percent in the year 2010 as compared to 2000, the number of graduates from nursing programs would fall by two percent in 2010 as compared to the number that graduated in 2000.

Thus, a reduction in projected growth in Hawai’i’s population may impact the future number of graduates from nursing programs. As shown in Table 1.1 (the default population projection and percent of women age 20 – 44) and Table 1.2 (the adjusted population projection and percent of women age 20 – 44).

Table 1.1 Default Population Projection & Percent of Women aged 20-44*

Table 1.2 Adjusted Population Projection & Percent of Women aged 20–44**

vii. Registered Nurse Participation

RN participation measures the probability that a nurse will be either employed or looking for work. The NSM uses pre-estimated national participation rates by age and education. For example, there may be a 96 percent chance that a 30 year old with a Masters Degree is participating in the RN labor market and only a 30 percent chance that a 64 year old with a diploma is participating. The NSM applies the participation rates to the nursing labor pool to estimate the number of nurses participating in the labor market each year.

viii. Registered Nurse FTE Equivalents

FTE equivalent rates estimate the proportion of nurses that work full time (for one FTE) and the proportion that work part time (for ½ FTE). The NSM creators used national data stratified by age and education to pre-estimate FTE RN equivalent rates. The model multiplies the full time and part time proportions to the nursing labor pool to estimate the supply of FTE RNs each year.

ix. FTE Nursing Supply

The NSM multiplies the population of nurses by participation rates and FTE RN equivalent rates to estimate the FTE RN supply in each year.

x. Nursing Supply Model & Hawai’i

The assumption is that most of the variables in the model reflect Hawai’i’s nursing supply experience. A number of variables, however, required adjustment. These variables, including population, were modified to reflect the recent population and economic projection for the state of Hawai’i using CDC data. Nurse graduate numbers from the recent HSCFN ‘Education Capacity Survey 2004-05’ were used. Policy adjustments include a reduction in retirement age of nurses by 5 years and a 5% increase in faculty to reflect current legislative support to the UH system and adjustments made by private schools of nursing to meet need.

B. Initial Number of Registered Nurses

The NSM projects the supply of licensed registered nurses, participation rates, and full time equivalent (FTE) of registered nurses. These numbers can be adjusted at the state level using a number of different elements found within the model.

C. Number of Nurses

i. Projected Graduate Registered Nurses

The NSM uses an initial value of the combined Associate and Diploma graduates, as well as pre-licensure BSN graduates. The first row of Table 1.3 shows that the default model assumed that in the year 2005 there were 133 Associate and 212 BSN graduates. The next row shows Hawai’i’s actual graduation experience in 2005. These figures are substituted into the model. The default model assumed in the year 2005 there were 39 RNs upgrading to BSN and 46 RNs upgrading to Masters or PhD. Hawai’i’s actual graduation numbers included 23 RNS upgrading to BSN and 23 RNS upgrading to MS or PhD. These numbers were added to the model.

Table 1.3 Numbers of Nurse Graduates from Hawai’i Nursing Programs

ii. Changing Projection Assumptions

The NSM allows RN supply projections to be made using alternate assumptions. Key assumptions about the determinants of the number of new RN graduates and the attrition and labor force participation rates can be changed using the model’s policy adjustment features. Previous nursing supply surveys ³ suggest be simulated, a retirement adjustment was made by shifting attrition rates forward by 5 years. An adjustment was also made to reflect current activities to increase nursing faculty i.e., the Hawai’i Legislature appropriations to public nursing programs and adjustments made by private schools of nursing to meet need. These changes allow the simulations to reflect the number of new RN graduates produced with a 5% increase in faculty. iii. Projected Registered Nurse supply Projections indicate an increase from a supply of 7,553 FTE RNs in 2005 to 8,286 FTE RNs in 2020. This represents an increase in supply of approximately 9.7% over the 15-year period. Figure 1.4 and Table 1.4 illustrate the projections of FTE RNs from 2005 to 2020 are well below both the projected licensed RN population and the active RN supply. that nurses retire earlier than 65 years of age. To enable retirement patterns to be simulated, a retirement adjustment was made by shifting attrition rates forward by 5 years. An adjustment was also made to reflect current activities to increase nursing faculty i.e., the Hawai’i Legislature appropriations to public nursing programs and adjustments made by private schools of nursing to meet need. These changes allow the simulations to reflect the number of new RN graduates produced with a 5% increase in faculty.

iii. Projected Registered Nurse supply

Projections indicate an increase from a supply of 7,553 FTE RNs in 2005 to 8,286 FTE RNs in 2020. This represents an increase in supply of approximately 9.7% over the 15-year period. Figure 1.4 and Table 1.4 illustrate the projections of FTE RNs from 2005 to 2020 are well below both the projected licensed RN population and the active RN supply.

Figure 1.4 Hawai’i RN Supply and Population Projections 2005 to 2020

Table 1.4 Estimated Supply FTE RNs, Active RNs, Licensed RNs and Total Population/100 from 2005 to 2020

iv. Projected Age Distribution of Employed Registered Nurses 2005 to 2020

Figure 1.5 shows projected age distribution of RNs in Hawai’i for 2005 and 2015. A significant aging of the RN population is occurring due to the large number of baby boom RNs, an increase in the age at which new RNs enter the profession, and a decline in younger women choosing nursing as a career. The aging bubbles are highlighted in Figure 1.5.

The age distribution of the RN population has important supply implications. As RNs age, they are more likely to leave the RN workforce due to retirement, disability, or death and are more likely to be working part time in nursing or to retain their license but not be working in nursing (Figure 2.5).

Figure 1.5 Projected Age Distribution of RNs in Hawai’i 2005 and 2015