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What is Behind HRSA's Projected Supply, Demand, and Shortage of Registered Nurses?

II. Nursing Supply Model

Tracking nurses by age, State, and highest education level attained (i.e., diploma or associate degree, baccalaureate degree, and graduate degree), the NSM produces annual, State-level projections of RN supply through 2020. Starting with the number of licensed RNs in 2000, the NSM adds the estimated number of newly licensed RNs, subtracts the estimated number of separations, and tracks cross-State migration patterns to calculate an end-of-year estimate of licensed RNs by State (Exhibit 1). The end-of-year estimate becomes the starting value for the next year’s projections.

To estimate the number of RNs active in the health workforce and the number of fulltime equivalent (FTE) RNs employed in healthcare, the model projects the number of licensed RNs and then applies workforce participation rates. In computing FTE RNs, nurses who work fulltime are counted as one FTE, while nurses who report working part time or for only part of the year are counted as one-half of an FTE.

Exhibit 1. Overview of the Nursing Supply Model


The NSM contains three major components: (1) modeling new graduates from nursing programs, (2) modeling location and employment patterns of the current licensed nurse population, and (3) modeling separations from the nurse workforce. For each of these components, we describe the data, assumptions, and methods used to project future RN supply.

A.  New Graduates from Nursing Programs

RNs typically enter the nurse workforce prepared at the diploma, associate, or baccalaureate level. (Some RNs enter at the master’s level but are modeled here as bachelor of science in nursing [BSN] graduates who upgrade their education). Data on the number of first-time candidates taking the National Council Licensure Examination for Registered Nurses (NCLEX-RN examination), as published by the National Council of State Boards of Nursing, suggest that in 2000 approximately 71,100 RNs graduated from U.S. nursing programs (Exhibit 2). Approximately two-thirds of these graduates were prepared at the diploma or associate level, with the remaining one-third prepared at the baccalaureate level or higher. The number of graduates in 2000 shows a continuing decline compared with earlier years (e.g., there were approximately 76,300 graduates in 1999 and 83,000 graduates in 1998). The literature discussing reasons for this trend is extensive (e.g., see Buerhaus et al. [2000] and Seago et al. [2001]) but reflects increasing professional opportunities for women outside nursing, stagnant pay and more onerous working conditions for many in nursing, and a decline in public perception of the attractiveness of the nursing profession.

Baseline projections of the number of new nursing school graduates are based on the assumption that the nursing profession will continue to attract its current share of the applicant pool. The population of women ages 20 to 44 is used as a proxy for the size of the applicant pool, and the population projections used in the NSM come from the U.S. Census Bureau’s middle series population projections. [2] Combining State-level NCLEX-RN data with State-level estimates of the number of women ages 20 to 44 creates a separate applicant pool share for each State. Over time, each 1 percent increase (or decrease) in the size of the applicant pool is assumed to cause a 1 percent increase (or decrease) in the number of RN graduates for that State. Under the baseline scenario, the number of new nurse graduates remains relatively constant through 2020 at the national level. The number of nurse graduates of each education type (E) in each State (S) and year (Y) is expressed mathematically:


The NSM software was built with algorithms to model the impact on the number of nursing graduates resulting from changes in RN compensation, working conditions, teaching capacity, and tuition costs. However, the research has yet to be completed for modeling the relationship between the number of nurse graduates and determinants that reflect the attractiveness of nursing as a career.

In addition to graduates from U.S. nursing programs, the NSM assumes net immigration of 3,500 RNs per year from foreign countries.

Exhibit 2. National Baseline Projections of Annual Nursing School Graduates Exhibit 2[D]

Source: Analysis of the 2000 SSRN.

B.  Licensed Nurse Population

The NSM tracks the population of licensed RNs, or “bodies,” regardless of whether the RN is providing nursing services. It applies estimated workforce participation rates to the projections of licensed RNs to forecast the active nurse supply (defined as number of nurses employed or seeking employment in nursing) and FTE supply (defined as the FTE number of nurses providing nursing services).

The model starts with the number of licensed RNs in each State, tracked by education level and age, as estimated using the 2000 SSRN (Exhibit 3). The education level and age composition of the licensed RN population has important implications for the current and future RN supply because workforce participation, cross-State migration, and retirement patterns vary systematically by education level and age.

Exhibit 3. RN Licensed Population, by Education Level and Age, 2000Chart with no title[D]

Source: Analysis of the 2000 SSRN

Over time, the nurse population has continued to age due to the large number of baby boom nurses and increasing difficulties in attracting new entrants to the profession. Also, the average age of new entrants is increasing (Exhibit 4).

Exhibit 4. Age Distribution Trend of the RN PopulationChart with no title[D]

Sources: 1980 and 2000 SSRN; NSM projections for 2010 and 2020.

1. Workforce Participation

The active RN supply is defined as the licensed RN population who provides nursing services or are seeking employment in nursing. This supply estimate excludes RNs who are licensed but not working in the nursing field (e.g., retired RNs who retain a license, RNs who have temporarily left the workforce, and licensed RNs employed in non-nursing positions). Responses to the SSRN are subjective, with individual respondents determining whether they are employed in a nursing position. The NSM applies national rates of workforce participation, by RN age and education level, to the projected licensed RN population in each State to project active nurse supply (Exhibit 5).

In a recent survey of approximately 7,300 licensed nurses (ANA, 2001), 672 respondents Stated reasons for their decision not to work in a nursing position. Approximately 25 percent found their current position more rewarding professionally, 20 percent cited better salaries in their current position, 20 percent reported more convenient work hours in their current position, and 18 percent cited personal safety concerns with working in a healthcare environment. If these estimates represent the entire licensed nurse workforce, then of the approximately 136,000 licensed RNs in 2000 employed in non-nursing positions (BHPr, 2001), an estimated:   

  • 34,000 would find their current position more rewarding professionally,
  • 27,000 would cite better salaries in their current position,
  • 27,000 would report more convenient work hours in their current position, and
  • 24,000 would cite personal safety concerns with working in a healthcare environment.

Only 70 percent of nurses in 2000 report being satisfied in their current position, which is significantly lower than U.S. workers in general (85 percent) and professionals in particular (90 percent) (BHPr, 2001). Job satisfaction among RNs was lowest in nursing homes and hospitals and highest in nursing education. Thus, of the approximately 2.2 million RNs employed in nursing in 2000, an estimated 672,000 were dissatisfied with their work.

The NSM software contains algorithms that allow users the potential to model changes in workforce participation rates over time based on projected changes in RN compensation and working conditions. There exists a paucity of research, however, identifying appropriate measures of working conditions and impact of changes in these factors on RN workforce participation.

Exhibit 5. Workforce Participation Rates of Licensed RNs, by Age and Highest Education Level AttainedChart with no title [D]

Source: Analysis of the 2000 SSRN.

The NSM also projects the FTE supply of RNs by applying FTE workforce participation rates that vary by RN age and education level (Exhibit 6). The FTE supply counts RNs working fulltime in nursing as one FTE and RNs working part time as one-half of an FTE.

Exhibit 6. FTE Workforce Participation Rates of Licensed RNs, by Age and Highest Education Level AttainedChart with no title[D]

Source: Analysis of the 2000 SSRN.

2. Cross-State Migration Patterns

Nurses migrate between States for better career opportunities, because of change in location of spouses’ employment, and for many other reasons. Some States are net exporters of RNs (i.e., more RNs leave than enter the State in a given year), while other States are net importers. The NSM estimates the number of RNs who will leave or enter the State each year by applying migration probabilities that vary by RN age, education level, and State. We estimated these migration probabilities by estimating a probit model using data from the 1992, 1996, and 2000 SSRNs. The SSRN asks survey participants in which State they resided at the time of the survey and one year before the survey. Nurses who change States between the survey date and the preceding year are identified as cross-State migrants. The probit model estimates the probability of leaving (or entering) a particular State as a function of RN age, education level, and State of residence. The NSM first estimates the number of nurses leaving each State by age and education level. Then, the NSM allocates this pool of migrating nurses to each State based on immigration probabilities that vary by State, RN age, and RN education level.

RNs prepared at the masters level or higher are more likely to migrate than are RNs prepared at the baccalaureate level, who in turn are more likely to migrate than are RNs with a diploma or associate degree (Exhibit 7). The analysis also shows significant variation across States in migration patterns. Younger RNs are more likely to migrate across States than are older RNs, reflecting factors such as greater transience among professionals early in their career as they seek employment after graduation.

Exhibit 7. Probability of Cross-State Migration, by Age and Education LevelChart with no title[D]

Source: Analysis of the 1992, 1996, and 2000 SSRNs.
Note: Probability of immigration and emigration varies by State.

3. Change in Education Level Attained

Some RNs will continue their schooling and thus move to a higher education category during the year. The NSM tracks two types of education upgrades: RNs prepared at the diploma or associate level who earn a baccalaureate degree and RNs prepared at the baccalaureate level who earn a master’s or higher degree (Exhibit 8). The probability that an RN will upgrade his or her education level varies by age and was estimated using a probit model and data from the 1992, 1996, and 2000 SSRNs.

Exhibit 8. Percentage of RNs Who Upgrade Their Education, by AgeChart with no title [D]

Source: Analysis of the 1992, 1996, and 2000 SSRNs.

Note: An analysis of the SSRN found that few nurses age 55 and older upgrade their education, and the drop in probability of education upgrade for nurses in their early 50s reflects this transition to a zero probability.

C.  Permanent Separation from the Nurse Workforce

Reasons why RNs permanently leave the workforce and do not renew their license include retirement, mortality, disability, and other factors. The NSM contains one set of attrition rates that combines all reasons for failing to renew one’s license. These rates do not, however, reflect temporary departures from the nurse workforce captured through the use of workforce participation rates as described previously.

We constructed separation rates (Exhibit 9) by combining mortality rates for women obtained from Minino et al. (2002) and estimated rates of attrition for reasons of disability and retirement using data from the 1998, 1999, 2000, and 2001 March Current Population Survey (CPS). The CPS collects data on respondent age, gender, education level, and workforce participation. These workforce departure rates were constructed based on data for all U.S. college–educated women. There exists a paucity of information on workforce separation rates for RNs, and, in particular, the number of RNs who fail to renew their license after changing careers. (The SSRN surveys only nurses with an active license.) Anecdotal evidence suggests that many RNs who leave nursing retain their license even when they have little intention of returning to nursing. We account for nurses who change careers but continue to renew their license in our workforce participation and FTE supply rates.

Exhibit 9. Workforce Separation Rates for College-Educated WomenChart with no title[D]

Source: Analysis of the 1998–2001 CPS files; mortality rates from Minino et al. (2002).

D. Nursing Supply Projections

Below we present projections from the NSM. The baseline projections assume the status quo, while projections for three alternative scenarios illustrate the supply implications of increasing the number of graduating RNs, increasing RN wages, and improving RN retention in the workforce.

1. Baseline Projections

The NSM baseline projections reflect the level of RN supply most likely to occur if current trends continue (Exhibit 10). At the national level, the number of licensed RNs is projected to remain relatively constant at about 2.7 million nurses between 2000 and 2020. The number of licensed RNs is projected to increase slightly through 2012 but to start declining as the number of retiring RNs exceeds the number of new graduates. The number of RNs active in nursing is projected to remain between 2.1 million and 2.3 million during this period, while the FTE supply of RNs is projected to decrease slightly from 1.89 million in 2000 to 1.81 million in 2020. At the State level, substantial variation occurs in the growth (or decline) of the RN population between 2000 and 2020 based on the number of new RN graduates in each State, net cross-State migration, and attrition from the RN population.

Exhibit 10. Baseline RN Projections, 2000 to 2020







Change from 2000–2020

Licensed RNs







RNs providing nursing services or seeking employment in nursing







FTE RNs providing nursing services







To assess the sensitivity of the model to key determinants of RN supply, we projected supply under alternative scenarios where we vary key assumptions.

2. Scenario 1: Change in Output from Nursing Programs

Under the baseline projections, the year-to-year percentage change in the number of graduates from nursing programs in each State is directly proportional to percentage change in size of the State’s female population ages 20 to 44 (which, as discussed previously, is used as a proxy for the size of the pool of nursing school candidates). The NSM uses State-level estimates of new RN graduates in 2000 as the starting point for the projections. Projections of the FTE RN supply increase substantially over time under alternative scenarios where the number of graduates from U.S. nursing programs, relative to the baseline projections, is 30 percent higher, 60 percent higher, and 90 percent higher year after year (Exhibit 11). Over time, the difference in projected total FTE RNs between each scenario grows such that by 2020 the difference in totals FTE RNs, relative to the baseline projections, is +314,000, +628,000, and +929,000 for, respectively, the +30 percent, +60 percent, and +90 percent scenarios. To meet projected growth in demand for RN services, the U.S. must graduate approximately 90 percent more nurses from U.S. nursing programs relative to the baseline graduate projections.

Exhibit 11. FTE Supply Implications of Changes in Projected Number of New Graduates from U.S. Nursing Programs [3]

Chart with no title[D]

3. Scenario 2: Change in RN Wages

If wages for nursing services increase relative to wages in alternative occupations, then, all else being equal, nursing becomes a more attractive career. In the short run, an increase in wages for nursing services would increase the FTE RN supply by motivating:

  • Licensed RNs not practicing nursing to return to nursing,
  • Part-time RNs to work more hours, and
  • RNs to delay retirement or leave retirement.

The short-term percentage increase in FTE RN supply attributed to each 1 percent increase in wages for nursing services is referred to as short-term wage elasticity of supply.

In the long run, an increase in wages for nursing services will also attract new entrants to the nursing workforce (assuming no constraints on nursing school capacity). Because of the time to recognize an increase in RN wages and the time to train new nurses, a delay of several years is expected between the time that RN wages increase and new entrants to the nursing profession increase. The long-term wage elasticity of supply, consequently, is larger than the short-term wage elasticity of supply.

There exists a paucity of research that estimates the wage elasticity of supply for nurses, and the few studies that have been published report a large range of elasticity estimates. One challenge when assessing the validity of these estimates for modeling the supply of RNs is to distinguish between short-term and long-term wage elasticities and to distinguish between market wage elasticities and wage elasticities specific to a particular provider (e.g., if one hospital increases RN wages, then that hospital will draw nurses away from other hospitals). Sloan and Richupan (1975) obtained wage elasticity estimates for RN workforce participation that ranged from 0.18 to 2.82. Using a sample of Norwegian nurses, Askildsen et al. (2002) estimate wage elasticities for workforce participation ranging from 0.253 to 0.843. For comparison, a review of the literature assessing the military’s ability to recruit finds most pay elasticity estimates in the 0.5 to 1.5 range (Hogan et al., 1995).

For this scenario, we assume annual growth in RN wages, relative to wage growth in alternative occupations, of 0 percent (the assumption in the baseline projections), +1 percent, +2 percent, and +3 percent annually between 2000 and 2020 [4] (Exhibit 12). The wage growth rates have a compounding effect over time, so a 1  percent growth rate over a 20-year period means that by 2020 RN wages would have increased, relative to other occupations, by 22 percent. We assume that each 1 percent increase in wages increases the number of RN graduates by 0.8 percent and increases workforce participation rates by 0.3 percent. By 2020, relative to the baseline projections, the number of FTE RNs is +228,000 (+13 percent), +518,000 (+29 percent), and +886,000 (+49 percent), respectively, for the scenarios with 1 percent, 2 percent, and 3 percent annual growth in real wages.

The baseline demand projections, discussed in more detail later, assume that RN wages will grow at the same rate as wages of licensed practical nurses (LPN) and other healthcare occupations. If RN wages were to rise faster than, say, LPNs, then employers of nurses would have a financial incentive to substitute lower-cost LPNs for higher-cost RNs, where feasible. Spetz and Given (2003) estimate that inflation-adjusted wages must increase by between 3 percent and 4 percent per year between 2002 and 2016 to bring RN labor markets into equilibrium. Assuming each 1 percent real increase in RN wages increases the number of new RN graduates by 0.8 percent and increases FTE activity rates by 0.3 percent, a continuous 3 percent annual increase in RN wages would still result in a shortfall of approximately 100,000 FTE RNs but would prevent the shortage from growing more severe (Exhibit 13).

Exhibit 12. Supply Implications of Rising RN Wages, 2020


Annual Wage Growth (relative to annual wage growth in alternative professions)

0% (Baseline)




Cumulative wage growth 2000–2020





Graduates/year 2020
(percentage different from baseline)








Licensed RNs 2020
(percentage different from baseline)








FTE RNs 2020
(percentage different from baseline)








FTE Rate 2020 (aggregate)





Exhibit 13. Projected FTE RN Supply under Alternative Wage Growth Scenarios

Chart with no title[D]

Note: Projections assume wage elasticities of 0.8 for new graduates and 0.3 for FTE workforce participation rates.

4. Scenario 3: Change in RN Retirement Patterns

The rate at which RNs permanently separate from the RN workforce varies by age and education level, with high rates of departure between age 62 and age 65 as nurses qualify for Social Security and Medicare benefits. Using the NSM, we project RN supply if each RN were to work an additional 4 years before retiring. Delays in average retirement age might occur as a result of (1) government policies delaying eligibility for Social Security and Medicare, (2) a healthier population able to remain longer in the workforce, or (3) improvements to RN working conditions that increase the likelihood that nurses will remain active in the workforce. Compared to the baseline projections, delaying retirement by an average of 4 years would increase the FTE RN supply by nearly 158,000 (9 percent) in 2020. Still, such an increase exerts only a modest effect on alleviating the projected growing RN shortage (Exhibit 14).

Exhibit 14. Impact of Changing Retirement Patterns on FTE RN Supply

Chart with no title[D]