The NDM projects State-level demand for FTE RNs, LPNs and vocational nurses, and nurse aides/auxiliaries and home health aides (NA) through 2020. Moreover, the NDM projects demand for RNs, the focus of this paper, in 12 employment settings. Nurse demand is defined as the number of FTE RNs whom employers are willing to hire given population needs, economic considerations, the healthcare operating environment, and other factors.

Changing demographics constitute a key determinant of projected demand for FTE RNs in the baseline scenario. The U.S. Census Bureau projects a rapid increase in the elderly population starting around 2010 when the leading edge of the baby boom generation approaches age 65 (Exhibit 15). Because the elderly have much greater per capita healthcare needs compared with the non-elderly, the rapid growth in demand for nursing services is especially pronounced for long-term care settings that predominantly provide care to the elderly.

Exhibit 15. Population Growth, 2000 to 2020

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In addition to State-level U.S. Census Bureau projections of changing demographics, the NDM projects nurse demand as a function of changing patient acuity, economic factors, and various characteristics of the healthcare operating environment.

The NDM (Exhibit 16), which combines input databases and projection equations to project demand, contains two major components: (1) the data and equations to project future demand for healthcare services and (2) the data and equations to project future nurse staffing intensity. It first extrapolates expected use of healthcare services by combining national healthcare use patterns and State population projections by age and gender. Then, the model adjusts the healthcare use extrapolations for each State to account for factors that cause healthcare use to deviate from expected levels (e.g., State-level variation in managed care enrollment rates).

Exhibit 16: Overview of the Nursing Demand Model

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The model next projects nurse staffing intensity (e.g., FTE RNs per hospital inpatient days) as a function of current staffing intensity and trends in major determinants  of nurse staffing intensity (e.g., average patient acuity). Combining projected healthcare use (e.g., inpatient days) with projected nurse staffing intensity (e.g., FTE RNs per inpatient day) produces projections of demand for FTE RNs by setting, State, and year. We describe the data, assumptions, and methods used to estimate demand for healthcare services and nurse staffing intensity, and we present our findings. A more complete description of the NDM is available in other reports. ^[5]

The demand for nurses derives from the demand for healthcare services. To accurately project the demand for nurses, therefore, one must first project the demand for healthcare services. The NDM projects demand for healthcare services for half of the 12 employment settings in the NDM (Exhibit 17). (For five settings, demand for RNs is projected using RN-per-population ratios. Demand for nurse educators is projected assuming that nurse educators remain a fixed proportion of total RN demand in each State). Measures of demand for NDM-projected healthcare services include inpatient days, outpatient visits, and emergency visits to short-term hospitals; inpatient days at long-term hospitals (e.g., psychiatric, rehabilitation, and all other hospitals); nursing facility residents; and home health visits.

Exhibit 17. Overview of the Nursing Demand Model

The NDM employs a two-step process to make State-level projections of demand for healthcare services for each of the six settings modeled. Step 1 applies national per capita use rates for 32 population subgroups to U.S. Census Bureau population projections for each State and year. ^[6] The 32 population subgroups are defined by eight age categories (ages 0–4, 5–17, 18–24, 25–44, 45–64, 65–74, 75–84, and 85 and older), gender, and metropolitan or non-metropolitan location.

Multiplying each per capita use rate by its respective State-level population projection creates a State-level extrapolation of the expected demand for healthcare services that controls for differences across States and over time in demographics. (Step 2 adjusts these extrapolations based on trends in the healthcare operating system and other factors.)

The following equation describes this step, where EU_S,H,Y is the expected level of healthcare use in State S in healthcare setting H in year Y. The variables P and R are, respectively, the size of the population in State S and the national per capita healthcare use for each age category (a), by gender (s) and by metropolitan or nonmetropolitan location (l). The first component of this equation is a calibration factor to ensure that base year estimates of expected healthcare use equal estimates of actual use. ^[7]

Step 2 adjusts up or down these initial extrapolations of healthcare use in each State and year based on projected changes in the healthcare operating environment, economic considerations, and other factors. The use adjustment factor differs by State, year, and setting and is calculated using projection equations whose parameters describe the relationship between healthcare use and exogenous variables.

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We estimated the parameters in the projection equations (bs) (Exhibit 18) using multiple regression analysis and a panel data set consisting of State-level data for the period 1996 to 2000. The dependent variable in the regression equations, measures the degree to which actual use (AU) of healthcare services deviate from expected use (EU) in a given State and setting during the period included in the regression analysis as described in Step 1.

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The actual regression equations contain the logged form of the dependent and many of the exogenous variables. Taking the logged form of these variables has two major advantages over the unlogged form. One, using a logged form ensures that the model will not project a negative value of the dependent variable. Two, the coefficients of logged exogenous variables can be interpreted as elasticities that represent the percentage change in the dependent variable for each 1% change in the exogenous variables (holding constant the other variables in the model). Having the coefficients in a common metric (e.g., elasticities) allows easier comparison of the magnitude and precision of coefficients between variables, across regression equations, and with empirical findings in the literature. The health maintenance organization (HMO) variable and the region dummy variables are the only variables not in log form.

Selection of the exogenous variables employed in the healthcare use regressions, as well as those employed in the staffing intensity regressions, was based on both theory and empirical analysis. We considered three criteria when determining which variables to include in the regression equations.

1. Theory-based model specification. A logical relationship should exist between the exogenous variable and the dependent variable. That is,  there should be a priori expectations of the direction of the relationship between the exogenous variable and the dependent variable based on theory and prior empirical evidence.
2. Identification of major determinants. We used stepwise regression to identify factors that exert a statistically significant effect on either demand for healthcare services or nurse staffing intensity. Stepwise regression considers the pool of potential exogenous variables—the pool consisted of only exogenous variables that logically would affect the dependent variable—and adds or subtracts variables based on the predictive power of each variable. One result of using this approach is that nearly all the exogenous variables in the final regression equations are statistically significant. Unfortunately, another result of using stepwise regression is that the statistical significance of the regression equations and the predictive power of the equation are overstated.
3. Reliable extrapolations of future values. We considered for inclusion in the final regression equations only variables whose future values can be extrapolated with some degree of reliability or that are important for policy modeling.

Several factors complicated the selection of exogenous variables in the regressions. First, in a few cases an exogenous variable is not statistically significant, though the factor that this variable reflects is presumed essential for developing a dynamic model (e.g., the HMO variable in the equation to estimate RN staffing patterns in hospital inpatient settings). We had to determine whether to include these variables with low statistical significance. In a few cases, variables deemed important that had a level of statistical significance between 0.05 and 0.2 were included in the final regressions. The coefficients on these variables are unbiased, despite the lack of precision. We closely scrutinized these coefficients and compared them with other findings from this analysis and from the literature to help ensure their reasonableness.

A second complication is that some of the exogenous variables that theory suggests are determinants of the dependent variable—and thus should be considered for inclusion in the equation—are correlated. For example, HMO enrollment rate is correlated with population density, and both HMO enrollment rates and population density might affect healthcare use and staffing intensity. (An example of how population density might affect nurse-staffing patterns is that healthcare providers in metropolitan areas might benefit from economies of scale that rural areas might not realize.) Multicollinearity among the exogenous variables means that their independent effects might not be precisely estimated even though the estimated effects are unbiased. Also, the stepwise regression approach might result in one variable forcing a correlated variable from the equation. Preliminary regressions were estimated to test the robustness of the regressions with respect to the inclusion or exclusion of correlated variables, and the results helped determine which variables to include or exclude from the final regression specifications.

A third complicating factor is that some regressions contain data from multiple years, and observations from the same State are not completely independent, meaning some heteroskedasticity occurs in the data. Heteroskedasticity can result in underestimates of the coefficient standard errors, which in turn overstates the statistical significance of the coefficients. ^[8]

The dependent and exogenous variables in the equations are estimates based on hospital census data and surveys of patients and healthcare providers. The concern that estimates for smaller States are less precise than estimates for larger States led to the decision to weight each observation in the regression by the square root of the State’s population.

Multiple regression analysis provides estimates of the relationship between healthcare use and its determinants. Note that the regressions predict the relationship between healthcare use and its determinants after adjusting for differences in the demographic composition by age category, gender, and urban or rural location.

Consistent with other studies, this analysis finds that HMOs decrease the number of inpatient days at short-term hospitals (Exhibit 18). The number of emergency department visits and nursing facility residents also decline as HMO enrollment rates rise. The baseline scenario assumes a 0.5 percentage point increase annually in enrollment rates, which equates to a 10 percentage point increase between 2000 and 2020. ^[9] Consequently, the NDM projects that, in 2020, inpatient days at short-term hospitals will decline by 3 percent, emergency department visits will decline by 2.8 percent, and the number of nursing facility residents will decline by 3.6 percent relative to the levels that would exist if no change in HMO enrollment rates occurred. State-level estimates of HMO enrollment rates for 1996 through 2000 come from the Interstudy Competitive Edge.

As improvements in technology and cost pressures shift more surgeries from an inpatient to an outpatient setting, the number of inpatient days at short-term hospitals will fall and the number of outpatient visits and home health visits is expected to rise. The baseline scenario assumes that per capita inpatient surgeries will decline by 2 percent annually from 2000 to 2020 and that these surgeries will instead be performed on an outpatient basis. For every 1 percent increase in the proportion of hospital-based surgeries performed on an outpatient basis, the regression findings suggest that inpatient days will decline by 0.47 percent, outpatient visits will increase by 1.66 percent, and home health visits will increase by 0.86 percent. State-level estimates of the proportion of hospital surgeries performed on an outpatient basis were obtained from American Hospital Association (AHA) annual Hospital Statistics publications.

An increase in the percentage of population uninsured decreases demand for healthcare services in long-term hospitals and nursing facilities. The baseline scenario assumes a modest decline in the percentage of population uninsured due to changing demographics. The variable was primarily included to increase the NDM’s policy analysis capabilities. A 1 percent increase in the proportion of the population that is uninsured decreases inpatient days at long-term hospitals by 0.38 percent and decreases nursing facility residents by 0.16 percent.

The percentage of population enrolled in Medicaid is positively correlated with higher use of healthcare services in five settings. Given that Medicaid enrollment is generally associated with higher need for healthcare services, access to medical services, and lower income (which some studies have found to be correlated with greater healthcare needs), this positive relationship is not surprising. The baseline scenario assumes a modest change in the percentage of population enrolled in Medicaid due to changing demographics. A 1 percent increase in the proportion of the population enrolled in Medicaid increases demand for inpatient days, outpatient visits, and emergency department visits at short-term hospitals by 0.26 percent, 0.17 percent, and 0.29 percent, respectively; increases demand for inpatient days at long-term hospitals by 0.26 percent; and increases demand for home health services by 0.34 percent.

An increase in the proportion of the population that is non-white is associated with a slight increase in the use of short-term hospital outpatient services and long-term hospital inpatient days. An increase in the proportion of the population that is Hispanic is associated with a slight decrease in emergency department visits. These demographic variables might be capturing differences across racial and ethnic groups in healthcare needs, behavior that affects healthcare use, or access to care via insurance and local availability of services.

Population density, as measured by percentage of population living in an urban area, is negatively correlated with use of inpatient services at short-term hospitals and nursing facilities. The reader will recall that the approach already controls for urban or rural location of the States’ population before estimating the regressions. Consequently, these findings are difficult to interpret. Population density is also correlated with HMO enrollment rates. When the population density variable is omitted from the short-term hospital inpatient day and nursing facility regressions, the coefficients on the HMO variable grow more negative.

The inclusion of regional dummy variables in the regressions improves the overall fit of many of the equations and helps estimate more precisely the relationship between the dependent and exogenous variables in the model. Over time, the values of these dummy variables remain constant. After controlling for differences in demographics and the exogenous variables in the model, the regressions show significant regional variation in demand for healthcare services.

Exhibit 18. Healthcare Use Regression Results

^a Regression coefficients with standard errors in parentheses.

Note: The projection method already controlled for population age, gender, and urban or rural location distribution before estimating the regression equations. Also, the use of stepwise regression to determine which exogenous variables to include inflates the statistical significance of the results.

Nurse staffing intensity is defined as the number of FTE RNs divided by some measure of workload specific to the setting being modeled (e.g., FTE RNs per 1,000 inpatient days at short-term hospitals). The NDM calculates base year values of nurse staffing intensity for each State and setting by dividing estimates of RN employment by estimates of healthcare use. Thus, in nursing facilities, base year estimates of employed FTE RNs per resident are used as the staffing intensity measures.

We use 1996 as the base year for several reasons. First, the importance of the SSRN in estimating base-year RN supply and demand limits the base year to a year in which the SSRN was conducted (e.g., 1992, 1996, 2000). Second, indications that the nurse shortage has grown more severe in recent years suggests that an earlier year (e.g., 1996 versus 2000) might produce nurse staffing intensity estimates that reflect a market where a relative equilibrium existed between nurse supply and demand. We make one exception to the argument that nurse employment in a setting is the best measure of nurse requirements. In hospitals, we estimate that RN demand was approximately 7 percent higher than RN employment in 1996. The lower-than-demanded number of RNs employed in hospitals reflects the rapid and significant changes taking place in the hospital sector during the early and middle 1990s when hospitals were downsizing in response to the rapid rise in managed care and hospital consolidations. We arrive at this 7 percent estimate by comparing RN staffing intensity in hospitals using SSRN and AHA data for 1992, 1996, and 2000.

After establishing base year nurse staffing intensity, the NDM then projects future nurse staffing intensity. For four employment settings, nurse staffing intensity is measured as a nurse-to-population ratio (because of data limitations) that is assumed constant over time. Demand for nurse educators is calculated as a constant fraction of total demand for RNs. For 7 of the 12 employment settings modeled, future nurse staffing intensity is projected as a function of changes in exogenous variables (X) such as average patient acuity levels, economic considerations, and characteristics of the healthcare operating environment. The projection formula is specified as

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where the parameters b represent the estimated relationship between nurse staffing intensity and its determinants and δ is an adjustment factor so the base year projections equal actual nurse staffing intensity in the base year. We estimated the parameters using multiple regression analysis with State-level data from 1996 through 2000 (although most regression equations were estimated using a subset of these years based on data availability).

Both theory and empirical analysis helped determine the exogenous variables to employ in the projection equations. As with the healthcare use regressions, the dependent variable and most of the exogenous variables enter into the regression equation in a log form. Also, we estimated the equations using a stepwise regression that results in a parsimonious model but that overstates the significance statistics often used to assess how robust the regression findings are.

The ratio of RN to LPN wages is used to estimate the degree to which employers substitute lower-cost LPNs for higher-cost RNs as RN wages rise relative to LPN wages. ^[10] In the baseline projections, we assume that this ratio stays constant over time. The regressions do not simultaneously control for nursing supply, which could bias the wage elasticities (e) towards zero. The size of the estimated elasticities, however, appears reasonable based on a priori expectations and a comparison with the literature. Demand for RNs is less responsive to changing relative wages in physicians’ offices (e=-0.64) and inpatient settings at short-term hospitals (e=-0.65) compared with home health (e=-1.06) and long-term hospitals (e=-1.20).

The wages elasticity estimates from this analysis are comparable to the few studies in recent literature that report wage elasticities. Lane and Gohmann (1995), in their analysis of nurse shortages, estimate the wage elasticity of nurse demand by simultaneously estimating a supply and demand equation. ^[11]   The authors combine both RNs and LPNs in their analyses. They estimate nurse own-wage elasticity in short-term hospitals to be approximately -0.9.

Spetz (1999) estimates a demand equation for RNs using hospital-level data for short-term, general hospitals in California during the period 1976 to 1994. To control for the endogeneity of nurse wages, Spetz uses an instrumental variables approach to estimate the RN demand curve, which she compares to a demand curve estimated using the ordinary least squares (OLS) regression. As expected, her estimate of wage elasticity from the OLS regression (e=-0.194) is less elastic than the estimate obtained using the instrumental variables approach (e=-2.778) when she models the daily services units of California hospitals. Similarly, when she estimates demand equations for the medical-surgical units of California hospitals, the wage elasticity estimates are less elastic from the OLS regression (e=-0.342) than from the instrumental variables regression (e=-3.653). Spetz also finds that an increase in LPN wages is associated with a statistically significant rise in RN employment in daily services units of hospitals, but the converse is untrue.

As discussed previously in the context of RN supply, the short-term wage demand elasticities are typically smaller than long-term wage elasticities. In the short term, employers might have few options to replace RNs as they become relatively more expensive. In the long term, employers can change nurse staffing practices and adopt new technologies that alter how RNs are used.

An increase in HMO enrollment rates produces mixed effects on staffing intensity. The HMO variables in the regressions are not logged, so the interpretation of the coefficients is different from the other variables. An increase in the HMO enrollment rate by one percentage point increases RN staffing intensity in short-term hospital inpatient, short-term hospital outpatient, and home health by 0.30 percent, 0.67 percent, and 0.97 percent, respectively. An increase in the HMO enrollment rate by one percentage point decreases RN staffing intensity in physician offices by 0.51 percent.

HMO enrollment rates affect nurse-staffing patterns for two possible reasons. One, HMOs decrease inpatient days in short-term hospitals through efforts at preventive care and efforts to channel patients with less-severe problems to less-expensive settings. This reduction in inpatient days might be raising the average acuity level of patients admitted to the hospital, which results in higher RN staffing per 1,000 inpatient days. Two, the efforts of HMOs to reduce costs could contribute to their adopting technologies or substituting between different types of healthcare professionals. As discussed previously, HMO enrollment rates are correlated with other variables such as percentage of population in urban area. Consequently, the coefficient on the HMO enrollment rate variable could be capturing some of the relationship between staffing intensity and other factors correlated with HMO enrollment rates. In both regressions where HMO enrollment rate affects staffing intensity, the variable percentage of population in urban area is also included.

Changes in technology can exert a mixed effect on the demand for healthcare services and staffing intensity. One measure used in the NDM that reflects, in part, technological advances is the percentage of hospital-based surgical procedures performed on an outpatient basis. Improvements in technology and medical procedures that shift some surgical procedures from an inpatient to an outpatient setting could affect nurse-staffing intensity in both inpatient and outpatient settings. If patients with less-severe health problems are shifted from an inpatient to an outpatient setting, then average patient acuity in both settings could rise. This situation could result in greater staffing intensity per inpatient day and per outpatient visit while decreasing overall nurse demand. Each 1 percent increase in the proportion of hospital surgeries performed in an outpatient setting increases staffing intensity for FTE RNs per 1,000 short-term hospital inpatient days by 0.64 percent. As discussed previously, a 1 percent increase in the proportion of hospital-based surgeries performed on an outpatient basis reduces short-term hospital inpatient days by 0.47 percent, increases outpatient visits by 1.64 percent, and increases home health visits by 1.86 percent. Surprisingly, a 1 percent increase in this surgery variable causes virtually no change in overall demand for RNs—it just shifts where the RNs are providing services.

A rise in average Medicare and Medicaid payments for services is associated with greater staffing intensity. Part of this increase might be due to greater patient acuity, and part might be due to the ability of healthcare providers to purchase nursing services. A 1 percent increase in average Medicare payments per home health visit increases demand for RNs by 1 percent. A 1 percent increase in average Medicaid daily rates for nursing facilities increases staffing intensity of RNs in nursing facilities by 0.34 percent.

The rate of uninsured in the population could increase the level of uncompensated care provided by healthcare providers. A 1 percent increase in the proportion of the population that is uninsured decreases RNs per 1,000 short-term hospital inpatient days by 0.37 percent and decreases RNs per 1,000 visits to physician offices by 0.21 percent. RN per 1,000 inpatient days in long-term hospitals rises by 0.3 percent for each 1 percent increase in the rate of uninsured, although the reason for this positive relationship is not readily surmised.

A 1 percent rise in the proportion of population that is Medicaid eligible decreases RN staffing per 1,000 emergency department visits by 0.19 percent. As discussed in the previous section, a 1 percent rise in percentage of population that is Medicaid eligible increases demand for emergency department services by 0.29 percent, so the net effect of a 1 percent rise in this variable is to increase demand for RNs in emergency departments by 0.05 percent.

As the population grows wealthier, the demand for higher-quality healthcare services likely will rise. A 1 percent rise in per capita income increases RN staffing intensity in physician offices by 0.33 percent.

A population with greater healthcare needs requires greater levels of services as measured by both the quantity of services provided and staffing intensity per unit of service provided. The NDM contains two measures that are proxies of population health status: (1) population mean age and (2) average number of activities of daily living (ADL) limitations of nursing facility residents. (In addition, the Medicare and Medicaid reimbursement rate variables discussed previously might also be capturing variation in average patient acuity across States and over time.) A 1 percent increase in population mean age increases RN staffing intensity in physician offices by 1.52 percent. A 1 percent increase in average number of ADL limitations of nursing facility residents increases demand for RNs per nursing facility resident by 0.63 percent.

The percentage of population living in urban areas exerts a mixed impact on nurse staffing intensity. A 1 percent increase in this variable decreases RN staffing per 1,000 inpatient days at long-term hospitals by 0.60 percent. In short-term hospitals, a 1 percent increase in this variable increases RN staffing intensity in inpatient settings and outpatient settings by 0.16 percent and 0.39 percent, respectively. As discussed previously, this variable is correlated with HMO enrollment rates; consequently, the precise relationship among HMO enrollment rate, percentage of population living in urban areas, and nurse staffing intensity is unclear. Significant regional variation occurs in nurse staffing intensity, but few visible patterns emerge in the findings (Exhibit 19). Changes in staffing intensity will vary by State depending on the projected values for exogenous variables and changing demographics.

Between 2000 and 2020, staffing intensity is projected to increase 34 percent in home health, from approximately 2.8 FTE RNs per 1,000 home health visits to approximately 3.8 FTE RNs per 1,000 visits (Exhibit 20). In short-term hospital inpatient settings, FTE RNs per 1,000 inpatient days is projected to increase by 18 percent at the national level (from 6.5 to 7.7). For nursing facilities and physician offices, we project a 13 percent increase in staffing intensity, while for short-term hospital outpatient settings we project a 6 percent increase in staffing intensity. In short-term hospital emergency settings and in long-term hospitals, we project virtually no change in staffing intensity. The staffing intensity measures for RNs in occupational health, school health, public health, nurse education, and “other” healthcare settings is assumed constant over time at their 1996 levels. To fully comprehend the magnitude of additional FTE RNs required, the overall impact of staffing intensity must be considered in conjunction with healthcare use projections.

Exhibit 19. Nurse Staffing Intensity Regressions

^a Regression coefficients with standard errors in parentheses.

Exhibit 20. National Measures of Projected Nurse Staffing Intensity

Below, we present projections from the NDM. We present projections for alternative scenarios that use different assumptions about the trends in the major demand determinants.

Under the baseline scenario, demand for FTE RNs is projected to increase 41 percent between 2000 and 2020 at the national level (Exhibit 21). As shown in the appendix, the projected change in demand varies substantially by State. In percentage terms, the fastest growth will occur in settings that predominantly serve the elderly (e.g., home health and nursing facilities) and in hospital outpatient settings.

Exhibit 21. Baseline Projections of Demand for FTE RNs

^a Due to rounding, category totals might fail to equal the sum across component settings.

Nurse demand will be determined, in part, by political decisions, changes in technology, changes in the healthcare operating environment, and changes in other factors difficult to predict. In addition, projection models such as the NDM are relatively simplistic simulations of a complex healthcare system that try to capture the major trends affecting demand for nurses, so the RN demand projections are made with some level of imprecision. The degree of imprecision is difficult to determine. A sensitivity analysis shows how the projections change as we change key assumptions in the model. We present projections under four alternative scenarios (Exhibit 22):

(1) Scenario 1 assumes no changes in managed care enrollment rates (compared to the baseline that assumes an annual 0.5 percentage point increase). At the national level across all settings, this modest change in the growth rate of managed care enrollment has virtually no effect on demand for RNs. However, substantial changes occur at the setting level. Managed care growth simply shifts care from inpatient to outpatient settings, and the decline in projected inpatient days is offset by a likely increase in staffing intensity as the average level of patient acuity increases.

(2) Scenario 2 assumes that RN wages increase 1 percent annually compared to LPN wages. (The baseline assumes that RN and LPN wages grow at the same rate.) Under this scenario, a rise in RN wages gives employers greater financial incentive to substitute lower-cost LPNs for higher-cost RNs, where possible. Between 2000 and 2020, the compounding effect of a 1 percent annual growth in relative wages for RNs results in a real increase of 22 percent relative to wages of LPNs. By 2020, demand for FTE RNs would be approximately 10 percent lower (or 285,000 FTE RNs) relative to the baseline.

(3) Scenario 3 assumes that the U.S. population grows 20 percent faster than projected by the U.S. Census Bureau. By 2020, this accelerated growth results in demand for 88,000 additional FTE RNs (or 3 percent) relative to the baseline.

(4) Scenario 4 assumes that the U.S. population grows 20 percent slower than projected by the U.S. Census Bureau. By 2020, this decelerated growth results in the demand for 85,000 fewer FTE RNs (or 3 percent) relative to the baseline.

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