Chapter 5:  Factors Affecting the Supply of and Demand for LPNs

The labor market for licensed practical and vocational nurses consists of two components: the supply of LPNs and the demand for LPNs.  Both supply and demand should be affected by the wage paid to LPNs.  When wages rise, LPNs should find employment more attractive and increase their supply of labor.  Conversely, higher wages increase the cost of hiring to employers and thus demand should decline.  When there is a shortage or surplus of LPNs, wages should adjust to rectify the imbalance.

Numerous other factors can affect the supply of and demand for LPNs, however.  The family circumstances of LPNs may prohibit them from working full-time, and regulatory requirements might lead to higher or lower demand for LPNs.  This chapter examines the underlying supply of and demand for LPNs to identify the factors that affect LPNs’ decisions to work and employers’ demands for them.

The Supply of LPNs

A Conceptual Model of the LPN Supply

Labor markets for licensed nurses generally are not national in scope.  In some geographic regions there are few employers and these employers may have a high degree of control over the local labor market.  Other nursing labor markets are very competitive, with a plethora of employers.  Because job opportunities for licensed nurses are plentiful at nearly all times, nurses usually do not need to relocate to find interesting and rewarding work. 

The supply of nurses consists of nurses with active licenses.  Some of these nurses are not working in nursing, but they are part of the current pool of nurses potentially available to work.  The supply of nurses to a local labor market increases as nurses flow into the labor market by graduating from nursing programs, migrating from other regions, immigrating from other countries, or increasing hours worked.  The supply of nurses declines with retirements, migration out of the region, decreasing hours worked, and career changes out of nursing.  Figure 5.1 summarizes the labor flows in and out of the stock of licensed nurses.

The primary source of growth in the nursing workforce is graduations from nursing programs.  These graduations generally stem from interest in the nursing profession.  For the first part of the 20th century, licensed nursing was one of a few occupations widely open to women.  Most women faced limited career choices, and nursing was an attractive option to women who were interested in science.  As career opportunities expanded for women in the last quarter of the 20th century, however, nursing had to compete with numerous other attractive professions for new entrants.  It has been suggested that women now are less likely to choose a traditionally female-dominated career such as nursing (Buerhaus, Staiger, & Auerbach, 2000) .  However, an annual survey of 350,000 first-year college students across the U.S. found that the percent of students planning on a career in nursing remained steady at five percent between 1966 and 1996 (Astin, 1998).

Regional and international migration of LPNs has not been measured in any data sources of which we are aware.  The National Council of State Boards of Nursing does not maintain a national database of LPN licenses, and States do not link their licensure files so that LPNs can be tracked as they move from State to State.  LPNs do not exist in most other countries, so international migration of LPNs is not an important source of new LPNs.  This is reflected in the fact that relatively small and stable shares of LPNs are immigrants, as reported in Chapter 2.  Some registered nurses educated in other Nations do not pass the RN licensing board examination when they immigrate and subsequently take the LPN licensing examination.  To our knowledge, no source of data measures the extent to which this occurs. 

Figure 5.9: Flows and Stock of Licensed Practical/Vocational Nurses

The outflow from the supply of LPNs consists of nurses who retire, choose to permanently leave the profession, or who migrate to other countries or regions.  Unfortunately, there is no data with which one can examine any of these phenomena.  If a LPN allows his or her State license to lapse, it is not possible to identify whether the LPN obtained a license elsewhere, and thus we do not know if the LPN has left the supply of nurses.  LPNs who have active licenses but are not working are not identified in any national survey.  National data such as that collected by the Bureau of Labor Statistics and Bureau of the Census identify LPNs by their current occupation, and thus very few LPNs who are not working are identified in these data.

Thus, little can be said about important components of the inflow and outflow of LPNs.  The behavior of LPNs who are actively licensed and consider their current occupation to be that of LPN can be examined using the annual Current Population Survey conducted by the Bureau of Labor Statistics and the Bureau of the Census.  Many characteristics of these LPNs are available from these surveys, and the factors that affect labor supply can be considered in depth.

Data for Supply Analyses

We use data from the 1994-2001 Current Population Survey (CPS) Outgoing Rotation Group (ORG) (U.S. Bureau of the Census, 2004) to analyze factors that influence the supply of licensed practical nurses.  In order to identify licensed practical/vocational nurses in the Current Population Survey, we utilize the occupation codes.  With these codes, we identified 4,736 LPNs in the 1994-2001 CPS ORG files.  The resulting dataset used to estimate the supply of licensed practical nurses in the U.S. has 4,616 observations.  This number does not match the total number of LPNs in the 1994-2001 CPS ORG files since we delete LPN observations that have extreme values (defined as over the 99th percentile) for the earnings and work hours variables used in our analysis. 

Methods of Analysis

Economic theory suggests that an individual’s work decision is a function of individual (demographic) characteristics, family characteristics, and labor market conditions.  We use the Current Population Survey’s demographic and labor force information on LPNs to create variables for our models of the supply of LPNs.  The demographic variables in our models include the following: gender, age, educational attainment, race/ethnicity, and citizenship status.  Family characteristics included in our analysis are marital status, number of kids in household by age category (e.g. number of kids aged 0 to 5 in same household as LPN), and household earnings (defined as the sum of weekly earnings of all household members minus the LPN’s weekly earnings). 

Labor market variables were generated using the geographic and earnings data in the CPS.  We created dummy variables for each region in the United States (Northeast, Midwest, South, and West), and for the population size of the metropolitan statistical area in which LPNs in our sample reside.  Also included is the percentage of licensed practical nurses unionized in the LPN’s State of residence.  The market wage for LPNs is an important labor market condition.  We generate State-level market wages using hourly earnings from our sample of LPNs.  Because we had small numbers of observations for some States, we used a complex method to determine markets wages.  Each wage is based on 3 years of data, so the wage of a single year is the median of the wages of that year and the years immediately preceding and following that year.  For example, the market wage for 1990 is the median of the wages for 1989, 1990, and 1991. 

We then group LPN observations in each State based on whether they resided in a metropolitan statistical area (MSA).  Those residing in an MSA are considered to be living in an urban area, while those not residing in an MSA are considered to be in a rural area.  Using this information, we calculate urban and rural LPN wages for each State.  Since sample sizes were small for several States, we decided that the market wage associated with each LPN would have to be calculated from at least 15 observations.  We used the following algorithm to assign market wages: if LPN lives in an urban area in a State and the median urban wage for that State is calculated from at least 15 observations, then the market wage is the median urban wage; otherwise, the market wage is the State-level median wage.  Substituting “rural” for “urban” in the above algorithm explains the logic for assigning a market wage to LPNs residing in rural areas of a State.  Thus, we have three potential market wages for each State, but only one is matched to each LPN in our sample.

Even though we assume market wages are exogenous in our labor supply equations, we cannot rule out the possibility that they are determined simultaneously with supply, thus potentially biasing our estimates.  To address this concern, we use two-stage least squares regression as a specification check.  This technique produces predicted values for wages after estimating a wage equation. ^[2] We then use these predicted wages in our labor supply regressions, and compare the results with those from the regressions in which market wages are used.  As a third specification, we calculate wages for the LPNs in our sample who report being employed.  The CPS has data on usual weekly earnings and usual weekly hours of work.  We divide usual weekly earnings by usual weekly hours of work to obtain a measure of own wage for each LPN in our sample who reports being employed.  We then estimate the supply equations using own wages for working LPNs and predicted wages for non-working LPNs.  Thus, we run three regressions for each supply model, each with a different measure of wage.

We focused on three outcome measures in our analysis: (1) the probability of working (labor participation), (2) the probability of working full-time, defined as usually works 30 or more hours per week, and (3) usual hours of work per week.    We model each of these to examine the factors that affect the supply of licensed practical nurses.  Appendix E1 reports the means of the variables in the dataset used to estimate the supply of LPNs.  We discuss trends in the variables here.

Several of the demographic variables show an upward trend in their mean values during our sample time period. These variables include age, and the proportion of LPNs who are black, Native American, have completed some college, and hold an AA degree.  Those with a downward trend are the proportion of LPNs who are white and the percent that have no more than a high school education.  These trends were discussed in detail in Chapter 2.

The data show an increase in the percent of LPNs holding more than one job, usual hours worked per week, and usual weekly earnings before deductions.  Notably, the means of our wage variables follow a similar pattern over our sample time period.  They decrease until 1997 and then climb to near their 1994 values by 2001. Most of the market characteristics in the dataset exhibit a trend in their mean values.  Union representation/coverage of LPNs decreased, as did the share of LPNs residing in the Northeast and West, and the percent living in metropolitan areas with a population of 500,000 to 2.5 million.  The percent of LPNs in our sample that live in the South increased between 1994 and 2001, as did the proportion residing in rural areas. 

LPNs in our sample also increasingly worked for private employers, in personnel supply services, and the offices of physicians.  The share working for government and the percent who are self-employed declined during our sample time period.  The only family characteristic exhibiting a trend during our sample time period is household earnings, which increased between 1994 and 2001.

Factors That Affect the Employment of LPNs

Table 5.1 presents the estimated coefficients and marginal effects from probit regression equations of the likelihood of a LPN being employed using the Current Population Survey data for 1994 through 2001.  The marginal effect measures the increase in probability resulting from increases in the explanatory variable in the regression equation.  For example, the marginal effect of living in the Midwest is 0.016.  The explanatory variable has a value of 1 if an LPN lives in the Midwest and 0 otherwise.  Thus, living in the Midwest increases the probability of being employed 1.6 percentage points, which is the product of the marginal effect and the change in the explanatory variable.  In the regression equation tables, the statistical significance of the coefficients is indicated.  We focus our discussion on explanatory variables that are significant with a p-value of 0.05, meaning there is a 5 percent chance that the identified relationship is spurious.

The first three columns in Table 5.1 report the estimated coefficients, robust standard errors, and marginal effects for the regression in which market wages are included as an explanatory variable.  The next three columns report estimates for the two-stage least squares model in which predicted wages are used, and the final three columns report results from the regression in which the wage is defined separately, as described above, for working and non-working LPNs. From this point forward, we refer to this last measure of wage as “own wage.”

The results from the probit regression with market wages as an independent variable are quite similar to the results from the two-stage least squares regression in which predicted wages are used to estimate the supply model.  The probit regression in which own wages are used produce surprising results, especially concerning the effect of wage.

Though not statistically significant, the estimated coefficients on market wage and predicted wage and their squared values have the expected sign.  However, when estimating the model using own wages, we find a negative and statistically significant coefficient on wage.  The marginal effect implies that a one-dollar increase in wage decreases the likelihood of a LPN being employed by 1.4 percentage points.  Furthermore, the wage-squared coefficient is positive and statistically significant, implying that as the wage increases beyond a certain point, LPNs are more likely to work. This result is opposite the pattern found in many studies of labor supply.  The likelihood of employment typically rises with wage at nearly all wage levels.  It is important to note that the LPNs in our sample have very high labor participation rates, ranging from 92 percent to 96 percent during our sample time period of 1994-2001.  Thus, there is little variation in our outcome variable, and this may affect our regression results.  Nevertheless, several of the coefficients of the remaining explanatory variables across all three specifications of our model are in agreement with economic theory.

Demographic characteristics are important predictors of employment of LPNs.  The likelihood of working initially increases with age, by 0.1 to 0.4 percentage points, and then decreases as indicated by the coefficients on age squared.  The inflection points calculated from the marginal effects indicate that LPNs are less likely to work after age 38 (first specification), 40 (second specification), or 50 (third specification). Native American LPNs are 2.5 to 7.6 percentage points less likely to be working than white LPNs.  Black LPNs also are less likely to be employed, although the degree of statistical significance is lower in two of the specifications.  In contrast, Asian LPNs are more likely to be working, although this result is only statistically significant at a higher p-value.  LPNs who are US citizens by naturalization are 0.6 to 3.4 percentage points less likely to be employed than are US-born LPNs.  In the regression with market wage as an independent variable, LPNs who are not U.S. citizens also are less likely to be employed.

Family characteristics do not appear to be strong predictors of labor force participation.  In all three specifications of the model, only household earnings have a statistically significant relationship with the likelihood of working for LPNs.  LPNs are less likely to work as the earnings of other household members (such as the LPN’s spouse/partner) increase.  However, the marginal effects are practically zero. 

Table 5.1:  Probit Results for Probability of Working