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
Inflow of Nurses
Education System
Migration from Other Regions
Migration from Other Countries |
Supply of Nurses
Active License Status
Currently working as a Nurse
Not Currently working as a Nurse
Inactive License Status |
Outflow of Nurses
Retirement, Not in Labor Force
Migration to Other Regions/Countries
Career Changes |
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
|
(1) |
(2) |
(3) |
Market
Wages |
Predicted
Wages |
Own
Wages
if Working,
Else Predicted
Wages |
Independent
Variables |
Coefficient |
SE |
Marginal
Effect |
Coefficient |
SE |
Marginal
Effect |
Coefficient |
SE |
Marginal
Effect |
Wage |
0.267 |
(0.255) |
0.014 |
0.303 |
(0.426) |
0.015 |
-2.220** |
(0.341) |
-0.014 |
Wage
Squared |
-0.010 |
(0.009) |
-0.0005 |
-0.014 |
(0.016) |
-0.001 |
0.080** |
(0.013) |
0.001 |
Demographic
Variables |
Male |
-0.034 |
(0.177) |
-0.002 |
0.030 |
(0.189) |
0.001 |
-0.040 |
(0.186) |
-0.0003 |
Age |
0.069** |
(0.022) |
0.003 |
0.079** |
(0.028) |
0.004 |
0.096** |
(0.025) |
0.001 |
Age
Squared |
-0.001** |
(0.000) |
-0.00004 |
-0.001** |
(0.000) |
-0.00005 |
-0.001** |
(0.000) |
-0.00001 |
Some
College |
0.188* |
(0.111) |
0.009 |
0.207* |
(0.112) |
0.010 |
0.187 |
(0.121) |
0.001 |
AA
Degree |
0.160 |
(0.108) |
0.008 |
0.188* |
(0.110) |
0.009 |
0.145 |
(0.117) |
0.001 |
Bachelor,
Master, PhD, or Professional School
Degree |
0.131 |
(0.191) |
0.006 |
0.198 |
(0.204) |
0.008 |
0.090 |
(0.207) |
0.001 |
Black |
-0.192* |
(0.111) |
-0.011 |
-0.189* |
(0.111) |
-0.011 |
-0.244** |
(0.118) |
-0.002 |
Hispanic |
-0.160 |
(0.202) |
-0.009 |
-0.172 |
(0.201) |
-0.010 |
-0.209 |
(0.219) |
-0.002 |
Native
American |
-0.690** |
(0.277) |
-0.068 |
-0.738** |
(0.287) |
-0.076 |
-0.945** |
(0.305) |
-0.025 |
Asian |
0.639* |
(0.361) |
0.018 |
0.655* |
(0.360) |
0.018 |
0.677* |
(0.370) |
0.002 |
Not
a U.S. Citizen |
-0.383** |
(0.238) |
-0.028 |
-0.436* |
(0.245) |
-0.033 |
-0.396 |
(0.261) |
-0.005 |
Citizen
by Naturalization |
-0.438** |
(0.208) |
-0.034 |
-0.422** |
(0.209) |
-0.032 |
-0.476** |
(0.228) |
-0.006 |
Family
Characteristics |
Weekly
Earnings of All Household Members
Except Nurse |
-0.0004** |
(0.000) |
-0.00002 |
-0.0004** |
(0.000) |
-0.00002 |
-0.0005** |
(0.000) |
-0.000003 |
Married |
0.005 |
(0.132) |
0.0002 |
0.011 |
(0.131) |
0.001 |
0.018 |
(0.140) |
0.0001 |
Previously
Married |
0.104 |
(0.153) |
0.005 |
0.106 |
(0.151) |
0.005 |
0.093 |
(0.166) |
0.001 |
No.
of Kids Aged 0-5 in Household |
-0.051 |
(0.074) |
-0.003 |
-0.054 |
(0.073) |
-0.003 |
-0.039 |
(0.082) |
-0.0003 |
No.
of Kids Aged 6-12 in Household |
-0.055 |
(0.057) |
-0.003 |
-0.057 |
(0.056) |
-0.003 |
-0.075 |
(0.060) |
-0.0005 |
No.
of Kids Aged 13-17 in Household |
0.015 |
(0.069) |
0.001 |
0.010 |
(0.069) |
0.001 |
-0.017 |
(0.078) |
-0.0001 |
Market
Characteristics |
Northeast |
0.217 |
(0.136) |
0.010 |
0.240* |
(0.136) |
0.011 |
0.243* |
(0.143) |
0.001 |
Midwest |
0.370** |
(0.139) |
0.016 |
0.347** |
(0.145) |
0.015 |
0.410** |
(0.146) |
0.002 |
South |
0.149 |
(0.127) |
0.007 |
0.100 |
(0.137) |
0.005 |
0.152 |
(0.125) |
0.001 |
MSA
Population 100,000-499,999 |
-0.038 |
(0.132) |
-0.002 |
0.009 |
(0.133) |
0.0004 |
0.023 |
(0.138) |
0.0001 |
MSA
Population 500,000-999,999 |
0.093 |
(0.170) |
0.004 |
0.150 |
(0.179) |
0.007 |
0.225 |
(0.183) |
0.001 |
MSA
Population 1,000,000-2,499,999 |
-0.138 |
(0.137) |
-0.008 |
-0.061 |
(0.150) |
-0.003 |
-0.029 |
(0.140) |
-0.0002 |
MSA
Population 2,500,000+ |
-0.016 |
(0.140) |
-0.001 |
0.153 |
(0.187) |
0.007 |
-0.015 |
(0.132) |
-0.0001 |
Year
Dummy Variables |
1995 |
0.172 |
(0.149) |
0.008 |
0.176 |
(0.150) |
0.008 |
0.198 |
(0.162) |
0.001 |
1996 |
0.235 |
(0.167) |
0.010 |
0.183 |
(0.176) |
0.008 |
0.207 |
(0.181) |
0.001 |
1997 |
-0.029 |
(0.148) |
-0.001 |
-0.100 |
(0.163) |
-0.005 |
-0.110 |
(0.162) |
-0.001 |
1998 |
0.014 |
(0.151) |
0.001 |
-0.019 |
(0.156) |
-0.001 |
-0.031 |
(0.167) |
-0.0002 |
1999 |
0.258 |
(0.175) |
0.011 |
0.258 |
(0.174) |
0.011 |
0.248 |
(0.190) |
0.001 |
2000 |
0.103 |
(0.154) |
0.005 |
0.076 |
(0.155) |
0.004 |
0.125 |
(0.171) |
0.001 |
2001 |
0.142 |
(0.157) |
0.006 |
0.146 |
(0.156) |
0.007 |
0.156 |
(0.167) |
0.001 |
|
Log-likelihood |
-529.04 |
-528.69 |
-472.19 |
N |
4,478 |
4,478 |
4,478 |
*p
< 0.10
**p < 0.05
Notes:
(1) dependent variable equals one if employed,
and equals zero otherwise; (2) all regressions
include a constant; and (3) standard errors
are estimated using the "robust"
option in Stata.
Source:
Current Population Survey Outgoing Rotation
Group Files, 1994-2001
The labor market in which the LPN resides
affects employment opportunities, and
cultural differences across regions also
may affect the likelihood of working.
As compared to LPNs living in the West,
Midwest LPNs are 0.2 to 1.6 percentage
points more likely to work.
It is important to note that LPNs are
identified by their self-reported occupation,
and thus LPNs who are not working in nursing
may not identify themselves as LPNs.
The CPS data thus likely overstate the
probability of employment, and regression
equations estimated for a broader sample
of LPNs might produce different results
The Hours Worked by
LPNs
Once an individual decides to work, a
decision must be made about the extent
to which to work. Employees can work
part-time or full-time, and the number
of hours per week they work can vary.
Personal, family, and labor market characteristics
affect the decision of how much to work.
To explore these relationships, we estimate
regression equations similar to those
estimated for whether a LPN is working.
Table 5.2 presents probit regression equations
for the probability of a LPN working full
time (i.e., 30 or more hours per week).
Again we run three regressions, each with
a different measure of wage. The first
specification, using market wages as an
explanatory variable, is restricted to
LPNs who report working, and thus the
regression results only apply to the population
of working LPNs. The remaining specifications
use the full sample of LPNs. Despite
differences in how we define the wage
variable (and, thus, the wage-squared
variable) in each of the three specifications
of the model, the regression results are
similar.
In all three specifications, the estimated
coefficient on wage is positive. It also
is statistically significant except in
the regression using predicted wages as
an independent variable for all observations.
For the sample of working LPNs (specification
(1)), a one-dollar increase in the market
wage increases the likelihood of working
full-time 6.8 percentage points. In specification
(3), a one-dollar increase in own wage
increases the likelihood of full-time
employment 2.6 percentage points.
Table 5.2: Probit
Results for Probability of Working Full-Time
|
(1) |
(2) |
(3) |
Market
Wages |
Predicted
Wages |
Own
Wages
if Working,
Else Predicted
Wages |
Independent
Variables |
Coefficient |
SE |
Marginal
Effect |
Coefficient |
SE |
Marginal
Effect |
Coefficient |
SE |
Marginal
Effect |
Wage |
0.356** |
(0.162) |
0.068 |
0.429 |
(0.299) |
0.080 |
0.142** |
(0.024) |
0.026 |
Wage
Squared |
-0.013** |
(0.006) |
-0.003 |
-0.016 |
(0.011) |
-0.003 |
-0.005** |
(0.001) |
-0.001 |
Demographic
Variables |
Male |
0.496** |
(0.160) |
0.071 |
0.505** |
(0.161) |
0.070 |
0.538** |
(0.161) |
0.072 |
Age |
0.091** |
(0.015) |
0.017 |
0.081** |
(0.018) |
0.015 |
0.082** |
(0.016) |
0.015 |
Age
Squared |
-0.001** |
(0.000) |
-0.0002 |
-0.001** |
(0.000) |
-0.0002 |
-0.001** |
(0.000) |
-0.0002 |
Some
College |
-0.192** |
(0.073) |
-0.038 |
-0.209** |
(0.074) |
-0.041 |
-0.214** |
(0.073) |
-0.041 |
AA
Degree |
-0.012 |
(0.072) |
-0.002 |
-0.033 |
(0.073) |
-0.006 |
-0.039 |
(0.072) |
-0.007 |
Bachelor,
Master, PhD, or Professional School
Degree |
0.018 |
(0.135) |
0.003 |
0.016 |
(0.141) |
0.003 |
0.018 |
(0.138) |
0.003 |
Black |
0.202** |
(0.087) |
0.035 |
0.217** |
(0.086) |
0.037 |
0.217** |
(0.087) |
0.036 |
Hispanic |
-0.097 |
(0.152) |
-0.020 |
-0.084 |
(0.151) |
-0.017 |
-0.047 |
(0.154) |
-0.009 |
Native
American |
-0.249 |
(0.251) |
-0.055 |
-0.187 |
(0.246) |
-0.039 |
-0.217 |
(0.238) |
-0.045 |
Asian |
-0.007 |
(0.251) |
-0.001 |
-0.068 |
(0.247) |
-0.013 |
-0.029 |
(0.235) |
-0.005 |
Not
a U.S. Citizen |
0.308 |
(0.243) |
0.049 |
0.326 |
(0.240) |
0.050 |
0.305 |
(0.236) |
0.047 |
Citizen
by Naturalization |
0.680** |
(0.215) |
0.085 |
0.715** |
(0.211) |
0.086 |
0.690** |
(0.203) |
0.083 |
Family
Characteristics |
Weekly
Earnings of All Household Members
Except Nurse |
-0.0001* |
(0.000) |
-0.00002 |
-0.0001 |
(0.000) |
-0.00001 |
-0.00004 |
(0.000) |
-0.00001 |
Married |
-0.424** |
(0.097) |
-0.076 |
-0.421** |
(0.096) |
-0.073 |
-0.444** |
(0.096) |
-0.076 |
Previously
Married |
0.019 |
(0.110) |
0.004 |
0.020 |
(0.109) |
0.004 |
0.006 |
(0.110) |
0.001 |
No.
of Kids Aged 0-5 in Household |
-0.128** |
(0.047) |
-0.024 |
-0.123** |
(0.046) |
-0.023 |
-0.111** |
(0.046) |
-0.020 |
No.
of Kids Aged 6-12 in Household |
-0.139** |
(0.034) |
-0.026 |
-0.133** |
(0.034) |
-0.025 |
-0.129** |
(0.034) |
-0.024 |
No.
of Kids Aged 13-17 in Household |
-0.119** |
(0.040) |
-0.023 |
-0.119** |
(0.039) |
-0.022 |
-0.112** |
(0.039) |
-0.021 |
Market
Characteristics |
Northeast |
-0.137 |
(0.086) |
-0.027 |
-0.146* |
(0.086) |
-0.029 |
-0.150* |
(0.086) |
-0.029 |
Midwest |
-0.004 |
(0.083) |
-0.001 |
0.004 |
(0.085) |
0.001 |
0.001 |
(0.082) |
0.000 |
South |
0.271** |
(0.089) |
0.049 |
0.290** |
(0.093) |
0.051 |
0.260** |
(0.085) |
0.045 |
MSA
Population 100,000-499,999 |
-0.236** |
(0.081) |
-0.050 |
-0.228** |
(0.082) |
-0.047 |
-0.210** |
(0.079) |
-0.042 |
MSA
Population 500,000-999,999 |
-0.189* |
(0.099) |
-0.039 |
-0.189* |
(0.099) |
-0.039 |
-0.184* |
(0.096) |
-0.037 |
MSA
Population 1,000,000-2,499,999 |
-0.142 |
(0.094) |
-0.029 |
-0.149 |
(0.098) |
-0.030 |
-0.143 |
(0.089) |
-0.028 |
MSA
Population 2,500,000+ |
-0.084 |
(0.087) |
-0.017 |
-0.071 |
(0.115) |
-0.014 |
-0.060 |
(0.082) |
-0.011 |
Year
Dummy Variables |
1995 |
-0.016 |
(0.093) |
-0.003 |
-0.010 |
(0.092) |
-0.002 |
-0.002 |
(0.093) |
0.000 |
1996 |
0.037 |
(0.101) |
0.007 |
0.038 |
(0.104) |
0.007 |
0.021 |
(0.100) |
0.004 |
1997 |
0.059 |
(0.100) |
0.011 |
0.076 |
(0.109) |
0.014 |
0.052 |
(0.100) |
0.009 |
1998 |
0.058 |
(0.101) |
0.011 |
0.064 |
(0.103) |
0.012 |
0.069 |
(0.100) |
0.012 |
1999 |
0.195* |
(0.106) |
0.034 |
0.187* |
(0.105) |
0.032 |
0.180 |
(0.106) |
0.030 |
2000 |
0.118 |
(0.102) |
0.021 |
0.120 |
(0.103) |
0.021 |
0.106 |
(0.102) |
0.019 |
2001 |
0.214** |
(0.104) |
0.037 |
0.209** |
(0.103) |
0.035 |
0.189* |
(0.104) |
0.032 |
|
Log-likelihood |
-1558.87 |
-1581.61 |
-1554.77 |
N |
4,351 |
4,478 |
4,478 |
*p
< 0.10
**p < 0.05
Source:
Current Population Survey Outgoing Rotation
Group Files, 1994-2001
Notes:
(1) dependent variable equals one if usually
works 30+ hours per week, and equals zero
otherwise; (2) in column one, sample is
restricted to licensed practical/vocational
nurses who reported being employed; (3)
all regressions include a constant; and
(4) standard errors are estimated using
the "robust" option in Stata.
To check for the possibility of backward-bending
supply, we included wage-squared as an
independent variable. The estimated coefficients
are negative in all three specifications,
and statistically significant in the regressions
with market wages and own wages. The negative
coefficients across the three specifications
provide evidence that the labor supply
of LPNs is backward bending, indicating
that after a point, further wage increases
reduce the likelihood of working full-time.
A possible explanation is that LPNs want
to earn a target income, and as wages
rise they need to work fewer hours to
reach this target.
Demographic characteristics are important
predictors of whether LPNs work full-time.
Notably, the same demographic variables
have statistically significant coefficients
regardless of how we define wages. Furthermore,
there is very little difference in the
marginal effects. For example, black
LPNs are 3.5 to 3.7 percentage points
more likely to work full-time than are
white LPNs. Male LPNs are 7.0 to 7.2
percentage points more likely than females,
and LPNs who are naturalized citizens
are 8.3 to 8.6 percentage points more
likely than U.S.-born LPNs. LPNs with
some college education but no degree are
less likely to work full-time than LPNs
who have never attended college. Finally,
LPNs are more likely to work full-time
until their late thirties or early forties,
after which time age has a negative association
with the likelihood of working full-time.
Family characteristics also are important
factors for LPNs in deciding whether to
work full-time. As the earnings of other
members of the household increase, the
likelihood of a LPN working full-time
decreases. However the estimated coefficients
in all three specifications are small
in magnitude and only the coefficient
in the regression with market wages is
statistically significant. All three
specifications of the model indicate that
married LPNs are less likely to work full-time
than are LPNs who have never been married.
As expected, the presence of children
in the household is negatively associated
with full-time work. The results are
similar for each age category and suggest
that each child under the age of 18 reduces
the likelihood of a LPN working full-time
by approximately two percentage points.
Several market characteristics affect
the probability of a LPN working full-time.
LPNs residing in the South are 4.5 to
5.1 percentage points more likely to work
full-time than are LPNs in the Western
region of the U.S. The results for all
three specifications of the model also
indicate that LPNs residing in urban areas
with a population between 100,000 and
499,999 are less likely to work full-time
than those residing in less populated
areas. Finally, compared to the beginning
of the sample time period, LPNs in 2001
were more likely to work full-time.
Table 5.3 presents regression equations
for the usual number of hours worked per
week in the past year. As before, we run
three regressions, each with a different
measure of wage. When market wages are
used, the sample is restricted to LPNs
who report being employed. Otherwise,
the full sample of working and non-working
LPNs is used.
The regression results are remarkably
similar; however, there are key differences
centered on the coefficients for wage.
In the specifications (1) and (2), wage
is positively associated with hours of
work. However, this result is only statistically
significant when we correct for the potential
endogeneity of wages. In this case, the
estimated coefficient implies that LPNs
on average work an additional 3.2 hours
per week for each dollar increase in wage.
In specification (3), the coefficient
on own wage is negative, but statistically
insignificant. Again, we find evidence
of a backward bending supply curve. In
all three specifications, the estimated
coefficient on wage squared is negative
and statistically significant, albeit
at a higher p-value.
Male LPNs work more hours per week than
do women, and black LPNs work more hours
than white LPNs. The number of hours
worked increases with age until age 39
(37 in specification (3)) after which
time age has a negative relationship with
hours worked per week. LPNs who are citizens
by naturalization work an average of 2.5
to 2.6 hours per week more than do US-born
LPNs.
Family characteristics affect the number
of hours worked per week in ways that
are consistent with the regression equations
that examine full-time versus part-time
work. Married LPNs work approximately
2.2 fewer hours per week than do unmarried
LPNs. Children also reduce hours worked
per week, with the effect being largest
for children younger than thirteen. The
earnings of other members of a LPN’s
household are negatively associated with
hours worked per week, but in all specifications
the size of the coefficient is so small
as to be negligible.
The average number of hours worked per
week varies across regions of the United
States. Southern LPNs work 1.2 to 1.4
hours per week more than do LPNs in Western
States, and LPNs living in the Northeast
work fewer hours.
The Demand for LPNs
The demand for licensed nurses is derived
from the demand for health care, and is
affected by a variety of factors, including
the general population’s demographics
and health, new medical treatments, health
care payment systems, and health care
regulations. Health care providers rely
on licensed nurses to provide the majority
of direct patient care. Registered nurses
assess patients, develop plans for their
care, perform tests, provide medical treatments,
plan for patients’ discharges, teach
patients and their families how to provide
ongoing care, and keep detailed records
of all these activities. Licensed practical
and vocational nurses assist in patient
assessments and the development of care
plans, provide medications to patients,
start intravenous fluids, obtain blood
samples, and participate in numerous other
components of patient care. Without licensed
nurses, many health care providers could
not care for patients.
Table 5.3: Regression
Results for Usual Hours Worked Per Week
|
(1) |
(2) |
(3) |
Market
Wages |
Predicted
Wages |
Own
Wages if Working, Else Predicted
Wages |
Independent
Variables |
Coef-ficient |
SE |
Coef-ficient |
SE |
Coef-
ficient |
SE |
Wage |
1.379 |
(0.928) |
3.198* |
(1.805) |
-0.003 |
(0.183) |
Wage
Squared |
-0.057* |
(0.033) |
-0.127* |
(0.066) |
-0.010* |
(0.006) |
Demographic Variables |
Male |
3.076** |
(0.615) |
3.303** |
(0.641) |
3.345** |
(0.599) |
Age |
0.624** |
(0.102) |
0.625** |
(0.116) |
0.667** |
(0.104) |
Age
Squared |
-0.008** |
(0.001) |
-0.008** |
(0.001) |
-0.009** |
(0.001) |
Some
College |
-0.495 |
(0.382) |
-0.490 |
(0.384) |
-0.504 |
(0.377) |
AA
Degree |
0.364 |
(0.359) |
0.381 |
(0.365) |
0.362 |
(0.355) |
Bachelor,
Master, PhD, or Professional School
Degree |
0.872 |
(0.601) |
1.135* |
(0.636) |
1.096* |
(0.607) |
Black |
1.212** |
(0.382) |
1.208** |
(0.381) |
1.220** |
(0.377) |
Hispanic |
-0.580 |
(0.616) |
-0.654 |
(0.615) |
-0.576 |
(0.606) |
Native
American |
0.091 |
(1.469) |
0.036 |
(1.463) |
-0.210 |
(1.454) |
Asian |
0.904 |
(1.154) |
0.802 |
(1.149) |
0.788 |
(1.085) |
Not
a U.S. Citizen |
0.476 |
(0.922) |
0.218 |
(0.944) |
0.300 |
(0.893) |
Citizen
by Naturalization |
2.513** |
(0.816) |
2.610** |
(0.807) |
2.487** |
(0.782) |
Family Characteristics |
Weekly
Earnings of All Household Members
Except Nurse |
-0.0005* |
(0.000) |
-0.0005* |
(0.000) |
-0.0004 |
(0.000) |
Married |
-2.203** |
(0.420) |
-2.179** |
(0.421) |
-2.170** |
(0.410) |
Previously
Married |
0.381 |
(0.452) |
0.389 |
(0.452) |
0.392 |
(0.443) |
No.
of Kids Aged 0-5 in Household |
-0.824** |
(0.282) |
-0.821** |
(0.282) |
-0.738** |
(0.276) |
No.
of Kids Aged 6-12 in Household |
-0.877** |
(0.204) |
-0.886** |
(0.205) |
-0.845** |
(0.203) |
No.
of Kids Aged 13-17 in Household |
-0.453** |
(0.230) |
-0.443* |
(0.231) |
-0.490** |
(0.228) |
Market Characteristics |
Percentage
of LPNs Unionized in State |
-0.262 |
(1.054) |
-0.199 |
(1.051) |
-0.174 |
(1.047) |
Northeast |
-0.909* |
(0.488) |
-0.828* |
(0.492) |
-0.877* |
(0.484) |
Midwest |
-0.594 |
(0.484) |
-0.466 |
(0.494) |
-0.542 |
(0.464) |
South |
1.212** |
(0.480) |
1.364** |
(0.501) |
1.235** |
(0.454) |
MSA
Population 100,000-499,999 |
-0.497 |
(0.452) |
-0.506 |
(0.462) |
-0.399 |
(0.437) |
MSA
Population 500,000-999,999 |
-0.698 |
(0.547) |
-0.691 |
(0.547) |
-0.598 |
(0.529) |
MSA
Population 1,000,000-2,499,999 |
-0.206 |
(0.487) |
-0.224 |
(0.512) |
-0.144 |
(0.466) |
MSA
Population 2,500,000+ |
-0.061 |
(0.450) |
0.269 |
(0.597) |
0.175 |
(0.416) |
Year Dummy Variables |
1995 |
-0.166 |
(0.476) |
-0.116 |
(0.478) |
-0.097 |
(0.476) |
1996 |
0.453 |
(0.527) |
0.451 |
(0.553) |
0.361 |
(0.522) |
1997 |
0.637 |
(0.538) |
0.599 |
(0.585) |
0.452 |
(0.531) |
1998 |
0.422 |
(0.539) |
0.437 |
(0.549) |
0.383 |
(0.532) |
1999 |
0.578 |
(0.492) |
0.613 |
(0.497) |
0.564 |
(0.490) |
2000 |
0.837 |
(0.524) |
0.816 |
(0.533) |
0.763 |
(0.522) |
2001 |
0.916* |
(0.506) |
0.987* |
(0.505) |
0.894* |
(0.501) |
|
|
|
|
|
|
|
R-squared |
0.0843 |
0.0836 |
0.1026 |
N |
4,002 |
4,002 |
4,002 |
*p < 0.10
**p < 0.05
Source: Current Population Survey Outgoing
Rotation Group Files, 1994-2001
Notes: (1) in the first column, the sample
is restricted to nurses who reported being
employed; (2) standard errors (in parentheses)
are estimated using the “robust”
option in Stata; and (3) all regressions
include a constant.
The dominant employer of licensed nurses
is the hospital industry, although RNs
are more likely to work in hospitals than
are LPNs. As the number of patients and
patient days in hospitals rise, demand
for RNs and LPNs rises (Spetz, 1999) .
The increasing acuity of illness of patients
in the hospital makes RNs particularly
important to hospital care, as does the
diffusion of high-technology medical services
in hospitals (Spetz, 1999) . LPNs are
generally restricted from giving patients
medications through intravenous lines
(IVs), administering blood products, and
providing other types of care that are
critical in the hospital setting. These
restrictions reduce the usefulness of
LPNs to hospitals.
A high share of LPNs work in nursing
homes and long-term care facilities; relatively
fewer RNs work in this setting. Patients
in nursing homes generally do not receive
complex treatments such as IV medication
therapy, and thus much of the patient
care in nursing homes can be provided
by LPNs and unlicensed nursing personnel.
LPNs assist in the ongoing assessment
of nursing home patients and the administration
of oral medications. In this section
we use hospital and nursing home data
to examine the demand for LPNs by these
employers.
Data for the Analysis
of Hospital Demand
To analyze the demand for licensed practical/vocational
nurses in general acute care hospitals,
we use 1990-2000 data from the American
Hospital Association (AHA) Annual Survey
of Hospitals. This database contains
hospital-level information on organizational
structure; facilities and services; community
orientation; total beds, utilization,
finances, and staffing; and location and
other geographic codes. The AHA surveys
all hospitals in the United States and
the response rate averages 85 to 95 percent
annually (American Hospital Association,
1999) . Thus, in any year, the AHA Annual
Survey Database has around 6,000 hospital
observations.
The AHA Annual Survey asks hospitals
to report full-time and part-time personnel
for the total facility and for specific
types of personnel, including registered
nurses and licensed practical/vocational
nurses. The survey specifically defines
full-time as working 35 hours or more
per week, and part-time as working less
than 35 hours per week (American Hospital
Association, 1999) . The staffing figures
reported by the hospitals are then converted
by the AHA into full-time equivalent (FTE)
measures. According to the AHA, full-time
equivalent figures are calculated by adding
the number of full-time personnel to half
the number of part-time personnel (American
Hospital Association, 1999) . We use
full-time equivalent LPN employment as
our measure of LPN staffing for short-term,
general acute care hospitals. However,
we should note that this measure potentially
overestimates or underestimates the use
of LPNs by hospitals. For example, a
nurse who works 20 hours per week and
one who works 34 hours per week each would
be counted as one-half of an FTE. Similarly,
a nurse who works 35 hours per week and
one who works 40 hours per week would
each count as one FTE.
We model hospital demand for LPNs as
a function of hospital, patient, and market
characteristics. This model is similar
to that used in previous studies of the
demand for nurses (Spetz, 1999) . We
construct hospital characteristic variables
using data from the AHA. We measure the
quantity of care provided by each hospital
in our sample as the number of patient
days. Also included in our model are
Medicare’s share of total patient
days, and the hospital’s service
mix. Our measure of service mix is the
Saidin technology index (Spetz and Maiuro,
2004) . The Saidin index provides a measure
of the degree of technology available
at hospitals by weighting each potential
service and calculating the sum of weighted
services available at each hospital.
The more rare the technology used by a
hospital, the higher the weight it receives
(Spetz & Maiuro, 2004) .
Patient characteristics in our demand
model are the average length of stay (available
from the AHA data) and the hospital’s
case mix index from Medicare files (available
from the Center for Medicare & Medicaid
Services). Both measures control for
changes in patient volume, but the case
mix index also controls for variation
in the complexity or severity of cases
treated by hospitals.
We use data from the 1989-2001 Current
Population Survey Outgoing Rotation files
and the Bureau of Health Professions Area
Resource File (ARF) (Bureau of the Health
Professions, 2003) to create market-level
variables. The CPS contains union status
information and we use this to create
variables denoting the percentage of LPNs,
RNs, and all workers in a given State
who are covered by or a member of a union.
We calculate market wages for registered
nurses, licensed practical nurses, and
nurse aides using earnings data from the
CPS ORG files. The market wages are median
values calculated from 3 years of data.
For example, 1990 LPN market wages are
based on hourly earnings reported by LPNs
in 1989, 1990, and 1991. Furthermore,
we calculate these at both the State level
and for urban and rural areas within a
State. Thus, for each nurse type, we
have with three potential market wages
per State. We attach an LPN, RN, and
nurse aide market wage to each hospital
observation in our sample depending on
the number of observations used in creating
the respective market wage. If the rural
or urban wage for a given State was calculated
from less than 15 observations, then we
assign the State-level wage to the hospital.
Otherwise, we assign the rural wage if
the hospital is in a rural area or the
urban wage if the hospital is in an urban
area. In the end, each hospital observation
in our sample is matched to three market
wages, one for each type of nurse.
We also include managed care variables
in our demand model, which were generously
provided by Douglas R. Wholey of the University
of Minnesota. Managed care activity is
measured with two variables: the number
of HMOs operating in the county and HMO
penetration. We also create a variable
interacting these two measures of the
managed care environment, and include
this in our analysis (Wholey, Christianson,
Engberg, & Bryce, 1997) . County-level
per capita income also is included in
the model, and was obtained from the Area
Resource File. Finally, we include the
two State-level scope of practice variables
described in Chapter 3 in some equations.
We estimated our demand equations including
several other variables from the ARF,
such as physicians per 1,000 population
and the share of population estimated
to be aged 65 and over; however, we do
not report the results of these regressions
because these variables had no statistically
significant relationship with our dependent
variable, nor did their inclusion affect
any other coefficients. Our dataset for
estimating hospital demand for licensed
practical nurses contains 54,258 hospital
observations over our sample time period
from 1990 to 2000.
As shown in Appendix E2, the average
number of full-time equivalent LPNs in
our sample of hospitals declined between
1990 and 2000. In contrast, the mean
number of full-time equivalent RNs increased.
As a result of these trends, the ratio
of LPNs to all licensed nurses declined
during our sample time period.
All of the variables denoting hospital
and patient characteristics exhibit trends
in their mean values. The average number
of inpatient days and length of stay declined
between 1990 and 2000. Medicaid’s
share of inpatient days increased, however,
as did the service mix and the severity
of cases treated in our sample of hospitals.
Market wages for LPNs, RNs, and nurse
aides were higher on average in 2000 compared
to 1990. However, the data do not show
a continuous upward trend during our sample
time period. RN and LPN market wages
increased between 1990 and 1994, and then
declined during the mid-1990s. In contrast,
market wages for nurse aides declined
during the first half of our sample time
period, and then increased between 1994
and 2000.
Other market characteristics in our dataset
also exhibit trends. The degree of HMO
penetration increased between 1990 and
2000, as did the average number of HMOs
operating in a county. In addition, the
average per capita income in the hospitals’
counties increased during our sample time
period.
Methods for Analyzing
Hospital Demand for LPNs
In our hospital demand analysis, our
dependent variable is the log of the number
of full-time equivalent LPNs. We also
log several of our independent variables
to normalize their distributions. Thus,
our demand equation is log-linear in form.
Each regression includes dummy variables
for each year in our sample. We estimate
robust standard errors using the “cluster”
command in Stata because it is possible
that observations within a State may not
be independent (StataCorp, 2003) .
We use several estimation methods in
our demand analysis. This is motivated
by two concerns. One is that there could
be some unknown factor inherent to each
hospital that affects its demand for licensed
practical nurses. If this is the case,
ordinary least squares (OLS) estimates
will be inefficient. To address this
possibility, we estimate fixed effects
models to allow for individual hospital
effects.
Another concern is the potential endogeniety
of LPN wages1.
If wages are endogenous in the demand
equation, then OLS estimates will be inconsistent.
Thus, we also estimate our demand equation
using the instrumental variable procedure
in Stata (StataCorp, 2003). To use this
procedure, we have to find variables that
are correlated with wages, but not correlated
with the error term in our demand equation.
County unemployment rates, obtained from
the Area Resource File, have been used
as an instrument for nurse wages in other
studies (Spetz, 1999) . As unemployment
rates rise, spouses are more likely to
be unemployed, and thus the nurse is more
likely to work. We also try two other
instruments: the average age of LPNs in
the hospital’s market area2,
and the percent of all workers unionized
within the State. We estimate first-stage
regressions for LPN wages including these
instruments as explanatory variables,
and consistently find that the estimated
coefficients on all but the county-level
unemployment rates are highly significant.
Thus, we determine that the average age
of LPNs and the percent of workers unionized
within a State are good instruments for
LPN wage in our demand equation. We further
check for the endogeneity of wages by
conducting a Hausman test (Hausman, 1978;
Kennedy, 1998; StataCorp, 2003) . The
test results provide no evidence that
LPN market wages are endogenous in our
model. Thus, we report regression results
both with and without instrumental variables,
because although theory suggests instrumental
variables are needed, the Hausman test
indicates they may not be appropriate.
Longitudinal Analysis
of Hospital Demand for LPNs
Table 5.4 presents regression equations
estimating hospital demand for licensed
practical nurses as a function of hospital,
patient, and market characteristics.
The first two columns present the ordinary
least squares equation coefficients and
standard errors. The second two columns
present the results of a fixed effects
equation, which includes a dummy variable
for each hospital to control for hospital
characteristics that are unobserved and
constant over time. The final two columns
contain the results of the model estimated
with fixed effects and instruments to
control for the endogeneity of wages.
Conventional economic theory predicts
that demand for employees will decline
as their wages rise. At the same time,
demand for a type of employee could rise
or fall with the wages of other employees,
depending on whether other employees are
complements or substitutes. The results
presented in Table 5.4 are consistent
with this theory. Higher LPN wages have
a negative effect on demand for LPNs when
instrumental variables are used to control
for the endogeneity of wages. The importance
of addressing endogeneity is demonstrated
by the positive, significant relationship
between wages and demand in the uninstrumented
fixed effects model. In all three models,
higher RN wages are associated with higher
demand for LPNs. This finding suggests
that LPNs are used as substitutes for
RNs, at least in part. The fixed effects
and instrumental variables models indicate
that a ten percent increase in the RN
wage will raise LPN demand about two to
three percent. Aide wages have a modest
positive relationship to demand for LPNs
in the fixed effects equations, with a
ten percent increase in the aide wage
having less than a one percent effect
on demand. In the ordinary least squares
equation, the aide wage has a very large,
negative effect on LPN demand.
The volume of patients cared for at a
hospital has an important effect on demand
for LPNs. The fixed effects and instrumental
variables models estimate that ten percent
growth in the number of inpatient days
increases the demand for LPNs by about
four percent. Conversely, as the length
of stay of these patients rises, the demand
for LPNs falls. The coefficient measuring
the relationship between case mix and
demand for LPNs is negative as well.
LPNs are less able to care for acutely
ill patients, and thus as acuity rises,
demand will fall. Hospitals with a higher
level of technology demand fewer LPNs.
The ability of hospitals to hire staff
depends on the revenue received in exchange
for patient care services. Several variables
measure the potential revenues available
to hospitals. As the share of patient
days reimbursed by Medicaid rises, demand
for LPNs also rises. Medicaid reimbursements
to hospitals are known to be low, and
hospitals that have high shares of Medicaid
patients also typically have large shares
of charity and non-paying patients. Thus,
it is possible that this relationship
results from hospitals with a high share
of Medicaid patients having smaller personnel
budgets. Another possibility is that
Medicaid patients are somewhat less acutely
ill than are other patients, and thus
as the share of Medicaid patients rises,
LPNs are better able to care for more
patients.
The next three variables measure the
relationship between the type of hospital
owner and demand for LPNs. For-profit,
district, and government hospitals have
greater demand for LPNs than do not-for-profit
hospitals, holding other factors constant.
The potential reasons for these findings
vary by type of owner. For-profit hospitals
have a financial incentive to hire less-expensive
LPNs to increase their profit margins.
District and government hospitals may
have smaller personnel budgets because
they rely at least in part on tax revenues;
thus, they may stretch their budgets with
LPNs.
Table 5.4: Estimates
of Demand for Licensed Practical/Vocational
Nurses in U.S. General Acute Care Hospitals,
1990-2000
|
OLS (s.e.) |
Fixed Effects (s.e.) |
Fixed Effects, Instrumenting
for LPN Wages (s.e.) |
log
(LPN Wage) |
-0.154 |
(0.259) |
0.290** |
(0.044) |
-0.804** |
(0.390) |
log
(RN Wage) |
0.645** |
(0.235) |
0.235** |
(0.047) |
0.286** |
(0.051) |
log
(Nurse Aide Wage) |
-1.140** |
(0.324) |
0.009 |
(0.046) |
0.095* |
(0.055) |
|
log
(Inpatient Days) |
0.754** |
(0.027) |
0.420** |
(0.013) |
0.424** |
(0.014) |
log
(Length of Stay) |
-0.512** |
(0.028) |
-0.192** |
(0.015) |
-0.192** |
(0.015) |
Case
Mix |
0.037 |
(0.087) |
-0.202** |
(0.034) |
-0.201** |
(0.035) |
Technology
(Saidin Index) |
-0.030** |
(0.012) |
-0.039** |
(0.002) |
-0.038** |
(0.002) |
|
log
(Medicaid Share of Inpatient Days) |
0.036* |
(0.020) |
0.024** |
(0.004) |
0.023** |
(0.004) |
For
Profit Hospital |
0.190** |
(0.050) |
0.142** |
(0.020) |
0.154** |
(0.020) |
District
Hospital |
0.221** |
(0.058) |
0.090** |
(0.025) |
0.098** |
(0.025) |
Government
(State or local) Hospital |
0.161** |
(0.053) |
0.117** |
(0.023) |
0.117** |
(0.023) |
|
Number
of HMOs Operating in County |
-0.022* |
(0.013) |
-0.006** |
(0.002) |
-0.004** |
(0.002) |
HMO
Penetration |
-0.328 |
(0.223) |
-0.139** |
(0.046) |
-0.115** |
(0.047) |
No.
of HMOs X HMO Penetration |
0.011 |
(0.029) |
-0.004 |
(0.004) |
-0.014** |
(0.005) |
|
Per
Capita Income in County |
-0.00002** |
(0.000) |
-0.00001** |
(0.000) |
-0.00001** |
(0.000) |
|
Percentage
of LPNs Unionized in State |
0.175 |
(0.154) |
0.060** |
(0.024) |
0.060** |
(0.025) |
Percentage
of RNs Unionized in State |
0.007 |
(0.263) |
-0.013 |
(0.049) |
-0.063 |
(0.052) |
|
1991 |
-0.006 |
(0.022) |
-0.001 |
(0.011) |
0.026* |
(0.014) |
1992 |
-0.063** |
(0.027) |
-0.054** |
(0.011) |
-0.012 |
(0.019) |
1993 |
-0.115** |
(0.033) |
-0.093** |
(0.012) |
-0.047** |
(0.020) |
1994 |
-0.031 |
(0.037) |
-0.023** |
(0.012) |
0.022 |
(0.020) |
1995 |
0.039 |
(0.041) |
-0.001 |
(0.013) |
0.039** |
(0.019) |
1996 |
0.072 |
(0.045) |
0.009 |
(0.014) |
0.046** |
(0.019) |
1997 |
0.140** |
(0.052) |
0.045** |
(0.015) |
0.078** |
(0.019) |
1998 |
0.163** |
(0.059) |
0.040** |
(0.017) |
0.100** |
(0.027) |
1999 |
0.137** |
(0.058) |
0.002 |
(0.018) |
0.083** |
(0.034) |
2000 |
0.121* |
(0.062) |
-0.029 |
(0.019) |
0.061* |
(0.037) |
|
R-Squared |
0.519 |
0.458 |
0.451 |
N |
42,401 |
42,317 |
42,299 |
*p < 0.10
**p < 0.05
Sources: American Hospital Association
Annual Survey of Hospitals, Current Population
Survey Outgoing Rotation Group Files,
and Area Resource File. Managed care
data courtesy of Douglas R. Wholey
Notes: (1) the dependent variable is
log (Number of Full-time Equivalent Licensed
Practical Nurses) (2) all regressions
include a constant; and (3) OLS regression
uses the cluster (on State) option in
Stata.
As HMO penetration and the number of
HMOs operating in a county rise, the demand
for LPNs falls, and these effects are
somewhat accelerated as the interaction
between penetration and the number of
HMOs rises. Greater HMO penetration in
a market is thought to have a primary
effect of reducing revenues available
to hospitals. Such revenue reduction
could reduce demand for LPNs because hospital
budgets are tighter. However, HMOs also
may value the quality of care offered
by hospitals, and thus as HMO penetration
increases, hospitals are pressured to
favor the hiring of more-skilled RNs while
reducing LPN staff.
County income affects demand for LPNs.
As per capita income rises, the demand
for LPNs falls. This relationship may
arise if wealthier patients prefer hospitals
with more highly skilled staff, and thus
hospital demand for LPNs falls.
Statewide unionization of LPNs is associated
with greater demand for LPNs in the instrumental
variables equation. This relationship
may indicate that unionized LPNs are better
able to ensure that they are retained
in hospital staffing models. Conversely,
LPNs may be more likely to unionize when
their numbers are higher in the hospital
industry. RN unionization has no statistically
significant relationship to LPN demand.
The coefficients of the yearly dummy
variables indicate that there has been
some change in hospital demand for LPNs
over time. In 1993, demand for LPNs was
lower than in 1990, while demand rose
from 1995 through 1999. This period of
increased demand coincides with reports
that hospitals were redesigning their
nursing services to emphasize team nursing
and less-skilled nursing personnel. In
these staffing strategies, LPNs would
have had a more prominent role, and thus
demand for LPNs would have risen.
Table 5.5 presents regression equations
similar to Table 5.4, but the dependent
variable is employment of LPNs as a share
of all licensed nurses. In these equations,
we can directly compare the effects of
explanatory variables on demand for LPNs
to demand for RNs. The results confirm
those of the level of LPN employment equations.
Relative demand for LPNs declines as the
LPN wage rises, and it rises with growth
in RN wages.
Increases in the number of inpatient
days has no effect on relative demand
for LPNs, suggesting that hospitals maintain
a consistent skill mix even as patient
volumes rise. Longer lengths of patient
stays increase relative demand for LPNs,
even though they decrease overall demand
for LPNs. Together, these findings suggest
that longer lengths of stay are associated
with lower overall demand for nursing
care, perhaps because the share of patients
in intermediate and rehabilitation units
increases.
A higher patient case mix reduces relative
demand for LPNs, although this relationship
is statistically significant only in the
ordinary least squares equation. The
coefficient on the technology index is
consistent with expectations, in that
higher technology reduces relative demand
for LPNs. It is possible that case mix
is collinear with both length of stay
and the technology index, so the statistically
insignificant coefficients for case mix
result from multicollinearity rather than
a lack of relationship.
Table 5.5: Estimates
of Relative Demand for Licensed Practical/Vocational
Nurses
|
OLS
(s.e.) |
Fixed
Effects (s.e.) |
Fixed
Effects, Instrumenting for LPN Wages
(s.e.) |
log
(LPN Wage) |
-0.055 |
(0.041) |
0.019** |
(0.006) |
-0.126** |
(0.055) |
log
(RN Wage) |
0.098** |
(0.045) |
0.039** |
(0.007) |
0.046** |
(0.007) |
log
(Nurse Aide Wage) |
-0.234** |
(0.056) |
-0.024** |
(0.006) |
-0.012 |
(0.008) |
|
log
(Inpatient Days) |
-0.016** |
(0.005) |
-0.002 |
(0.002) |
-0.002 |
(0.002) |
log
(Length of Stay) |
0.027** |
(0.004) |
0.016** |
(0.002) |
0.016** |
(0.002) |
Case
Mix |
-0.070** |
(0.013) |
-0.006 |
(0.005) |
-0.006 |
(0.005) |
Technology
(Saidin Index) |
-0.004** |
(0.001) |
-0.001** |
(0.000) |
-0.001** |
(0.000) |
|
log
(Medicaid Share of Inpatient Days) |
0.007** |
(0.003) |
0.005** |
(0.001) |
0.005** |
(0.001) |
For
Profit Hospital |
0.027** |
(0.008) |
0.015** |
(0.003) |
0.017** |
(0.003) |
District
Hospital |
0.040** |
(0.010) |
0.022** |
(0.004) |
0.023** |
(0.004) |
Government
(State or local) Hospital |
0.020* |
(0.010) |
0.024** |
(0.003) |
0.024** |
(0.003) |
|
Number
of HMOs Operating in County |
-0.003** |
(0.001) |
-0.002** |
(0.000) |
-0.002** |
(0.000) |
HMO
Penetration |
-0.070** |
(0.027) |
-0.020** |
(0.007) |
-0.017** |
(0.007) |
No.
of HMOs X HMO Penetration |
0.005* |
(0.003) |
0.004** |
(0.001) |
0.003** |
(0.001) |
|
Per
Capita Income in County |
-0.000002** |
(0.000) |
0.000001** |
(0.000) |
-0.000001** |
(0.000) |
|
Percentage
of LPNs Unionized in State |
0.014 |
(0.022) |
0.006* |
(0.003) |
0.006* |
(0.003) |
Percentage
of RNs Unionized in State |
0.004 |
(0.044) |
0.001 |
(0.007) |
-0.005 |
(0.007) |
|
1991 |
-0.008** |
(0.004) |
-0.009** |
(0.002) |
-0.005** |
(0.002) |
1992 |
-0.021** |
(0.005) |
-0.024** |
(0.002) |
-0.018** |
(0.003) |
1993 |
-0.033** |
(0.006) |
-0.036** |
(0.002) |
-0.030** |
(0.003) |
1994 |
-0.030** |
(0.007) |
-0.038** |
(0.002) |
-0.032** |
(0.003) |
1995 |
-0.021** |
(0.007) |
-0.040** |
(0.002) |
-0.035** |
(0.003) |
1996 |
-0.022** |
(0.007) |
-0.049** |
(0.002) |
-0.044** |
(0.003) |
1997 |
-0.012 |
(0.007) |
-0.049** |
(0.002) |
-0.044** |
(0.003) |
1998 |
-0.009 |
(0.009) |
-0.057** |
(0.002) |
-0.049** |
(0.004) |
1999 |
-0.008 |
(0.010) |
-0.063** |
(0.003) |
-0.052** |
(0.005) |
2000 |
-0.010 |
(0.010) |
-0.071** |
(0.003) |
-0.058** |
(0.005) |
|
R-Squared |
0.378 |
|
0.098 |
|
0.181 |
|
N |
43,289 |
|
43,204 |
|
43,186 |
|
*p
< 0.10
**p < 0.05
Notes:
(1) the dependent variable is log (LPNs
as a Proportion of Total Licensed Nurse
Staff) (2) all regressions include a constant;
and (3) OLS regression uses the cluster
(on State) option in Stata.
Sources:
American Hospital Association Annual Survey
of Hospitals, Current Population Survey
Outgoing Rotation Group Files, and Area
Resource File. Managed care data courtesy
of Douglas R. Wholey.
The effects of payer mix and hospital
ownership in the relative demand equations
are similar to those in the level of demand
equations. Hospitals with higher shares
of Medicaid inpatient days have greater
relative demand for LPNs, and the relative
demand for LPNs falls as HMO penetration
and the number of HMOs increases. For-profit,
district, and government hospitals have
greater demand for LPNs relative to RNs
than not-for-profit hospitals. Per capita
county income also has a negative effect
on relative demand for LPNs. Hospitals
in States with higher shares of LPNs in
unions have greater relative demand for
LPNs.
Relative demand for LPNs declined from
1991 through 2000 (relative to 1990).
Combined with Table 5.4, these findings
indicate that although absolute demand
for LPNs stabilized in the late 1990s,
hospitals have demanded relatively more
RNs over time.
These findings demonstrate the importance
of wages, hospital characteristics, and
payer mix on hospital demand for LPNs.
As hospitals face increased pressure to
reduce costs, or face higher wages for
RNs and LPNs, the demand for LPNs changes
significantly. There have been periods
of time during which LPNs have been considered
attractive substitutes for RNs, and other
times when demand for LPNs dropped because
hospitals preferred RNs. These demand
changes have large effects on the career
opportunities of LPNs.
The Effect of Scope
of Practice on Hospital Demand for LPNs
The longitudinal models presented above
omit one important factor that could affect
demand for LPNs: scope of practice regulations.
Using the categorizations of LPN scope
of practice created as part of this study,
we examined the relationship between the
scope of practice of LPNs and hospital
demand for LPNs. This is a complex undertaking,
because these things are determined jointly.
For example, a liberal scope of practice
may encourage employers to demand LPNs
and reduce demand for other workers such
as RNs. However, when there is a shortage
of RNs, employers are likely to increase
their demand for LPNs and also to lobby
State legislatures for expanded scope
of practice for LPNs. Because the relationship
between demand and scope of practice is
likely to be endogenous, we use instrumental
variables to predict scope of practice
regulations, in a fashion similar to that
used to control for endogeneity of wages.
Our instruments are a set of variables
measuring the political characteristics
of each State: whether there is Democratic
control of both legislative houses and
the governorship, whether there is divided
control of the legislature and/or governorship,
the ratio of per capita State debt to
per capita income, whether the governor
has a line item veto, the percent of the
upper legislative house that is Democratic,
and the percent of the lower legislative
house that is Democratic. Mark W. Smith
from the Veterans Health Administration
Health Economics Resource Center in Menlo
Park kindly provided these variables.
Because we have scope of practice data
for only 1 year, we estimate the demand
for LPNs using only a single year of data.
Table 5.6 presents the results of regression
equations for hospital demand for LPNs
using data from 2000, and Table 5.7 presents
analogous equations for relative demand
for LPNs (as a share of total licensed
nurse employment). The tables are organized
in the same way as Tables 5.4 and 5.5.
As seen in the first two rows of Table
5.6, hospitals in States with restrictive
scopes of LPN practice tend to have lower
employment of LPNs. However, once the
potential endogeneity of wages and scope
of practice are addressed using instrumental
variables, the relationship is no longer
statistically significant. A similar
pattern holds for the specificity of scope
of practice. However, Table 5.7 demonstrates
that as the scope of practice of LPNs
becomes more restrictive, the demand for
LPNs falls relative to the demand for
all licensed nurses, even when controlling
for the endogeneity of scope of practice.
There are some differences in the effects
of other explanatory variables between
the cross-section and longitudinal results.
LPN wages continue to have a negative
effect on demand for LPNs, but this effect
is not significant when instrumental variables
are used to control for the endogeneity
of LPN wages. RN and aide wages are not
significantly associated with LPN demand,
except in the uninstrumented equations.
In these equations, higher aide wages
are associated with greater demand for
LPNs. As seen in Table 5.7, wages have
little to no effect on relative demand
for LPNs.
Table 5.6: Estimates
of Demand for Licensed Practical/Vocational
Nurses in U.S. General Acute Care Hospitals,
2000
|
OLS
(s.e.) |
Instrumenting
for Scope of Practice (s.e.) |
Instrumenting
for Scope of Practice & LPN
Wages (s.e.) |
|
Specific |
-0.077* |
(0.040) |
-0.085** |
(0.041) |
0.221 |
(0.354) |
Restrictive |
-0.137** |
(0.032) |
-0.136** |
(0.032) |
-0.060 |
(0.056) |
|
log
(LPN Wage) |
-0.857** |
(0.281) |
-0.838** |
(0.289) |
-4.929 |
(3.977) |
log
(RN Wage) |
-0.092 |
(0.350) |
-0.093 |
(0.348) |
1.912 |
(1.373) |
log
(Nurse Aide Wage) |
0.667** |
(0.275) |
0.725** |
(0.277) |
0.183 |
(0.601) |
|
log
(Inpatient Days) |
0.615** |
(0.024) |
0.615** |
(0.024) |
0.631** |
(0.030) |
log
(Length of Stay) |
-0.418** |
(0.030) |
-0.420** |
(0.031) |
-0.436** |
(0.033) |
Case
Mix |
0.098 |
(0.080) |
0.087 |
(0.081) |
0.076 |
(0.091) |
Technology
(Saidin Index) |
-0.022* |
(0.012) |
-0.021* |
(0.012) |
-0.022* |
(0.012) |
|
log
(Medicaid Share of Inpatient Days) |
0.067** |
(0.023) |
0.069** |
(0.024) |
0.083** |
(0.032) |
For
Profit Hospital |
0.035 |
(0.039) |
0.039 |
(0.039) |
0.044 |
(0.039) |
District
Hospital |
0.154** |
(0.050) |
0.159** |
(0.051) |
0.137** |
(0.055) |
Government
(State or local) Hospital |
0.127** |
(0.055) |
0.134** |
(0.056) |
0.132** |
(0.060) |
|
Number
of HMOs Operating in County |
-0.049** |
(0.008) |
-0.049** |
(0.008) |
-0.026 |
(0.025) |
HMO
Penetration |
-0.138 |
(0.261) |
-0.120 |
(0.265) |
0.131 |
(0.332) |
No.
of HMOs X HMO Penetration |
0.042 |
(0.032) |
0.040 |
(0.032) |
-0.003 |
(0.058) |
|
Per
Capita Income in County |
-0.00001** |
(0.000) |
-0.00001** |
(0.000) |
-0.000009** |
(0.000) |
|
R-Squared |
0.542 |
0.539 |
0.498 |
N |
3,890 |
3,798 |
3,798 |
*p
< 0.10
**p < 0.05
Notes:
(1) dependent variable is log (No. of
Full-time Equivalent Licensed Practical
Nurses), (2) all regressions include State
dummy variables and a constant; and (3)
all regressions use the cluster (on State)
option in Stata.
Sources:
American Hospital Association Annual Survey
of Hospitals, Current Population Survey
Outgoing Rotation Group Files, and Area
Resource File. Managed care data courtesy
of Douglas R. Wholey Political variables
courtesy of Mark W. Smith, Health Economics
Resource Center, VA Palo Alto Health Care
System.
Higher patient volumes increase the demand
for LPNs, and this relationship is larger
in magnitude in the cross-section than
it was in the longitudinal data. However,
higher volumes reduce the relative demand
for LPNs in the cross section, suggesting
that larger hospitals demand fewer LPNs,
all other things held equal. LPN demand
is negatively associated with length of
stay, but relative demand for LPN rises
with length of stay, again suggesting
that the acuity of patients declines with
length of stay. Thus, both overall demand
for nursing staff and demand for RNs drops
as length of stay rises. Relative demand
for LPNs falls as the case mix of patients
rises.
Table 5.7: Estimates
of Demand for Licensed Practical/Vocational
Nurses in U.S. General Acute Care Hospitals,
2000
|
OLS
(s.e.) |
Instrumenting
for Scope of Practice (s.e.) |
Instrumenting
for Scope of Practice & LPN
Wages (s.e.) |
|
Specific |
-0.025** |
(0.006) |
-0.0001 |
(0.010) |
0.045 |
(0.056) |
Restrictive |
-0.004 |
(0.024) |
-0.038** |
(0.009) |
-0.027** |
(0.009) |
|
log
(LPN Wage) |
-0.108 |
(0.084) |
-0.106 |
(0.083) |
-0.722 |
(0.621) |
log
(RN Wage) |
-0.154* |
(0.090) |
-0.152* |
(0.089) |
0.150 |
(0.244) |
log
(Nurse Aide Wage) |
0.054 |
(0.064) |
0.059 |
(0.065) |
-0.022 |
(0.116) |
|
log
(Inpatient Days) |
-0.025** |
(0.002) |
-0.026** |
(0.002) |
-0.024** |
(0.003) |
log
(Length of Stay) |
0.034** |
(0.004) |
0.035** |
(0.004) |
0.033** |
(0.004) |
Case
Mix |
-0.057** |
(0.013) |
-0.057** |
(0.014) |
-0.059** |
(0.015) |
Technology
(Saidin Index) |
-0.001 |
(0.001) |
-0.001 |
(0.001) |
-0.001 |
(0.001) |
|
log
(Medicaid Share of Inpatient Days) |
0.006** |
(0.003) |
0.006** |
(0.003) |
0.008** |
(0.004) |
For
Profit Hospital |
-0.001 |
(0.007) |
-0.0002 |
(0.007) |
0.0003 |
(0.007) |
District
Hospital |
0.022** |
(0.007) |
0.022** |
(0.007) |
0.019** |
(0.008) |
Government
(State or local) Hospital |
0.015* |
(0.008) |
0.016* |
(0.009) |
0.015* |
(0.009) |
|
Number
of HMOs Operating in County |
-0.006** |
(0.002) |
-0.006** |
(0.002) |
-0.003 |
(0.003) |
HMO
Penetration |
-0.046** |
(0.019) |
-0.045** |
(0.019) |
-0.008 |
(0.039) |
No.
of HMOs X HMO Penetration |
0.009** |
(0.004) |
0.009** |
(0.004) |
0.002 |
(0.007) |
|
Per
Capita Income in County |
-0.000001** |
(0.000) |
-0.000001** |
(0.000) |
-0.000001** |
(0.000) |
|
R-Squared |
0.529 |
0.527 |
0.464 |
N |
3,963 |
3,867 |
3,867 |
*p
< 0.10
**p < 0.05
Sources:
American Hospital Association Annual Survey
of Hospitals, Current Population Survey
Outgoing Rotation Group Files, and Area
Resource File. Managed care data courtesy
of Douglas R. Wholey Political variables
courtesy of Mark W. Smith, Health Economics
Resource Center, VA Palo Alto Health Care
System.
Notes:
(1) dependent variable is log (LPNs as
a Proportion of Total Licensed Nurse Staff),
(2) all regressions include State dummy
variables and a constant; and (3) all
regressions use the cluster (on State)
option in Stata.
As in the longitudinal models, hospitals
with a higher share of Medicaid inpatient
days have greater demand for LPNs. District
and government hospitals demand more LPNs
both in absolute and relative terms.
The only cross-sectional effect of managed
care is that as the number of HMOs operating
in a county rises, demand for LPNs falls.
Relative demand for LPNs also falls as
the number of HMOs and HMO penetration
rise. However, neither of these findings
is observed when instrumental variables
are used to account for the potential
endogeneity of wages. County per capita
income continues to be negatively associated
with LPN demand and relative LPN demand.
The Demand for LPNs
by Long-Term Care Facilities
The above analysis demonstrates that
restrictive scopes of LPN practice reduce
hospital demand for LPNs, both in absolute
terms and relative to total licensed nurse
demand. How does scope of practice affect
demand for LPNs by nursing homes? To
answer this question, we turned to Medicare’s
Online Survey, Certification, and Reporting
System (OSCAR). These data provide information
about long-term care facilities, including
staffing, limitations in the activities
of daily living of residents (ADLs), the
share of residents insured by Medicaid,
and facility number of beds. To examine
the factors that affect long-term care
facility demand for LPNs, we estimate
regression equations similar to those
used to study hospital demand for LPNs.
The dependent variables in our analysis
are LPN hours per facility resident day,
and LPN hours as a share of licensed nurse
hours per resident day. We anticipate
that demand for LPNs will be a function
of the scope of practice, measured as
above; the number of beds in the facility;
the resident case mix index; State Medicaid
reimbursement rates; nurse wages; the
share of residents on Medicaid; whether
the State uses a case mix reimbursement
method; the facility’s ownership,
including profit status, and chain membership;
whether the nursing facility is based
in a hospital; whether is certified to
accept patients insured by Medicaid, Medicare,
or both; and the concentration of nursing
homes in the market, measured as the Herfindahl
index. All data are from 2002, except
for RN and LPN wages, which are measured
as in the hospital demand models.
Previous research has demonstrated that
many of the variables that affect demand
for LPNs are endogenous (Harrington &
Swan, 2003; Zinn, 1993) . Specifically,
the case mix of residents is simultaneously
determined with LPN demand, and State
Medicaid rates are endogenous. In order
to estimate the demand equations, we implemented
instrumental variables techniques to address
this endogeneity. The instrumental variables
for case mix, which is measured as the
dependency of residents in activities
of daily life, are the proportion of the
MSA population aged 65 and over, the percentage
of females in the labor force, personal
per capita income, and the percent excess
beds in the county. The instrumental
variables for State Medicaid rates are
the proportion of the MSA population aged
65 and over, personal per capita income,
whether the governor is Democratic, and
whether the legislature and/or governorship
are split between political parties.
Wages also are endogenous, and we use
RNs per 100,000 population, the share
of the population over age 65, percentage
of females in the labor force, and personal
income per capita as instrumental variables.
Finally, we assume that scope of practice
regulations may be endogenous with demand
for LPNs, and we use the same instrumental
variables as in the hospital equations.
Tables 5.8 and 5.9 present LPN demand
equations for long-term care facilities.
In Table 5.8, the dependent variable is
LPN hours per resident day, and in Table
5.9 it is LPN hours divided by total licensed
nursing hours per resident day. The first
two columns of both tables present an
equation in which instrumental variables
are used for Medicaid reimbursement rates,
case mix, and scope of practice. The
second two columns include instrumental
variables for LPN wages as well.
Table 5.8: Estimates
of Demand for Licensed Practical/Vocational
Nurses in U.S. Long-Term Care Facilities,
2002
|
Not
instrumenting for wages |
Instrumenting
for wages |
Restrictive
scope of practice |
-0.028** |
(0.006) |
-0.022** |
(0.006) |
Specific
scope of practice |
-0.030** |
(0.004) |
-0.033** |
(0.004) |
|
LPN
wage (or relative wage) |
-0.025** |
-0.004 |
-0.097** |
(0.006) |
|
Number
of beds |
-0.0004** |
(0.00006) |
-0.0005** |
(0.00006) |
|
Case
mix Index |
0.390** |
(0.018) |
0.344** |
(0.018) |
Rate
of Medicaid residents |
-0.004** |
(0.0002) |
-0.004** |
(0.0002) |
Accepts
Medicare and Medicaid |
-0.263** |
(0.012) |
-0.232** |
(0.012) |
|
Medicaid
reimbursement rate |
0.0001 |
(0.0003) |
0.003** |
(0.0003) |
Case
mix reimbursement method |
0.011 |
(0.009) |
-0.021** |
(0.009) |
|
For-profit
facility |
0.002 |
(0.008) |
0.003 |
(0.008) |
Chain
facility |
0.021** |
(0.008) |
0.028** |
(0.008) |
Hospital-based
facility |
0.022* |
(0.012) |
0.014 |
(0.012) |
Market
concentration |
-0.062** |
(0.017) |
-0.163** |
(0.018) |
|
Intercept |
-0.595** |
(0.108) |
0.347** |
(0.121) |
|
R-squared |
0.138 |
0.154 |
N |
14029 |
14029 |
*p
< 0.10
**p < 0.05
Notes:
(1) Dependent variable is LPN hours per
resident day; (2) both equations instrument
for Medicaid Reimbursement Rate, Case
mix, and Scope of Practice
As seen in Table 5.8, long-term care
facilities located in States with more
restrictive and specific scopes of LPN
practice demand fewer LPNs. This effect
is statistically significant in both the
level of demand and the relative demand
equations. This result persists in the
equations for relative LPN demand, although
the relationship is not statistically
significant when instrumental variables
are used for relative wages. Thus, as
with hospitals, it appears that the restrictiveness
of the LPN scope of practice has an important
effect on the demand for LPNs by long-term
care facilities.
Other factors affect long-term care facility
demand for LPNs. As the market wage rises,
demand for LPNs falls, as expected. However,
in the relative demand equation, the opposite
relationship is found: higher LPN wages,
relative to RN wages, are associated with
increased demand for LPNs relative to
RNs. We have not been able to explain
this contrary finding. It may be that
the higher wages for LPNs are related
to having additional training and certification.
That would also explain the increase in
demand for LPNs. If the LPNs have acquired
higher skills, they are more attractive
to hospitals than RNs, even though they
have a higher wage. They can perform
more complex activities and they cost
less than RNs.
Table 5.9: Estimates
of Relative Demand for Licensed Practical/Vocational
Nurses in U.S. Long-Term Care Facilities,
2002
|
Not
instrumenting for wages |
Instrumenting
for wages |
Restrictive
scope of practice |
-0.005** |
(0.003) |
-0.004 |
(0.003) |
Specific
scope of practice |
-0.016** |
(0.002) |
-0.012** |
(0.002) |
LPN
wage (or relative wage) |
0.055* |
(0.031) |
0.659** |
(0.083) |
Number
of beds |
0.0002** |
(0.00003) |
0.0002** |
(0.00003) |
Case
mix Index |
0.157** |
(0.008) |
0.188** |
(0.009) |
Rate
of Medicaid residents |
0.002** |
(0.00007) |
0.002** |
(0.00008) |
Accepts
Medicare and Medicaid |
-0.030** |
(0.005) |
-0.043** |
(0.006) |
Medicaid
reimbursement rate |
-0.002** |
(0.0001) |
-0.002** |
(0.0001) |
Case
mix reimbursement method |
-0.007* |
(0.004) |
-0.014** |
(0.004) |
For-profit
facility |
0.035** |
(0.004) |
0.038** |
(0.004) |
Chain
facility |
-0.006* |
(0.003) |
-0.008** |
(0.004) |
Hospital-based
facility |
-0.007 |
(0.005) |
-0.005 |
(0.006) |
Market
concentration |
-0.0001 |
(0.007) |
0.004 |
(0.008) |
Intercept |
-0.211** |
(0.048) |
-0.785** |
(0.089) |
R-squared |
0.143 |
0.131 |
N |
14029 |
14029 |
*p
< 0.10
**p < 0.05
Notes:
(1) dependent variable is log (No. of
Full-time Equivalent Licensed Practical
Nurses), (2) all regressions include State
dummy variables and a constant; and (3)
all regressions use the cluster (on State)
option in Stata. (3) Dependent variable
is (LPN hours/(LPN+RN hours)) per resident
day; 4) both equations instrument for
Medicaid Reimbursement Rate, Case mix,
and Scope of Practice
Facilities with more beds demand fewer
LPNs per resident day, but demand more
LPNs relative to RNs. These figures suggest
there are economies of scale in providing
long-term care. The absolute and relative
demand for LPNs rises with the ADL dependency
of residents. A higher share of Medicaid
residents is associated with lower demand
for LPNs per resident day, but with a
greater share of LPNs relative to RNs.
In sum, these coefficients suggest that
as the share of Medicaid residents rises,
long-term care facilities rely more on
less-skilled licensed nursing personnel.
Facilities that have certification for
both Medicare and Medicaid patients demand
fewer LPNs overall and also fewer LPNs
relative to RNs.
Payment rates for long-term care facilities
have significant effects on demand for
LPNs. Increases in the Medicaid reimbursement
rate result in higher LPN demand, and
also lower LPN demand relative to RN demand,
probably because facilities can better
afford more skilled nurses when reimbursement
rates are higher. Case mix reimbursement
methods are associated with lower demand
for LPNs and lower LPN/RN ratios.
The ownership of the long-term care facility
affects demand for LPNs. For-profit facilities
demand more LPNs relative to RNs, although
the absolute level of demand for LPNs
is not associated with profit status.
This suggests that for-profit facilities
employ fewer RNs than do other facilities.
Chain-owned long-term care facilities
demand more LPNs, and also demand fewer
LPNs relative to RNs (indicating that
they demand more RNs).
Finally, LPN demand is affected by market
characteristics. Facilities in markets
where there is less competition between
facilities have lower demand for LPNs,
and competition has no effect on the LPN
to RN mix. This finding suggests that
competition between long-term care facilities
may increase quality of care, because
the facilities compete for patients by
hiring more licensed staff.
The earnings of LPNs
In general, the wages of LPNs result
from the intersection of market supply
and market demand. As demand rises relative
to supply, wages will rise. This wage
inflation will, in turn, increase the
supply of LPNs and reduce demand for LPNs.
These movements bring the labor market
into balance. Thus, it is difficult to
examine the earnings of LPNs separately
from demand and supply. The above sections
on demand and supply explore these relationships.
In this section, we present the results
from the first-stage regression used to
obtain predicted values of wage. Recall
that these predicted values were used
in our supply regressions.
We use Current Population Survey data
from 1994 through 2001 to estimate the
wage of each LPN, controlling for demographic,
market, and job characteristics. We omit
family characteristics because in theory
family characteristics should not affect
the human capital of workers. The yearly
dummy variables included in the equation
control for secular changes in wages nationwide,
such as those that result from economy-wide
inflation. We also include the number
of physicians per 100,000 people and the
average manufacturing wage in the LPN’s
State of residence as explanatory variables
in the wage equation. These two variables
serve as instruments in our two-stage
least squares regressions of the supply
of LPNs. The dependent variable is created
for each LPN in our sample by dividing
their usual weekly earnings (before deductions)
by their usual hours of work per week,
and is adjusted for inflation.
Table 5.10 presents ordinary least squares
regression results for LPN wages. Notably,
the estimated coefficients on the two
variables serving as instruments are positive
and statistically significant, and imply
that LPN wages increase as the Statewide
average manufacturing wage and the number
of physicians relative to the population
increase.
Demographic characteristics affect the
wages received by LPNs. Male LPNs earn
higher wages than do female LPNs, and
LPNs with a college degree have higher
wages than do those who do not have a
college degree. Furthermore, the wage
differential is greater for LPNs with
at least a 4-year degree (i.e., bachelor’s
degree or higher). LPNs who are not citizens
earn lower wages than US-born LPNs, though
this result is only statistically significant
at a higher p-value. Age has a significant
effect on LPN wages. Wages rise with
age until age 52, after which time they
decline. This finding suggests that,
adjusted for inflation, LPN wages do not
progress consistently with potential experience.
Table 5.10: Regression
Results for Log of LPN/LPN Earnings Per
Hour
|
Coefficient |
SE |
Instruments |
Number of Physicians Per 100,000
People in State |
0.004** |
(0.001) |
Average Manufacturing Wage in State |
0.270** |
(0.044) |
Demographic Variables |
Male |
0.782** |
(0.323) |
Age |
0.207** |
(0.040) |
Age Squared |
-0.002** |
(0.000) |
Some College |
0.274 |
(0.185) |
AA Degree |
0.445** |
(0.180) |
Bachelor, Master, PhD, or Professional
School Degree |
0.987** |
(0.357) |
Black |
-0.265 |
(0.190) |
Hispanic |
-0.053 |
(0.391) |
Native American |
-0.903 |
(0.604) |
Asian |
0.357 |
(0.567) |
Not a U.S. Citizen |
-0.846* |
(0.491) |
Citizen by Naturalization |
0.026 |
(0.436) |
Government Worker |
-0.262 |
(0.185) |
Market Characteristics |
Percentage of LPNs Unionized in
State |
-0.498 |
(0.550) |
Northeast |
-0.235 |
(0.281) |
Midwest |
-0.829** |
(0.220) |
South |
-0.671** |
(0.229) |
MSA Population 100,000-499,999 |
0.508** |
(0.198) |
MSA Population 500,000-999,999 |
0.548** |
(0.227) |
MSA Population 1,000,000-2,499,999 |
0.993** |
(0.211) |
MSA Population 2,500,000+ |
1.599** |
(0.214) |
Type of Industry |
Personnel Supply Services |
0.935 |
(0.601) |
Offices and Clinics of Physicians |
-0.918** |
(0.203) |
Private Households |
-2.455** |
(1.012) |
Health Services (not else where
classified) |
0.021 |
(0.227) |
Hospitals |
0.154 |
(0.147) |
Other Industries |
-0.459 |
(0.309) |
Year Dummy Variables |
1995 |
-0.092 |
(0.233) |
1996 |
-0.782** |
(0.242) |
1997 |
-1.117** |
(0.240) |
1998 |
-0.608** |
(0.250) |
1999 |
-0.328 |
(0.252) |
2000 |
-0.495** |
(0.250) |
2001 |
-0.047 |
(0.238) |
|
R-squared |
0.1057 |
N |
3,994 |
*p < 0.10
**p < 0.05
Source: Current Population Survey Outgoing
Rotation Group Files, 1994-2001
Notes: (1) the dependent variable is
created by dividing usual weekly earning
by usual hours of work per week; (2) standard
errors (in parentheses) are estimated
using the "robust" option in
Stata; and (3) all regressions include
a constant.
Market characteristics are important
predictors of wages. Compared to those
living in the Western region of the U.S.,
LPNs residing in the Midwest and South
earn lower wages. Also, LPNs in rural
areas earn lower wages than do their urban-dwelling
counterparts. The more populated an urban
area is, the higher the wage relative
to wages in rural areas. This may reflect
higher costs of living in cities, especially
in cities of 2.5 million or more.
Employment setting has some effect on
the wages of LPNs. LPNs working in physician
offices and private households have lower
wages than do LPNs working in long-term
care settings. Finally, wages in 1996-1998
and in 2000 were lower compared to wages
in 1994. Thus, there is some evidence
that inflation adjusted wages for LPNs
declined during our sample time period.
Conclusions about
Supply and Demand of LPNs
The supply of LPNs is affected by characteristics
common to other professions. Male LPNs
are not more likely to be employed, but
they tend to work more hours and are more
likely to be employed full time than are
females. LPNs reduce their participation
in the labor force after some age, the
probability of employment drops after
age 40 or 50 (depending on how the model
is specified) and the probability of full-time
work declines after LPNs reach their early
forties. Black LPNs are more likely to
work full time and tend to work more hours
than white LPNs. Likewise for LPNs living
in the South, relative to those in the
Western States. Furthermore, Midwestern
LPNs are more likely to be employed than
their counterparts in the West. LPNs who
are foreign-born are less likely to be
employed, but work more hours than do
LPNs who are US-born. LPNs with children
in their households tend to work fewer
hours. Finally, as LPN wages rise, LPNs
are more likely to work full-time. LPNs
enjoy higher earnings with experience,
until they are in their early 50s. They
also have higher wages if they have a
college degree, especially if they have
a 4-year or graduate degree. LPN earnings
vary by employment sector; the highest
earnings are enjoyed by LPNs working in
personnel supply services (such as temporary
and home health agencies), hospitals,
and long-term care facilities, and the
lowest earnings are received by those
working in private households and physician
offices.
The demand for LPNs varies with LPN wages,
wages of other nursing personnel, patient
volumes, case mix of patients, and market
characteristics. In general, demand for
LPNs drops as LPN wages rise, and demand
for LPNs rises as wages of RNs rise.
Higher patient volumes are associated
with higher demand for LPNs. In hospitals,
rising patient acuity reduces demand for
LPNs, while demand increases in long-term
care facilities with higher ADL dependency
of patients.
Hospital demand for LPN rises as the
share of patients insured by Medicaid
increases. Long-term care facility demand
for LPNs declines as the share of residents
insured by Medicaid rises, and demand
for RNs also declines. Thus, both types
of employers shift their labor demand
to the least skilled nursing personnel
possible when Medicaid is more prominent
in the patient mix. Increases in the
Medicaid reimbursement rate cause long-term
care facilities to demand more skilled
nurses.
Finally, the scope of practice of LPNs
affects demand for them. Restrictive
scopes of practice have a significant,
negative effect on hospital and long-term
care facility demand for LPNs. Demand
for LPNs also is lower in States with
more specific scopes of practice. If
States want to encourage the employment
of LPNs as substitutes for RNs, they can
liberalize the scope of practice of LPNs
to achieve this goal. However, because
there is little research indicating whether
these skill mix changes would have negative
effects on quality of care, policymakers
should tread carefully before moving in
this direction.
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[1] We refer
to these governmental authorities as “boards”
in the remainder of this chapter.
[2] The explanatory
variables in the wage equation are dummy
variables for male, citizenship status,
highest education attained, race, work
setting, type of employer, region, city
size, and year in sample, as well as age,
age squared, and the percentage of licensed
practical nurses unionized in state of
residence. The average manufacturing
wage and number of physicians per 100,000
people in the LPN’s state of residence
serve as instrumental variables.
1 We assume
that the market wages for registered nurses
and nurse aides are exogenous in our model
of hospital demand for licensed practical
nurses. While individual hospitals’
wages to nurses may indeed be simultaneously
determined with demand, market wages should
not be influenced significantly by any
single hospital’s demand for LPNs.
2 Average
ages were computed in the same way as
were market wages and merged to each observation
in the same fashion.
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