Right-to-work laws and manufacturing employment: the importance of spatial dependence.
Lacombe, Donald J.
1. Introduction
Right-to-work (RTW) laws prohibit the requirement that a person
become a union member as a condition of employment. Such a prohibition,
if effective, raises the cost of union organizing activity, leading to a
decline in union membership and thus in union bargaining power. To the
extent that this reduction in the bargaining power of unions occurs,
firms considering locating in an RTW state may expect lower wages and a
more favorable business climate than would be the case in a non-RTW
state, leading to greater employment in RTW states, all else equal.
Therefore, it is important to determine whether these laws are
effective.
Many studies have documented a negative correlation between RTW
laws and unionization rates, suggesting that RTW laws do indeed decrease
unionization rates, although some of this correlation is thought to be
due to negative public perceptions of unions in right-to-work states
rather than the effect of the laws themselves. There have also been a
few studies that have investigated the effects of RTW legislation on the
level of and growth in manufacturing employment, but the results of
these studies are mixed, with the results hinging on the particular
econometric specification used. A regression of the manufacturing
employment share on an RTW dummy variable often suffers from omitted
variable bias, and each study attempts to deal with this bias
differently. Potential omitted variables are climate, soil quality, the
availability of natural and labor resources, infrastructure, and public
attitudes toward unions and business, all of which are thought to be
determinants of employment but are not easily measured. If public
attitudes are probusiness, then omitting measures of public attitudes
may positively bias the RTW coefficient. This is because probusiness
attitudes may lead both to passage of RTW legislation and to other
probusiness policies that increase employment. Another example may be
the percentage of the population that consists of recent immigrants.
Businesses that employ recent immigrants may be more likely to vote for
RTW legislation and may employ more people. In addition, there may also
be spatial correlation in the errors, because these omitted variables
are likely correlated across counties. Weather, resources,
infrastructure, and public attitudes usually do not change abruptly at
political borders, and therefore these omitted factors would necessarily
be geographically correlated. Another possibility is that manufacturing
employment itself may be correlated across counties as a result of
agglomeration economies, which are cost savings that result when firms
locate in close proximity to one another. For example, firms may locate
close to bodies of water such as rivers to take advantage of natural
shipping routes, and any variables associated with firm activities would
be geographically correlated. Finally, measurement error is also a
possibility if the relevant unit of measurement is the city but we are
measuring our variable at the county level. (1) The potential presence
of omitted variables bias and spatial dependence and the techniques used
to address them are the primary concern of this paper.
As will be seen in the literature review below, manufacturing has
been the primary industry of interest with respect to analyzing the
effects of RTW legislation because of the high percentage of
manufacturing workers that are unionized. However, manufacturing is not
the most unionized industry. According to www.unionstats.com, several
other industries, including construction and transportation, were more
highly unionized than manufacturing in 2000. Therefore, this analysis
will also investigate the relationships between RTW laws and employment
in several different industries.
2. Literature Review
Three early studies of the effects of RTW legislation on employment
are discussed in two literature reviews by Moore and Newman (1985) and
Moore (1998). The first study by Softer and Korenich (1961) took a
simple analysis of variance approach and concluded that RTW laws do not
contribute to the expansion of a state's nonagricultural jobs and
industrial development. However, this model ignored the influence of
factors other than RTW laws on employment in a state. A later study by
Newman (1983) used multiple regression analysis to control for variables
thought to be important determinants of employment to find a significant
positive relationship between RTW legislation and relative changes in
employment, particularly for labor-intensive industries. The third
paper, also by Newman (1984), demonstrated the existence of RTW effects
and also that they diminished over time and eventually disappeared.
However, both of Newman's studies likely suffered from omitted
variables bias. In addition, none of these early studies addressed
spatial autocorrelation in either the dependent variable or the error
term.
Later studies have attempted to address these issues, however.
Perhaps the most significant attempt to deal with the problem of omitted
variables bias due to the exclusion of unmeasurable geographic
characteristics was made by Holmes (1998). He defined the problem as one
of distinguishing the effects of state policies from the effects of
other state characteristics unrelated to policy, and proposed that the
solution is to analyze what happens at the border of two counties, one
in an RTW state and the other in a non-RTW state. As Holmes noted, if
state policies are an important determinant of the location of
manufacturing, one should find an abrupt change in manufacturing
activity when one crosses a state border at which policy changes. This
is because state characteristics unrelated to policy are likely to be
the same on both sides of the border. Thus, Holmes' approach was to
focus on border counties in his analysis.
Holmes used two measures of manufacturing activity: manufacturing
employment in a county as a percentage of total private nonagricultural
employment in the county, taken from 1992 County Business Patterns (CBP)
and Census of Manufactures data, and the growth rate in manufacturing
employment over the postwar period from 1947 to 1992. Holmes regressed
manufacturing's share of total private employment on an RTW dummy and two distance functions to control for a county's proximity to
an RTW border and the side of the border on which a county is located.
Holmes generated and analyzed several specifications of the geographic
functions and found that controlling for geography affected the
estimated RTW coefficient. Holmes found that when one crosses the border
into the RTW state, the estimated average increase in manufacturing
share was approximately 6.6%, an estimate about one-third higher than
the estimate that did not control for geography.
Although Holmes dramatically improved controls for unmeasured
geographic factors, his use of ordinary least squares (OLS) to estimate
the regression coefficients and standard errors may have been
inappropriate. Holmes did not consider that the dependent variable
itself may have been spatially correlated because of agglomeration
economies or measurement error or that the residuals were spatially
correlated. If OLS is used to estimate a model where the dependent
variable is spatially correlated, the resulting coefficient estimates
will be biased and inconsistent. On the other hand, if there is evidence
of spatial correlation in the residuals of an OLS regression, the top
estimator will be unbiased but inefficient. (2) The distance functions
used by Holmes are also a concern, as they may have only crudely
approximated geographic reality (see Barry, Pace, and Sirmans 1998). If
this is the case, Holmes' error terms across counties were still
likely correlated.
Two studies since Holmes' have attempted to address the issues
of omitted variables bias and correlated errors but have been limited in
scope, focusing on only one RTW state, Idaho, and on the growth in
manufacturing employment after the implementation of an RTW law rather
than on the level of manufacturing employment at a point in time.
Wilbanks and Reed (2001) investigated the effects of RTW legislation on
manufacturing employment in Idaho following that state's adoption
of an RTW law in 1986. They performed a county-level analysis but did
not use Holmes' technique to control for geographic factors,
instead making comparisons based on alternative treatment and control
groups, and found that manufacturing employment growth was significantly
greater in Idaho than in the control groups. Unlike Holmes, they
included several variables to control for demographic and geographic
characteristics such as measures of educational attainment, population
growth prior to the adoption of RTW legislation, the share of the
population that is black, measures of industry composition, and dummy
variables indicating the urban or rural status of a county. Although not
directly concerned with spatially dependent errors, they did attempt to
address heteroskedasticity concerns caused by their dependent variable
and concerns about the independence of the error terms across
observations using robust cluster estimation, where the clusters
appeared to be Bureau of Economic Analysis Economic Areas in some
specifications and states in others. However, the potential for
spatially correlated errors remained, as these cluster analyses allowed
for correlation in the error terms within clusters but not across
clusters. In addition, their analysis did not control for agglomeration
economies or measurement error by allowing the dependent variable to be
spatially correlated across counties.
Dinlersoz and Hernandez-Murillo (2002) also attempted to determine
the effects of RTW legislation on Idaho's industrial performance as
measured by employment growth. Their method of controlling for
geographic factors was similar to that of Wilbanks and Reed (2001) in
that they used neighboring states as controls for common region-specific
factors. They found that postlaw, Idaho experienced a significant and
persistent annual growth in manufacturing employment compared to almost
zero growth in manufacturing employment in neighboring states. They also
found that the difference between prelaw and postlaw growth rates in
Idaho was significantly larger compared with other states in the region.
However, they did not include demographic controls, nor did they address
correlation in the error terms or in the dependent variable across
counties.
This paper attempts to improve upon the existing literature on the
relationship between manufacturing employment and RTW laws by better
controlling for omitted factors that may be spatially correlated. In
particular, correlations across counties in the error term and in the
dependent variable are separately addressed. In addition, several
county-level demographic characteristics likely to affect the supply of
or the demand for labor or to reflect public attitudes or tastes
regarding state policies are included. These variables describe the age
distribution, race and ethnic composition, gender composition, and
educational level of a county's population as well as measuring the
degree of urbanization or population density. The paper also improves
upon the existing literature by exploring the relationships between RTW
laws and employment in other industries besides manufacturing.
3. Model and Estimation Technique
As noted above, many studies of RTW legislation fail to adequately
control for unobserved factors that may vary systematically over space
or for possible spatial dependence in the dependent variable, a
phenomenon known as spatial autocorrelation. Spatial autocorrelation may
be formally defined as follows (Anselin and Bera 1998, p. 241):
Cov([y.sub.i],[y.sub.j]) = E([y.sub.i],[y.sub.j]) -
E([y.sub.i])E([y.sub.j]) [not equal to] 0 for i [not equal to] j,
where [y.sub.i] and [y.sub.j] are observations on a random variable
at locations i and j in space. The subscripts i and j can refer to
states, counties, or any other geographic designation. The important
point is that the observations are correlated across space. Why might we
see such correlation across observations in space? One reason mentioned
earlier with respect to employment is agglomeration economies, with
firms wishing to locate near other firms for cost savings. It may be the
case that there are "employment centers" in one county that
draw employees from surrounding counties. Alternatively, measurement
error may also cause random variables to be spatially correlated if a
city is the relevant unit of measurement but we are measuring our
variable at the county level. In either of these cases, employment is
correlated across counties. It is also possible that omitted variables
such as climate or political attitudes that are difficult or impossible
to measure may be correlated across counties. For example, a cursory look at the so-called red/blue county map showing support for the two
main political parties in the most recent presidential election bears
this out (USA Today 2006). (3)
When the dependent variable is spatially correlated, as when
employment is due to agglomeration economies, employment centers, or
measurement error, we can use what Anselin (1988, p. 35) refers to as a
mixed regressive-spatial autoregressive model given by
y = [rho] [W.sub.y] + X[beta] + [epsilon]
[epsilon] ~ N([[sigma].sup.2], ln), (1)
where y contains an n x 1 vector of the percentage employment in
manufacturing or other employment variable in a county, X is an n x k
matrix of several demographic control variables as well as an RTW dummy
variable, [epsilon] is an n x 1 error term, and 9 and [beta] are the
coefficients to be estimated. The W term represents a first-order spatial weight matrix, "which expresses for each observation (row)
those locations (columns) that belong to its neighborhood set as nonzero elements" (Anselin and Bera 1998, p. 243). (4)
It is important to note that the inclusion of the [W.sub.y] term on
the right hand side of Equation 1 introduces simultaneity bias, and
therefore the use of OLS as an estimation strategy will produce biased
and inconsistent parameter estimates (Anselin 1988, pp. 57-59).
Therefore, maximum likelihood estimation is used to estimate the
parameters in the mixed regressive-spatial autoregressive model. The log
likelihood for the model expressed in Equation 1 under the assumption of
normally distributed error terms, [epsilon], and homoskedasticity is
given by Anselin (2001, p. 320):
ln L = - (n/2) ln (2[pi])--(n/2) ln [[sigma].sup.2] + ln|I - [rho]
W| -(1/2[[sigma].sup.2]))(y - [rho] [W.sub.y] - X[beta])' (y -
[rho] [W.sub.y] - X[beta]).
The key coefficients of interest are [rho] and the coefficient on
the RTW dummy variable, as we are interested in whether or not spatial
dependence exists and in the relationship between RTW legislation and
the employment variable of interest. In particular, if [rho] is
statistically significantly different from zero there is spatial
dependence, suggesting that agglomeration economies, employment centers,
or measurement error in the dependent variable exists. If the RTW
coefficient is greater than zero, then RTW laws are positively
associated with employment. Alternatively, if the RTW coefficient is
less than zero, RTW laws are negatively associated with employment.
Henceforth, the mixed regressive spatial autoregressive model will be
referred to as the SAR model.
A second type of spatial dependence involves correlation across the
error terms. It is possible that when an econometric model is specified
and estimated, there may be variables that are omitted. With respect to
the determinants of employment, such omitted variables may include
natural resources or infrastructure, public attitudes towards unions
and/or businesses, and/or labor supply characteristics. These omitted
variables are usually subsumed into the error term and thus may bias the
RTW coefficient. If public attitudes are probusiness, then omitting
measures of public attitudes may positively bias the RTW coefficient.
This is because probusiness attitudes may lead both to passage of RTW
legislation and to other probusiness policies that increase employment.
Another example may be the percentage of the population that consists of
recent immigrants. Businesses that employ recent immigrants may be more
likely to vote for RTW legislation and may employ more people. Hence, in
this case as well the RTW coefficient may be biased upward. If these
omitted variables vary in a systematic manner over space, there may also
be spatial error dependence. For example, a supply of natural resources
may exist beyond the boundary of a single county. Alternatively, public
attitudes and/or characteristics of the supply of labor may be similar
across county lines. Anselin (1988, p. 35) defines this as the linear
regression model with a spatial autoregressive disturbance. It is given
by:
y = X[beta] + u,
u = [lambda][W.sub.u] + [epsilon],
[epsilon] ~ N(0,[[sigma].sup.2][I.sub.n]), (2)
where [beta] is defined as before but now there is no spatially
weighted y term on the right hand side and the error term is now
specified by u. In this model, the key coefficients of interest are
[lambda] and the coefficient on the RTW dummy variable. [lambda] [not
equal to] 0 implies that the errors are spatially correlated, and a
positive RTW coefficient again indicates that RTW laws are positively
associated with the employment variable. Estimation of this model via
OLS results in parameter estimates that are unbiased (as long as the
omitted variables are uncorrelated with the other included explanatory variables) but inefficient, so maximum likelihood techniques are used.
Anselin (2001, p. 320) provides the following log likelihood function
under the assumption of normally distributed error terms, [epsilon], and
homoskedasticity used in estimating the spatial error model:
ln L = -(n/2)ln(2[pi]) - (n/2)ln [[sigma].sup.2] + ln|I -[lambda]W]
- (1/2[[sigma].sup.2])(y - X[beta])'(I - [lambda]W)'(I -
[lambda]W)(y - X[beta]).
The [lambda] [W.sub.u] term in Equation 2 uses the same row
stochastic spatial weight matrix W used in the first model, but now it
defines the contiguity relationship among the error terms. Henceforth,
the model with a spatial autoregressive disturbance is referred to as
the SEM model.
4. Data
RTW legislation is thought to make it harder for unions to organize
and thus weakens their bargaining power, potentially leading to an
increase in employment in heavily unionized sectors. One such heavily
unionized sector is manufacturing. (5) Because of its high level of
unionization and the heavy emphasis on manufacturing employment in
previous studies, the primary focus of this paper is also manufacturing
employment. We define our key dependent variable to be manufacturing
employment as a percentage of total private wage and salary employment
in a county in 2000. We choose to investigate employment at a point in
time because of the cross-sectional nature of the models used to deal
with spatial correlation and the economic question of whether or not RTW
laws still matter many years after their passage, and also because
previous studies have focused on this variable. However, we also
investigate alternative dependent variables, including the number of
manufacturing employees in 2000, manufacturing employment as a
percentage of total employment (not just private), and, to a lesser
extent, the growth rate in manufacturing over the 1947-1997 period, to
determine whether results are sensitive to the way the manufacturing
employment variable is defined. The data used to construct all the
point-in-time variables were taken from the 2000 Decennial Census
Profile of Selected Economic Characteristics (U.S. Bureau of the Census 2000). The data used to construct the growth rate were taken from
various issues of the City and County Data books that are available
online from the University of Virginia's Geospatial and Statistical
Data Center. (6) Only 194 observations were available for the growth
rate model because of missing data. (7) As mentioned earlier, we employ
standard spatial econometric techniques and estimate only
cross-sectional models. Spatial panel data models and methodologies do
exist (for example, see Elhorst 2003), but we do not employ them here
for two reasons. First, the demographic data are unavailable for the
appropriate sample period. Second, the weight matrix, W, is assumed to
remain constant over time, which would pose problems for our county
level sampling technique given that counties have been added or
otherwise changed over the years (U.S. Bureau of the Census 2006) (8).
Manufacturing, however, is not the most highly unionized industry.
According to www.unionstats.com, other industries with higher
percentages of employees covered by unions in 2000 were forestry, metal
and coal mining, construction, transportation, communications, utilities
and sanitary services, theatres and motion pictures, educational
services associated with elementary and secondary schools, legal
services, labor unions, and public administration (Hirsch and Macpherson 2004). Although industries are grouped differently by www.unionstats.com
than they are in the Profile of Selected Economic Characteristics, we do
analyze employment in other industries in order to investigate whether
RTW laws play a role in any other highly unionized industries and
perhaps even affect industries that are not very unionized. The
industries we consider include: agriculture, forestry, fishing and
hunting, and mining; construction; wholesale trade; retail trade;
transportation and warehousing and utilities; information; finance,
insurance, real estate, and rental and leasing; professional,
scientific, management, administrative, and waste management services;
educational, health, and social services; arts, entertainment,
recreation, accommodation, and food services; other services (except
public administration); and public administration. For each of these
industries we examine the number of employees and industry employment as
a percentage of total employment.
The key explanatory variable is an RTW dummy variable equal to one
if a state is an RTW state and equal to zero otherwise. Issues from 1998
through 2003 of the Monthly Labor Review (U.S. Bureau of Labor
Statistics 2004) that provide yearly updates on state labor legislation
were checked to ensure the accuracy of the list of RTW states. Although
there were 22 RTW states in 2000, only 14 of these states, Arizona,
Arkansas, Idaho, Iowa, Kansas, Nebraska, Nevada, North Dakota, South
Dakota, Tennessee, Texas, Utah, Virginia, and Wyoming, are used in this
analysis because they are the only ones that border at least one non-RTW
state. The non-RTW states that border the RTW states used in our sample
are California, Colorado, Illinois, Kentucky, Maryland, Minnesota,
Missouri, Montana, New Mexico, Oklahoma, Oregon, Washington, West
Virginia, and Wisconsin.
Earlier studies have noted that it is difficult to distinguish
between the effects of an RTW policy and other probusiness policies
(see, for example, Holmes 1998). In an attempt to distinguish between
the effect of RTW legislation and just a generally business-friendly
climate, a Small Business Survival Index (SBSI) obtained from the Small
Business & Entrepreneurship Council is included along with the RTW
dummy in the manufacturing employment share regressions presented in
this paper. A higher value for this index means a less friendly business
climate. The existence of an RTW law was netted out of this index in
order to obtain a measure of other probusiness policies and/or public
attitudes toward unions. This index is comparable to the ranking of
state business climates constructed by Fantus Consulting (FANTUS) in
1975 that Holmes used, but the SBSI is much more recent than the FANTUS
ranking. (9)
The SBSI may be an imperfect measure of business climate, however,
so there may still be omitted variables bias. One technique for dealing
with omitted variables bias is to include as many relevant regressors on
the right-hand side of a regression as are available. Therefore, several
other county-level population characteristics are included as
explanatory variables to control for characteristics of a county's
labor supply and for public attitudes towards unions or business in
general that, if omitted, might bias the RTW coefficient. These include
the size of the population, the percentage of the population aged 18-64,
the percentage of the population aged 25 and over whose highest level of
educational attainment is a bachelor's degree, the percentage of
the population that is female, the percentage of the population that is
Hispanic, the percentage of the population that is nonwhite, the
percentage of the population that speaks a language other than English at home, the population per square mile, and the mean travel time to
work for individuals aged 16 and over. The spatial techniques we use to
control for spatially correlated omitted variables also help eliminate
omitted variable bias, at least that portion due to spatially correlated
omitted variables. Although we cannot be certain that we have eliminated
all omitted variables bias, we believe we have made greater progress
than previous studies in controlling for omitted variables.
Table 1 provides the descriptive statistics for the variables used
in the analysis. Note that, on average, manufacturing accounted for over
18% of a county's private wage and salary employment in 2000 and
that just over 52% of the border counties were located in RTW states.
Data sources are contained in the Appendix.
5. Results
Table 2 shows the estimated relationships between manufacturing as
a percentage of private wage and salary employment and the existence of
an RTW law in a state in 2000. These estimates are based on data for the
427 counties that lie on the borders between RTW and non-RTW states.
Results for three different specifications are shown. The first column
of results shows the OLS estimates and the second and third columns of
results show those from the SAR and SEM models, respectively. Recall
that the SAR and SEM models apply spatial techniques and are estimated
via maximum likelihood.
The OLS coefficient on the RTW dummy variable in Column 1 is
positive and statistically significant at the 1% level, indicating that
states with right to work laws have higher manufacturing employment by
over 3% on average, all else equal. The estimated SAR coefficient on the
RTW dummy variable in Column 2 is also positive and significant at the
5% level, but the estimated effect is much smaller at 1.63%. Note that
the estimated [rho] is positive and highly significant, indicating the
existence of spatial dependence. The estimated SEM coefficient on the
RTW dummy variable in Column 3 is also positive and significant at the
1% level and falls between the OLS and SAR estimates at 2.12%. The
estimated [lambda]. is positive and highly significant, also indicating
the existence of spatial dependence.
Given that both the SAR and SEM models account for spatial
correlation, how do we choose which model to use? A general spatial
model that combines the two models by including both a spatially lagged
dependent variable and a spatial error component could theoretically be
utilized along with standard Lagrange multiplier (LM) diagnostic tests.
Such a model is given by the following:
y = [rho] [W.sub.1y] + X[beta] + u,
u = [lambda] [W.sub.2] u + [epsilon],
[epsilon] ~ N(0,[[sigma].sup.2] [I.sub.n]). (3)
In this model, setting [rho] = [lambda] = 0 results in the familiar
OLS specification. Allowing [lambda] = 0 results in the SAR model,
whereas setting [rho] = 0 results in the SEM model. However, as noted in
Anselin and Bera (1998, p. 252), the estimation of the general spatial
model in Equation 3 can lead to identification issues in that the [rho
and [lambda] parameters cannot be separately identified. We avoid this
problem by following the standard procedure in the spatial econometrics literature, which is to estimate the SAR and SEM models separately and
utilize LM diagnostic tests to assist in model choice. (10) This
methodology consists of first estimating the OLS specification and then
testing for spatial error correlation using an LM test, where the null
hypothesis [H.sub.0]: [lambda] = 0 is tested against the alternative
hypothesis [H.sub.a]: [lambda] [not equal to] 0. Under the null
hypothesis, the LM test statistic is distributed as [chi square] with
one degree of freedom. If spatial error correlation is detected in the
residuals, we first try to correct for this by employing the SAR model,
as Anselin (1988, Chapter 8) notes that spatial error correlation may be
due to a spatially correlated dependent variable. Using an LM test, we
then test the residuals of the SAR model to determine if any spatial
error correlation remains. Again, the null hypothesis [H.sub.0]:
[lambda] = 0 is tested against the alternative hypothesis [H.sub.a]:
[lambda] [not equal to] 0. The LM test statistic is once again
distributed as [chi square] with one degree of freedom under the null
hypothesis. If there is still spatial error dependence, we estimate the
SEM model. For the models estimated in Table 2, the LM test statistic is
highly significant for the OLS specification, indicating that spatial
error dependence is a concern. A similar conclusion is drawn from the LM
test associated with the SAR model, in that spatial error dependence is
still present even after including a spatially lagged dependent
variable. Therefore, the SEM model would be the most appropriate model.
Thus, we estimate the relationship between RTW legislation and
manufacturing employment as a percentage of private wage and salary
employment to be approximately 2.12%, the SEM estimate.
Table 3 shows the results of analyses based on alternative
dependent variables. Given the sheer number of models estimated (168 in
all), only the estimates of the RTW coefficients are presented, although
the full set of results is available from the authors. The first three
rows are based on alternatives to the manufacturing as a percentage of
private wage and salary employment variable. The first of these is the
absolute number of manufacturing employees in 2000. Such a variable was
suggested by an anonymous reviewer because of the question of whether
RTW laws actually influence the absolute level of employment or just the
industrial mix of employment within a state. This variable is not
significantly affected by RTW legislation in any of the three
specifications. The next variable, however, is significantly related to
RTW legislation in all specifications. It is manufacturing employment as
a percentage of total employment. The preferred SEM estimate is 1.86 and
is highly significant and smaller in magnitude than the OLS estimate.
The third alternative manufacturing variable, the growth rate in
manufacturing employment in 1947-1997, is not statistically related to
RTW legislation in any specification. However, these results must be
viewed cautiously, because demographic controls were unavailable for
this specification.
The rest of the estimates in Table 3 are for measures of employment
in other industries and for total employment. Given the results for
manufacturing and the fact that none of the absolute employment
variables is significantly related to RTW legislation, it is likely that
rather than having an absolute effect on employment, RTW legislation
affects only the industrial mix. In fact, if one adds up both the
statistically significant and not statistically significant estimated
coefficients on the RTW dummy for all the percentages of total
specifications, the sum is zero. With respect to the other industry
estimates there are several significant results. Agriculture, forestry,
fishing and hunting, and mining employment as a percentage of total
employment is negatively and marginally statistically significant in all
specifications, a result that suggests that perhaps RTW laws are related
to a movement away from agriculture, an industry that is not very
unionized. However, this is a very broad census category that includes
mining, a highly unionized industry that may be positively affected by
RTW legislation, potentially masking a much larger negative effect on
agriculture. Unfortunately we are limited by the broad census
categories. On the other hand, information industry employment as a
percentage of total employment is positive and statistically
significantly related to the existence of an RTW law in all
specifications, suggesting that this industry may also benefit from RTW
laws, although the coefficient estimate is small, only 0.2% in the SEM
model. Similarly, employment in the professional, scientific,
management, administrative and waste management services industry as a
percentage of total employment is also positively and significantly
affected by the existence of an RTW law. This makes sense given that
some types of workers in this broad category are highly unionized.
However, because the category is so broad and also includes several
types of workers with low unionization rates, this may explain the small
coefficient estimate on the RTW variable of 0.25 in the SEM model.
Finally, employment in the other services (except public administration)
industries as a percentage of total employment is negatively associated
with RTW legislation, a result suggesting that RTW legislation may steer local employment away from services and towards manufacturing, although
the effect is small, -0.23% in the SEM model. A result that is
surprising, however, is that no relationship between RTW legislation and
employment is found in other heavily unionized industries such as
construction, transportation and warehousing, and utilities. However,
such insignificant results may help explain why the literature has
primarily focused on manufacturing.
Table 4 shows the results from LM tests to ascertain whether or not
spatial dependence exists in the errors of the OLS and SAR
specifications for the alternative outcome models. The vast majority of
the models tested reject the null hypothesis [H.sub.0]: [lambda] = 0
against the alternative hypothesis [H.sub.a]: [lambda] [not equal to] 0
for both the OLS and SAR specifications, thereby justifying the use of
the SEM model for purposes of inference.
6. Conclusion
RTW laws are thought to decrease the power of unions and thus to
attract manufacturing employment to a state. Previous evidence on the
effectiveness of these laws is mixed, although many recent studies
suggest that RTW laws do positively affect employment. However, many of
these studies suffer from omitted variable bias because of unmeasurable
geographic characteristics such as public attitudes or natural or labor
resources. In addition, failure to correct for spatial autocorrelation
can result in coefficient estimates that are both biased and
inconsistent. Our estimates that do not account for geographically
correlated omitted factors dramatically overstate the positive
relationship between RTW legislation and manufacturing employment. When
we do control for geographically correlated omitted factors, we estimate
that RTW legislation is associated with an increase in
manufacturing's share of private wage and salary employment of
2.12%, an estimate almost 30% lower than the estimate that does not
control for these spatially correlated omitted factors. Results for
other industries indicate that right to work legislation is negatively
associated with employment shares in the agriculture, forestry, fishing
and hunting, and mining industries and in some service industries but
positively associated with employment shares in the information and
professional, scientific, management, administrative, and waste
management services industries. Improperly controlling for geographic
factors can lead to incorrect inferences and misinform policy.
Appendix
Data Sources
Variable Description
2000 manufacturing employment as
a percentage of total private wage and
salary employment
Growth rate in manufacturing employment
1947-1997
2000 percentage of population aged 18-64
2000 percentage of population that is female
2000 percentage of population that is
Hispanic or Latino
2000 percentage of population that is
nonwhite
2000 percentage of population aged 25 or
above with at least a high school degree
2000 percentage of population aged 25 or
above with a bachelor's degree
2000 percentage of population that speaks
a language other than English at home
2000 persons per square mile
2000 mean travel time to work in minutes
for persons aged 16 +
2000 Small Business Survival Index
2000 right-to-work dummy variable
Source
2000 Decennial Census Summary File 4 Sample
Data: Profile of Selected Economic
Characteristics. U.S. Bureau of the Census.
City and County Data Books. http://
fisher.lib.virginia.edu/collections/stats/ccdb.
Computed using total population and
populations under 18 and 65 and older. State
and County Quick Facts. U.S. Bureau of the
Census. http://quickfacts.census.gov/qfd/.
http://quickfacts.census.gov/qfd/.
Subtracted percentage white from 100. http://
quickfacts.census.gov/qfd/.
http://quickfacts.census.gov/qfd/.
http://quickfacts.census.gov/qfd/.
http://quickfacts.census.gov/qfd/.
http://quickfacts.census.gov/qfd/.
http://quickfacts.census.gov/qfd/.
Small Business and Entrepreneurship
Council. http://www.sbsc.org/index.asp.
Right-to-work status was determined by
checking 1998-2003 issues of the Monthly
Labor Review published by the Bureau of
Labor Statistics, http://www.bls.gov/opub/
mlr/archive.htm
The authors gratefully acknowledge invaluable comments from Barry
Hirsch, Jim LeSage, and an anonymous reviewer, and would also like to
thank Tatevik Sekhposyan and Nicholas Prala for their excellent research
assistance.
Received February 2005; accepted March 2006.
References
Anselin, Luc. 1988. Spatial econometrics. Methods and models.
Dordrecht, Netherlands: Kluwer Academic Publishers.
Anselin, Luc. 2001. Spatial econometrics. In A companion to
theoretical econometrics, edited by Badi Baltagi. Oxford, England:
Blackwell, pp. 310-30.
Anselin, Luc, and Anil Bera. 1998. Spatial dependence in linear
regression models with an introduction to spatial econometrics. In
Handbook of applied economic statistics, edited by A. Ullah and D. E. A.
Giles. New York: Marcel Dekker, pp. 237-89.
Barry, Ronald, R. Kelley Pace, and C. F. Sirmans. 1998. Spatial
statistics and real estate. Journal of Real Estate Finance and Economics
17:5-13.
Dinlersoz, Emin M., and Ruben Hernandez-Murillo. 2002. Did
'right-to-work' work for Idaho? Federal Reserve Bank of St.
Louis Review 84:29-41.
Elhorst, J. Paul. 2003. Specification and estimation of spatial
panel data models. International Regional Science Review 26:244-58.
Garrett, Thomas A., and Thomas L. Marsh. 2002. The revenue impacts
of cross-border lottery shopping in the presence of spatial
autocorrelation. Regional Science and Urban Economics 32:501-19.
Hirsch, Barry T., and David A. Macpherson. "Union Membership
and Coverage Database from the CPS (Documentation)." Accessed 3
March 2004. Available http://www.unionstats.com.
Holmes, Thomas J. 1998. The effect of state policies on the
location of manufacturing: Evidence from state borders. Journal of
Political Economy 106:667 705.
LeSage, James P. 1997. Regression analysis of spatial data. The
Journal of Regional Analysis and Policy 27:83-94.
Moore, William J. 1998. The determinants and effects of
right-to-work laws: A review of the recent literature. Journal of Labor
Research 19:445-69.
Moore, William J., and Robert J. Newman. 1985. The effects of
right-to-work laws: A review of the literature. Industrial and Labor
Relations Review 38:571-85.
Newman, Robert J. 1983. Industry migration and growth in the South.
Review of Economics and Statistics 65:76-86.
Newman, Robert J. 1984. Growth in the American South. New York: New
York University Press.
Soffer, Benson, and Michael Korenich. 1961. 'Right to
work' laws as a location factor: The industrialization experience
of agricultural states. Journal of Regional Science 3:41-56.
U.S. Bureau of the Census. "Profile of Selected Economic
Characteristics: 2000." Accessed 22 June 2004. Available
http://factfinder.census.gov/servlet/QTGeoSearchByListServlet?
ds_name=DEC_2000_SF4_U&_lang=en&_ts=163693878929.
U.S. Bureau of the Census. "Significant Changes to Counties
County Equivalent Entities: 1970 Present." Accessed 2 January 2006.
Available http://www.census.gov/geo/www/tiger/ctychng.html.
U.S. Bureau of Labor Statistics. Monthly Labor Review, Various
issues. Accessed 22 June 2004. Available http://
www.bls.gov/opub/mlr/archive.htm.
University of Virginia Geospatial and Statistical Data Center.
County and City Data Books, Accessed 2 January 2006. Available
http://fisher.lib.virginia.edu/collections/stats/ccdb.
USA Today. "Election 2004: Latest Vote, County by
County." Accessed 2 January 2006. Available http://
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Wilbanks, James R., and W. Robert Reed. 2001. The impact of
right-to-work on state economic development: Evidence from Idaho.
Unpublished paper, University of Oklahoma.
(1) Anselin (1988, p. 12) provides an example of measurement error.
(2) Anselin (1988) provides all of the appropriate mathematical
derivations.
(3) For a graphical depiction of this county map, please see
http://www.usatoday.com/news/politicselections/vote2004/countymap.htm.
(4) An excellent introduction to spatial econometric techniques can
be found in LeSage (1997). All MATLAB code used to estimate the models
in this paper is available from Jim LeSage's website, found at
www.spatial-econometrics.com.
(5) Data on unionization rates are available from
www.unionstats.com.
(6) Please see http://fisher.lib.virginia.edu/collections/stats/ccdb for details.
(7) A number of growth rate models were estimated using different
growth measures and different measures of manufacturing employment.
Regardless of the methodology used to calculate the growth variable, the
results were similar.
(8) One example is the creation of Cibola County, NM, which was
created in part by taking land from Valencia County, NM on June 19,
1981. Please see http://www.census.gov/geo/www/tiger/ctychng.html for
further examples.
(9) The FANTUS ranking and the SBSI are similar measures. However,
the FANTUS ranking is from 1975 whereas the SBSI is from the year 2000.
(10) For an example of a similar diagnostic methodology to the one
outlined here, please see Garrett and Marsh (2002).
Charlene M. Kalenkoski, Department of Economics, Ohio University,
Bentley Annex 351. Athens, OH 45701, USA; E-mail kalenkos@ohio.edu.
Donald J. Lacombe, Department of Economics, Ohio University,
Bentley Annex 345, Athens, OH 45701, USA; E-mail lacombe@ohio.edu;
corresponding author.
Table 1. Descriptive Statistics
for Variables (N = 427)
Variable Description (a) Mean Minimum
Manufacturing employment 2728.21 2
Manufacturing employment as
a percentage of
private wage and salary
employment 18.441 0.717
Manufacturing
employment as
a percentage of
total employment 13.40 0.398
Growth rate in
manufacturing
employment 1947-1997 (b) 1983.43 -92.65
Agriculture, forestry,
fishing and hunting,
and mining employment 759.95 61
Agriculture, forestry,
fishing and hunting,
and mining employment
as a percentage
of total employment 10.83 0.16
Construction employment 1804.27 17
Construction employment
as a percentage
of total employment 7.46 2.17
Wholesale trade employment 736.71 2
Wholesale trade
employment as
a percentage of
total employment 2.81 0.52
Retail trade employment 2844.96 13
Retail trade
employment as
a percentage
of total employment 11.32 3.10
Transportation
and warehousing
employment and
utilities employment 1280.88 15
Transportation
and warehousing
employment and
utilities employment
as a percentage of
total employment 5.70 1.64
Information employment 757.19 0
Information employment
as a percentage
of total employment 1.79 0
Finance, insurance,
real estate, and rental
and leasing employment 1425.68 0
Finance, insurance,
real estate, and rental
and leasing employment
as a percentage
of total employment 4.22 0
Professional, scientific,
management,
administrative,
and waste management
services employment 2183.40 0
Professional, scientific,
management,
administrative,
and waste management
services employment as
a percentage of
total employment 4.68 0
Educational, health
and social services
employment 4620.04 29
Educational, health
and social services
employment as a
percentage of total
employment 20.32 6.55
Arts, entertainment,
recreation,
accommodation,
and food services
employment 2278.59 8
Arts, entertainment,
recreation,
accommodation,
and food services
employment as a
percentage of total
employment 7.36 1.14
Other services
(except public
administration)
employment 1229.29 10
Other services
(except public
administration)
employment as
a percentage of
total employment 4.83 1.84
Public administration
employment 1533.36 17
Public administration
employment as
a percentage of
total employment 5.27 1.58
Percentage of population
aged 18-64 58.645 48.50
Percentage of population
aged 25 or above with
a bachelor's degree 15.948 5.40
Percentage of population
who are female 50.331 37.20
Percentage of population
who are Hispanic or
Latino 7.931 0.10
Percentage of population
age 25 or above who
are high school
graduates or higher 77.810 46.10
Percentage of the population
nonwhite 11.24 0.30
Percentage of population
that speaks language
other than English at home 9.446 1.10
Persons per square mile 81.168 0.30
Small business survival index 41.285 24.88
Mean travel time to work
in minutes for
persons age 16+ 21.299 10.80
Right to work (RTW)
dummy variable 0.522 0
Standard
Variable Description (a) Maximum Deviation
Manufacturing employment 84,166 6674.80
Manufacturing employment as
a percentage of
private wage and salary
employment 46.176 11.467
Manufacturing
employment as
a percentage of
total employment 35.217 8.915
Growth rate in
manufacturing
employment 1947-1997 (b) 56,208 5997.14
Agriculture, forestry,
fishing and hunting,
and mining employment 13,063 918.52
Agriculture, forestry,
fishing and hunting,
and mining employment
as a percentage
of total employment 56.66 9.08
Construction employment 62,115 5472.19
Construction employment
as a percentage
of total employment 20.41 2.37
Wholesale trade employment 27174 2258.10
Wholesale trade
employment as
a percentage of
total employment 7.23 1.07
Retail trade employment 84,460 8102.52
Retail trade
employment as
a percentage
of total employment 26.90 2.34
Transportation
and warehousing
employment and
utilities employment 46,776 3864.32
Transportation
and warehousing
employment and
utilities employment
as a percentage of
total employment 17.25 1.74
Information employment 36,721 3041.98
Information employment
as a percentage
of total employment 10.23 1.05
Finance, insurance,
real estate, and rental
and leasing employment 43,631 4805.91
Finance, insurance,
real estate, and rental
and leasing employment
as a percentage
of total employment 14.16 1.63
Professional, scientific,
management,
administrative,
and waste management
services employment 112,036 9145.03
Professional, scientific,
management,
administrative,
and waste management
services employment as
a percentage of
total employment 23.47 2.64
Educational, health
and social services
employment 140,063 12,873
Educational, health
and social services
employment as a
percentage of total
employment 45.06 4.58
Arts, entertainment,
recreation,
accommodation,
and food services
employment 191,596 10,629
Arts, entertainment,
recreation,
accommodation,
and food services
employment as a
percentage of total
employment 30.06 3.90
Other services
(except public
administration)
employment 34,428 3827.73
Other services
(except public
administration)
employment as
a percentage of
total employment 8.08 1.01
Public administration
employment 65,619 5838.18
Public administration
employment as
a percentage of
total employment 26.92 3.05
Percentage of population
aged 18-64 79 4.106
Percentage of population
aged 25 or above with
a bachelor's degree 60.20 7.375
Percentage of population
who are female 55.60 1.738
Percentage of population
who are Hispanic or
Latino 78.20 13.059
Percentage of population
age 25 or above who
are high school
graduates or higher 95.30 8.920
Percentage of the population
nonwhite 83.60 12.387
Percentage of population
that speaks language
other than English at home 74.10 12.24
Persons per square mile 7323.30 403.157
Small business survival index 52.15 7.024
Mean travel time to work
in minutes for
persons age 16+ 39.70 5.45
Right to work (RTW)
dummy variable 1 0.5
(a) All data are for 2000 except for growth rate in manufacturing
employment 1947-1997.
(b) Based on 194 observations instead of 427 because of missing
values.
Table 2. Manufacturing as a Percentage of Private Wage and
Salary Employment Regression Results for the OLS, SAR, and
SEM Models
Independent OLS SAR
Variable Estimates Estimates
Constant -95.49 (-3.68) *** -31.35 (-1.82) *
Right-to-work
dummy variable 3.02 (3.07) *** 1.63 (2.49) **
Small Business
Survival Index 0.20 (2.83) *** 0.09 (1.85) *
Population 0.00 (1.40) 0.00 (1.27)
Percentage of
population aged
18-64 0.89 (4.99) *** 0.27 (2.21) **
Percentage of
population age 25 or
above with a
bachelor's degree -0.68 (-5.18) *** -0.27 (-3.14) ***
Percentage of
population who are
female 1.64 (5.14) *** 0.52 (2.43) **
Percentage of
population who are
Hispanic or Latino -0.20 (-2.18) ** -0.04 (-0.67)
Percentage of
population age 25 or
above who are
high school
graduates or higher -0.20 (-2.13) ** -0.07 (-1.04)
Percentage of
the population
nonwhite -0.13 (-2.42) ** -0.07 (-1.97) **
Percentage of
population that
speaks language
other than
English at home 0.02 (0.14) 0.05 (0.58)
Persons per
square mile 0.00 (1.120 0.001 (0.62)
Mean travel time
to work in
minutes for
persons age 16+ -0.07 (-0.70) -0.02 (-0.30)
Rho 0.76 (21.46) ***
Lambda
Adjusted [R.sub.2] 0.3282 0.3672
LM test-[H.sub.0]:
[lambda]=0 324.34 *** 5380.24 ***
Log-likelihood -1269.8383
Independent SEM
Variable Estimates
Constant -4.72 (-0.25)
Right-to-work
dummy variable 2.12 (2.65) ***
Small Business
Survival Index 0.09 (1.49)
Population 0.00 (1.28)
Percentage of
population aged
18-64 0.34 (2.42) **
Percentage of
population age 25 or
above with a
bachelor's degree -0.19 (-1.99) **
Percentage of
population who are
female 0.58 (2.62) ***
Percentage of
population who are
Hispanic or Latino 0.03 (-3.53) ***
Percentage of
population age 25 or
above who are
high school
graduates or higher -0.33 (-3.53) ***
Percentage of
the population
nonwhite -0.11 (-2.30) **
Percentage of
population that
speaks language
other than
English at home 0.01 (0.06)
Persons per
square mile -0.001 (-0.62)
Mean travel time
to work in
minutes for
persons age 16+ -0.05 (-0.55)
Rho
Lambda 0.84 (28.32) ***
Adjusted [R.sub.2] 0.7231
LM test-[H.sub.0]:
[lambda]=0
Log-likelihood -1265.2561
t-statistics are in parentheses.
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.
Table 3. Alternative Outcome Regressions: Estimated
Right to Work Coefficients (a)
Outcome (b) OLS Estimate SAR Estimate
Manufacturing
employment 315.15 (1.07) 329.83 (1.14)
Manufacturing
employment as
a percentage of
total employment 2.58 (3.39) *** 1.48 (2.94) ***
Growth rate in
manufacturing
employment
1947-1997 (c) 1168.91 (1.36) 1177.60 (1.38)
Agriculture,
forestry, fishing
and hunting,
and mining employment -99.27 (-1.33) -83.25 (-1.17)
Agriculture,
forestry, fishing
and hunting, and
mining employment as
a percentage of
total employment -1.30 (-1.77) * -1.16 (-1.95) *
Construction employment 29.79 (0.24) 35.77 (0.29)
Construction
employment as
a percentage of
total employment -0.26 (-1.31) -0.18 (-1.00)
Wholesale trade employment 8.54 (0.13) 12.43 (0.19)
Wholesale trade
employment as
percentage of total
employment 0.11 (1.00) 0.10 (0.99)
Retail trade employment 131.43 (1.47) 130.33 (1.48)
Retail trade
employment as
a percentage of
total employment 0.07 (0.31) 0.06 (0.29)
Transportation
and warehousing
employment and
utilities
employment 67.98 (0.83) 69.67 (0.87)
Transportation
and warehousing
employment and
utilities
employment as a
percentage of total
employment 0.04 (0.23) 0.02 (0.12)
Information employment 99.00 (0.69) 104.47 (0.74)
Information
employment as
a percentage of
total employment 0.22 (2.68) *** 0.19 (2.55) **
Finance, insurance,
real estate, and
rental and leasing
employment -34.87 (-0.26) -38.91 (-0.29)
Finance, insurance,
real estate, and
rental and leasing
employment as
a percentage of
total employment -0.12 (-0.85) -0.10 (-0.77)
Professional,
scientific,
management,
administrative,
and waste
management services
employment 71.65 (0.18) 89.44 (0.24)
Professional, scientific,
management,
administrative,
and waste management
services employment
as a percentage of
total employment 0.28 (1.96) * 0.27 (2.07) **
Educational, health,
and social
services employment -315.11 (-1.29) -358.87 (1.50)
Educational,
health, and social
services employment
as a percentage
of total employment -0.58 (-1.34) -0.32 (-0.82)
Arts, entertainment,
recreation,
accommodation,
and food services
employment 324.72 (0.49) 267.34 (0.41)
Arts, entertainment,
recreation,
accommodation, and
food services
employment as a
percentage of total
employment
Other services
(except public
administration) -0.69 (-1.92) * -0.44 (-1.41)
employment -86.84 (-0.96) -88.11 (-0.99)
Other services
(except public
administration)
employment as
a percentage of total
employment -0.23 (-2.26) ** -0.21 (-2.17) **
Public administration
employment -10.79 (-0.04) 17.50 (0.06)
Public administration
employment as
a percentage of total
employment -0.12 (-0.50) -0.09 (-0.41)
Total employment 501.39 (0.57) 427.26 (0.49)
Outcome (b) SEM Estimate
Manufacturing
employment 329.71 (1.06)
Manufacturing
employment as
a percentage of
total employment 1.86 (3.02) ***
Growth rate in
manufacturing
employment
1947-1997 (c) 1207.95 (1.40)
Agriculture,
forestry, fishing
and hunting,
and mining employment -101.40 (-1.29)
Agriculture,
forestry, fishing
and hunting, and
mining employment as
a percentage of
total employment -1.26 (-1.77) *
Construction employment -29.18 (-0.31)
Construction
employment as
a percentage of
total employment -0.04 (-0.20)
Wholesale trade employment 8.54 (0.12)
Wholesale trade
employment as
percentage of total
employment 0.11 (0.93)
Retail trade employment 138.53 (1.51)
Retail trade
employment as
a percentage of
total employment 0.04 (0.18)
Transportation
and warehousing
employment and
utilities
employment 49.42 (0.68)
Transportation
and warehousing
employment and
utilities
employment as a
percentage of total
employment 0.03 (0.16)
Information employment 78.75 (0.51)
Information
employment as
a percentage of
total employment 0.20 (2.37) **
Finance, insurance,
real estate, and
rental and leasing
employment -43.69 (-0.32)
Finance, insurance,
real estate, and
rental and leasing
employment as
a percentage of
total employment -0.17 (-1.20)
Professional,
scientific,
management,
administrative,
and waste
management services
employment 14.73 (0.04)
Professional, scientific,
management,
administrative,
and waste management
services employment
as a percentage of
total employment 0.25 (1.70) *
Educational, health,
and social
services employment -316.35 (-1.38)
Educational,
health, and social
services employment
as a percentage
of total employment -0.26 (-0.59)
Arts, entertainment,
recreation,
accommodation,
and food services
employment 273.89 (0.52)
Arts, entertainment,
recreation,
accommodation, and
food services
employment as a
percentage of total
employment
Other services
(except public
administration) -0.54 (-1.47)
employment -101.47 (-1.07)
Other services
(except public
administration)
employment as
a percentage of total
employment -0.23 (-2.19)**
Public administration
employment -50.75 (-0.16)
Public administration
employment as
a percentage of total
employment -0.10 (-0.40)
Total employment 500.13 (0.57)
t-statistics are in parentheses.
(a) The full set of regression coefficients is available
from the authors upon request.
(b) All data are for 2000 except for growth rate in
manufacturing employment 1947-1997.
(c) Based on 194 observations because of missing values.
No demographic variables are included for this specification
because of a lack of data.
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.
Table 4. Specification Tests for Spatial
Autocorrelation in Alternative Outcome Regressions (a)
LM Test LM Test
Statistic Statistic
for OLS for SAR
Models Models
([H.sub.0]: ([H.sub.0]:
Outcome (b) [lambda] = 0 [lambda] = 0
Manufacturing employment 9.34 *** 18.56 ***
Manufacturing employment
as a percentage of total
employment 327.81 *** 5128.74 ***
Growth rate in
manufacturing
employment
1947-1997 (c) 0.09 26.54 ***
Agriculture, forestry,
fishing and hunting,
and mining
employment 20.27 *** 101.14 ***
Agriculture, forestry,
fishing and
hunting, and mining
employment as a
percentage of
total employment 171.52 *** 1531.74 ***
Construction employment 22.64 *** 27.35 ***
Construction employment
as a percentage
of total employment 69.44 *** 450.43 ***
Wholesale trade employment 8.44 *** 11.63 ***
Wholesale trade employment
as a percentage of total
employment 10.41 *** 230.27 ***
Retail trade employment 3.84 ** 3.90 **
Retail trade employment
as a percentage of
total employment 19.91 *** 297.74
Transportation and
warehousing employment
and utilities
employment 8.10 *** 8.60 ***
Transportation and
warehousing employment
and utilities
employment as a percentage
of total employment 44.49 *** 676.14 ***
Information employment 1.11 6.90 ***
Information employment
as a percentage of
total employment 13.18 *** 47.07 ***
Finance, insurance, real
estate, and rental
and leasing
employment 1.64 1.58
Finance, insurance,
real estate, and
rental and leasing
employment as a percentage
of total employment 44.48 *** 272.77 ***
Professional, scientific,
management,
administrative, and
waste management
services employment 0.11 2.14
Professional, scientific,
management,
administrative, and
waste management services
employment as a
percentage of
total employment 15.67 *** 23.17 ***
Educational, health,
and social services
employment 0.99 4.81 **
Educational, health,
and social services
employment as
a percentage of
total employment 77.62 *** 778.29 ***
Arts, entertainment,
recreation,
accommodation and food
services employment 13.71 *** 27.08 ***
Arts, entertainment,
recreation, accommodation
and food services
employment
as a percentage of
total employment 93.00 *** 870.03 ***
Other services (except
public administration)
employment 0.02 0.08
Other services (except
public administration)
employment as a
percentage of total
employment 6.31 ** 96.30 ***
Public administration
employment 0.94 14.12 ***
Public administration
employment as a
percentage of total
employment 74.95 *** 611.14 ***
Total employment 0.01 0.04
(a) The LM test statistic is distributed chi-square with one
degree of freedom under the null hypothesis.
(b) All data are for 2000 except for growth rate in
manufacturing employment 1947-1997.
Based on 194 observations because of missing values. No
demographic variables are included because of a lack of data.
* Significant at the 10% level.
** Significant at the 5% level.
*** Significant at the 1% level.