Do economic effects justify the use of fiscal incentives?
Murray, Matthew N.
1. Introduction
Recruitment of large industrial and commercial facilities is a key
aspect of most local development strategies. Policy makers presumably act on the assumption that new sitings will create positive shocks and
significant net benefits for the local community. It is this premise
that justifies the significant inducements and incentives that appear
very large relative to the magnitude of the locating firm. (1)
Economists have dabbled around the margin of this debate. Oates and
Schwab (1991), for example, argue that interjurisdictional fiscal
competition is efficiency enhancing by bringing business tax payments in
closer alignment with services received from the public sector. Inman and Rubinfeld (1996), on the other hand, argue that competition for
mobile capital may lead to a local public sector that is too small. This
paper explores the question of whether large, mobile companies, the
typical targets of aggressive industrial recruitment activities and
recipients of lucrative incentives, have positive net impacts on
regional economics. In the spirit of Jacobson, LaLonde, and Sullivan
(1993), we apply the tools of the program evaluation literature to
address this question.
There are several reasons why the net economic effect of a new
location could in fact be well short of the direct (or gross) employment
and investment effects that are the basis for granting concessions.
First, concessions erode the fiscal gains of location. Tax incentives
limit revenue gains, and other concessions (such as site acquisition and
development) require expenditure of public funds. Foregone tax revenues
and higher public expenditures mean that state and local governments
must either provide fewer public services or impose higher taxes on
existing industry and residents to maintain balanced budgets.
Second, a significant new location can easily crowd out other
economic activity. For example, Porter (1999) found that megasports
events, specifically Super Bowls, have no real effects on spending in
host communities, a result he attributes in part to the crowding-out
phenomenon. The 1996 Atlanta Olympics provides another example. Spending
by Olympic patrons created significant short-term jobs and income. At
the same time, the Olympics crowded out other economic activity, as
fewer other special events were held during the summer of 1996 and many
local businesses not tied to the Olympics lost sales. In fact, anecdotal
evidence suggests more than complete crowding out since the total number
of visitors to Atlanta during 1996 was down 8.8% from 1995. (2) Also,
wages were driven up by labor shortages during both the construction and
operational phases. (3)
More generally, the location of a large company can crowd out other
economic activity by shifting sales from existing firms, congesting
local infrastructure, and raising prices in factor markets; Porter
(1999) noted the possibility of diminishing returns in production. The
factor market effects can be pronounced, especially for existing firms
that produce in highly competitive national or international markets.
Further, large companies (like Mercedes Benz and BMW) may prefer
locations without other significant, visible firms sited in the same
jurisdiction so that they can dominate the business and civic community.
(4) Thus, the incentive for other firms to locate may be diminished by
the preexisting presence of large firms in an area.
The business location literature has focused on the ways in which
market forces and public policies influence firm location, investment,
and job creation. (5) This paper reverses the focus of previous research
by modeling the discrete location behavior of large firms with an eye on
estimating the net impact of location on traditional measures of
regional economic growth. Although our reduced form model does not
explicitly take into account the concessions that are granted to firms
nor the specific channels through which firm location positively or
negatively influences regional economic performance, we are nonetheless
able to identify the net effect of large firm location on regional
income and employment. Panel data techniques and extensive statistical
tests are applied to a primary database of large companies that made
location decisions in the 1980s. We control for nonrandom site selection
on the part of firms and nonrandom company selection on the part of
communities in order to estimate the time path and magnitude of net
economic effects on host communities. The primary finding is that the
location of a large firm has no measurable net economic effect on local
economies when the entire dynamic of location effects is taken into
account. Thus, the siting of large firms that are the target of
aggressive recruitment efforts fails to create positive private sector
gains and likely does not generate significant public revenue gains
either.
The next section of the paper details the model of firm and
community selection leading to an empirical model of regional economic
growth. An important aspect of this model is the discrete and
noncompetitive process that typifies the siting of large firms. This is
followed by discussion of the data used in the empirical model.
Empirical findings are then reported and discussed.
2. Conceptual Framework
Economic growth and decline occur as firms locate, expand,
contract, and exit the economy in response to market forces and public
policies. The focus of this analysis is on the role that large, newly
locating firms play in the regional growth process and the extent to
which the large firms influence the propensity of other firms to locate,
expand, contract, or exit. The large firms can be viewed as demanders of
sites, and the communities can be seen as suppliers, either directly
(through industrial parks) or indirectly (through accommodating land use
controls). (6) The negotiations that characterize the siting process of
large new companies suggest the presence of market power on both the
demand and supply sides of the market. Other firms are modeled
separately (below) as competitive agents in the regional economy.
Large firms determine their demand for sites by examining the
expected profitability associated with each site based on a
region's demand, cost, policy structure, and amenities. Note that
firms may not choose locations with the best profit potential, possibly
accepting lower market returns in exchange for amenities (see Fox and
Murray 1990). During the negotiating process with communities, firms
reveal their general intentions including planned production, capital
investment, employment, and nonpayroll spending. Communities, as
discussed below, may choose to induce location through provision of
incentives that improve site-specific profits or may seek to limit
access to sites through zoning or other restrictions. Together, market
forces and information on incentives enable firms to formulate expectations on intersite profitability. Generally this information is
not available to a researcher.
Just as firms evaluate site-specific profits, states and localities
determine their willingness to supply sites based on evaluations of
potential returns from the location of large companies within their
jurisdiction. (7) In practice states and substate jurisdictions have
separate indices of expected returns from large company locations. The
state would have greater interest in state-wide benefits and costs,
whereas localities would be expected to hold a more parochial view of
benefits and costs. It is difficult to know exactly how specific states
and communities will evaluate the returns to location since the weights
applied to benefit and cost factors will vary dramatically. Narrowly,
the calculus might reflect the perceived surplus (deficit) of revenues
over public sector costs. Other factors that might be included are
economic effects such as jobs, income, and changes in industry structure
as well as the prestige effects that accrue to political leaders when a
large location occurs.
In cases where net economic, fiscal, and/or political surpluses are
anticipated, states and communities may seek to increase the probability
of a large firm locating through grants, abated taxes, provision of
training services, site development assistance, and other concessions.
There is presumably some maximum amount that a community would be
willing to give away to attract a large new company, some or all of
which may be provided to a locating firm through concessions and other
forms of support. A stick, such as zoning or other regulatory controls,
might be used instead of a carrot in cases where policy makers perceive
net costs instead of net benefits from a specific location. Information
on pieces of incentive packages actually accepted by firms is often
available, whereas unaccepted incentive offers are generally unobserved.
The noncompetitive location process begins as firms reveal their
production and investment plans and scrutinize alternative sites.
Communities then make their best offer based on expected returns. Firms
respond by accepting or rejecting community bids or by making a counter
offer. (8) A new round of negotiations could begin if the offer is not
accepted. Generally, it is impossible to observe the sites examined by a
large firm and the firm's assessment of intersite profitability as
well as incentives offered and incentives accepted. But actual company
locations are observed to take place in specific regions, in which case
it can be assumed that the location satisfies both the company's
required minimum or reservation level of profit and the community's
required return on available sites.
An important question is the ex post effect of location on regional
economic performance. One approach to estimating the economic effect of
a large company's intervention is to specify a detailed structural
model capturing the optimization behavior of firms, factors of
production, and policy makers. Unfortunately, we cannot directly
estimate nor indirectly identify the component structural equations
corresponding to firms' expected profitability and
communities' expected returns from large company locations. To do
so would require detailed information on each company and each community
including the range of factors, especially incentives or restrictive
policies, considered in evaluating rates of return across sites.
However, a structural model is not necessary to measure the net economic
effect that large firms have on the siting area. Net effects can be
assessed through a reduced form model that differentiates between the
competitive behavior of most locating, expanding, contracting, and
exiting firms and the noncompetitive behavior and location of many
large, newly locating firms and their potential host communities.
The behavior of competitive industry is reflected in a stylized reduced form model of regional economic growth,
(1) [E.sub.it] = [X.sub.it]B,
where [E.sub.t] is some aggregate measure of region i's
economic activity in period t (such as the level of employment or the
growth in personal income), [X.sub.it] includes the attributes of the
economy that encourage/retard growth (including taxes and labor costs),
and B are the agent's responses to market and public policy
variables. As a reduced form, this specification encompasses the
behavior of firms, factors of production, and policy makers.
For empirical implementation, Equation 1 is amended to accommodate
the location of large firms as follows:
(1) [E.sub.it] = [X.sub.it]B + [D.sub.i][alpha] + v,
(2) [MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII.]
(3) v = [v.sub.i] + [v.sub.t] + [v.sub.it].
The variable D reflects the presence or absence of a company
location in a given community, and a is the estimated mean impact of
location on the specific measure of substate growth. (9) The term a,
which measures only employment or income effects in the analysis that
follows, is a subset of the returns that communities consider when
evaluating the returns to large firm location. The term v is expanded to
accommodate a two-way error structure for panel data applications
including time-specific and placespecific effects on economic activity.
A random-effects model assumes each term is independently and
identically distributed as (O, [[isgma].sup.2.sub.v]); alternatively, a
fixed-effects model assumes that [v.sub.i] and [v.sub.t] are fixed
parameters to be estimated.
3. Data
An exhaustive process was followed to generate a viable database
and an appropriate econometric model, and each step is discussed in
detail below. The setup for the empirical analysis relies on three
data-intensive issues and three econometric issues based on concerns
prior to development of the econometric findings. This section addresses
the three data issues: (i) identifying locations and the general issue
of control regions, (ii) selecting the measure of economic performance,
and (iii) choosing an appropriate unit of regional analysis.
A set of treatment counties and two sets of control counties were
developed for the study. The treatment group is a 100% sample of all
counties where locations of large firms have occurred. Large locations
were defined as all new sitings between 1980 and 1989 that involved an
expected employment of at least 1000 people. The sitings range in size
from 1000 employees to 7594 employees with a median of 1200. The number
of locations by year is given in Table 1 and by state of location in
Table 2. The requirement that locations take place in the 1980s permits
the time path of economic performance before and after the location to
be examined. A very comprehensive process was used to identify the
sitings of new firms versus firms that simply relocated or changed
ownership. A primary data collection effort was undertaken as every
state's economic development agency or other appropriate agency was
contacted to determine where and when such locations occurred. (10) In
addition, secondary data were collected by reviewing a number of popular
publications such as Site Selection Magazine, which commonly provides
listings of large firms and their sites of location. Locating firms and
local government agencies were also contacted when necessary to ensure
the data on place, time of location, and announced employment were
accurate. Locations that proved to be expansions at existing sites were
omitted. No limitations were placed on the industries in which firms
could operate. The industry could not always be identified, but the data
include firms in manufacturing; services; construction; finance,
insurance, and real estate; retail and wholesale trade; and
transportation, communications, and public utilities industries. Because
of the size of these firms and the nature of their products or services,
it can be assumed that they are producing their goods or services for
national or international markets.
Since the location of large firms is not a random event, control
groups composed of untreated (nonlocation) counties were necessary to
identify the impact of location on treated (location) counties. (11) One
control group was formed by taking a 10% random sample of all
nonlocation counties. (As described below, this control group indicated
evidence of selectivity bias, and pooling was rejected.) A second
control group was developed that reflected a larger regional economy and
was composed of all Metropolitan Statistical Areas (MSAs) as of 1996.
(12) The possibility of contamination bias (i.e., the inclusion of a
control county or MSA where a large location has in fact occurred)
exists to the extent that the primary and secondary data collection
efforts failed to identify all large firm locations.
The 93 locations that occurred in MSAs took place in 46 different
MSAs located in 26 states. The control sample includes 188 MSAs located
in 33 states (reliable data were unavailable for 17 states). The
location MSAs were on average larger with a median population of 852,372
in 1990 compared with a median population of 193,889 in the control
MSAs. As can be seen in Figure 1, the location MSAs grew faster. For
example, the median location MSA had a compound annual employment
increase of 2.36% during the 1980s compared with 1.84% for the control
MSAs.
[FIGURE 1 OMITTED]
The empirical work is undertaken using both personal income and
employment growth as separate indicators of economic effects from large
locations. However, the basic findings are the same so that the results
are only provided for employment effects, which are measured for both
the county and the broader area in which the location occurs. The county
is the area of primary economic impact, and this well-defined local
government unit is often responsible for at least part of the local
government location incentives as well as policies that may discourage
firm location. The area is defined as the entire MSA for metropolitan
locations or the location county and all contiguous counties for
nonmetropolitan locations. The area is expected to encompass a wider set
of the economic effects that result from locations.
A time series for both the location and nonlocation communities is
necessary to study the time path of economic performance. (13)
Employment and income data for all counties and areas in the samples
were obtained for 1972-1995. (14) For purposes of this study, the
relevant time dimension is the number of years before or after the
location, rather than the specific calendar year in which the location
occurred. Thus, data for each treatment place (either county or area)
were centered on the location year. Time is then reflected as the number
of years before or after the location. Centering was not directly
possible for control counties since by definition no location has
occurred. Control places were randomly centered (without replacement)
across the years 1980-1989, and time is measured relative to the number
of years before or after the randomly selected centering point.
A balanced and an unbalanced data panel are used alternately in the
empirical analysis. (15) Both panels include the entire cross section
and a constant, although different, length for the time series. The
difference is that the balanced panel includes a fixed number of years
before and after the location. Specifically, the balanced panel is
composed of annual data for five years before and five years after the
location plus the year of the location. Thus, the time series for every
place has exactly the same number of years before and after the
location. The unbalanced panel has a time series using all years from
1972 to 1995. However, the number of years before and after the location
varies based on the calendar year in which the data are centered. Thus,
for a 1980 location, eight years of data are available before it and 15
years after it; however, for a 1989 location, 17 years of data are
available before and six years of data after.
Unweighted average employment growth in treatment areas was
generally higher than for the control MSAs, both before and after the
location (see Figure 1). (16) Of course, the growth in some prelocation
years potentially can be explained at least partially by construction
activity, whereas growth in the postlocation years can be explained by
the new firm's operations. But the location areas had higher growth
in 23 of the 32 years shown in the figure. The higher growth rates suggest that there is something systematically different about the two
samples that could go beyond the direct locational effects. (17) One
explanation is that there is a different growth-generating process
arising because the geographic site of location areas is consistently
preferred over nonlocation MSAs, a potentiality that is addressed in the
empirical analysis. This same phenomenon heightens concerns over the
potential for selection bias, since locations seem to occur
predominantly in high-growth areas. On the other hand, the reverse is
true for location counties (see Figure 2). The location counties
experienced consistently slower growth than the average control county,
suggesting that firms locate in slow-growing counties in high-growth
metropolitan areas.
[FIGURE 2 OMITTED]
4. Empirical Analysis
The key issue for this paper is estimation of [alpha] in Equation
2. Proper econometric analysis of this question requires a number of
steps. First, the reduced form equation necessary to estimate Equation 2
must be developed. Second, a statistical analysis must be undertaken to
determine whether the treatment and control panels described in the data
section can be pooled for estimation purposes. Third, it is necessary to
test whether the error term and the right-hand side variables are
correlated (i.e., that selection bias is present), which would lead to
biased parameter estimates. Finally, the econometric estimates can be
generated once the previous econometric issues have been settled. The
remainder of this section describes the detailed reasoning and findings
for the first four steps, whereas the econometric estimates are provided
in the following section.
Proxy variables are used in the reduced form equation to control
for market and public policy influences on growth. First, GDP growth is
used to control for macroeconomic trends, national product demand, and
the national policy environment. Weak national growth may depress product demand, in turn reducing the absolute attractiveness of all
potential sites and vice versa. Second, measures of state growth are
used to control for cross-sectional variation in the business climate
including factor market, public policy, and regulatory conditions across
state and substate regions. The assumption is that measures of statewide
growth embody, as a composite, the diverse structural elements like tax
burdens and labor costs that influence growth within counties and MSAs.
Third, having accounted for national and state market conditions,
substate regions may still display differential growth due to variations
in industry mix and other unique place-specific factors. For example,
during recovery from recession an area characterized by a dominant
manufacturing sector may enjoy stronger growth than regions with a large
agricultural or service sector. Thus, the percentages of employment in
manufacturing and in agriculture are included as separate regressors.
(18) Finally, we utilize random and fixed effects to control for
remaining features of the regional economy.
Location of a large business is evaluated as a noncompetitive
activity occurring through a negotiated process that takes place
simultaneously with the normal growth process of MSAs and counties. The
effect of these locations is examined through two indicators. The first,
called Location, is a dummy variable with a value of one in the location
and all subsequent years and a zero otherwise. Location always takes on
a zero value for control places. The second is a set of six dummy
variables with a value equal to zero in prelocation years and one in the
location year (Location 1), the year after location (Location 2), and so
forth. The first approach requires that the economic effects from a
location be the same for all years after the location; the second allows
for the impacts to vary by year after the location.
The empirical analyses are performed separately for counties
(control and location counties) and MSA areas (control and location
areas). Estimation of the model depends on the relationship between v in
Equation 4 and the right-hand-side variables of Equation 2. Of
particular interest is the variable D, since the presence of any
correlation would lead to biased parameter estimates. In accounting for
these problems, we followed Bassi (1984) who suggested a step-by-step
approach to model specification and identification of intervention
impacts in her analysis of job training programs. (19) The approach
explores alternative estimators--ordinary least squares (OLS), random
effects, and fixed effects--that sequentially accommodate a more
sophisticated structure between v and D based on model specification
tests. For a given estimator, we first conducted F-tests to determine
whether the prelocation economic growth process captured by Equation 2
is the same for the treatment and control groups. This is essential to
ensuring that a picks up any regime shift from location as opposed to
other causes of different patterns of economic growth across the two
groups. Alternative control groups or estimators are required if there
are underlying differences in the growth patterns. The F-tests are
estimated using prelocation data, but no simple determination of what is
meant by a prelocatinn time period can be made because some growth
effects from construction or pre-announcement expectations may exist
prior to the actual location. Thus, two time periods were examined: one
for the time up to three years before the location and one for the time
up to one year before the location. The shorter time period was used to
minimize any bias in the estimates from pre-announcement expectations or
construction activity prior to the location.
The F-tests were conducted using each control group and its
counterpart of location areas, that is, all MSA areas and the 10% random
sample and both the MSA area and location county treatment groups. State
employment growth, the GDP growth rate, and various subsets of the
control variables that account for the economic structure (the
percentages of employment in manufacturing and in agriculture) were used
in the equations. The F-tests, estimated using a wide variety of model
specifications, were always significant using the county control group,
indicating that there were systematic differences in the growth
processes of the location and control counties during the prelocation
period. Although the remaining analysis emphasizes the results for the
MSA control group, we also present estimates for counties, but offer the
caution that these findings may be biased if fixed effects do not
adequately control for different regional patterns of growth. Little
evidence was found of systematic differences between the MSA control
group and location counties.
The second step is to look for correlations between the error term
v and the regressors in the growth equations, in particular D, to
determine if selection bias is present. Correlations between the two
could provide evidence that firms systematically selected high (or low)
growth places for location and that differences in growth between
location and control places are due to some preexisting differences,
rather than to the effects of a large facility. A series of growth
equations was estimated using data from the two prelocation time
periods, OLS, and random effects estimators. (20) Separate equations
were run using the county database and the area database. The key issue
is whether dummy variables D for the location counties or MSA areas were
significant in the prelocation time period, providing evidence of
selection bias as firms chose places that were already growing at a
different pace. In addition to the dummy variable, every combination of
the regressors listed above was tried.
Evidence is found that selection based on fixed effects is taking
place in the location of employment in the area specifications with the
dummy variable being positive and statistically significant in the OLS
equations. The dummy variable is significant in most of the random
effects equations, but no estimates could be obtained with the industry
structure variables present. The fixed effects model is an acceptable
control for nonrandom selection when selection takes place on the basis
of fixed factors and therefore is the preferred estimator for the area
equations. (21) The t-values on the fixed effects are always significant
at a higher degree of confidence for the panel data up to one year
before location than for the panel up to three years before location,
suggesting the occurrence of a growth spurt immediately before location
that is perhaps linked to construction of the large facility or
expectations regarding future supplier linkages. Overall, these findings
indicate that businesses are likely to select high-growth places for
locations, but the higher growth can be explained with fixed effects or
with characteristics of the local economic structure. The dummy variable
was negative but only statistically significant in one of the county
equations, suggesting more limited evidence of selectivity bias.
Growth Equation Results
A listing of empirical results for employment growth equations is
presented in Table 3 for counties and in Table 4 for MSAs. (22) In each
case, the dependent variable is annual growth in employment for the
place. The preferred fixed effects results are presented when the
estimated variance is positive; OLS and random effects estimates are
also shown for comparison. Equations based on a balanced time series,
with the same number of years of data before and after each location,
and an unbalanced time series, with the total available number of years,
are provided for each equation.
The state growth variable, which is included to account for
cross-section effects of the full range of policy variables and growth
conditions, is highly significant in every equation. In general, the
coefficient estimates are very close to 1, indicating a strong
relationship between statewide factors and regional economic growth. On
average, substate regions grow at a rate roughly commensurate with the
growth of the state itself. GDP, intended to pick up time series effects
of the national policy environment and national market conditions, is
entered into every equation and is significant in some cases. (23) The
percentages of employment in manufacturing and in agriculture, which
operate as fixed effects across areas, are generally significant. The
fixed effects estimator could not be used with these variables since
they include no intertemporal variation. (24)
The Location variable, taking the value one in the location and all
subsequent years, is never significant, suggesting the large plant
locations had no effect on growth. There is some evidence that location
has a net positive effect using the annual location variables (Location
1-Location 6), although the results are mixed. With the unbalanced
equations and the preferred fixed effects model, significant effects are
found contemporaneously with the location (Location 1) and one (Location
2) and three (Location 4) years after location. An F-test of the
combined significance of the annual location variables was
insignificant. Further, the only effect with the balanced equation is
negative five years after the location. At least one location variable
is significant in each of the OLS equations and the unbalanced equations
with the random effects model. An F-test of the combined effect of
including the six annual location variables was significant only in the
OLS equation without fixed effects for industry structure.
5. Discussion
Together these findings yield little support for the case that
large company locations appreciably influence the path of regional
economic growth. The descriptive data indicate that large companies are
choosing low-growth counties in rapidly growing MSAs. Low-growth
counties may offer less congested infrastructure, relatively lower
priced land, more aggressive recruitment efforts, and so on. At the same
time, the locating firm will be able to exploit agglomeration economies,
infrastructure, amenities, the availability of business services, and so
forth that are generally available in the broader metropolitan area, as
well as to take advantage of the visibility and prestige associated with
a rapidly growing regional economy.
Once we control for national and state economic conditions as well
as time and place fixed effects, there is little evidence of positive or
negative growth impacts associated with the location of large firms.
(25) These findings are consistent with the longer term development of
this research project where a wide variety of estimators, data, and
model specifications failed to produce a consistent pattern of positive
or negative results. (26) The strongest evidence for positive growth
impacts comes from the preferred fixed effects model applied to MSAs,
but even here there is no consistent finding of any economic stimulus.
When the analysis is conducted at the county level, where there is less
confidence that control and location counties can be pooled, in only one
instance does the coefficient of the location intervention variable show
statistical significance. More generally, across all estimators
(including the less preferred OLS and random effects models), there are
only three instances of positive growth effects and four instances of
negative growth effects.
What might explain the findings that large firms have little or no
net impact on regional growth? Perhaps the political leaders'
and/or communities' goals in business and industrial recruitment
were to enhance prestige and community visibility or to promote changes
in industry structure (for example, by replacing old, cyclical jobs with
new jobs). If these were in fact the communities' objectives, they
may or may not have been realized. One thing seems clear: recruitment
did not lead to more rapid regional growth. In all likelihood, the
absence of significant growth impacts means that large companies simply
displace other sources of job and income growth in the regional economy.
As discussed above, the underlying dynamics of this displacement may
reflect the fiscal consequences of granting incentives, the crowding of
regional infrastructure, and/or higher prices in local factor markets.
Since growth would have taken place absent the large firm's
location, typical business recruitment strategies focused on larger
enterprises along with the liberal granting of tax and other incentives
to the same firms is simply not a cost-effective economic development
strategy.
Table 1. Large Firm Locations by Year
Year Number of Locations
1980 14
1981 10
1982 6
1983 5
1984 8
1985 9
1986 10
1987 17
1988 17
1989 13
Total 109
Table 2. Large Firm Locations by State
Alabama 6
Arkansas 3
Colorado 3
Connecticut 4
Florida 28
Georgia 1
Illinois 1
Indiana 12
Kansas 7
Kentucky 1
Louisiana 1
Maryland 2
Massachusetts 3
Michigan 1
Mississippi 3
Missouri 3
Nebraska 2
North Carolina 7
Ohio 1
Oklahoma 1
Rhode Island 1
South Carolina 2
Table 3. Regression Estimates for County Location
State Employment Percent
Growth GDP Growth Agriculture
Ordinary least squares
Balanced 0.99 *** -0.00006
Unbalanced 0.98 *** 0.00003
Balanced 0.99 *** -0.00005
Unbalanced 0.97 *** 0.00004
Balanced 0.98 *** -0.00001 0.06 *
Unbalanced 0.96 *** 0.00009 0.07 ***
Balanced 0.98 *** -0.00006 0.06 ***
Unbalanced 0.96 *** 0.0001 0.07 ***
Fixed effects
Balanced 0.94 *** -0.0002
Unbalanced 0.93 *** 0.0003 **
Balanced 0.94 *** 0.002
Unbalanced 0.93 *** 0.0003 **
Random effects
Balanced 0.95 *** 0.0002
Unbalanced 0.95 *** -0.0002 *
Balanced 0.95 *** 0.0002
Unbalanced 0.94 *** 0.0002 **
Balanced 0.95 *** 0.0002 0.07
Unbalanced 0.94 *** 0.0002 ** 0.06
Balanced 0.95 *** 0.0002 0.07
Unbalanced 0.94 *** 0.0002 * 0.06
Percent
Manufacturing Location Location 1
Ordinary least squares
Balanced -0.0009
Unbalanced -0.001
Balanced -0.0009
Unbalanced -0.001
Balanced -0.03 *** -0.001
Unbalanced -0.02 *** -0.001
Balanced -0.03 *** -0.001
Unbalanced -0.02 *** -0.002
Fixed effects
Balanced 0.01
Unbalanced 0.002
Balanced 0.002
Unbalanced 0.003
Random effects
Balanced 0.0006
Unbalanced 0.001
Balanced 0.001
Unbalanced 0.001
Balanced -0.03 *** 0.0005
Unbalanced -0.02 * 0.002
Balanced -0.03 *** 0.001
Unbalanced -0.02 *** 0.001
Location 2 Location 3 Location 4
Ordinary least squares
Balanced
Unbalanced
Balanced 0.0003 0.003 0.0005
Unbalanced 0.0001 0.002 0.0003
Balanced
Unbalanced
Balanced -0.0002 0.002 -0.00005
Unbalanced -0.0003 0.002 -0.0002
Fixed effects
Balanced
Unbalanced
Balanced 0.003 0.006 0.002
Unbalanced 0.004 0.007 ** 0.003
Random effects
Balanced
Unbalanced
Balanced 0.002 0.005 0.001
Unbalanced 0.003 0.005 * 0.003
Balanced
Unbalanced
Balanced 0.002 0.005 0.001
Unbalanced 0.003 0.005 * 0.003
Location 5 Location 6
Ordinary least squares
Balanced
Unbalanced
Balanced -0.003 -0.006 *
Unbalanced -0.003 -0.006 **
Balanced
Unbalanced
Balanced -0.004 -0.006 **
Unbalanced -0.004 -0.007 ***
Fixed effects
Balanced
Unbalanced
Balanced -0.002 -0.005
Unbalanced -0.0003 -0.003
Random effects
Balanced
Unbalanced
Balanced -0.002 -0.005
Unbalanced -0.001 0.004
Balanced
Unbalanced
Balanced -0.002 -0.005
Unbalanced -0.001 -0.004
* t-test significant at the 0.10 level.
** t-test significant at the 0.05 level.
*** t-test significant at the 0.01 level.
Table 4. Regression Estimates for Metropolitan Statistical
Area (MSA) Location
State
Employment Percent
Growth GDP Growth Agriculture
Ordinary least squares
Balanced 0.99 *** -0.00001
Unbalanced 0.99 *** 0.00007
Balanced 0.99 *** 0.00003
Unbalanced 0.99 *** 0.00007
Balanced 0.95 *** 0.0002 0.27 ***
Unbalanced 0.95 *** 0.0003 ** 0.34 ***
Balanced 0.95 *** 0.0002 0.27 ***
Unbalanced 0.95 *** 0.0003 *** 0.33 ***
Fixed effects
Balanced 0.97 *** 0.0001
Unbalanced 0.97 *** 0.0002
Balanced 0.97 *** 0.0001
Unbalanced 0.97 *** 0.0002 *
Random effects
Balanced 0.97 *** 0.00009
Unbalanced 0.98 *** 0.0001
Balanced 0.97 *** 0.0001
Unbalanced 0.97 *** 0.0002
Balanced 0.96 *** 0.0001 0.27 ***
Unbalanced 0.97 *** 0.0002 * 0.33 ***
Balanced 0.96 *** 0.0001 0.27 ***
Unbalanced 0.97 *** 0.0002 * 0.33 ***
Percent
Manufacturing Location Location 1
Ordinary least squares
Balanced 0.003
Unbalanced 0.002
Balanced 0.003 *
Unbalanced 0.002
Balanced -0.02 *** 0.001
Unbalanced -0.01 *** 0.002
Balanced -0.01 *** 0.003
Unbalanced -0.01 *** 0.002
Fixed effects
Balanced 0.0008
Unbalanced 0.003
Balanced 0.0001
Unbalanced 0.003 *
Random effects
Balanced 0.001
Unbalanced 0.003
Balanced 0.001
Unbalanced 0.003
Balanced -0.02 ** 0.001
Unbalanced -0.01 ** 0.002
Balanced -0.02 ** 0.001
Unbalanced -0.01 ** 0.003
Location 2 Location 3 Location 4
Ordinary least squares
Balanced
Unbalanced
Balanced 0.004 ** 0.004 ** 0.004 **
Unbalanced 0.004 ** 0.003 * 0.003
Balanced
Unbalanced
Balanced 0.004 ** 0.003 * 0.003
Unbalanced 0.003 * 0.003 0.002
Fixed effects
Balanced
Unbalanced
Balanced 0.001 -0.0005 0.0002
Unbalanced 0.005 ** 0.003 0.004 *
Random effects
Balanced
Unbalanced
Balanced 0.003 0.002 0.002
Unbalanced 0.004 ** 0.003 * 0.003 *
Balanced
Unbalanced
Balanced 0.002 0.001 0.001
Unbalanced 0.004 ** 0.003 0.003 *
Location 5 Location 6
Ordinary least squares
Balanced
Unbalanced
Balanced 0.001 0.0001
Unbalanced 0.0006 0.0003
Balanced
Unbalanced
Balanced 0.0006 0.0002
Unbalanced -0.00009 0.0005
Fixed effects
Balanced
Unbalanced
Balanced -0.003 -0.004 *
Unbalanced 0.0002 -0.0004
Random effects
Balanced
Unbalanced
Balanced -0.001 -0.002
Unbalanced 0.001 -0.0003
Balanced
Unbalanced
Balanced -0.002 -0.002
Unbalanced -0.000006 -0.0005
* t-test significant at the 0.10 level.
** t-test significant at the 0.05 level.
*** t-test significant at the 0.01 level.
(1) Political explanations for incentive packages also exist (see
Fox and Mayes 1994).
(2) The number of conventions held in Atlanta fell 10.9%, and the
number of convention attendees dipped 10.4% in 1996. Net of Olympic
visitors and travel to Atlanta was down 36% in 1996. (Atlanta Convention
and Visitors Bureau 2001).
(3) For example, see Business Bulletin, Wall Street Journal,
September 28, 1995.
(4) On the other hand, some firms may choose locations where they
benefit from agglomeration benefits and direct supplier linkages derived
from the location of other large firms.
(5) Recent reviews of the literature are in Fisher (1997) and
Wasylenko (1997).
(6) For a discussion of the supply and demand for industrial sites
in competitive markets, see Fox (1978).
(7) Economic and fiscal impact studies are common ways of
evaluating some of the private and public sector benefits and costs
associated with a newly locating company. In practice, such studies
typically examine gross rather than net economic and fiscal impacts,
often ignoring important factor market and consumer substitution effects.
(8) This is the process that took place between the Boeing
Corporation and well over a dozen states in 2003.
(9) It is not necessary to estimate explicitly the firm and
community selection equations to identify [alpha]. See Bassi (1984) and
Heckman and Robb (1985).
(10) Some states, including Alaska, California, Hawaii, and New
York, were omitted from the sample because they either did not cooperate
in the data collection or the data were not of acceptable quality.
(11) Greenstone and Moretti (2003) use an alternative experimental
design that includes counties that became hosts to large new companies
and runner-up counties. They argue that the use of framer-up counties is
superior to randomly chosen control groups, since the former can be
viewed as close substitutes to sites actually chosen for location, thus
reducing any unobserved heterogeneity in the empirical analysis.
However, this may not be the case in practice since no information is
available on why potential sites were revealed or not revealed by firms
and communities. For example, firms may behave strategically and reveal
a runner-up county only because it made a lucrative incentive offer that
served the company's interest of eliciting a higher bid from the
county of ultimate location. In such a case, unobserved heterogeneity
may still exist across location and runner-up counties.
(12) Ninety-three of the locations were in MSAs. The control group
only includes MSAs from states where data on locations were used.
(13) Depending on the specific application, cross-section, and/or
panel data, including pre- and postlocation information, may be adequate
to identify the impact of location (Heckman and Robb 1985).
(14) In what follows we discuss only the employment data as a
similar pattern emerged for income data.
(15) An unbalanced panel does not lead to econometric problems when
the lack of balance is random and not the result of agent choice. See
Wooldridge (2002).
(16) The number of observations drops for years above six and below
eight because of the truncation at the ends of the data set.
(17) The control MSAs grew faster in seven of the prelocation years
and only two postlocation years.
(18) The economic structure variables are the shares of employment
five years before the location so that there is no simultaneity between
the share and the location. This approach precludes time series
variation in the economic structure data.
(19) More generally, see Heckman and Robb (1985) and Friedlarlder,
Greenberg, and Robins (1997).
(20) This test cannot be run using the fixed effects estimator.
(21) No evidence of selection bias is found using income as the
dependent variable.
(22) Results for the income equations are available from the
authors upon request. The location variables are never statistically
significant in the county income equations and are significant in only
two cases in the MSA equations.
(23) The effects of national growth may also be picked up in the
state growth variable.
(24) Random effects estimates are based on a one-way random en-or,
including only the error across areas.
(25) By contrast, Greenstone and Moretti (2003) found evidence that
wages in the one-digit industry and county of location were stimulated
by the siting of a large firm. These results do not directly conflict
with ours since Greenstone and Moretti's (2203) findings are for a
single industry and a single county. This more narrowly construed result
fails to account for the possibility of other activity being crowded out
in surrounding counties or in other industries. They separately
estimated effects on other industries in the location county and on
contiguous counties and generally found a positive, although
statistically insignificant, effect. However, a direct comparison with
our results would require that their analysis be conducted for the
overall economy in the broad economic area in which the location occurs.
They also concluded that property values in the location county were
increased, but the property value analysis was based on a significantly
restricted sample. Further, they speculated that the effects were caused
by the state providing part of the location subsidies, but payment of
the subsidies increased the chance of property value losses in other
parts of the state (including other parts of the MSA) that could result
in no net effect, as was concluded in the current study.
(26) Fox and Murray (1998) provide a preliminary version of the
analysis that is confined to only locating areas and only 24 company
locations.
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William F. Fox * and Matthew N. Murray ([dagger])
* Department of Economics and Center for Business and Economic
Research, 1000 Volunteer Boulevard, 100 Glocker Building, The University
of Tennessee. Knoxville, TN 37996-4170, USA; E-mail billfox@utk.edu;
corresponding author.
([dagger]) Department of Economics and Center for Business and
Economic Research, 1000 Volunteer Boulevard, 100 Glocker Building, The
University of Tennessee, Knoxville, TN 37996-4170, USA; E-mail
mmurray1@utk.edu.
Received July 2001; accepted August 2003.