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AGGLOMERATION SPILLOVERS:
EVIDENCE FROM MILLION DOLLAR PLANTS
IDENTIFYING
Michael Greenstone
Richard Hornbeck
Enrico Moretti
Working Paper 07-31
December 19, 2007
Revised: April 26, 2010
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This paper can be downloaded without charge from the
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Social Science Research
at
Identifying Agglomeration Spillovers:
Evidence from Winners and Losers of Large Plant Openings
Michael Greenstone
Massachusetts Institute of Technology
Richard Hornbeck
Harvard University
Enrico Moretti
University of California, Berkeley
April 2010
An earlier version of this paper was distributed under the title "Identifying Agglomeration Spillovers: Evidence
from Million Dollar Plants." We thank Daron Acemoglu, Jim Davis, Ed Glaeser, Vernon Henderson, William Kerr,
Chad Syverson, seminar participants at several
and comments. Steve Levitt and two anonymous
Jeffrey Kling, Jonathan Levin, Stuart Rosenthal, Christopher Rohlfs,
conferences and
many
universities for helpful conversations
referees provided especially insightful
comments. Elizabeth Greenwood provided valuable research
assistance.
Any
opinions and conclusions expressed herein are those of the authors and do not necessarily represent the views of the
U.S. Census Bureau. All results have been reviewed to ensure that no confidential information
for this research at the
Boston
RDC
from
NSF
(ITR-0427889)
is
also gratefully acknowledged.
is
disclosed. Support
Abstract
We
quantify agglomeration spillovers by estimating the impact of the opening of a large
in the same
compare incumbent plants in
the county where the new plant ultimately chose to locate (the "winning county"), with
incumbent plants in the runner-up county (the "losing county"). Incumbent plants in winning and
losing counties have similar trends in TFP in the seven years before the new plant opening. Five
years after the new plant opening, TFP of incumbent plants in winning counties is 12% higher
than TFP of incumbent plants in losing counties. Consistent with some theories of agglomeration
economies, this effect is larger for incumbent plants that share similar labor and technology
manufacturing plant on the
county.
We use
pools with the
total factor productivity
new
on
profits
is
model, we find evidence of a
winning counties. This indicates that the ultimate
plant. Consistent with a spatial equilibrium
relative increase in skill-adjusted labor costs in
effect
(TFP) of incumbent plants
the location rankings of profit-maximizing firms to
smaller than the direct increase
in productivity.
Digitized by the Internet Archive
in
2011 with funding from
Boston Library Consortium Member Libraries
http://www.archive.org/details/identifyingaggloOOgree
Introduction
most countries, economic
In
concentration
activity
is
spatially concentrated.
explained by the presence of natural
is
While some of
this
advantages that constrain specific
productions to specific locations, Ellison and Glaeser (1999) and others argue that natural
advantages alone cannot account for the observed degree of agglomeration. Spatial concentration
is
particularly remarkable for industries that produce nationally traded goods, because the areas
where economic
activity
is
concentrated are typically characterized by high costs of labor and
land. Since at least Marshall (1890), economists
economic
activity
may
have speculated
that this concentration
be explained by cost or productivity advantages enjoyed by firms
of
when
they locate near other firms. The potential sources of agglomeration advantages include: cheaper
and
faster
supply of intermediate goods and services; proximity to workers or consumers; better
quality of worker-firm matches in thicker labor markets;
lower risk of unemployment for
workers and lower risk of unfilled vacancies for firms following idiosyncratic shocks; and
knowledge
The
tantalizing,
that
1
spillovers.
possibility
because
of documenting productivity advantages through agglomeration
could provide insights into a series of important questions.
it
produce nationally traded goods willing
London
that are characterized
to locate in cities like
by extraordinary production costs?
Why
and what explains their historical development?
New
Why
is
are firms
York, San Francisco, or
In general,
why do
do income differences
cities exist
persist across
regions and countries?
Beside
an
obvious
interest
for
urban
and
growth
economists,
the
existence
of
agglomeration spillovers has tremendous practical relevance. Increasingly, local governments
compete by offering
The main economic
substantial subsidies to industrial plants to locate within their jurisdictions.
rationale for these incentives
depends on whether the
attraction
plants generates agglomeration externalities. In the absence of positive externalities,
to justify the use
it is
of new
difficult
of taxpayer money for subsidies based on economic efficiency grounds. The
optimal magnitude of incentives depends on the magnitude of agglomerations spillovers, if they
2
exist.
The existence and exact magnitude of agglomeration
1
spillovers are considered
open
See Duranton and Puga (2004), Glaeser and Gottlieb (2009), and Moretti (forthcoming) for recent surveys.
e discuss in more detail the policy implications of local subsidies in Greenstone and Moretti (2004). See also
W
Card, Hallock, and Moretti (2007), Glaeser (2001), and Glaeser and Gottlieb (2008).
questions by many, despite their enormous theoretical and practical relevance.
three objectives. First,
estimating
county.
how
We
we
test for
and quantify agglomeration spillovers
the productivity of incumbent plants changes
when
from the Annual Survey of Manufactures. Second,
including input and output flows,
we measure
technological linkages. Third,
by the spillover are reflected
Because the new
opening and
identification
of
af
new
county that
shed light on the possible
plant.
We
economic
consider different measures of
measures of labor flows between firms, and
which the productivity gains generated
the extent to
plant's location decision is
in future periods.
plant, using plant-
higher local factor prices.
in
likely to differ substantially
we
new
the magnitude of the spillovers depends on
linkages between the incumbent plant and the
is
manufacturing by
estimate augmented Cobb-Douglas production functions that allow the total factor
mechanisms by investigating whether
linkages,
This paper has
a Jarge plant opens in their
productivity (TFP) of incumbent plants to depend on the presence of the
level data
in
3
made
to
maximize
profits, the
from an average or randomly chosen county, both
chosen county
at the
time of
Valid estimates of the plant opening's spillover effect require the
is
identical to the county
where the plant decided
to locate in the
determinants of incumbent plants' TFP. These determinants are likely to include factors that
affect the
new
infrastructure,
plant's
TFP and
that are
difficult to
measure, such as local transportation
current and future costs of factors of production, quality of the workforce,
presence of intermediate input suppliers, and any other local cost
This paper's solution
is
shifter.
to rely on the reported location rankings of profit-maximizing
firms to identify a valid counterfactual for what would have happened to incumbent plants'
in the
absence of the plant opening. These rankings come from the corporate
Site Selection,
which includes a regular
a large plant decided where to locate.
real estate journal
feature titled "Million Dollar Plants" that describes
When
TFP
how
firms are considering where to open a large plant,
they typically begin by considering dozens of possible locations. They subsequently narrow the
3
To
two primary approaches in testing for spillovers. The first tests for an unequal geographic
These "dartboard" style tests reveal that firms are spread unevenly and that co-agglomeration
rates are higher between industries that are economically similar (Ellison, Glaeser, and Kerr 2009). This approach is
based on equilibrium location decisions and does not provide a direct measure of spillovers. The second approach
uses micro data to assess whether firms' total factor productivity (TFP) is higher when similar firms are located
nearby (see, for example, Henderson 2003). The challenge for both approaches is that firms base their location
decisions on where their profits will be highest, and this could be due to spillovers, natural advantages, or other cost
shifters. A causal estimate of the magnitude of spillovers requires a solution to this problem of identification.
date, there are
distribution of firms.
list
to
roughly 10
among which
sites,
2 or 3 finalists are selected.
county that the plant ultimately chose
articles report the
two runner-up counties
(i.e.,
the "losers").
The
(i.e.,
The "Million Dollar Plants"
the "winner"), as well as the
losers are counties that
one
or
have survived a long
selection process, but narrowly lost the competition.
The
identifying assumption
is
incumbent plants
that the
in
the losing counties form a
valid counterfactual for the incumbents in the winning counties, after conditioning on differences
in
pre-existing trends, plant fixed effects,
variables.
Compared
to the rest
industry-by-year fixed effects, and other control
of the country, winning counties have higher rates of growth
income, population, and labor force participation. But compared to losing counties
before the opening of the
variables. This finding
is
new
plant,
winning counties have similar trends
in
in
in the years
most economic
consistent with both our presumption that the average county
is
not a
credible counterfactual and our identifying assumption that the losers form a valid counterfactual
for the winners.
We
first
measure the
effect
of the new Million Dollar Plant
(MDP) on
total
factor
productivity of all incumbent manufacturing plants in winning counties. In the 7 years before the
MDP opened,
we
find statistically equivalent trends in
TFP
for
incumbent plants
in
winning and
losing counties. This finding supports the validity of the identifying assumption.
After the
MDP
relative increase in
opened, incumbent plants in winning counties experienced a sharp
TFP. Five years
later,
the
MDP
increase in incumbent plants' TFP. This effect
substantial:
on average, incumbent plants' output
is
in
opening
is
associated with a
statistically
significant
winning counties
is
is
equivalent to
moving a county from
increase in the distribution of county TFP.
We
and economically
A 12%
it
is
five
increase in
TFP
10th percentile of the county-level
the
distribution to the 27th percentile; alternatively,
relative
$430 million higher
years later (relative to incumbents in losing counties), holding constant inputs.
TFP
12%
equivalent to a 0.6 standard deviation
interpret this finding as evidence
of large
productivity spillovers generated by increased agglomeration.
Notably, the estimated productivity gains experienced by incumbent plants
counties are highly heterogeneous. The average county-level
instances, small in
some
increase
other cases, and even negative for a non-negligible
Having found evidence
to the question
TFP
in favor
is
very large
We
winning
in
some
number of counties.
of the existence of agglomeration spillovers,
of what might explain these spillovers.
in
we then
turn
follow Moretti (2004b) and Ellison,
Glaeser, and Kerr (2009) and investigate
how
the magnitude of the spillovers depends on
MDP.
measures of economic proximity between the incumbent plant and the
test
whether incumbents that are geographically and economically linked to the
we
Specifically,
MDP experience
larger spillovers, relative to incumbents that are geographically close but economically distant
MDP. We
from the
use several measures of economic links including input and output flows,
measures of the degree of sharing of labor pools, and measures of technological linkages.
We
find that spillovers are larger for
MDP
flows with the
transition
is
industry.
A
incumbent plants
4
worker
in industries that share
one standard deviation increase
in
our measure of worker
associated with a 7 percentage point increase in the magnitude of the spillover.
Similarly, the measures of technological linkages indicate statistically meaningful increases in
the spillover effect. Surprisingly,
flows
we
find
little
support for the importance of input and output
determining the magnitude of the spillover. Overall, this evidence provides support for
in
the notion that spillovers occur between firms that share workers and use similar technologies.
To
we
interpret the results,
set out a straightforward
Roback (1982)
style
model
that
incorporates spillbvers between producers and derives an equilibrium allocation of firms and
workers across locations.
In the
model, the entry of a
new
firm produces spillovers. This leads to
entry of firms that are interested in gaining access to the spillover.
and subsequent new entry leads
and other local
for labor, land,
cannot raise output prices
obtained
when
in
to competition for inputs, so
inputs. In the
activity in the
sloping
the
model, firms produce nationally traded goods and
is
is
equal to the increased costs
prices.
Consistent with these predictions,
economic
opening
response to higher input prices. Thus, the long-run equilibrium
of production due to higher input
MDP
original plant
incumbent firms face higher prices
the value of the increase in output due to spillovers
following
The
we
find
increases in quality-adjusted labor costs
openings. These higher wages are consistent with the documented increase in
(at least in the
model predicts
winning counties and with a local labor supply curve
medium
run).
We
that
is
upward
also find positive net entry in winning counties,
which
will occur if there are sufficiently large positive spillovers to generate an
overall increase in profitability.
The findings
We
in this
are deeply indebted to
paper are related to two earlier studies that use a similar approach to
Glenn
Ellison,
these measures of economic distance.
Edward
Glaeser, and William Kerr for providing their data for five of
productivity
identify
spillovers
the
at
local
Henderson
level:
who documents
(2003),
agglomeration spillovers for the machinery and high-tech industries, and Moretti (2004b),
estimates productivity spillovers generated by increased concentration of
human
who
capital in a
location. Consistent with the findings in this paper, the findings in Moretti (2004b) point to the
economically non-trivial,
of productivity spillovers that are
existence
depending on economic distance, and are largely offset by increased labor
Our
have two
findings
of important
sets
implications for local economic development policies.
spillovers
welfare
is
crucial to understanding the
consequences.
intervention
may
In
a
implications.
be efficient from the point of view of a
economic development
discuss
our
findings
have
The magnitude and form of agglomeration
economic rationale
We
significantly
costs.
First,
for location-based policies
world with significant agglomeration
point of view of aggregate welfare.
vary
how
spillovers,
and
their
government
although not always from the
locality,
our results inform the debate on local
policies.
Second, our findings have implications for understanding industrial clusters. Urban
economists have long noted that economic activity
industrial concentration appears to
economic
activity in
we
find
is
it
county-wide, while the productivity spillovers decline
economically further away
that are
profitable. In the long-run, this process
may result
MDP
location.
The
industry. This
appears to be fairly stable over time. Because the
economic distance, incumbent firms
each
by
spatially concentrated
be a pervasive feature of the geographical distribution of
most countries and
increase in labor costs that
is
interaction
in increased
may become
in
less
agglomeration of similar plants
in
between spillovers and input costs may therefore help
explain the existence and persistence of industrial clusters.
The remainder of
Section
II
the paper
is
organized as follows. Section
discusses the identification strategy. Section
presents the econometric model. Sections
interprets the results
I.
We
V
III
I
presents a simple model.
introduces the data sources. Section IV
and VI describe the empirical
results.
Section VII
and discusses implications for policy. Section VIII concludes.
Theories of Agglomeration and Theoretical
are interested in identifying
how
the opening of a
productivity, profits, and input use of existing plants in the
Framework
new
plant in a county affects the
same county.
We
begin by briefly
reviewing theories of agglomeration.
5
We
then present a simple theoretical framework that
guides the subsequent empirical exercise and aids in interpreting the results.
*
A. Theories of Agglomeration
Economic
activity is geographically concentrated (Ellison
the forces that can explain such agglomeration of
economic
and Glaeser 1997). What are
Here we summarize
activity?
five
possible reasons for agglomeration, and briefly discuss what each of them implies for the
relationship between productivity and the density of economic activity.
(1) First,
it
is
possible that firms (and workers) are attracted to areas with a high
concentration of other firms (and other workers) by the size of the labor market There are
.
least
two
different reasons
more productive
6
larger labor markets
may be
attractive. First, if there are search
and jobs and workers are heterogeneous, then a worker-firm match will be on average
frictions
jobs.
why
at
in areas
where there
Second, large labor markets
the firm side or
unpredictable
On the worker
demand shocks
are
may
side
many
firms offering jobs and
looking for
provide insurance against idiosyncratic shocks, either on
(Krugman 1991a).
that lead to layoffs
If firms experience idiosyncratic
and moving/hiring
then thicker labor markets will reduce the probability that a worker
unfilled vacancies.
many workers
is
is
and
costly for workers/firms,
unemployed and a firm has
7
These two hypotheses have different implications
for the
relationship
between the
concentration of economic activity and productivity. If the size of the labor market leads only to
better worker-firm matches,
we
should see that firms located
in
denser areas are more productive
than otherwise identical firms located in less dense areas. The exact form of this productivity
gain depends on the shape of the production function.
5
8
See Moretti, forthcoming, and Glaeser and Gottlieb, 2009, for comprehensive surveys of this
For
A
a related point in a different context, see
third alternative hypothesis has to
example,
in
Acemoglu
do with spillovers
(1996), plants have
literature.
Petrongolo and Pissarides (2005).
more
capital
that arise because of endogenous capital accumulation. For
and better technology in areas where the number of skilled
and workers find each other via random matching and breaking the match is costly,
even without learning or technological externalities. The intuition is simple. The
privately optimal amount of skills depends on the amount of physical capital a worker expects to use. The privately
optimal amount of physical capital depends on the number of skilled workers. If the number of skilled workers in a
city increases, firms in that city, expecting to employ these workers, will invest more. Because search is costly, some
of the workers end up working with more physical capital and earn more than similar workers in other cities.
For example, it is possible that the productivities of both capital and labor benefit from the improved match in
denser areas. It is also possible that the improved match caused by a larger labor market benefits only labor
workers
is
larger. If firms
externalities will arise naturally
On
the other hand, if the only effect of thickness in the labor market
unemployment
for
a lower risk of
is
workers and a lower risk of unfilled vacancies for firms, there should not be
improved
differences in productivity between dense and less dense areas. Unlike the case of
matching described above, the production function does not change: for the same
capital inputs, the output
of firms
in
set
of labor and
denser areas should be similar to the output of firms in less
dense areas. While productivity would not vary, wages would vary across areas depending on the
thickness of the labor market, although the exact effect of density on
ambiguous.
9
wages
is
a priori
This change in relative factor prices will change the relative use of labor and
capital.
(2)
A
second reason
why
the concentration of
economic
activity
may
be beneficial has to
do with transportation costs (Krugman 1991a and 1991b; Glaeser and Kohlhase 2003). Because
in this
paper
we
focus on firms that produce nationally traded goods, transportation costs of
Only a small
finished products are unlikely to be the relevant cost in this paper's setting.
of buyers of the
The
relevant
final
costs
intermediate goods
.
product
are
the
is
likely to
be located
transportation
Firms located
in
costs
in the
same area
fraction
as our manufacturing plants.
of suppliers of
local
services
and
local
denser areas are likely to enjoy cheaper and faster delivery
of local services and local intermediate goods. For example, a high-tech firm that needs a
specialized technician to fix a
is
machine
located in Silicon Valley than in the
is
likely to get service
Nevada
more quickly and
at
lower cost
if
it
desert.
This type of agglomeration spillover does not imply that the production function varies as
a function of the density of economic activity: for the
same
set
of labor and capital inputs, the
output of firms in denser areas should be similar to the output of firms in less dense areas.
However, production costs should be lower
(3)
A
third reason
why the
with knowledge spillovers
.
in
denser areas.
concentration of economic activity
There are
at least
two
may be
different versions
of
beneficial has to do
this hypothesis. First,
economists and urban planners have long speculated that the sharing of knowledge and
through formal and informal interaction
may
skills
generate positive production externalities across
productivity. This has different implications for the relative use of labor and capital, but total factor productivity will
be higher regardless.
9
Its sign depends on the relative magnitude of the compensating differential that workers are willing to pay for
lower risk of unemployment (generated by an increase in labor supply in denser areas) and the cost savings that
firms experience due to lower risk of unfilled vacancies (generated by an increase in labor
demand
in
denser areas).
workers.
10
more
high-tech industries. For example, patent citations are
metropolitan area as the originating patent (Jaffe et
geographic proximity of high-tech firms
flow of
may
Empirical evidence indicates that this type of spillover
new
likely to
come from
is
associated with a
ideas and ultimately causes faster innovation." Second,
proximity results
sharing of information on
in
new
the
in
same
some
state or
1993). Saxenian (1994) argues that
al.
Valley
in Silicon
be important
it
is
more
efficient
also possible that
technologies and therefore leads to faster
technology adoption. This type of social learning phenomenon applied to technology adoption
was
first
proposed by Griliches (1958).
If
of economic activity
density
results
we
agglomeration would lead to higher productivity. In particular,
in
externalities,
intellectual
in
this
form of
should see that firms located
denser areas are more productive than otherwise identical firms located
in less
dense areas.
As
with the search model, this higher productivity could benefit both labor and capital, or only one
of the two
depending on the form of the production function.
factors,
On
the other hand, if
density of economic activity only results in faster technology adoption and the price of
technologies
refrects
their
higher productivity,
should
there
new
no relationship between
be
productivity and density, after properly controlling for the quality of capital.
(4)
is
It
spillover, but
possible that firms concentrate spatially not because of any technological
because local amenities valued by workers are concentrated. For example, skilled
workers
may
employ
relatively
prefer certain amenities
more
skilled
more than unskilled workers. This would
workers to concentrate
in locations
where these amenities are
available. In this case, there should not be differences in productivity
less
dense areas, although there would be differences
in
wages
lead firms that
between dense areas and
that reflect the
compensating
differential
(5) Finally, spatial concentration
of some industries
natural advantages or productive amenities
limited
number of
wine industry
is
states
.
For example, the
concentrated
See, for example, Marshall
be explained by the presence of
oil
industry
because those states have the most accessible
in
California due to
manufacturing productions, the presence of a harbor
10
may
(1
suitable
may be
890), Lucas (1988), Jovanovic and
Rob
is
concentrated in a
oil fields. Similarly,
the
weather and land. For some
important. Natural advantages imply
(1989),
Grossman and Helpman (1991),
Saxenian (1 994), Glaeser (1 999), and Moretti (2004a, 2004b and 2004c).
" The entry decisions of new biotechnology firms in a city depend on the stock of outstanding scientists there, as
measured by the number of relevant academic publications (Zucker et al. 1998). Moretti (2004b) finds stronger
human
capital spillovers
between
pairs of firms in the
same
city that are
economically or technologically closer.
that firms located in areas with a high concentration of similar firms are
course this correlation
is
unrelated to agglomeration spillovers. Since most natural advantages
are fixed over time, this explanation
which exploit variation over time
B.
in
is
not particularly relevant for our empirical estimates,
agglomeration.
A Simple Model
We
begin by considering the case where incumbent firms are homogenous
technology.
Later
we
consider
what happens when incumbent firms
Throughout the paper, we focus on the case of factor-neutral
Homogeneous Incumbents.
(i)
We
assume
capital,
fixed and
is
1.
land, T, to
maximize the following expression:
normalized to
r,
and q are input prices and A
factors that affect the productivity
agglomeration spillovers,
we
if
and
heterogeneous.
are
spillovers.
that all
Incumbent firms choose
Maxijc,T f(A,
where w,
in size
incumbent firms use a production
and land to produce a nationally traded good whose price
technology that uses labor,
all
more productive, but of
of
is
L,
K, T)
-
their
amount of
labor, L, capital, K,
is
and
wL-rK- qT
a productivity shifter (TFP). Specifically,
labor, capital,
A includes
and land equally, such as technology and
they exist. In particular, to explicitly allow for agglomeration effects,
allow A to depend on the density of economic activity
in
an area:
A = A{N)
(1)
where
N
is
the
number of firms
that are active in a county,
and
define factor-neutral agglomeration spillovers as the case where
dA/dN =
instead
0,
we
all
A
counties have equal size.
increases in N:
dA/dN >
We
0. If
say that there are no factor-neutral agglomeration spillovers.
Let L*(w,r,q) be the optimal level of labor inputs, given the prevailing wage, cost of
and cost of industrial land. Similarly,
capital,
capital
K*(w,r,q) and T*(w,r,q) be the optimal level of
and land, respectively. In equilibrium, V, K", and T* are
of each of the three factors
We
demand
local
let
assume
is
equal to
that capital
or supply conditions.
economic conditions.
infinitely elastic at the
As
in
is
its
county
marginal product
price.
internationally traded, so
However, we allow
In particular,
set so that the
we
its
for the price
price does not
depend on
of labor and land
to
local
depend on
allow the supply of labor and land to be less than
level.
Moretti (forthcoming),
we
attribute the
upward sloping labor supply curve
to the
We
existence of preferences for location.
assume
that workers'
wages, cost of housing and idiosyncratic preferences for location, and that
we
marginal workers are indifferent across locations. For simplicity,
decisions within a given location and assume that
To
new
illustrate this,
plant. In particular,
consider that there are
m
such
is
that,
all
and staying
rising,
in the original
and some workers find
county.
it
county
c
amount of labor.
before the opening of the
given the distribution of wages and the housing costs
across localities, the marginal worker in another county
c
in
equilibrium
in
ignore labor supply
residents provide a fixed
m workers
depends on
indirect utility
When
optimal to
new
a
move
is
indifferent
between moving
plant opens in county
to county
c.
c,
to
county
wages there
start
The number of workers who move,
and therefore the slope of the labor supply function, depend on the importance of preferences for
location (see Moretti, forthcoming, for details). Let w(7V) be the inverse of the reduced-form
labor supply function that links the
wage
level,
firms,
TV,
active in a county to the local
nominal
w.
Similarly,
county
number of
level.
we
allow the supply of industrial land to be less than infinitely elastic
For example,
it is
possible that the supply of land
land-use regulations. Alternatively,
industrial land has already
more expensive
it
may
been developed, so that the marginal land
that links the
number of
therefore write the equilibrium level of profits,
77*
=
fixed because of geography or
not be completely fixed, but
to develop. Irrespective of the reason,
form land supply function
is
77*,
we
call
at the
it is
is
possible that the best
of decreasing quality or
q(N) the inverse of the reduced
firms, N, to the price
of land,
q.
We
can
as
/[/l(JV),L'(w(JV),r,gW),)('(w(N),r,(|(W)),r*(w(N),r,(,(W))]
- w(N)L'(w(N),r,q(N))where we now make
explicit the fact that
r/C'(w(yV),r,q(iV))
- q(N)T'(w(N),r,q(N))
TFP, wages, and land prices depend on the number of
firms active in a county.
Consider the
total derivative
of incumbents' profits with respect
to a
change
in
the
number of firms:
(2)
dn'/dN = (df/dA x dA/dN)
+ dw/dN {[df/dw (df/dL - w) + dq/dN {[dL'/dq (df/BL - w)] +
If all firms are price takers
and
considerably and can be written
all
L']
+ [dK'/dw (df/dK -
[dK*/dq (df/dK
- r)] +
r)]
+ [dT'/dw (df/dT -
[dT'/dq (df/dT
-
q)
q)]}
- 7*]}
factors are paid their marginal product, equation (2) simplifies
as:
10
dn'/dN = (df/dA x dA/dN) - [dw/dN V + dq/dN T ].
(3)
Equation (3) makes clear that the effect of an increase
First, if there are positive spillovers, the
effect
on TFP
represented by the
is
it
inputs. Formally,
df/dA >
sum of two
first
(df/dA x dA/dN). This
term,
effect
L*
+ dq/dN T*],
dw/dN >
that
of economic activity
is
in the
Intuitively,
>
0,
entrant.
The
and
to
change
it
an increase
dK*/dN >
negative
N
in
is
an increase
in the local
demand
in the level
for labor
and
two
prices
is
for locally scarce resources with
effects
on incumbents.
mechanically raises production costs. Second,
in
N, the firm
on the use of labor and land
productivity of all factors increases.
On
it
First, for
a
leads the firm
is
likely to
end up using more
is
ambiguous.
On one
hand, the
the other hand, the price of labor and land increases.
on the magnitude of the
(i.e.,
compete
in factor
0.
contrast, the effect
the production function
to
prices has
not affected by an increase
capital than before:
capital,
is
use of the different production inputs. In particular, given that
its
is
net effect depends
now have
wages and land
increase in
the price of capital
By
term
illustrated in a similar context in Moretti (2004b).
given level of input utilization,
to re-optimize
0.
while the magnitudes depend on the
county and therefore an increase
costly for incumbent firms, because they
new
dA/dN >
this
Unlike the beneficial effect of agglomeration spillovers, the increase
the
unambiguously
represents the negative effect from increases
and dq/dN
of the supply of labor and land.
land. This point
opposite effects.
is
if there are positive spillovers,
of production, specifically the prices of labor and land. Formally,
we have assumed
elasticity
the
is
productivity of all factors increases. In equation (3), this
by assumption and,
The second term, -[dw/dN
because
N
allows an incumbent firm to produce more output using the same amount of
positive, because
in the cost
in
factor price increases, as well as
The
on the exact shape of
the strength of technological complementarities between labor,
and land).
It
is
instructive to apply these derivations to the case
positive spillovers.
would occur when
this case, the
We
initially
MDP
opening that causes
consider the case where for incumbent firms dFl*/dN
the agglomeration spillover
MDP's
of a
is
<
0.
This
smaller than the increase in production costs. In
opening would not lead to entry and could cause some existing firms to
exit.
The
alternative case
spillover due to the
is
that
dU* /dN >
0,
which occurs when the magnitude of the
MDP opening exceeds the increase in factor prices due to the MDP's demand
11
be positive for
for local inputs. In the short run, profits will
of local
will disappear over time as the price
In the long run, there
is
new
such as land and possibly labor,
factors,
likely that
wages
bid up.
is
will increase. This
may
occur due to a
supply, as noted above. These adjustments
amount of land
locales. Since the
of productivity are likely to be capitalized into land
fixed, the higher levels
profits
an equilibrium such that firms and workers are indifferent
between the county where the new plant has opened and other
is
These positive
entrants.
prices.
less than infinite elasticity
make marginal workers
also
It is
of local labor
between the
indifferent
county with the new plant and other counties. Similarly, the changes in factor prices mean that
same
firms earn the
spillovers)
context to
and
profits in the
in other locations.
know when
new
county with the
From
plant (even in the presence
a practical perspective,
it is
of the
not possible in our empirical
the short run ends and the long run begins.
There are two empirical predictions that apply when there are positive spillovers.
the magnitude of the spillovers
is
new
large enough,
MDP's
firms will enter the
First, if
county to gain
access to the spillover. This prediction of increased economic activity holds at any point after
new
potential
entrants have had sufficient time to respond.
prices of locally traded inputs will rise as the
(ii)
Assume
12
some
some
extent from the type of workers
overlap.
Assume
if
that the
new
that the
is
for these inputs.
the population of incumbent firms
where there are two types of
that for technological reasons, the type
differs to
is
the case
prediction
MDP and the new entrants bid
Heterogeneous Incumbents. What happens
homogeneous? Consider
The second
is
12
non-
firms: high-tech and low-tech.
of workers employed by high-tech firms, L H
employed by low-tech
entrant
is
firms, L L
,
,
although there
a high-tech firm. Equations (4) and (5)
This model focuses on the case where the productivity benefits of the agglomeration spillovers are distributed
equally across
all factors.
What happens when agglomeration
Assume,
spillovers are factor biased?
example, that
for
agglomeration spillovers raise the productivity of labor, but not the productivity of capital. As before, the technology
is
f(A,L,K,T), but now L represents
physical workers and 9
We
0. If
dA/dN =
of the economic
,
where
H
is
the
economic
activity in the
county 6
and factors are paid their marginal product, then the effect of an increase
activity
in
county on incumbent firms simplifies
a
[dw/dN H* + dq/dNT']. The
increased productivity of labor.
effect
It
is
on
number of
define factor-biased agglomeration spillover as the case where
the productivity shifter 8 depends positively on the density of the
68/ dN >
= 6H
units of effective labor. In particular, L
a productivity shifter.
is
profits can
be decomposed
in
to
two
— 0(N)
and
in the density
dU'/dN = (df/dH x dd/dN)H —
parts.
The
the product of the sensitivity of output to labor
first
term represents the
(df/dH >
0),
times the
magnitude of the agglomeration spillover (38 /dN >
by definition), times the number of workers. The second
term is the same as in equation (3), and represents the increase in the costs of locally supplied inputs. The increase in
N changes the optimal use of the production inputs. Labor is now more productive, and its equilibrium use
increases:
dT'/dN <
dL'/dN <
0.
0.
Land
is
equally
productive but
its
Neither the price nor the productivity of capital
depends on technology;
specifically,
it
depends on the
elasticity
12
price
is
increases,
so
its
equilibrium
affected by an increase in N.
of substitution between labor and
Its
use declines:
equilibrium use
capital.
.
characterize the effect of the
new
high-tech firm on high-tech and low-tech incumbents:
(4)
dn-H /dNH = (dfH /dA H x 3A H /dNH ) - [dw H /3NH VH + dq/8NH
(5)
dnt/dNH = (dfL /dA L x 8A L /dNH ) - [dwL /dNH
+ dq/dNH
T'}.
plausible to expect that the beneficial effect of agglomeration spillovers generated
It is
new
l\
T*]
high-tech entrant
is
by
a
larger for high-tech firms than for low-tech firms:
CdfH /dA H x dA H /dNH ) > {dfL /dA L x 3A L /dNH ).
(5')
At the same time, one might expect
that the increase in labor costs is also higher for the high-
now competing
tech incumbents, given that they are
for
workers with an additional high-tech
firm:
dwH /dNH > dwL /dNH
(5")
The
effect
.
on land prices should be similar
for both firm types, since the
assumption of a single
land market seems reasonable.
This model of heterogeneous incumbents has two main implications.
First,
it
reasonable to expect larger spillovers on firms that are economically "closer" to the
Second, the relative impact of the new plant on profits
is
may
new
be
plant.
unclear, because the economically
"closer" plants are likely to have both larger spillovers and larger increases in production costs.
C. Empirical Predictions
The simple
theoretical
framework above generates four predictions
that
we
bring to the
data. Specifically if there are positive spillovers, then:
1
the opening of a
2.
the increase in
new
plant will increase
TFP may
TFP
of incumbent plants;
be larger for firms that are economically "closer" to the
new
plant;
3.
the density of
economic
activity in the
county will increase as firms
move
in to
gain
access to the positive spillovers (if the spillovers are large enough); and
4.
the price of locally supplied factors of production will increase.
in the price
of quality-adjusted
labor,
factor for manufacturing plants.
13
which
is
We
test for
changes
arguably the most important local
II.
Plant Location Decisions and Research Design
main econometric challenge
In testing the four empirical predictions outlined above, the
is
do not choose
that firms
where
their expectation
their location
randomly. Firms maximize profits and choose to locate
of the present discounted value of future
profits
is
many
present value varies tremendously across locations depending on
This net
greatest.
including:
factors,
transportation infrastructure, the availability of workers with particular skills, subsidies, etc.
These factors are frequently unobserved and, problematically, they are
with the
TFP of existing
plants.
TFP of incumbents
Therefore, a naive comparison of the
plant opening with the
TFP of incumbents
likely to yield biased estimates
plant opening on
the location
TFP
effect
13
intent
of incumbent plants require the identification of a location that
is
to locate in the determinants
how
BMW
may
circumvent these
similar to
is
of incumbent plants' TFP.
picked the location for one of
to demonstrate the empirical difficulties that arise
of plant openings on the TFP of incumbent
our research design
plants. Further,
when
illustrates
it
BMW announced in
months
counties. Six
informally
BMW
announced
Greenville-Spartanburg, South Carolina, and
they would
site
that the
decision.
did
The
list
two
of potential candidates
finalists
Omaha, Nebraska.
BMW
first
was
by the
choose Greenville-Spartanburg?
BMW's
in the
In 1992,
to
technology.
state
and
Two
factors
expected future costs of production
According
to
BMW,
Spartanburg more attractive than the other 250
the
local
in
characteristics
sites initially
a supply of qualified workers; numerous global firms
13
This plant
we
is
in
14
20 U.S.
BMW announced that
governments.
were important
in this
Greenville-Spartanburg,
that
made
BMW's
Greenville-
considered were: low union density;
in
the
Greenstone and Moretti's (2004) set of 82 MDP plants. Due
this plant is part of this paper's analysis.
cannot report whether
new
competition were
which are presumably a function of the county's expected supply of inputs and
production
its
would receive a package of
the plant in Greenville-Spartanburg and that they
incentives worth approximately $115 million funded
Why
how
difficulties.
1991 that they had narrowed the
later,
its
estimating the
After overseeing a worldwide competition and considering 250 potential sites for
plant,
is
of productivity spillovers. Credible estimates of the impact of a
where the plant decided
The
counties that experience a
in
do not experience a plant opening
in counties that
This section provides a case study for
plants.
be correlated
likely to
to
area,
including 58
Census confidentiality
German
restrictions,
companies; high quality transportation infrastructure, including
air,
rail,
highway, and port
access; and access to key local services.
For our purposes, the important point to note here
While these
potential source of unobserved heterogeneity.
the
is
that these
county characteristics are a
characteristics are well
BMW case, they are generally unknown and unobserved.
the growth of TFP of existing plants, a standard regression that
documented
in
If these characteristics also affect
compares Greenville-Spartanburg
with the other 3,000 United States counties will yield biased estimates of the effect of the plant
A
opening.
standard regression will overestimate the effect of plant openings on outcomes
example, counties that have more attractive characteristics
infrastructure)
tend to have faster
underestimate the effect
if,
TFP
for
improving transportation
(e.g.,
growth. Conversely, a standard regression would
example, incumbent plants' declining
for
if,
TFP
encourages new
entrants (e.g., cheaper availability of local inputs).
A
second important factor
in
BMW's
decision was the value of the subsidy
Presumably Greenville-Spartanburg was willing to provide
because
it
facility's
this
$2
expected economic benefits from
BMW's
was expected
indirectly. In principle, these
presence. According to local officials, the
to create 2,000 jobs directly
As
2,000 additional jobs could reflect the entry of
be a function of the expected gains from agglomeration for the county.
is
a part of
and another 2,000 jobs
new
plants or the
expansion of existing plants caused by agglomeration economies. Thus, the subsidy
This possibility
received.
BMW with $115 million in subsidies
ex ante expected five-year economic impact on the region was $2 billion.
billion, the plant
it
is
likely to
14
relevant for this paper's identification strategy, because the magnitude
of the spillover from a particular plant depends on the level and growth of a county's industrial
structure, labor force,
determine the
represent
a
heterogeneity
and a series of other unobserved variables. For
total size
second
is
this reason, the factors that
of the potential spillover (and presumably the size of the subsidy)
potential
source
of unobserved
heterogeneity.
If
this
unobserved
correlated with incumbent plants' TFP, standard regression equations will be
misspecified due to omitted variables, just as described above.
In order to
14
The
valid inferences in the presence of the heterogeneity associated with the
fact that business organizations
the case with
model
make
BMW)
such as the Chambers of Commerce support these incentive plans (which was
suggests that incumbent firms expect such increases. Greenstone and Moretti (2004) present a
that describes the factors that determine local
governments' bids for these plants and whether successfully
attracting a plant will be welfare-increasing or welfare-decreasing for the county.
15
plant's expected local production costs
and the county's value of attracting the plant, knowledge
of the exact form of the selection rule
necessary.
As
BMW
the
decisions are generally
difficult to
that
determines plants' location decisions
example demonstrates, the two
unknown
where they are known, are
to researchers and, in the rare cases
factors that determine the plants'
in
generally
factors that determine plant location
measure. Thus, the effect of a plant opening on incumbents'
confounded by differences
is
TFP
very likely to be
is
profitability at the
chosen
location.
As
a solution to this identification problem,
we
rely
on the reported location rankings of
profit-maximizing firms to identify a valid counterfactual for what would have happened to
incumbent plants
in
winning counties
in the
absence of the plant opening.
We
implement the
research design using data from the corporate real estate journal Site Selection. Each issue of this
journal includes an article titled "Million Dollar Plants" that describes
where
to locate.
These
that in
many
the "losers").
(i.e.,
winner and losers are usually chosen from an
cases
a large plant decided
always report the county that the plant chose
articles
and usually report the runner-up county or counties
indicates, the
how
number more than one hundred. The
initial
15
As
(i.e.,
the
the "winner"),
BMW case study
sample of "semi-finalist"
losers are counties that
sites
have survived
a
long selection process, but narrowly lost the competition.
We
use the losers to identify what would have happened to the productivity of incumbent
plants in the
winning county
incumbent firms'
in
the absence of the plant opening. Specifically,
TFP would have
trended identically
of winning and losing counties belonging
so our identifying assumption
is
to the
approach
incumbent plants
in
is
more
the absence of the plant opening in pairs
case. In practice,
we
if this
assumption
adjust for covariates
fails to hold,
reliable than using regression adjustment to
counties with
that
weaker. The subsequent analysis provides evidence that
supports the validity of this assumption. Even
this pair-wise
same
in
we assume
new
we presume
that
compare the TFP of
plants to the other 3,000 United States counties, or to
using a matching procedure based on observable variables.
In
some
were able
is
we
did
the original 82,
we
instances the "Million Dollar Plants" articles do not identify the runner-up county. For these cases,
a Lexis/Nexis search for other articles discussing the plant opening and
to identify the losing counties.
Comprehensive data on
unavailable in the Site Selection articles.
16
in
four cases,
the subsidy offered
among
by winning and losing counties
Data Sources and Summary
III.
Statistics
Data Sources
A.
The "Million Dollar Plants"
county where the
articles typically reveal the
new
firm (the
"Million Dollar Plant") ultimately chooses to locate (the "winning county") and one or two
runner-up counties (the "losing counties"). The articles tend to focus on large manufacturing
plants that are the target of local
is
An
government subsidies.
important limitation of these articles
magnitude of subsidy offered by winning counties
that the
offered by losing counties
losing county, there
We
is
is
often unobserved and the subsidy
is
almost always unobserved. In addition, when there
no indication of the
plants' relative preferences
among
is
more than one
the losing counties.
identify the Million Dollar Plants in the Standard Statistical Establishment List
(SSEL), which
is
the
Census Bureau's "most complete,
business establishment,"
16
and matched the plants
to the
and consistent data for U.S.
current,
Annual Survey of Manufactures (ASM)
17
MDP openings in all
industries used in Greenstone and Moretti (2004), we identified 47 useable MDP openings in the
manufacturing data. In order to qualify as a useable MDP manufacturing opening, we imposed
and the Census of Manufactures (CM) from 1973-1998.
the following criterion: 1) there had to be a
reported firm, appearing in the
the
the
MDP
MDP
SSEL
new
plant in the manufacturing sector,
SSEL had
owned by
the
to be located in the county indicated in
had to be incumbent plants
present for each of the previous 8 years.
identified 10 openings in the retail
the 82
within 2 years before and 3 years after the publication of
article; 2) the plant identified in the
article; and, 3) there
Of
Among
the 35
in
both winning and losing counties
MDP
we
openings that did not qualify,
and wholesale trade sectors whose
effects
we examine
in
robustness specifications.
To
the
ASM
obtain information on incumbent establishments in winner and loser counties,
and CM. The
ASM
and
materials, total value of shipments,
location
are
also
reported
CM
we
use
contain information on employment, capital stocks,
and firm
identifiers.
The
and these play a key role
manufacturing data contain a unique plant
identifier,
4-digit
the
in
making
it
SIC code and county of
analysis.
Importantly,
the
possible to follow individual
The SSEL is confidential and was accessed in a Census Data Research Center. The SSEL is updated continuously
and incorporates data from all Census Bureau economic and agriculture censuses and current business surveys,
quarterly and annual Federal income and payroll tax records, and other Departmental and Federal statistics and
administrative records programs.
17
The sample
is
because of minor
cut at 1998 because sampling methods in the
known
inconsistencies with the 1972
CM.
17
ASM changed
for 1999.
The sample begins
in
1973
plants over time.
ASM
Our main
were continuously present
analysis uses a sample of plants that
in the
the 8 years preceding the year of the plant opening plus the year of the opening.
in
Additionally,
we
drop
all
plants
owned by
firms that
own
sampling scheme was positively related to firm and plant
MDP.
a
Any
size.
In this period, the
ASM
was
establishment that
part
of a company with manufacturing shipments exceeding $500 million was sampled with certainty,
as
were establishments with 250
or.
more employees.
There are a few noteworthy features of
this
sample of potentially affected plants.
First,
the focus on existing plants allows for a test of spillovers on a fixed sample of pre-existing
plants,
which eliminates concerns
compositional bias. Second,
a disadvantage
is
it
is
that the results
related
to
54%
plants
and
possible to form a genuine panel of manufacturing plants. Third,
may
not be externally valid to smaller incumbent plants that are
not sampled with certainty throughout this period. Nevertheless,
plants accounts for
new
the endogenous opening of
relevant that this sample of
it is
of county-wide manufacturing shipments
in the last
CM before the MDP
opening.
In addition to testing for
agglomeration effects are larger
some measure of economic
categories. First, to
an average spillover effect,
We
to the
MDP
based on
focus on six measures of economic distance in three
measure supplier and customer linkages,
industry's manufactured inputs that
also test whether the estimated
more closely linked
in industries that are
distance.
we
come from each
we
use data on the fraction of each
3-digit industry
and the fraction of each
industry's outputs sold to manufacturers that are purchased by each 3-digit industry. Second, to
measure the frequency of worker mobility between
transitions
industries,
we
use data on labor market
from the Current Population Survey (CPS) outgoing rotation
measure the fraction of separating workers from each 2-digit industry
2-digit industry. Third, to
measure technological proximity,
we
that
file.
In particular,
move
we
to firms in each
use data on the fraction of
patents manufactured in a 3-digit industry that cite patents manufactured in each 3-digit industry.
We
also use data
digit industries.
,b
on the amount of R&D expenditure
in
a 3-digit industry that
is
used
in
other 3-
18
have two sources of information on the date of the plant opening. The first is the MDP articles, which often
when ground is broken on the plant but at other times are written when the location decision is made or
when the plant begins operations. The second source is the SSEL, which in principle reports the plant's first year of
operation. However, it is known that plants occasionally enter the SSEL after their opening. Thus, there is
We
are written
uncertainty about the date of the plant's opening. Further, the date at which the plant could affect the operations of
existing
plants depends
on the channel
for
agglomeration spillovers.
18
If the
agglomeration spillovers are a
Summary
B.
Table
Statistics
1
summary
presents
the basis of the analysis.
MDP
openings that
we
As
statistics
on the sample of plant location decisions that forms
discussed in the previous subsection, there are 47 manufacturing
can match to plant level data. There are plants
same
in the
2-digit
SIC
industry in both winning and losing counties in the 8 years preceding the opening for just 16 of
these openings.
The
some
table reveals
accompanying
other facts about the plant openings.
19
We
refer to the
winner and
opening as a "case." There are two or more
loser(s) associated with each plant
losers in
16 of the cases, so there are a total of 73 losing counties along with 47 winning
counties.
Some
average county
the
MDP
counties appear multiple times in the sample (as winner and/or loser), and the
in
the sample appears a total of 1.09 times.
article publication
across the categories -2 to
refer to cases
where the
and the year the plant appears
-1
years,
years,
and
in the
to 3 years.
1
appears after the plant
article
The
is
difference between the year of
SSEL
For
is
roughly spread evenly
clarity, positive differences
identified in the
SSEL. The dates of the
plant openings range from the early 1980s to the early 1990s.
The remainder of Table
assigned opening date. These
1
provides
MDPs
summary
statistics
on the MDPs,
are quite large: they are
five years after their
more than twice
the size of the
average incumbent plant and account for roughly nine percent of the average county's
total
output one year prior to their opening.
Table 2 provides summary
descriptions of these variables. In
statistics
all
on the measures of industry linkages and further
cases, the proximity
between industries
is
increasing in the
value of the variable. For ease of interpretation in the subsequent regressions, these variables are
normalized to have a mean of zero and a standard deviation of one.
Table 3 presents the means of county-level and plant-level variables across counties.
consequence of supplier relationships, then they could occur as soon as the plant is announced. For example, the
new plant's management might visit existing plants and provide suggestions on operations. Alternatively, the
agglomeration spillovers may be driven by the labor market and therefore may depend on sharing labor. In this case,
agglomeration spillovers
there
is
possibility,
we emphasize
year that the matched
19
A
may
not clear guidance on
number of
not be evident until the plant
when
the
new
is
operating. Based on these data and conceptual issues,
plant could affect other plants.
results using the earliest
To
be conservative and allow for each
of (1) the publication year of the magazine
article
and
(2) the
MDP appears in the SSEL.
the statistics in Table
1
are reported in broad categories to
confidentiality restrictions and to avoid disclosing the identities
19
of any individual
comply with
plants.
the
Census Bureau's
These means are reported
and
(3), respectively.
among
20
for winners, losers,
In the
and the entire United States
the incumbent plants present in the
ASM in
the 8 years preceding the assigned opening
across years to produce statistics for the year of the average
Further, the plant characteristics are only calculated
Column
9 consecutive years.
and
(2) are equal,
(3).
Columns
the
same
(6)
2-digit
calculated
among
while
(1), (2),
winner and loser columns, the plant-level variables are calculated
date and the assigned opening date. All entries in the entire United States
least
Columns
in
Column
(5) repeats this for a test
MDP.
as the
the plants in the
same
among
opening
in
are weighted
our sample.
plants that appear in the
(4) presents the t-statistics
through (10) repeat this exercise
SIC industry
among
MDP
column
from a
ASM for at
test that the entries in (1)
of equality between Columns (1) and
the cases
where there
are plants within
In these columns, the plant characteristics are
2-digit industry.
This exercise provides an opportunity to assess the validity of the research design, as
measured by pre-existing observable county and plant
observable characteristics are balanced
characteristics.
among winning and
To
losing counties, this should lend
The comparison between winner counties and
credibility to the, analysis.
the extent that these
the rest of the United
States provides an opportunity to assess the validity of the type of analysis that
undertaken
in the
absence of a quasi-experiment.
The top panel
reports county-level characteristics
measured
in
the year before the
assigned plant opening and the percentage change between 7 years and
opening.
Compared
to the rest
Among
compared
are at the
1%
to losers: 3
level.
rates
and growth, and a higher share of labor
in
the 8 variables in this panel, 6 of the 8 differences are statistically
significant at conventional levels.
are
year before the
1
of the country, winning counties have higher incomes, population
and population growth, labor force participation
manufacturing.
would be
of the
These differences are substantially mitigated when the winners
8 variables are statistically different at the
5%
level,
and none
Notably, the raw differences between winners and losers within the subset of
cases where there are plants in the same 2-digit SIC industry are generally smaller, and none are
statistically significant.
20
The
The second panel
reports
losing county entries in
Column
the inverse of their
number
is
(2) are
in that case.
multiplied by the inverse of the
plant within each county)
on the number of sample plants and provides information on
weighted
in
the following manner. Losing counties are weighted
Losing plants are weighted by the inverse of
number of
losing counties in their case.
given equal weight within the case and then
20
all
The
result
is
their
by
number per-county,
that each
county (and each
cases are given equal weight.
some of their
characteristics. In light
On
special interest.
of our sample selection
The
plants in winning and losing counties; in fact, there are
among
plants or
all
among
shows
Overall, Table 3
(although not
all)
number of plants
is
of
average, there are 18.8 plants in the winner counties and 25.6 in the loser
counties (and just 8.0 in the average U.S. county).
either
criteria, the
same
plants within the
MDP
that the
covariates are well-balanced
no
between
statistically significant differences
2-digit industry.
21
winner-loser research design balances
Of
observable county-level and plant-level covariates.
many
course, this exercise
does not guarantee that unobserved variables are balanced across winner and loser counties or
their plants. In the
subsequent analysis,
losing counties prior to the
we
MDP opening, which
section outlines our full econometric
TFP were
find that trends in
similar in
winning and
The next
lends further credibility to this design.
model and highlights the exact assumptions necessary
for
consistent estimation.
IV. Econometric
Building on the model in Section
we
I,
start
Model
by assuming
that
incumbent plants use the
following Cobb-Douglas technology:
w=v4CC*
(6)
where p references
changes
in inventories;
capital stock
M
pij t
plant,
to
Kpij
t
,
i
industry,
A pijt
is
;'
case,
t
TFP; and we allow
is
total labor
the total value of shipments minus
hours of production L pijt
Kpijt
,
,
building
and the dollar value of materials
have separate impacts on output. In practice, the two capital stock variables are
method
and deflated values of subsequent investment.
Recall that equation (1) in Section
Roughly
Ypijt
machinery and equipment capital stock
calculated with the permanent inventory
21
year; and
20%
of the winners were
defined as MI, IN,
OH, PA, NJ,
IL,
I
that uses earlier years
of data on book values
22
allows for agglomeration spillovers by assuming that
in the Rust Belt, compared to roughly 25% of the losers (where the Rust Belt is
WI, NY). Roughly 65% of the winners were in the South, compared to roughly
45%
of the losers.
For the first date available, plants' historical capital stock book values are deflated to constant dollars using BEA
data by 2-digit industry. In all periods, plants' investment is deflated to the same constant dollars using Federal
Reserve data by 3-digit industry. Changes in the capital stock are constructed by depreciating the initial deflated
capital stock using Federal Reserve depreciation rates and adding deflated investment. In each year, productive
capital stock is defined as the average over the beginning and ending values, plus the deflated level of capital rentals.
The analysis is performed separately for building capital and machinery capital. This procedure is described further
by Becker et al. (2005), Chiang (2004), and Davis, Haltiwanger, and Schuh (1996), from whose files we gratefully
obtained deflators.
21
TFP
number of
a function of the
is
some
also allow for
varying shocks to
TFP
fi it
ln
The goal
do
we need
so,
in
TFP
and a stochastic error term
,
0W
)
£ pijt
ap
,
we
).
Here we
generalize equation (1) by
cases X p
,
industry-specific time-
:
= a v + ftt + h + £vm + ^C^pyt )•
of winning a plant on incumbent plants' TFP.
impose some structure on A(Npijt
to
In particular,
.
across plants
to estimate the causal effect
is
).
In particular,
we
To
use a specification that
plant in winning counties to affect both the level of TFP as well as
new
allows for the
A piJt
additional heterogeneity in
allowing for permanent differences
A pijt = A(Npijt
firms that are active in a county:
its
growth
overtime:
ln(i4 yj )
p
(7)
= 81(Winner) pj + \pTrendjt + fXJrend x l(Winner)) pjt
>
k1(t
+
ft (1 (Winner)
0) ;t
+ a p + n it
where l(Winner) Pj
year, but
The
it
a
>
0)) ;t
x 1(t > 0)) p;t
+
6 2 (Trend x
+Ap + e
dummy
l(W inner) x
1(t
>
0)) pjt
pijt
equal to
1
if plant
p
is
located in a winner county; and r denotes
normalized so that for each case the assigned year of the plant opening
is
variable
is
1(t
+ y (Trend X
+
TrendJt
is
is
t
=
0.
a simple time trend.
Combining equations
and
(6)
(7)
and taking
we
logs,
obtain the regression equation that
forms the basis of our empirical analysis:
(8)
\n(Ypijt
)
=A
ln(L pyt
+ ft ln(^;t ) +
)
ln(^yt ) + ft ln(Mpiyt )
ft
+ 61(Winner) pj + xpTrendjt + H(Trend x l(Winner)) pjt
>
k1(t
+
ft (1 (dinner)
0) jt
x 1(t
+ a p + n + Ap +
it
Equation (8)
is
1(t
>
0)) ;t
0)) pyt
+
9 2 (Trend x l{Winner) x 1(t
+ y(Trend x
+
>
an augmented Cobb-Douglass production function that allows labor, building
machinery
focus
the estimation of the spillover effects of the
capital,
parameters of interest are
and materials
9i
and
82.
to
have
The former
differential impacts
TFP among
the
same
In practice,
we
new
plant
tests for a
plants in the winning county after the opening of the
in
0)) p;t
E pijt
capital,
is
>
on incumbent
mean
MDP,
on output. The paper's
shift in
while the
plants'
TFP, so the
TFP among incumbent
latter tests for
a trend break
plants.
estimate two variants of equation
more parsimonious model
that
simply
tests for a
22
mean
(8). In
shift.
some
In this
specifications,
we
fit
a
model, any productivity
effect
is
assumed
to
the restrictions that
occur immediately and to remain constant over time. Specifically,
i/>
=
/2
=
y
=
£>
=
2
0,
which
rules out differential trends. This specification
and
essentially a difference-in-difference estimator
adjustment for the inputs,
1
(W inner) pj
,
restrictions
we
in
>
refer to
,
it
Model
as
0)) p;t spij t |a p ,^i t Ap]
,
=
Formally, after
1.
the consistency of
is
in this
X
model
0.
estimate the entirety of equation (8) without imposing such
on the trends, and label
and trend break
we
and l(r>0) ;t
requires the assumption that E[(l(Winner) x 1(t
In other specifications,
we make
this
Model
productivity. In theory,
2.
This specification allows for both a
Model 2 allows
mean
shift
us to investigate whether any
productivity effect occurs immediately and whether the impact evolves over time. In practice,
disentangling these effects
t
=
is
demanding of the data because our sample
and there are only six years per case to estimate 6 X and 6 2
5
difference between
incumbent
plants'
Model
1
and Model 2
is
in
equation (8) control for unobserved determinants of
MDP
TFP
in
winning and losing counties
losing counties after the
to the
MDP
important
MDP opening (y),
opening (H), This
way
after the
MDP
opening
opening. These terms control for
to assess the validity
(k),
(ip),
a
a trend break in winning and
and a differential time trend
differential pre-trend in
might
that
differences in winning counties (5), a time trend in winning and losing counties
change
in
winning counties prior
winning counties
(fi)
will serve as an
of this research design. The specification also includes three
of fixed effects: plant fixed effects (a p ), so the comparisons are within a plant; 2-digit SIC
industry
by year fixed
effects (n it ) to account for industry-specific
fixed effects for each case (A ) to ensure that the impact of the
p
comparisons within a winner-loser
framework the
across
all
A
intuitive appeal
pair.
These case fixed
MDP
effects
TFP
shocks; and separate
opening
is
recreate
identified
in
from
a regression
of pair-wise differencing within cases, averaging
this effect
cases.
few further estimation
to affect input utilization,
(see, e.g., Griliches
are
practical
that the latter allows for differential pre-trends in
otherwise be confounded with the spillover effects of the
sets
The other main
.
TFP.
The other terms
TFP
only balanced through
is
unobserved demand shocks are likely
this raises the possibility that the estimated P's are inconsistent
and Mairesse 1995). This has been a topic of considerable research and
we
we implement
the
unaware of a complete
standard fixes,
and
details bear noting. First,
including:
solution. In a variety
of robustness specifications,
modeling the inputs with alternative functional forms
23
(e.g.,
the
translog); fixing the P's equal to their cost shares at the plant
of investment,
flexible functions
capital, materials,
and
and industry-level; controlling for
and instrumenting for current
labor;
2004a and 2004b; van Biesebroeck 2004; Olley
inputs with lagged changes in inputs (Syverson
and Pakes 1996; Levinsohn and Petrin 2003; Ackerberg, Caves, and Frazer 2006; Blundell and
Bond
we
1998). Additionally,
experiment with adding fixed effects for region-by-year or region-
by-industry-by-year, and allowing the effect of inputs to differ by industry or by winner and
post-MDP
The
status.
basic results are unchanged by these alterations in the specification.
also note that unobserved
demand shocks
main parameters of interest
and
(9i
counties in the years after the
are only a concern for the consistent estimation
02) if they systematically affect
MDP opening,
incumbent plants
in
We
of our
winning
controlling for the rich set of covariates in equation
(8).
Second,
some cases
in
is
estimated on a sample of plants from the entire
most specifications the sample
country, but in
ASM for
counties in the
this equation
every year from x
=
is
in these
dates of the
MDP openings, this
we probe
Third,
changes
is
we
prices of incumbents' output.
corroborating evidence for
Fourth,
in
the years between t
= -7 and
A
TFP
how
of sample
may
all
we
of the reported standard
all
Due
to the
cases.
24
number of supplementary
be influenced by unobserved
many of the
factor input
demand provides
biases associated with estimating
outcomes among plants
in the
same county, both within periods and over
focus on weighted versions of equation
total
(8).
data from the entire country
is
used, the sample
value of shipments
is
time.
Specifically, the specifications are
in
t
= -8
to
heteroskedasticity associated with differences in plant size. This weighting also
limited to plants that are in the
/ISM for
account for
means
at least
that the
14
consecutive years.
Data from
in
errors are clustered at the county level to account for the
weighted by the square root of the
When
5.
mismeasurement of TFP, and changes
complementary analysis of plants'
increases, without
=
which we have data from
the estimates
plants,
t
we
TFP.
correlation in
Fifth,
of the inputs and the industry
the longest period for
investigate
in plant inputs, attrition
plant-level
for the impact
the validity and robustness of our estimates with a
For example,
specifications.
This smaller sample of plants
counties from the rest of the country. For most of the analysis,
sample to observations
further restrict the
23
=
0.
-8 through x
from only winning and losing counties allows
shocks to differ
limited to plants from winning and losing
all
cases
is
also available for x
=
-8,
but shipments
24
in this
period are used to weight the regressions.
results
measure the change
more meaningful than
in
productivity for the average dollar of output, which in our
the impact of the
view
is
MDP on the average plant. 25
V. Results
This section
effect
the
is
divided into three subsections.
The
first
reports baseline estimates
of the
of the opening of a new "Million Dollar Plant" on the productivity of incumbent plants
same county through
the estimation of equation (8).
The second subsection explores
in
potential
channels for the agglomeration effects by testing whether the estimated spillovers vary as a
function of economic distance.
The
of the estimates for
third subsection explores the implications
the profits of local firms.
A.
Baseline Estimates
Columns
(1)
and (2) of Table 4 report estimated parameters and
from a version of equation
log of inputs, year
by
(8). Specifically,
2-digit
and the event time indicators
the natural log of output
SIC industry fixed
in a
sample
that
is
Column
and losing counties (Column
1)
(3) reports the yearly difference
2),
regressed on the natural
is
effects, plant fixed effects, case fixed effects,
restricted to the years t
reported coefficients on the event time indicators reflect yearly
(Column
their standard errors
= —7
mean TFP
through t
in
in
5.
The
winning counties
MDP
relative to the year before the
between estimated mean TFP
=
opened.
winning and losing
counties.
Figure
mean TFP
in
1
graphs the estimated coefficients from Table
winning and losing counties (Columns
1
trends
The top panel
and 2 of Table
the differences in the estimated winner and loser coefficients
The Figure has
4.
(Column
4).
3
The bottom panel
of Table
three important features. First, in the years before the
among incumbent
plants
were very similar
statistical test fails to reject that the trends
in
separately plots
plots
4).
MDP opening, TFP
winning and losing counties. Indeed, a
were equal. This finding supports the
validity
of our
identifying assumption that incumbent plants in losing counties provide a valid counterfactual for
incumbents
25
Finally,
we
in
winning counties.
hired a graduate student at Princeton to review publicly disclosed and annotated versions of all
STATA programs.
This person was not associated with the authors or their institutions prior to serving as the
program proofreader. To the best of his knowledge, the computer codes were correct. The authors remain fully
responsible for any coding errors in the analysis.
25
MDP
Second, beginning in the year of the
TFP between
difference in
improvement
relative
flattening
of the
availability
TFP
to the
continued
TFP
a sharp
is
upward break
The top panel shows
the winning and losing counties.
mainly due
is
opening, there
in the
that this
decline in losing counties and a
trend in winning counties. This underscores the importance of the
of losing counties as a counterfactual. For example, a naive comparison of
winning counties before and
after the
MDP opening would
suggest that
TFP
in
had a negligible impact
it
on incumbents' TFP. Overall, these graphs reveal much of the paper's primary finding. This
relative increase in
TFP among incumbent
plants in winning counties
TFP
variety of tests in the remainder of the paper. Third,
is
confirmed throughout a
We
displays a negative trend.
discuss
this feature in detail in Section VI.
Turning
fitting different
parameter,
B
Panel
which
9i,
to the statistical
models, the
versions of equation (8). For
and
its
four columns of Table 5 present results from
Model
1,
Panel
A
determined by the reported
0i
TFP
evaluated at t
=
reports the estimated
"Mean
standard error (in parentheses) in the
reports the estimated change in
is
first
Model
("Level Change" row) and 62 ("Trend Break" row).
plants in winning and losing counties. In
is
Shift" row. For
all
shift
2,
5 in the "Effect after 5 years" row,
"Pre-Trend" row contains the coefficient measuring the difference
opening
mean
in pre-existing trends
specifications, the estimated
change
determined during the period where t ranges from -7 through
5,
The
between
after the
as the
26
MDP
sample
is
balanced during these years.
In
Columns
(1)
and
(2), the
sample includes
all
report data for at least 14 consecutive years, excluding
Column
(3), the
sample
is
manufacturing plants
all
plants
owned by
restricted to include only plants in counties that
ASM that
MDP firm. In
in the
the
won
or lost a
MDP.
This restriction means that the input parameters and the industry-year fixed effects are estimated
solely from plants in these counties. Incumbent plants are
-8
<
x
<
Column
now
required to be in the data only for
(not for 14 consecutive years, though this does not change the results). Finally, in
(4),
the sample
is
restricted further to include only plant-year observations within the
period of interest (where x ranges from -7 through
5).
This forces the input parameters and
industry-year fixed effects to be estimated solely on plant-by-year observations that identify the
spillover parameters. This sample
estimation details are noted
26
This
is
calculated as 9,
at
is
used throughout the remainder of the paper. Further
the bottom of the table and apply to both
+ 69 2 because we allow
,
the
MDP to affect outcomes
26
from
x
Models
=
1
and
through x =
2.
5.
The
is
associated with a substantial relative increase in
Model
counties. Specifically,
on TFP appears
more
Model
appropriate. Results from
approximately
12%
increase in
in
In
change
Column
4,
in millions
of 2006
percent change by the
=
-1.
For Model
1,
dollars.
large,
row demonstrate
in
also
owned by
from both models are
all
the
of equal trends
winning counties
plants' total shipments in
$170
increase in total output of
results
TFP
following a
same years and
own
the
TFP
at i
=
5 nearly the
its
set
randomly chosen
ASM for
plants.
opening
in
year t
average level of
=
5.
MDP
is
These
output.
magnitude.
that
is
based on using plant
of 47 plant openings was randomly
industries as the
in the
MDP
in t
The Model 2 estimate
million.
from a "naive" estimator
manufacturing plants
firms that
statistically
These numbers are calculated by multiplying the estimated
with the Model 2 effect
ASM in
associated with an
square brackets evaluate the average magnitude of
openings without an explicit counterfactual. To begin, a
the sample includes
is
an increase in output of roughly $429 million
(5) presents
chosen from the
opening
that the null hypothesis
Section VII discusses the interpretation of this change and
Column
MDP
this calculation indicates that the increase in
larger, suggesting
numbers are
roughly 4.8%. As the figure
and are unaffected by the specification changes.
mean value of incumbent
was associated with an annual
even
winning
plants in
winning and losing counties cannot be rejected.
in
numbers
the
MDP opening
that the
be increasing over time so Model 2 seems
2 suggest that the
criteria,
the "Pre-trend"
TFP among incumbents
TFP of
five years later. Estimates
from zero by conventional
Furthermore, entries
in
TFP
to
1
TFP among incumbent
implies an increase in
1
highlights, however, the impact
different
from Figure
entries in Table 5 confirm the visual impression
MDP
openings.
The remainder of
fourteen consecutive years, and not
With these
data,
we
fit
a regression of
the natural log of output on the natural log of inputs, year by 2-digit SIC fixed effects, and plant
fixed effects. In
in a
Model
winning county 7 to
reported
mean
two additional
1,
1
dummy
variables are included for whether the plant
years before the randomly chosen opening or
shift is the difference in these
following the opening). In Model
and post-trend variables. The
2,
the
two
same two
shift in level
coefficients
dummy
(i.e.,
mean and
the average change in
The
TFP
variables are included along with pre-
and trend are reported, along with the pre-trend and
the total effect evaluated after five years. Finally, this procedure
the reported parameters are the
to 5 years after.
is
is
implemented 1,000 times and
standard deviation of those estimates.
This naive "first-difference" style estimator indicates that the opening of a
27
new
plant
is
associated with a
-3%
estimates from the
MDP
TFP of incumbent
plant openings. This
in
plants
15% (Model
1) to
was on a downward trend
similar to
is
incumbent plants' TFP, depending on the model. If the
research design are correct, then this naive approach understates the
10% (Model
extent of spillovers by
that
-5% change
to
what
is
observed
in
2).
in
our
The estimated
advance of the randomly selected new
MDP
sample of winners. Overall, the
absence of a credible research design can lead to misleading inferences
It is
"pre-trend" indicates
in this setting.
important to document the degree of heterogeneity in the treatment effects from the
47 separate case studies
that underlie the estimates presented thus far. Figure 2 explores this
heterogeneity by plotting case-specific estimates of parameter
8]
in
Model
confidence intervals. Specifically, the Figure plots results from a version of Model
interacts
the
(l{Winner) x 1(t > 0)) with indicators
variable
specification yields 45 estimates of 0i, as results from
Census Bureau's confidentiality
the estimated impacts on
TFP of incumbent
MDP
from zero
at the
5%
characteristics, but the limited
we
with confidence. Specifically,
size,
MDP
whether the
When
is
We
level.
to
substantial heterogeneity in
Twenty-seven of the 45 estimates are
explored whether this heterogeneity
number of cases provide
insufficient
is
power
statistically
related to the
to detect
regressed the estimates against three measures of the
owned by
a foreign
that
comply with the
and 9 of the negative estimates are
positive. Thirteep of the positive estimates
different
plants.
is
1
each of the cases. This
two cases were omitted
Figure 2 reveals that there
rules.
for
95%
and their
1
company, and whether
it
much
MDP's
an auto company.
is
these multiple measures were included jointly, none were significantly related to the
estimated effect of the
Ultimately,
alternative
way
to
MDP's
TFP
is
opening.
27
a residual, and residual labeling must be done cautiously.
examine the
MDP
impact,
we
As an
estimate directly the changes in incumbent plant
output (unadjusted for inputs) and inputs following a
MDP
opening. Contrasting changes in
outputs and inputs can shed light on whether productivity increased without imposing the
structural
assumptions of the production function. Put another way,
producing more with
27
less after the
MDP
opening? Factor input decisions also
Separate regressions of the case specific effects on the
statistically significant negative coefficients.
This result
MDP's
is
total
output or the
MDP's
total
consistent with the possibility that
large incumbents are left to hire labor and other inputs that are inferior in unobserved ways.
incumbents
the
are
reflect firms'
labor force generated
when
On
the
MDP
is
very
the other hand,
we
any significant differences when separately testing whether the productivity effect varied by the ratio
of the MDP's output to county-wide manufacturing output, whether the MDP is owned by a foreign company, or
whether the MDP is an auto company.
failed to find
28
many of
optimization decisions, and do not share
technology
(e.g.,
Model
exclude the inputs as covariates. For Model
to
13% (Columns
increase less. Across
specifications,
all
Table
MDP
less after the
28
Furthermore,
5.
1,
is
it
Model
in output. Overall,
is
it
output increases by
2,
change
in
in all
MDP
(8),
but
1)
and inputs
8%
and inputs
of the inputs
is
roughly
appears that incumbent plants produced
consistent with the
some evidence of increased
is
12% (Column
output increases by
striking that the
opening, which
there
and Model 2 versions of equation
1
2 through 5). For
equal to or less than the increase
more with
changes
incumbent plant output and inputs following a
in
opening. These estimates are from the
by 4
potential biases as
output price effects).
Table 6 reports estimated changes
increase
same
the
TFP
increases uncovered in
input
use,
reflecting
firms'
optimization in the face of higher potential productivity.
Estimates of Spillovers by Economic Distance
B.
What mechanisms might
discussed
some mechanisms
subsection attempts to shed
explain the productivity gains estimated above? Section IA
may
that
some
light
be responsible for agglomeration spillovers.
This
on the possible mechanisms by investigating how the
estimated spillover effect varies as a function of economic distance.
A
similar approach has been
used by Moretti (2004b) and Ellison, Glaeser, and Kerr (2009).
(i)
By
Industry. Table 7 shows separate estimates from the baseline model for samples
of incumbent plants
in the
MDP's
2-digit industry
and
all
other industries. In general, one might
expect agglomeration spillovers to decline with economic distance (equation
it
is
natural to explore whether spillovers are larger within an industry.
substantial heterogeneity in technologies
industry, only 16
[5']).
As
a
first
pass,
While there can be
and labor forces among plants within a 2-digit SIC
of the 47 cases have incumbent plants
in
the
MDP's
2-digit industry. Thus, the
research design and available data do not permit a discrete analysis at finer industry definitions.
The mode! suggests
that firms should substitute
away from
labor and toward capital.
supportive of this prediction, though directly estimating changes
making
19
The
in the capital/labor ratio
29
point estimates are not
gives imprecise estimates,
definitive conclusions unwarranted.
In the spirit of
work by Bloom
et al.
(2007) and Jaffe (1986),
technological overlap between industries. Lacking patent data,
share of industrial output that
is
we
we
explore defining a continuous measure of
define
at
the 3-digit
SIC industry
level: (1) the
sold to each manufacturing industry; and (2) the share of manufactured inputs that
from each manufacturing industry. For each measure, we calculate the overlap between an incumbent
and the MDP industry by taking the product of those vectors. We then estimate equation (9),
interacting the MDP effect with each measure of industrial overlap. There is evidence of differential spillovers based
on overlap defined with inputs, but not with outputs. We suspect that overlap in plant output consumed by the
are received
firm's industry
29
Column
of Table 7 reports estimates for
1
Columns
basis of comparison.
incumbent plants
in the
MDP's
and
(2)
(3) report estimates
2-digit industry
and
of Table
(4)
Table
in
5,
as a
from the baseline specification for
other industries, respectively.
all
columns are from the same regression. As
in these
from Column
industries
all
the
5,
numbers
The
entries
square brackets
in
convert the estimated percent changes into millions of 2006 dollars.
The estimated changes
are substantially larger in the
example, the estimated increase
17%
significant
in
Model
TFP
in
for plants in the
same
33%
and a poorly determined
1
MDP's own
2-digit industry
at x
=
in
Model
sample
size,
which was
1.
The
TFP
2-digit
in the
confirm this visual impression. As
trend in
Model
in
MDP
MDP
in
1
and
industry and other
industry estimates are noisy due to the small
also evident in the statistical results. Importantly, there
evidence of differential trends
downward
2. In contrast,
2.
Figures 3 and 4 graph annual changes in TFP, providing 2-digit
industry analogues to Figure
3.3%
For
a statistically
is
Model
5 in
estimates for plants in other industries are a statistically insignificant
marginally significant 8.9%
2-digit industry.
years before the
Figure
in losing counties
1,
MDP's
not any
is
opening, and statistical tests
the estimated impact reflects the continuation of a
and a cessation of the downward trend
in
winning
counties.
To probe
the role of economic distance further,
Dollar Plant" openings that were in the
MDP
the original 82
MDP
retail
we
identified an additional ten "Million
and wholesale trade
sectors.
openings, but are not included in the main sample of 47 manufacturing
openings. Estimating equation (8) for these ten trade sector
changes of -2.2% (2.9%)
in
Model
year observations in 31 counties).
generate similar
TFP
These plants are part of
increases,
1
It
and 4.9% (6.5%)
in
MDP
Model 2 (on
a
openings,
we
find
TFP
sample of 12,105 plant-
appears that the non-manufacturing sector openings did not
though the estimates
in
Model 2
are too imprecise to reject their
equality with the baseline estimates. These findings provide further evidence that the spillovers
are concentrated
among
manufacturing sector only
plant inputs
These
inputs
is
is
plants that are economically close to the
new
30
plant.
a poor reflection of overall industrial overlap, and are not confident that overlap
results
may
also provide a test of
causing plants to
move
closer
to
whether the estimated spillovers are due
their
production possibility
frontier.
to increased
Specifically,
competition for
these
new non-
manufacturing plants increase competition for land, labor, and other local inputs. The resulting increase
prices
may cause
all
in input
plants (regardless of industry) to search for opportunities to increase productivity. In such a
situation, all local plants
this paper's
in
more persuasive.
would exhibit increased TFP. These
primary findings.
30
results suggest that this
mechanism does not explain
By Continuous Measures of Economic
(ii)
economic proximity more
worker flows,
proximity,
and
flows
input-output
economic proximity variables are standardized
interpretation, these
standard deviation of one. In
investigate the role of
by using several measures of economic proximity
directly
technological
We now
Distance.
(Table
that capture
To ease
2).
have a mean of zero and
to
cases, a positive value indicates a "closer' relationship
all
the
between
the industries.
we
Specifically,
(9)
\n(Ypijt
)
= ft
+
estimate the following equation:
\n(L pijt
>
k1(t
0) yt
+ 7t 2 (1(t >
the
MDP
between the
1(t
>
0)) p;£
ln(Mpi/t
proximity
dummy
^(KW'inner)
+
+ n 3 (l(Winner) pj x
for a
with
variables
is
713,
l(Winner) pj,
which
0)
i}
x Proximity^)
)
+ ap
winning county, the
is
new
economically distant from the
A zero
plant, relative to
new
dummy
(1 (Winner)
and
0),
for after the
MDP
x
opening, and the
in "closer" industries
experience
Table 8 reports estimates of
713
for six
columns include the proximity measures one
one standard deviation increase
MDP's
is
same comparison among incumbents
that the estimated productivity spillover
the incumbents in a county, regardless of their
spillover. This finding
>
adds
incumbents that are geographically close but
plant (relative to the
means
coefficient
and the
1(t
that
1
after the
economically close to the
plants' industry
>
x Proximity
MDP opening. A positive coefficient means that the estimated
larger after the MDP opening for incumbents that are geographically and
TFP
productivity spillover
all
1(t
pj
the coefficient on the triple interaction
is
measure of proximity. This coefficient assesses whether plants
loser counties).
+ 81(Winner) pj
simply an augmented version of Model
is
coefficient of interest
a greater increase in
)
Epijt
This equation
of the
The
+ B^KWinner) x
ft
a measure of economic proximity between the incumbent plant industry and
industry.
interactions
lf>0)pjt.
is
ln(^;t ) +
ft
0) x Proximityij)
+ nit + Ap +
where Proximityij
+ ft ln(^% ) +
)
economic proximity
industry
is
new
the
at a time.
For example, Column
same
for
plant.
measures of economic proximity. The
CPS Worker
in the
to the
is
in
first six
(1) reports that a
Transitions variable between incumbent
associated with a 7 percentage point increase in the
consistent with the theory that spillovers occur through the flow of
workers across firms. One possibility
production or information on
new
is
that
new workers
share ideas on
how
to organize
technologies that they learned with their previous employer.
This measure tends to be especially high within 2-digit industries, so
31
this
finding
was
foreshadowed by the
In
results in
Columns
(2),
(3),
and
is
2-digit industry.
the measures of intellectual or technological linkages
(4),
indicate meaningful increases in the spillover.
shared
own
Table 7 based on the plant's
The
mechanism by which these ideas
precise
unclear, although both the flow of workers across firms
and the mythical exchange of
ideas over beers between workers from different firms are possibilities. Notably, there
variation in these measures within 2-digit industries than in the
Columns
(5)
and
(6)
provide
an auto manufacturer encourages (or even forces)
all
plants
owned by
the
its
MDP's
finding does not rule out this channel within firms.
technology flows
is
CPS
labor transitions measure.
fail
to support the types
suppliers to adopt
more
of
stories
where
efficient production
firm are dropped from the analysis, so this
The finding on
the importance of labor and
consistent with the results in Ellison, Glaeser and Kerr (2009) and
and Glaeser (2002), while the finding on input and output flows stands
Ellison,
more
is
support for the flow of goods and services in
little
determining the magnitude of spillovers. Thus, the data
techniques. Recall,
are
Dumais,
in contrast
with
these papers' findings.
In the
Column
simultaneously.
The
(7) specification,
we
include
all
the measures of economic proximity
labor flow, the citation pattern, and the technology input interactions
remain positive but are
statistically insignificant.
The customer and supplier
all
interactions are
negative and statistically insignificant.
Overall, this analysis provides support for the notion that spillovers occur between firms
that share workers
evidence
workers
is
and between firms that use similar technologies. In terms of Section IC,
consistent with intellectual externalities, to the extent that they are
who move from
firm to firm, and to the extent that they occur
technologies that are reasonably similar. The estimates
in
Table 8 seem
among
embodied
less consistent
all
in
firms that use
with the
hypothesis that agglomeration occurs because of proximity to customers and suppliers.
caution against definitive conclusions, because the utilized measures are
this
We
imperfect proxies for
the potential channels. Further, the possibility of better matches between workers and firms
could not be directly tested with these data.
C.
Firm Entry and Labor Costs as Indirect Tests of Spillovers
Baseline estimates found economically substantial productivity gains for incumbent
establishments following the opening of the
new MDP.
32
In the
presence of positive spillovers, the
model makes two empirical predictions
and increased local input
costs.
productivity spillovers are larger than short-run increases in the cost of local
First, if
inputs, the
that are explored in this subsection: increased firm entry
MDP county should experience entry by new firms (relative to
The
9 tests this prediction at the county level.
entries in Panel
data from the Census of Manufactures, which
manufacturing output (Column 2)
counties, and
owned by
plants
all
come from
1
county.
in the
MDP
regressions that use
conducted every five years. The dependent
number of establishments (Column
log of the
variables are the
is
losing counties). Table
The sample
is
1)
and the log of
total
winning and losing
restricted to
firms are excluded from both dependent variables.
The
covariates include county fixed effects, year fixed effects, case fixed effects, and an indicator for
whether the observation
is
from
MDP
after the
opening.
The parameter of
interest
is
associated
with the interaction of indicators for an observation from a winning county and from after the
MDP opening, so
Column
it is
(1) reports that the
winning counties
in
all
a difference-in-difference estimator of the impact of the
MDP
after the
plants are of an equal size.
because
it
treats
an increase
number of manufacturing
in
reports that the opening of a
The
total
A
limitation of this
value of output
is
associated with a
measure
new
attracted
new economic
assumes
it
plant equally.
14.5% increase
that
in total
Column
(2)
output in the
this is not estimated precisely.
Overall, these results are consistent with estimated increases in
MDP
1
by roughly 12.5%
that
is
•3
economically more meaningful,
is
output at an existing plant and a
MDP
manufacturing sector, although
opening.
plants increased
MDP opening.
activity to the
winning counties
TFP,
as
it
appears that the
(relative to losing counties) in the
manufacturing sector. Presumably, these new manufacturing establishments decided to locate
the winning counties to gain access to the productivity advantages generated
by
in
the spillover
effect.
The second
theoretical prediction
is
that,
if spillovers
are positive, the prices of local
inputs will increase as firms compete for these factors of production.
supplied input for manufacturing plants
wage
31
is
labor. This prediction
is
The most important
locally
tested using individual-level
data for winning and losing counties from the 1970, 1980, 1990, and 2000 Censuses of
Because data
years are
1
-
is
available every 5 years, depending on the Census year relative to the
associated with one earlier
MDP
opening, the sample
opening and 4-8 years after the MDP opening. Thus, each
date and one later date. Models are weighted by the number of plants
5 years before the
years -6 to -10 and column 4
is
MDP
weighted by the county's
total
33
manufacturing output
in
MDP
in the
years -6 to -10.
opening
county
is
in
Population.
32
These data are preferable
Manufactures
(i.e.,
the aggregate
wage
to the
measure of labor costs reported
bill for
we
year, education
US
We
and case fixed
effects.
from a winning county, occurs
indicators.
33
This interaction
is
the
Model
version of equation
1
Column
(3) in Panel 2
counties after the
MDP
wage
million after the
MDP
MDP
opening, and the interaction of these two
an adjusted difference-inis
analogous to
(8).
of Table 9 reports
that
wages increase by 2.7%
wage
and
is
winning
in
marginally statistically significant. Multiplying the
by the average labor earnings
bill
for
MDP opening. This
who
is
opening on wages. This equation
employers
finding
is
in
winning counties implies that the
by roughly $151
in all industries increased
consistent with positive spillovers and an
sloping labor supply curve, as in the model in Section
Moretti (2004),
is
opening, after adjusting for observable individual heterogeneity. This
increase
quality-adjusted ^annual
of
for interactions
and year, sex and race and Hispanic and
the focus of the regression and
effect appears quantitatively sizable
estimated 2.7%
dummies
also include indicators for whether the observation
after the
difference estimator of the impact of the
education and experience).
(e.g.,
estimate changes in log wages, controlling for
worker age and year, age-squared and
citizenship,
Census of
production and non-production workers), which
does not provide information on the quality of the labor force
Specifically,
in the
I.
This finding
finds significant productivity spillovers
is
upward
also consistent with
and increases
in
wages of similar
magnitude.
It is
possible to use the estimated increase in
calculations of the
MDP's
impact on incumbent plants'
Table 5 indicated an increase
in
TFP
not possible to estimate a version of
assume
that
wages
to
make some back of the envelope
profits. Recall, the
Model
of approximately 4.8% (we focus on Model
Model 2 with
1
1
result in
because
it is
the decennial population Census data). If
workers are homogenous or that high and low
substitutable in production, then the labor market-wide increase in
workers are perfectly
skill
wages applies throughout
manufacturing sector. In our sample, labor accounts for roughly
we
23%
32
of
total costs,
the
so the
The sample is limited to individuals who worked last year, worked more than 26 weeks, usually work more than
20 hours per week, are not in school, are at work, and who work for wages in the private sector. One important
limitation of the Census data is that they lack exact county identifiers for counties with populations below 100,000.
Instead, it is possible to identify PUMAs in the Census, which in rural areas can include several counties. This
introduces significant measurement error, which is partly responsible for the imprecision of the estimate.
The pre-period is defined as the most recent census before the MDP opening. The post-period is defined as the
most recent census 3 or more years after the MDP opening. Thus, the sample years are - 10 years before the MDP
opening and 3 - 12 years after the MDP opening.
1
34
estimated
2.7%
increase in skill adjusted
wages implies
by
that manufacturers' costs increased
13%
approximately 0.62%. The increased production costs due to higher wages are therefore
of
the gain in TFP.
These calculations demonstrate
due
TFP do
not translate directly to profits
higher costs of local inputs. Since the prices and quality of other inputs are not
to the
observable,
not possible to determine the total increase
is
it
wage
expect the
that the gains in
plants enter or
increase to be larger for plants and industries that experience
expand and compete
workers with the
for
we
production costs. Further,
in
TFP
increases as
skills relevant for these sectors.
these reasons, this back of the envelope calculation should be interpreted as a lower
For
bound of the
increase in input costs. In the long run, an equilibrium requires that the total impact on profits
is
zero.
VI. Validity and Robustness
Our main empirical finding
substantial average increase in
incumbent plants
research design
of
many
is
in counties that
in
V
Section
that
is
TFP among incumbent
MDP
openings are associated with a
plants in those counties, relative to
narrowly missed receiving the
supported by the similarity of pre-trends
in
new
TFP
plants.
(Figure
The
1)
validity
of this
and the balancing
ex-ante observable characteristics of winning and losing counties and their incumbent
plants (Table 3). Nevertheless, the possibility remains that the paper's identifying assumption
invalid
and that
incumbent plants
productivity shocks coincident to the
Consequently,
specifications,
this
section
winning counties experienced unobserved positive
in
new
plant's opening.
explores
the
robustness
(i)
unobserved
mismeasurement; and,
(i)
estimates
to
various
the role of functional form assumptions,
unobserved industry and regional shocks, and weighting;
(iii)
of the
and investigates several possible alternative interpretations of the estimated
spillover effects. Specifically, this section analyzes:
inputs;
is
(vi)
changes
changes
in
inputs;
in the price
(ii)
attrition;
(iv)
the general endogeneity of plant
(v)
declining
plant
TFP
and
of plant output.
Functional Form, Industry and Regional Shocks, and Weighting. Table 10 reports
estimates from a series of specification checks.
the results from the preferred specification in
We
As
a basis for comparison,
Column
(4)
of Table
Column
(1) reports
5.
begin by generalizing our assumption on plants' production technology. Estimates
35
in
Table 5 assume a Cobb-Douglas technology. In Column (2) of Table
Column
the translog functional form.
of each production input to
effect
(3)
is
10, inputs are
modeled with
based on a Cobb-Douglas technology but allows the
differ at the 2-digit
SIC
level.
This model accounts for possible
differences in technology across industries, as well as for possible differences in the quality of
inputs used
by
different industries.
For example,
some
similar across different manufacturers,
Column
the
(4) allows the effect
of the inputs
it
is
possible that even if technology were
industries use
more
labor than others.
skilled
to differ in winning/losing counties
and before/after
MDP opening.
Columns
(5)
and
add census division by year fixed effects and census division by year
(6)
by 2-digit industry fixed
effects.
These specifications aim to purge the spillover
unobserved region-wide shocks or region by industry shocks
correlated with the probability of winning a
Until
this
point,
we have
of
effects
might be
to productivity that
MDP (e.g., a declining Rust Belt).
presented
results
based
on specifications
weight
that
observations by the square root of the plant's total value of shipments eight years prior to the
MDP
opening.
As
discussed above, the resulting estimates measure the change in productivity
per average dollar of output, which reflects the
Column
(7)
reports
the
results
2,
the estimated change
is
economic impact of the
from unweighted regressions
productivity for the average plant. For
Model
full
Model
1,
concentrated
why
among
smaller plants
Taken
the largest plants.
fail to
34
benefit from the
is set
A
is
the
in
None of the
within one standard error of the baseline estimate
to
is
to explore
The weighted estimates appear
estimates are smaller than the baseline ones, the magnitude of the decline
fail
be
plant's presence.
contradict the findings from the baseline specification in Table 5. Although
Overall, these results
may
both models, which
insensitive to the specific functional form of the production function.
all
for
aside, the results indicate that the spillovers are
together, the results in Table 10 are striking.
example, they are
in
1.46% (1.07%);
promising avenue for future research
new
change
findings should be interpreted cautiously
because the building capital coefficient becomes slightly negative
a sign of misspecification. If this concern
reveal
that
the estimated change
0.65% (2.81%). These
plant. Nevertheless,
undermine the conclusion from Table
34
in
is
specifications
many of
the
modest. For
both Models
5 that the
to be
1
opening of a
and
2.
MDP
unweighted regressions, the estimated effect among incumbent plants in the largest decile (8 years prior to the
is: 2.90% (3.12%) higher than the average effect of 1.16% (0.98%) for Model l;and 16.7%(11.0%)
higher than the average effect of -1.1 6% (3.04%) for Model 2.
In
MDP opening)
36
TFP among incumbent
leads to a substantial increase in
theories of spillovers.
(ii)
and
plants,
with
this is consistent
35
General Endogeneity of Inputs.
An
important conceptual concern
labor inputs should be treated as endogenous, because the
same
is
that capital
and
forces that determine output also
determine a firm's optimal choice of inputs (Griliches and Mairesse 1995). Unlike the usual
estimation of production functions,
parameters,
results
in
9i
and
62,
our aim
is
the consistent estimation
so the endogeneity of capital and labor
of the spillover
only relevant to the extent that
is
it
biased estimates of these parameters. This subsection employs the productivity
literature's techniques to control for the
endogeneity of capital and labor to assess
this issue's
relevance in this paper's setting.
We
First, in
employ
Columns
three
(2)
main approaches, and the
and
(3),
we
calculate
TFP
results are collected in
for each plant
by
Appendix Table
fixing the parameters
1.
on the
inputs at the relevant input's share of total costs (van Biesebroeck 2004; Syverson 2004a; Foster,
Haltiwanger, and Syverson 2008). This method
may
mitigate any bias in the estimation of the
parameters on the inputs associated with unobserved demand shocks. In these two columns, the
cost shares are calculated at the plant level and the 3-digit
SIC industry
level over the full
sample, respectively.
Second, Columns (4) through (6) present estimates based on methodologies that build on
work by Olley and Pakes
(1996). These methods are based on the result that, under certain
conditions, adjustment for investment or intermediate inputs (e.g., materials) will
correlation between input levels and unobserved shocks to output.
Column
remove the
(4) controls for 4th
degree polynomial functions of log capital and log investment, and the interaction of both
functions (separately for both types of capital).
Column
(4),
Column
(5)
includes the
same controls
as
but replaces investment with materials; an alternative proposed by Levinsohn and
Petrin (2003). Building on
Column
log materials, as collinearity
may
(5),
Column
(6) includes interactions
between log labor and
complicate the estimation of the labor coefficient (Ackerberg,
Caves, and Frazer 2006).
Third,
3
We
Column
(7) presents estimates that instrument for current input levels with
also tested whether the results are sensitive to the choice of the date of the
year that the plant
is
first
observed
in the
SSEL
as the
MDP
MDP
opening date. Model
1
opening.
lagged
When we
estimates a change of
use the
5.23%
(2.39%) and Model 2 estimates a change of 11.2% (5.57%). When we use the year of the MDP article for the
opening date, Model 1 estimates a change of 4.58% (2.45%) and Model 2 estimates a change of 4.88% (4.21%).
37
changes
from
= -2
t
to
Bond
a technique proposed by Blundell and
in inputs;
t
= -1 may
unobserved output shocks
in
Column
expected sign, and
=
0.
=
t
0,
change
change due
to potential
(7) reports the
weak instrument
Appendix Table
1
The
may
but
2SLS
Of course,
results.
"it
that
is
we have
used only the
The
72%
summed
of the
0.86% increase
endogeneity
typical
in variable inputs (labor)
and
statistically significant,
but not
1%
and the
36
and
coefficients for labor
overall production function has mild decreasing returns to scale, with a
leading to a
way of
downward biased and loaded onto
is
labor. In the baseline specification, all inputs are positive
an expected
lagged
first
also reports coefficients from the production function, as a
fixed inputs (capital), so the estimated effect of capital
is
a strong assumption that
bias.
unobserved productivity shocks lead to changes
labor coefficient
not be correlated with
is
assessing the effectiveness of the production function estimation.
concern
increase in each input
Indeed, the estimated first-stage results (not shown) have the
not otherwise correlated with output, and
this lagged
is
t
predict input levels at
(1998).
capital.
increase in
all
The
inputs
in output.
Overall, the estimated changes in
TFP
are consistent with the findings from the baseline
specification. This exercise fails to suggest that the possible endogeneity
of labor and capital
is
the source of the estimated productivity spillovers.
Unobserved Changes
(iii)
comprehensive of
all
in
The
Inputs.
input
inputs that affect plant output.
measures
the
in
ASM
Further, the available data
are
not
may
not
adequately measure the degree of input usage or the quality of inputs. Consequently,
it
is
possible that the estimated spillovers reflect changes in unobserved inputs, unobserved usage,
and/or input quality. This subsection explores these possibilities.
State
and
local
governments frequently offer substantial subsidies
plants to locate within their jurisdictions.
to
new manufacturing
These incentives can include tax breaks, worker
training funds, the construction of roads, and other infrastructure investments.
these investments benefits firms other than the
road intended for a
MDP
may
(Chandra and Thompson 2000).
investment, then
36
As
it
is
and
1
.84%
If the productivity gains
inappropriate to interpret
for building capital,
For example, the construction of a new
also benefit the productivity of
a basis of comparison, the weighted cost shares are
capital,
MDP.
among
them
some of
the incumbent firms
we have documented
are
due
to public
as evidence of spillovers.
70.9%
for materials,
plants the year before the
38
possible that
It is
23.2%
for labor,
MDP opening.
3.98%
for
machinery
To
we
investigate this possibility,
total capital
estimated the effect of
MDP
openings on government
expenditures and government construction expenditures with data from the
Survey of Governments.
In
models similar
to equation (8),
we
find that the opening
of a
Annual
MDP
is
associated with statistically insignificant increases in capital and construction expenditures. In
most specifications the estimated impact of a
insignificant.
Even
MDP
opening
is
negative and statistically
produce positive insignificant estimates, there
in the specifications that
is
no
plausible rate of return that could generate a meaningful portion of the productivity gains in
winning counties. Based on these measures of public investment,
it
seems reasonable
to
conclude
that public investment cannot explain the paper's results.
Incumbent plants may respond
capital usage. If
MDP
to the
opening by increasing the intensity of their
winning counties had been depressed and the
capacity, then incumbent plants might increase production simply
closer to capacity.
As an
indirect test
of this
possibility,
we
models
and insignificant changes
find small
capacity utilization
The
is
is
unclear. If the
incumbent plants or the opening
may upgrade
of equation
(8)
used
by unobserved changes
would lead
(iv)
Sample
may
decline. If the
attracts higher quality
Plants. If the
attrition
though the
MDP
receives bad workers from
workers to the county, then incumbent
TFP
MDP
TFP change
in
for
for their quality,
incumbent plants.
increases competition for inputs and raises
in
quality-adjusted wages, this might
to close. Indeed, for a variety
sample of incumbent plants
in
of the true
by the estimated changes
encourage plants with declining
differential
we
poaches good workers from incumbent plants, the
to an underestimate or overestimate
local input prices, as suggested
measured
6,
the quality of their workforce. Since the specifications control for the
Attrition of
attrition in the
Table
in labor quality,
number of hours worked by production and non-production workers, but not
this
in
measure. This finding suggests that greater capital
MDP
quality of the workforce in existing plants
plants
MDP opening
unlikely to be the source of estimated productivity spillovers.
results could also be influenced
direction of this bias
their capital stock
increasing in the use of the capital
is
identical to the version
in this
by operating
was used below
estimated whether the
affected the ratio of the dollar value of energy usage (which
stock) to the capital stock. In
capital stock
of reasons, differential
winning and losing counties could contribute to the
productivity trends
among
could either result from plants shutting
survivors after the
down
MDP
opening. This
operations or from plants continuing
operations but dropping out of the group of plants that are surveyed with certainty as part of the
39
ASM.
The
available evidence suggests that differential attrition
winning counties. Similar numbers of winning and losing plants remained
of spillovers
in
sample
end:
at i
=
at its
72%
5 as a fraction
counties
is
unlikely to explain the finding
is
winning counties and
in
of the number of plants
68%
at x
-
in
losing counties
0).
The
(i.e.,
slightly larger attrition rate in losing
that the
is
MDP
opening allowed some winning county plants to
remain open that would have otherwise closed. Thus to the extent that a
weakening plants operating, the baseline analysis
Within aggregate
MDP
plants in
some
MDP
such
attrition
numbers, the
location. This
may
plants
all
we might
industries, then
MDP, 71%
sample
attrition
increases input prices for
each
the
will underestimate the overall
MDP
TFP
increase.
37
If
and disproportionally increases productivity for
expect to see increased agglomeration of those plants in
of incumbents
(v)
Declining Plant
in
winning counties and
5 years).
69%
Within the same 2-digit SIC as
in losing
counties remained in the
5.
TFP and Mismeasurement.
incumbent plant TFP was declining prior
counties, and in losing counties after the
to the
MDP
MDP
of the
results
The decline
in
and conclude
TFP
appear continuously
in the
is
it
in
opening. This finding
we
is
will
it
affects the
TFP
in the
overall
of large and aging manufacturing plants that
in the eight years prior to the
and sample
that
striking, as productivity
explore whether
MDP
specifications include both plant and industry-by-year fixed effects.
this specification
show
does not.
restricted to a set
ASM
1
both winning and losing
not necessarily inconsistent with rising
is
economy. Recall, the sample
that
Table 4 and Figure
opening
generally increases over time in the overall economy. Here,
" The
opening keeps
might change the mix of existing firms.
only occur in the long-run (more than
=
interpretation
MDP
an intriguing hypothesis, but difficult to test in this setting because
is
at t
TFP from
number of plants
consistent with the paper's primary result. Specifically, one seemingly reasonable
interpretation of this result
the
the
in the
opening. Further, the
The estimated
decline in
miss the process of creative destruction where
less
winning and losing counties prior to the MDP
TFP trend in losing counties was -0.0052
(0.0080). Further, the estimation of equation (8) on the sample of plants that is present for all years from -7 to +5
yields results that are qualitatively similar to those from the full sample.
null hypothesis
of equal trends
opening cannot be rejected; the
TFP
in
TFP among
attriting plants in
trend in winning counties minus the
40
no
productive plants are replaced by more productive plants.
The estimated downward trend appears
of plants
in all
analysis of
the
U.S. counties, and
MDP
be a general phenomenon for similar samples
TFP among
plants declined annually
similar plants in
openings. Specifically,
we
From our
not limited to winning and losing counties.
randomly selected manufacturing plant openings
TFP of incumbent
declining
is
to
the U.S. (Table 5,
in
by 0.5% prior
Column
[5]),
we
find
to the opening. Further,
U.S. counties over the 6-year periods following the
all
created a sample of
all
U.S. plants that appear
in
the
ASM
for
fourteen straight years, deflated output and materials by the CPI, and regressed log output on log
capital stocks (building
and machinery, created using the same permanent inventory method), log
labor hours, log materials, plant fixed effects, industry fixed effects, year fixed effects, and
weighted the regression by plant output. The estimated year effects report average changes
TFP
over each year, and
we
correspond to the periods following
declined
in all
words, the
MDP
openings.
U.S. counties by an average of
39
4.7% (with
pre-MDP opening TFP changes among
over 6-year periods that
Over these periods, we
find that
TFP
a standard error of 0.4%). In other
large and aging plants in our
winning and losing counties (and post-MDP opening changes
TFP
TFP
calculated average changes in
in
sample of
in losing counties) are similar to
changes among large and aging plants throughout the U.S.
An
reflects
alternative explanation
measurement
is
that
measured declines
error, particularly in the construction
practice in the existing literature,
we
in
TFP
are a statistical artifact that
of capital stocks. Following standard
construct capital stocks based on depreciating plants' past
inputs and adding deflated investments in
40
new
capital.
This procedure uses standard
NBER
depreciation rates, but if these rates are too low for firms in our sample then aging firms will
begin to have more measured capital than they have
firms'
TFP
appear to decline
in firm age.
TFP changes
and plant fixed
effects,
appear
labor or materials
if firms'
affect the relative
Mechanically, this will
Because the regressions control
became unobservably worse
to affect our
comparison of firm
38
TFP
in
make
for industry-by-year
are estimated solely on aging plants. Similar biases
Such measurement problems are unlikely
need not
in reality.
would
as plants aged.
main
results
of interest, as
this bias
winning and losing counties. For
it
to
Moreover, we note that our sample overlaps the late 1970s and early 1980s, which was a period of poor economic
performance and low productivity. Foster, Haltiwanger, and Krizan (2000) have documented that within-plant
productivity growth is cyclical, and is particularly low during downturns.
39
For example, if there was one MDP opening in 1987, the period 1987 to 1993 received a weight of 1/47.
40
This is necessary in the later portions of the sample, when book values for current capital are no longer reported.
41
affect
our estimates, measurement of capital stocks (or other inputs) would need to be
systematically biased in winning counties after the
checks provide some reassurance on
Column
vary by industry and
this issue:
MDP
from Table
opening. The previous robustness
10,
(4) allows input effects to
Column
(3) allows input effects to
MDP
vary after the
opening or
winning counties (but not the interaction of those two). Further, the specifications
Table
would be affected
1
differently
increases in winning counties after the
Changes
(vi)
is
Output. Another concern
the quantity of output.
of the productivity
virtually all
literature, the
However, due
dependent variable
output or price multiplied by quantity. Consequently,
do not expect
this to
show TFP
all
it
is
that the theoretically
is
to the data limitations faced
in
our models
is
the value of
possible that the estimated spillover
of higher productivity.
effect reflects higher output prices, instead
We
error in capital stocks, but
Appendix
in
MDP opening.
in the Price of Plant
correct dependent variable
by
by measurement
in
be a major factor in our context. The sample
comprised of
is
manufacturing establishments that generally produce goods traded outside the county.
41
In the
extreme case of a perfectly competitive industry that produces a nationally traded good, output
prices
would not increase disproportionally
To
explore this possibility further,
in industries that are
equation
(8)
incumbents'
that
more
regional or
interacts
production and consumption.
industry concentration.
more
local or
more concentrated.
measure
We
,
1(t>0) j£
of average
,
the productivity change
We
and
estimate a
industries; in fact, there is
by
traveled
also estimate this regression with a
Model
x 1(t >
(1 (Winner)
distance
These specifications do not find
more concentrated
incumbent plants
To
43
a county that experienced increased demand.
we examine whether
l(Winner) pj
industry-specific
in
1
is
42
larger
version of
0)) p;t
output
,
with
between
measure of incumbents'
that estimated
changes are larger
some evidence
for larger effects
in
on
that ship their products further.
we use data by detailed
good between production and consumption (Weiss 1972).
Across all sample plants, the 10th centile is 239 miles, 25th centile is 355 miles, median is 466 miles, 75th centile is
602 miles, and 90th centile is 722 miles. This suggests that most establishments in our sample produce goods that
are widely traded outside the county. Across all industries in the Weiss data, distance varies between 52 and ,337
miles, with a mean of 498. Examples of regional industries are: hydraulic cement, iron and steel products, metal
scrap and waste tailings, ice cream and related frozen desserts, and prefabricated wooden buildings.
give an indication of the tradability of goods produced by firms in a given industry
industry code on the average distance travelled by a
1
42
Similarly, input (labor) spillovers
goods (Black
43
et al.
may be
less
pronounced for incumbent plants
that
produce nationally traded
2005).
The information on
distance
is
from Weiss (1972). The information on industry concentration
of Census ("Concentration Ratios" 2002).
42
is
from the Bureau
were not
Earlier estimates found that the estimated spillovers
industries that tend to ship products to the
might be
largest. Further, the
on demand
incumbent
for
MDP
MDP
plants' output, particularly since these
to similar estimated
TFP
These exercises suggest
increases.
effects
might have similar effects
non-manufacturing
MDPs
and wholesale trade sectors. However, these non-manufacturing
in the retail
incumbent
which output price
industry, a context in
opening of a non-manufacturing
larger for
MDPs
were
did not lead
that output price increases are not
the source of the estimated spillover effects.
VII. Discussion and Implications for Policy
A.
Discussion
The
preferred
increased by
a
5%
12%
increase.
Model 2 estimates suggest
The 12% TFP
interpret this large effect
fraction
incumbent
plants' total factor productivity
Model
following the opening of a Million Dollar Plant, while
increase implies an additional
manufacturing output five years after the
To
that
and what
it
MDP
$430 million
opening. In this Section,
opening. There
is
TFP
discuss
amount of
that
is
56%
that plants located in counties at the top
we
4
level
A 12%
TFP
increase in
TFP
is
higher than the county
th
at the
10
equivalent to
moving from
MDP
TFP
percentile, indicating
productive that similar
all
production
the 10th percentile of the county-
it
deviation increase in the distribution of county TFP. Attracting a
we
calculate the
percentile of the
th
56% more
of the distribution are
distribution to the 27th percentile; alternatively,
counties, and
to
cross-sectional variation in productivity across U.S.
plants located in counties at the bottom of the distribution, holding constant
inputs.
how
implies for the spatial distribution of economic activity.
counties in the manufacturing sector. For example, the county at the 90
distribution has average
annual county
average manufacturing productivity explained by the
in
a tremendous
in
we
put the magnitude of the estimated spillover effect in perspective,
of overall variation
estimates find
1
is
equivalent to a 0.6 standard
MDP
find this implied shift in the relative standing
is
a major event for these
of counties large but not
unrealistic.
Our estimates have
44
Specifically,
these
numbers
interesting implications for the distribution
are
of economic activity
obtained using cross-sectional plant-level data from the
1987 Census of
Manufactures (the mid-point of our sample period). We regress log output on log inputs (log building capital, log
machinery capital, log materials, log labor) and a full set of county fixed effects. We then look at the distribution of
the county fixed effects,
which represent
the average
TFP among
43
all
manufacturing firms
in a
given county.
across locations.
Along with
substantial increases in
suggests that profits increased, at least
TFP
in
TFP, increased firm entry and expansion
However, the documented increase
the short-run.
does not translate necessarily as a similarly large increase
Increased economic activity generated by the
MDP
in profits for
in
incumbent firms.
leads to firms bidding up local factor prices
like labor
and land. Difference-in-difference estimates found that wage rates increased by 2.7%,
compared
to
TFP
4.8%
increases of
in the difference-in-difference
wages because we have
estimate a trend break model for skill-adjusted
Census
data).
Since this
receive a spillover
is
may become
plants in each
more
new
location. This
agglomeration,
we
important, because
of the
it
spatial distribution
open
In interpreting the
it is
would
fully
activity. It
in
winning counties and the same
TFP
2-digit
SIC staying
in other industries.
TFP
other
new
reflects the
plants
impact of the plant opening and
opened
in the
county following the
output increased (Table 10). Consequently, the
TFP
all
new
plants and
Second, the effect of a
MDP
MDP
opening and overall manufacturing
estimates should be interpreted as a general
expanded output from incumbent
opening
is
economy.
other associated changes. For example,
equilibrium reduced-form effect that combines both the direct impact of the
impacts of subsequent
First,
as the partial equilibrium impact of
the "Million Dollar Plant" opening, holding constant everything else in the county's
it
increases,
magnitude of our estimates, three points need to be highlighted.
inappropriate to interpret the estimated increase in
Instead,
seems
agglomerate around
industries. Indeed, despite experiencing substantially larger
sample more than winning incumbent plants
in the
of economic
a great deal of documented cross-sectional agglomeration and co-
no evidence of incumbent plants
is
helps explain the existence of
expect that local wages rise by more than 2.7% for the particular workers
demanded by such
there
is
on decennial
might be long-run agglomeration of similar
unlikely, however, that industries receiving larger spillovers
there
to rely
to
incumbents and the productivity gains are larger for
plant, there
is
industrial clusters, a pervasive feature
MDPs. While
(we are unable
less profitable.
similar to the
MDP
1
a county-wide increase in labor costs, incumbent firms that do not
If labor costs increase equally for all
plants that are
Model
MDP
and the
plants.
not representative of the typical plant opening.
The
MDPs
differ
size.
MDPs
are significantly larger than the average
from the average manufacturing plant
new
selected sample. Unlike most manufacturing plants, the
44
in several respects,
most importantly
plant in the U.S. Moreover, they are a
MDPs
generated bidding from local
governments, presumably because there was an ex ante expectation of substantial positive
spillovers. If spillovers vary
in
the
by
industry, then
it
may
be important to note that
MDPs
tend to be
automotive, chemical, computer, and electronics industries (relative to the average
manufacturing plant opening).
Third, the counties bidding for plants
new manufacturing
plant opening.
We
may
be those that would particularly benefit from a
do not expect that winners and losers were great counties
that almost attracted special plants; rather, they
were counties willing
industrial stimulus. In considering potential locations, the
MDPs
to provide tax subsidies for
might be attracted
to
a declining
manufacturing sector and the expectation of lower future wages.
These estimates of agglomeration spillovers come from a selected
counties for which
we
bound (and perhaps
set
of plants and
set
of
expect large spillovers, which implies that our estimates are a likely upper
substantially so). This
is
an issue of external
validity,
rather than the
consistency of our estimates. However, our estimates are representative of the benefits generated
by large plants bid on by these
policy.
From
local
governments, which
is
a research standpoint, finding spillovers from
a population of interest for public
MDPs
appears to be a necessary
condition for agglomeration spillovers from a broader set of plants (rather than a sufficient
condition) and a call for further research.
B.
Implications for Local Economic Development Policies
The presence of
new
significant agglomeration externalities implies that the attraction
plant to a locality generates external productivity benefits for existing firms.
question
whether
is
efficiency-enhancing.
in
this
From
agglomeration
significant
externalities
and
may
context publicly-financed subsidies to attract
the point of view
externalities
of an individual
indicates
increase efficiency in
some
that
cases.
providing
locality,
subsidies
An
new
of a
important
plants
are
presence of
the
can
internalize
However, from the aggregate point of
view, the efficiency of policy depends on whether the benefits of attracting a
new
plant for the
receiving county are homogeneous.
Consider the case where agglomeration spillovers are homogeneous. This could happen,
for example,
if
the functional form for agglomeration economies
is
linear
and productivity
spillovers
do not depend on economic distance between existing plants and the new
Assuming
that the
new
plant will locate
somewhere
45
in the
plant.
U.S. irrespective of the provision of
of the
subsidies, providing subsidies for plants to locate in a particular city, county, or region
country
2009).
is
socially wasteful from a national perspective (Glaeser 2008; Glaeser and Gottlieb
Further, even from a local perspective, the bidding for plants
game where
all
conventional
wisdom
of the benefits are bid away.
that the provision
likely to
is
sum
be a zero
This special case forms the intuition for the
of incentives for firms to locate
in particular locations is
wasteful.
However, our
results indicate that productivity spillovers
distance between the industry of the
the county in advance of
when
new
its
profits are high but the spillovers are
8).
This heterogeneity
is
important because
plant are heterogeneous, the socially efficient
where the sum of
cannot capture these spillovers on
spillovers
plant and the industrial composition of plants located in
opening (see Table
its
the benefits of attracting a
for a plant to locate
new
vary based on the economic
its
profits
own and
and the spillovers are
greatest.
outcome
The new
plant
consequently might choose a location where
minimal.
In this case,
payments
is
its
to plants that generate
can increase national welfare because they can cause plants to internalize the
externalities in rnaking their location decision.
Further, from the point of
government, heterogeneous spillovers imply local governments
may
view of the
not bid
away
all
local
of the
benefits (Greenstone and Moretti 2004).
There are
at least
two
issues
localities to plants are a one-for-one
supply of land
is
inelastic.
If the
of incidence
that bear noting.
First, the
exchange of land rents from the former
supply of land
payments from
to the latter if the
elastic, the increase in land rents is
not
substantial variability in the spillovers
and
is
necessarily one-for-one (see Moretti 2010).
Second, Figure 2 demonstrated that there
this
is
is
could affect the provision and/or magnitude of subsidies. For example, the estimated impact
negative
in
40%
of the cases. Consequently, risk-adverse local governments
to provide tax incentives with this distribution
may be
of outcomes or only willing to bid
unwilling
less than the
average spillover.
VIII. Conclusions
This paper makes three main contributions.
increases in
is
TFP among incumbent
First, the
estimates document substantial
plants following the opening of "Million Dollar Plants". This
consistent with firms agglomerating in certain localities, at least in part because they are
46
more
productive from being close to other firms.
Second, the estimates shed light on the channels that underlie the estimated spillovers.
Estimated spillovers are larger between plants that share labor pools and similar technologies.
This
consistent with intellectual externalities, to the extent that they occur
is
use similar technologies or are embodied in workers
finding
is
consistent with higher rates of
TFP due
matches. Clearly, our evidence on the mechanisms
to
understand
in
more
detail the sources
who move between
to
among
firms. Additionally, this
improved efficiencies of worker-firm
not conclusive. Further research
is
firms that
is
needed
of agglomeration economies.
Third, firms appear to pay higher costs in order to receive these productivity spillovers.
Spatial equilibrium requires that increases in
TFP
are
prices, so that firms are indifferent across locations.
adjusted labor
increased
is
consistent with this prediction.
demand
to locate in the
accompanied by increases
The
in local
input
finding of higher prices for quality-
The increased
levels of
economic
activity reflect
winning county, which leads to higher local prices and a new
equilibrium.
This paper has demonstrated that
directly
measuring TFP. These
of co-agglomeration rates that
spirit,
sector.
it is
tests
may
tests for the
can serve as an important complement to the measurement
reflect spillovers, cost shifters, or natural advantages. In this
important to determine whether impacts on
Further,
presence of spillovers can be conducted by
TFP
are evident outside the manufacturing
the significant heterogeneity in estimated spillovers across cases and the
variation in estimates across industries underscore that there
structural source
of these
spillovers.
47
is
still
much
to learn
about the
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Human
American Economic Review 88
Table
The "Million Dollar Plant" Sample
1.
01
Sample
MDP Openings:
1
Across All Industries
47
Within Same 2-digit SIC
16
Across All Industries:
Number of Loser
Counties per Winner County:
31
1
2+
16
Reported Year - Matched Year:
-2 to
2
20
-1
15
12
to 3
1
Reported Year of MDP Location:
1981-1985
1986-1989
1990-1993
MDP
11
18
18
Characteristics, 5 years after opening: 3
452801
Output ($1000)
Output, relative to county output
1
year prior
(901690)
0.086
(0.109)
Hours of Labor (1000)
2986
(6789)
'M
illion
Dollar Plant openings that were matched to the Census data and for which there were incumbent plants
in
both winning and losing counties that are observed in each of the eight years prior to the opening date (the opening
date
is
defined as the earliest of the magazine reported year and the year observed
restricted to include
matches for which there were incumbent plants
in the
in the
SSEL). This sample
Million Dollar Plant's 2-digit SIC
is
then
in
both
locations.
2
Only a few of these differences are 3. Census confidentiality rules prevent being more specific.
original 47 cases, these statistics represent 28 cases. A few very large outlier plants were dropped so that the
mean would be more representative of the entire distribution (those dropped had output greater than half of their
county's previous output and sometimes much more). Of the remaining cases: most SSEL matches were found in
the ASM or CM but not exactly 5 years after the opening date; a couple of SSEL matches in the 2xxx-3xxx SICs
were never found in the ASM or CM; and a couple of SSEL matches not found were in the 4xxx SICs. The MDP
characteristics are similar for cases identifying the effect within same 2-digit SIC. Standard deviations are reported
in parentheses. All monetary amounts are in 2006 U.S. do liars.
3
Of the
51
Table
2.
Summary
Statistics for
Measures of Industry Linkages
Mean
Only
Measure of
Industry Linkage
Description
st
1
Only 4
m
Standard
All Plants
Quartile
Quartile
Deviation
0.119
0.002
0.317
0.249
0.022
0.001
0.057
0.033
0.022
0.000
0.106
0.084
0.011
0.000
0.042
0.035
0.017
0.000
0.075
0.061
0.042
0.000
0.163
0.139
Labor Market Pooling:
CPS Worker
Proportion of workers leaving a job
Transitions
in this industry that
MDP
move
to the
industry (15 months later)
Technology Spillovers:
Citation pattern
Percentage of manufactured industry
patents that cite patents manufactured
Intellectual or
Technology
in MDP industry
R&D flows from MDP
Input
percentage of all private sector
industry, as a
technological expenditures
Technology
Output
R&D flows to MDP
industry, as a
percentage of all original research
expenditures
Customers and Suppliers:
Manufacturing
Industry inputs from
Proximity
to
Input
as a percentage
MDP
industry,
of its manufacturing
inputs
Industry output used by
Output
industry, as a percentage of
to
Notes:
CPS
MDP
Manufacturing
its
output
manufacturers
CPS Worker Transitions was
calculated from the frequency of worker industry
movements
in
the rotating
by Census Industry codes, matched to 2-digit SIC. The last 5 measures of
cross-industry relationships were provided by Ellison, Glaeser, and Kerr (2009). These measures are defined in a 3digit SIC by 3-digit SIC matrix, though much of the variation is at the 2-digit level. In all cases, more positive values
indicate a closer relationship between industries. Column
reports the mean value of the measure for all incumbent
plants matched to their respective MDP. Column 2 reports the mean for the lowest 25% and Column 3 reports the
mean for the highest 25%. Column 4 reports the standard deviation across all observations. The sample of plants is
all incumbent plants, as described for Table 1, for which each industry linkage measure is available for the
incumbent plant and its associated MDP. These statistics are calculated when weighting by the incumbent plant's
survey groups. This variation
is
1
total
value of shipments eight years prior to the
MDP opening.
52
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Table
Incumbent Plant Productivity, Relative
4.
Event Year
T
= -7
T
= -6
T
T
T
T
=
Winning
In
Q)
(2)
(3)
0.067
0.040
0.027
(0.058)
(0.053)
(0.032)
0.047
0.028
0.018
(0.044)
(0.046)
(0.023)
0.041
0.021
0.020
(0.036)
(0.040)
(0.025)
-0.003
0.012
-0.015
(0.030)
(0.030)
(0.024)
= -3
= -l
T
=
T=
0.011
-0.013
0.024
(0.022)
(0.022)
(0.021)
-0.003
0.001
-0.005
(0.027)
(0.011)
(0.028)
-2
0.013
-0.010
0.023
(0.018)
(0.011)
(0.019)
0.023
-0.028
0.051*
(0.026)
(0.024)
(0.023)
1
T=2
T
T
x
=
=4
=
0.004
-0.046
0.050
(0.036)
(0.046)
(0.033)
0.003
-0.073
0.076+
(0.047)
(0.057)
(0.043)
3
0.004
-0.072
0.076*
(0.053)
(0.062)
(0.033)
'
-0.023
-0.100
0.077*
(0.069)
(0.067)
(0.035)
5
R-squared
0.9861
Observations
28732
Notes: Standard errors are clustered
regression:
machinery
The
natural log of output
capital, materials),
dummy variables
When a plant is
at
of a
MDP Opening
Columns
the county level.
1
and 2 report coefficients from the same
(all worker hours, building capital,
regressed on the natural log of inputs
is
year x 2-digit SIC fixed effects, plant fixed effects, case fixed effects, and the reported
whether the plant
for
Ye ar
(l)-(2)
Counties
-5
T
to the
Difference
Counties
= -4
=
Losing
In
a winner or loser
is in
a winning or losing county
more than once,
it
receives a
in
each year relative to the
dummy
observations are weighted by the plant's total value of shipments eight years prior to the
plants in
all
cases
is
MDP
incumbent plants
at
is
the
5% level;
opening.
MDP
opening. Data on
were
method from early book values and subsequent investment. The sample of
Columns 1 - 2 of Table 3. ** denotes significance at 1% level; * denotes
only available 8 years prior to the
calculated using the permanent inventory
significance
MDP
variable for each incident. Plant-year
same as in
+ denotes significance
at
1
0%
level.
54
opening and
5 years after. Capital stocks
Table
Changes
5.
in
Incumbent Plant Productivity Following
mdpw:inners MDP Losers
(1)
Model
Mean
a
MDP Opening
MDP Counties
MDPW:inners MDP Losers
All Counties
(2)
All Counties
Random Winners
(4)
(3)
(5)
1:
0.0442+
Shift
0.0435+
(0.0233)
(0.0235)
0.0524*
0.0477*
-0.0496**
(0.0225)
(0.0231)
(0.0174)
[$170m]
R-squared
0.9811
0.9812
0.9812
0.9860
-0.98
Observations
418064
418064
50842
28732
-400000
(plant x year)
Model
2:
0.1301*
0.1324*
(0.0533)
(0.0529)
Effect after 5 years
0.1355**
(0.0477)
0.1203*
-0.0296
(0.0517)
(0.0434)
[$429m]
Level Change
0.0277
0.0251
0.0255
0.0290
0.0073
(0.0241)
(0.0221)
(0.0186)
(0.0210)
(0.0223)
0.0171 +
0.0179*
0.0183*
(0.0091)
(0.0088)
(0.0078)
Trend Break
Pre-trend
-
0.0057
-
0.0062
(0.0063)
0.0058
-0.0048
-
0.0044
-0.0048
(0.0046)
(0.0046)
(0.0044)
(0.0040)
-
(0.0046)
0.0152+
(0.0079)
R-squared
0.9811
0.9812
0.9813
0.9861
-0.98
Observations
418064
418064
50842
28732
-400000
YES
YES
YES
YES
YES
YES
YES
YES
N/A
(plant x year)
& Ind-Year FEs
Case FEs
Plant
NO
Years Included
All
All
Notes: The table reports results from
fitting several
-7
All
<
x
<
All
5
versions of equation (8). Specifically, entries are from a regression of
the natural log of output on the natural log of inputs, year x 2-digit SIC fixed effects, plant fixed effects, and case fixed
effects. In
Model
1,
dummy
two additional
MDP
variables are included for whether the plant
is in
a winning county 7 to
1
The reported mean shift indicates the difference in these two
coefficients, i.e:,
in TFP following the opening. In Model 2, the same two dummy variables are
included along with pre- and post-trend variables. The shift in level and trend are reported, along with the pre-trend and
the total effect evaluated after 5 years. In Columns (1), (2), and (5), the sample is composed of all manufacturing plants
in the ASM that report data for 14 consecutive years, excluding all plants owned by the MDP firm. In these models,
additional control variables are included for the event years outside the range from x = -7 through x = 5 (i.e., -20 to -8
opening or
the average change
years before the
and 6
to 17).
Columns
(3)
Column
and
(2)
(4), the
to 5 years after.
adds the case fixed effects that equal
sample
is
1
during the period that
restricted to include only plants in counties that
industry-year fixed effects to be estimated solely from plants
in
these counties. For
won
x
ranges from -7 through
or lost a
Column
MDP.
(4), the
5. In
This forces the
sample
is
restricted
further to include only plant-year observations within the period of interest (where x ranges from -7 to 5). This forces the
industry-year fixed effects to be estimated solely on plant-by-year observations that identify the parameters of interest. In
Column
(5), a set
industries as the
of 47 plant openings
MDP
those estimates). For
openings
all
(this
in
the entire country
were randomly chosen from the
ASM in
the
same years and
procedure was run 1,000 times and reported are the mean and standard deviation of
regressions, plant-year observations are weighted by the plant's total value of shipments eight
years prior to the opening. Plants not
two uncommon
in
a winning or losing county are weighted by their total value of shipments
in that
SIC values were excluded so that estimated clustered variance-covariance
matrices would always be positive definite. In brackets is the value in 2006 U.S. dollars from the estimated increase in
productivity: The percent increase is multiplied by the total value of output for the affected incumbent plants in the
winning counties. Reported in parentheses are standard errors clustered at the county level. ** denotes significance at 1%
year. All plants from
level; * denotes significance at
2-digit
5% level; + denotes
significance at
55
10%
level.
Table
Model
6.
1
:
Changes
Mean
in
Incumbent Plant Output and Inputs Following a
Output
Worker
Hours
Machinery
Building
Capital
Capital
(1)
(2)
(3)
(4)
0.1200**
Shift
(0.0354)
Model
2:
After 5 years
(in logs).
0.0401
(0.0348)
0.0826+
0.0562
(0.0478)
(0.0469)
Notes: The table reports results from
outcome variables
0.0789*
(0.0357)
See the
fitting
text for
at
10%
0.0911**
0.0089
(0.0691)
-
(0.0302)
0.0077
0.0509
(0.0541)
(0.0375)
versions of equation (8) for each of the indicated
more
Standard errors clustered
details.
are reported in parentheses. ** denotes significance at
denotes significance
-
(5)
0.1327+
(0.0300)
MDP O pening
Materials
1%
level; *
level.
56
at
the county level
denotes significance at
5%
level;
+
:
7. Changes
Incumbent Plants
Table
in
in
Incumbent Plant Productivity Following a MDP Opening
the MDP's 2-Digit Industry and All Other Ind ustries
All Industries
(1)
Model
Mean
MDP's
All Other
2-digit Industry
2-Digit Industries
(2)
01
1
0.0477*
0.1700*
(0.0231)
(0.0743)
(0.0253)
[$170m]
[$102m]
[$104m]
Shift
0.0326
R- squared
0.9860
0.9861
Observations
28732
28732
Model
2:
0.3289
(0.2684)
(0.0504)
[$429m]
[$197m]
[$283m]
Level Change
0.2814**
0.0290
(0.0210)
Trend Break
0.0152+
0.0044
-0.0174
(0.0265)
R-squared
0.9861
Observations
28732
Notes: The table reports results from
0.0147+
0.0079
(0.0081)
(0.0344)
(0.0044)
-
0.0004
(0.0171)
(0.0895)
(0.0079)
Pre-trend
0.0889+
0.1203*
(0.0517)
Effect after 5 years
-
all
industries
0.0026
(0.0036)
0.9862
28732
fitting
versions of equation (8).
estimates from the baseline specification for incumbent plants
plants in
for
As
a basis for comparison,
in all industries
Column
(baseline estimates for
1
reports
incumbent
5). Co lumns 2 and 3 report estimates from a single regression, which
and pre/post variables with indicators for whether the incumbent plant is in the same
or a different industry. Reported in parentheses are standard errors clustered at the
(Column 4 of Table
fully interacts the winner/loser
2-digit industry as the
county
level.
MDP
The numbers
in brackets are the
value (2006 U.S.S
)
from the estimated increase
in productivity:
The
percent increase is multiplied by the total value of output for the affected incumbent plants in the winning counties.
** denotes significance at 1% level; * denotes significance at 5% level; + denotes significance at 10% level.
57
.
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Table 9. Changes in Counties'
Following a MDP Opening
of Plants, Total Output, and Skill-Adjusted
Panel
Panel 2
(Census of Population)
1
(Census of Manufactures)
Dependent Variable:
Dependent Variable:
Dependent Variable:
Log(Plants)
Log(Total Output)
Log(Wage)
(JJ
(2)
(3)
0.1255*
Difference-in-Difference
(0.0900)
0.9984
0.9931
0.3623
209
209
1057999
Observations
Notes: The table reports results from
number of establishments and
0.0268+
0.1454
(0.0550)
R-squared
fitting three regressions. In
Wages
Panel
(0.0139)
1,
of
Census of
the dependent variables are the log
the log of total manufacturing output in the county, based
on data from
the
Manufactures. Controls include county, year, and case fixed effects. Reported are the county-level difference-in-
MDP opening. Because data are available every 5 years, depending on the Census
MDP opening, the sample years are defined to be - 5 years before the MDP opening and 4-8
years after the MDP opening. Thus, each MDP opening is associated with one earlier date and one later date. The
difference estimates for receiving a
year relative to the
Column
(1)
model
1
is
weighted by the number of plants
weighted by the county's
total
manufacturing output
In Panel 2, the dependent variable
is
log
in
in the
county
in
years -6 to -10 and the
wage and
controls include
education*year, sex*race*Hispanic*citizen, and case fixed effects. Reported
difference estimate for receiving a
defined to be
opening
is
-
1
dummies
is
for
in the private sector.
individual
individual the
significance
at
may
1,
The sample
in
all
individuals in a county group were
more than one FIPS and observations
are
parentheses are standard errors clustered
level; * denotes significance at
who worked more
not in school, are at work, and who
unique individuals - some IPUMS
is restricted to
work more than 20 hours per week, are
The number of observations reported refers to
then appear in
same weight. Reported
1%
is
MDP opening. Because data are available every 10 years, the sample years are
MDP opening and 3-12 years after the MDP opening. As in Panel each MDP
county groups include more than one FIPS, so
The same
model
for age*year, age-squared *year,
10 years before the
associated with one earlier date and one later date.
wages
(2)
the county-level difference-in-
than 26 weeks in the previous year, usually
work
Column
years -6 to -10.
5%
level;
+ denotes
59
individuals
matched
weighted
at
to each potential
to give
FIPS.
each unique
the county level. ** denotes
significance at
10%
level.
5
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Incumbent Plants' Productivity
the Year of a MDP Opening
Figure
1.
All
All Industries:
Difference:
in
Winning
Winners
vs.
vs.
Losing Counties, Relative to
Losers
Winners - Losers
0.1
-K
0.05
-
-7
-0.05
-6
J
Year, relative to opening
Notes: These figures accompany Table
4.
62
X
Figure
2.
Distribution of Case-Specific
Mean
Shift Estimates, Following a
MDP Opening
0.5
0.4
0.3
0.2
9-1
W
ww
ffM^
\u
\\\\\-
]
-
n
45
-0.2
-0.3
-0.4
-0.5
Notes: The figure reports results from a version of Model
cases. T he figure reports only
1
that estimates the
45 estimates because two cases were excluded
63
parameter
for
6] for
each of the 47
MDP
Census confidentiality reasons.
Figure 3. Incumbent Plants' Productivity in the MDP's 2-Digit Industry, Winning
Losing Counties, Relative to the Year of a MDP Opening
2-digit
0.2
-
0.1
-
vs.
MDP Industry: Winners Vs. Losers
A
\><r
-
-7
-0.1
-
-0.2
-
-6
-5
,•'--..._,
a...
'"'•.
•'
^"^•\$
/~'i-
-2
/
A\
.-• A--
"'•£•'"
1
Year, relative to opening
——
Winning Counties
---a--- Losing Counties
Difference (Winners - Losers)
Notes: These figures accompany Table
''•..
7,
Column 2 (MDP's
64
2-digit Industry).
2
3
4
^\§
Figure
4.
Incumbent Plants' Productivity in Other Industries (not the MDP's 2-Digit
Winning vs. Losing Counties, Relative to the Year of a MDP Opening
Industry),
Other Industries: Winners Vs. Losers
Year, relative to opening
Winning Counties
-
a-
•
•
Losing Counties
Difference (Winners - Losers)
12
3
4
-0.05
Year, relative to opening
Notes: These figures accompany Table
7,
Column
3 (All 2-digit Industries,
65
except the
MDP's
2-digit Industry).
5