# On Spatial Heterogeneity in Environmental Compliance Costs

Randy A. Becker

Center for Economic Studies

Census Advisory Committee of Professional Associations Meetings

October 26-27, 2006

Abstract

This paper examines sub-state variation in the stringency of
environmental regulation. In particular, establishment-level data from
the U.S. Census Bureau's Pollution Abatement Costs and Expenditures
(PACE) survey are used to construct a first-of-its-kind, county-level
index of environmental compliance costs. This paper then documents
whether counties within a state are statistically -- and meaningfully --
different from each other (and the state average) in terms of their
regulatory intensity, as measured by this index. If they are, this
suggests that potentially important spatial variation is lost in
state-level studies of environmental regulation.

Results suggest that spatial heterogeneity in environmental compliance
costs is indeed real, widespread, and significant. The index constructed
here could therefore prove to be quite useful in regulatory analyses, by
expanding the research laboratory to include U.S. counties. Among the
issues that could be (re)examined using this index are the effects of
environmental regulation on industrial location, employment, output,
investment (including foreign direct investment), industrial emissions,
and ambient pollution levels. Subsequent work will further explore,
test, and refine the cost index introduced here in this paper.

Questions:

1\. Is there value in such an index?

2\. Are there perhaps better empirical specifications for the index? Are
there robustness checks that you would like to see?

3\. Are there other descriptive exercises that would be illustrative?

4\. What recommendations do you have for future research in this area?

*Disclaimer*: Note that the opinions and conclusions expressed herein
are those of the author and do not necessarily represent the views of
the U.S. Census Bureau. All results have been reviewed to ensure that no
confidential information is disclosed. Otherwise, this work has not
undergone the review accorded official Census Bureau publications. The
results presented herein are preliminary. Please do not cite or
circulate this draft without permission.

**1. Introduction**

That the stringency of environmental regulation varies spatially in the
United States hardly seems a noteworthy point anymore. It is rather
well-established that states exercise discretion in their enforcement of
federal environmental regulations, and states can of course adopt
standards that are more stringent than those promulgated by the federal
government. Over the past couple of decades, a number of different
proxies that attempt to measure the extent of these regulatory
differences between states have been constructed and subsequently used
by researchers wishing to explore the impact of environmental
regulations on industrial location and industrial activity (see Levinson
2001 and Brunnermeier and Levinson 2004 for reviews).

What is less well-studied and less appreciated is the degree of
heterogeneity in regulatory stringency *below* the state level.
Duffy-Deno (1992) uses the variation in pollution abatement costs across
Standard Metropolitan Statistical Areas (SMSA) to examine the effects of
environmental regulations on economic activity, but -- with only 63
SMSAs -- this analysis is not much richer than a state-level study and
it obviously excludes a good deal of economic activity. Berman and Bui
(2001) examine the impact on oil refineries of the uniquely stringent
air quality regulations of the South Coast Air Basin (i.e., the Los
Angeles area) versus those of the rest of California and the rest of the
United States. Meanwhile, a growing number of studies have looked at the
effects county non-attainment of the Clean Air Act's national ambient
air quality standards (NAAQS) have had on manufacturing activity (e.g.,
McConnell and Schwab 1990, Henderson 1996, Kahn 1997, Becker and
Henderson 2000, Greenstone 2002, List *et al.* 2003). While county-level
NAAQS non-attainment status may be the best, most
geographically-detailed measures of environmental regulation currently
available, they cover only six air pollutants, and they are dichotomous
(rather than continuous) in nature, thereby cloaking the true variation
in regulatory intensity across counties, even within a state.

In this paper, I employ a unique database to measure and examine more
fully the extent of variation in regulatory stringency below the state
level. In particular, I use fourteen year's worth of establishment-level
data from the U.S. Census Bureau's Pollution Abatement Costs and
Expenditures (PACE) survey to construct a county-level index of
environmental compliance costs. Here, pollution abatement operating
costs per unit of economic activity (employment or output) is modeled as
a function of plants' industry, size, age, and year --- factors known to
determine both regulatory scrutiny and environmental expenditures --- as
well as plants' location. The resulting index is the first measure of
its kind, capturing extra-normal environmental costs at a detailed level
of geography, due (presumably) to additional environmental regulation
faced by industry at the locale.

The paper proceeds as follows. In the next section, I discuss the
construction of my county-level index of environmental compliance costs.
Section 3 then documents and examines whether counties within a state
are indeed statistically -- and meaningfully -- different from each
other (and different from the state average) in terms of their
conditional environmental compliance costs. If they are, this suggests
that potentially important spatial variation is lost in state-level
studies of environmental regulation. Section 4 concludes the paper with
a summary of findings and a brief discussion of the potential uses for
such an index.

**2. A County-level Index of Environmental Compliance Costs**

The literature is full of studies that have used pollution abatement
expenditure data from the PACE survey to measure geographic differences
in the stringency of environmental regulations.[^1] At the heart of each
of these measures is an estimate of pollution abatement expenditures,
divided by some measure of total manufacturing activity, such as gross
state product, value added, or value of shipments. In recognition of the
inherent variation in the pollution-intensiveness of industries, some
measures attempt to adjust for a location's industrial composition
(e.g., Bartik 1988, Levinson 1996, Gray 1997, Levinson 2001, Keller and
Levinson 2002); others do not (e.g., Duffy-Deno 1992, Friedman *et al.*
1992, List and Kunce 2000, List and Co 2000). With the exception of
Levinson (1996), all of these previous studies have used *published*
PACE statistics, versus the underlying establishment-level microdata.
And with the exception of Duffy-Deno (1992), who analyzed 63 SMSAs, the
unit of geography is the state in all of these studies.

In this paper, I use the *establishment-level* data from the PACE
survey.[^2] For my purposes, these microdata have a few substantial
advantages over the published PACE statistics that are commonly used.
First and foremost, the location information associated with each
establishment allows me to contemplate pollution abatement expenditure
at the sub-state level. Second, the industrial classification of
establishments in these data is the most detailed available, by *any*
level of geography, which is extremely valuable in any effort to explain
variation in pollution abatement expenditures. Finally, by merging these
PACE microdata to information reported by these same establishments in
the Annual Survey of Manufactures (ASM) and the Census of Manufactures
(CM), I am uniquely able to control for the size and age of
establishments --- factors that have been shown to be important
determinants of regulatory scrutiny and, hence, establishments'
compliance expenditures (e.g., Becker and Henderson 2001; Becker 2005).

Here I use the establishment-level data from the PACE surveys of
1979-1982, 1984-1986, and 1988-1994.[^3] As in most of the
aforementioned studies, I will employ data on total pollution abatement
operating costs (*PAOC*), which includes salaries & wages, parts &
materials, fuel & electricity, capital depreciation, contract work,
equipment leasing, and other operating costs associated with a plant's
abatement of its air and water pollution as well as its solid waste in
that calendar year.[^4] To this I merge data on these establishments
from the ASM or CM, including employment, payroll, value of shipments,
four-digit SIC industry, county, and plant vintage (as measured by an
establishment's first appearance in the Census of Manufactures). After
restricting the sample to cases that had linkable PACE and ASM/CM
records in a given year, and after eliminating inactive establishments,
plants in Alaska and Hawaii, and those with missing or incomplete data
on critical items, there are 200,532 establishment-years of observations
for my empirical work. This rather sizable sample contains approximately
49,000 unique manufacturing plants, encompassing virtually all
four-digit SIC manufacturing industries and located in 2,514 different
U.S. counties.

In harmony with the previous literature, the basis for my index is an
establishment's PAOC intensity --- that is, its pollution abatement
operating costs per unit of economic activity. In this paper, I choose
plant employment (*EMP*) for the denominator. This *PAOC~i~/EMP~i~*
ratio might be said to encapsulate a regulator's implicit choice between
environmental protection and jobs.[^5]

The degree of regulatory scrutiny faced by a manufacturing plant -- and
hence its PAOC intensity -- is most certainly dependent on the industry
it is in (its inherent pollution-intensiveness), as well as the year and
its size, and it has been shown that the *combination* of these three
factors can affect regulatory intensity (Becker and Henderson 2000).
Accordingly, I model PAOC intensity as a function of an
industry-year-size quartile effect. In lieu of an overwhelming number of
dummy variables (of which there would be over 21,000), I employ the data
at hand to compute an estimate of the expected PAOC intensity for each
industry-year-size quartile class. In particular, for an establishment
in industry *n´*, year *t´*, and size quartile *q´*, the relevant PAOC
intensity is assumed to be[^6]

$\sum_{j \in \left\{ n = n^{'},t = t^{'},q = q^{'} \right\}}^{}\text{PAOC}_{j}\sum_{j \in \left\{ n = n^{'},t = t^{'},q = q^{'} \right\}}^{}\text{EMP}_{j}$
(1)

Here, an establishment's size quartile is determined by its position in
the employment-weighted plant employment distribution for its industry
in that period.[^7]

In principle, a plant's age category could be a fourth dimension used in
computing an establishment's expected PAOC intensity (above), but this
would significantly increase the number of applicable cells and severely
reduce the average number of observations per cell. Instead,
establishment age is controlled for by a separate series of plant
vintage indicators (***V~k~***) based on an establishment's first
appearance in the CM, with *k*$\in${1963, 1967, 1972, 1977, 1982, 1987,
1992, 1997}. It is therefore assumed that plant vintage has equivalent
effects on regulation and environmental compliance costs across
industry, year, and size classes.[^8]

My county-level index of environmental compliance costs is the vector of
$\varphi_{m}$ parameters from the following regression equation:

$\text{ln}\left( \text{PAOC}_{i}/\text{EMP}_{i} \right) = \alpha + \beta \cdot \text{ln}\left( \sum_{j \in \left\{ n^{'},t^{'},q^{'} \right\}}^{}\text{PAOC}_{j}/\sum_{j \in \left\{ n^{'},t^{'},q^{'} \right\}}^{}\text{EMP}_{j} \right) + \sum_{k \in K}^{}\left( \gamma_{k} \cdot V_{k} \right) + \sum_{m \in M}^{}\left( \varphi_{m} \cdot C_{m} \right) + \varepsilon_{i}$    (2)

where observation *i* is an establishment in industry *n´*, year *t´*,
size quartile *q´*, and *j* also indexes establishments in the sample.
*K* is the set of possible first CM appearances, less one omitted
possibility (1963). *M* is the set of U.S. counties*, m* indexes those
counties, and *C~m~* is one in a series of county indicator variables,
less one omitted county (Washington DC). $\varepsilon_{i}$ is an error
term. Note that a comparable state-level index can be computed from a
version of equation (2) with
$\sum_{s \in S}^{}\left( \varphi_{s} \cdot C_{s} \right)$ in place of
$\sum_{m \in M}^{}\left( \varphi_{m} \cdot C_{m} \right)$, where *S* is
the set of U.S. states*, s* indexes those states, and *C~s~* is one in a
series of state indicator variables, less one omitted state (again,
Washington DC).[^9]

Since the value of the dependent variable is bounded from below for a
significant number of observations, the parameters of equation (2) are
estimated via a Tobit specification.[^10] The parameter $\alpha$ is the
estimated constant, representing the omitted group (establishments in
Washington DC, observed in existence in the 1963 Census of
Manufactures). In estimation, $\beta$ is restricted to be equal to one,
forcing the notion that an establishment is *expected* to have PAOC
intensity equivalent to the estimate for its industry-year-size class,
as specified in equation (1).[^11] Deviations from this are explained by
differences in plant vintage, as captured by the $\gamma_{k}$
parameters,[^12] and by differences between counties, as measured by the
estimated $\varphi_{m}$ parameters --- my county-level index. The index,
assumed here to be time invariant, reveals any extra-normal
environmental compliance costs, generally due to above- or below-normal
environmental regulation faced by manufacturers at the county
level.[^13] Exploring the variation in this index is the subject of the
next section.

**3. On Spatial Heterogeneity in Environmental Compliance Costs**

I begin by noting that, according to the respective pseudo-R^2^
statistics, the county dummy variables in equation (2) explain 18 times
more of the variation in excess PAOC intensity than a version of
equation (2) with state dummy variables in their place, relative to a
model with no geography variables at all. The R^2^ statistics from the
analogous OLS regressions tell a similar story. Here, the county dummy
variables explain over 16 times more of the variation in excess PAOC
intensity than do state dummy variables. Even the *adjusted* R^2^
statistics suggest that county variation explains about 14 times more of
the variation in excess PAOC intensity than state variation, relative to
a model without any geographic controls. These results clearly indicate
that, collectively, counties have substantially more explanatory power
than do states, on the matter of environmental compliance costs.

Table 1 begins to illustrate the degree of heterogeneity in regulatory
intensity *within* each of the 48 states in scope to this analysis. In
particular, I present two statistics here: The first is the difference
in the index values between the counties with the maximum and minimum
index value in the state (*Max-Min*). The second is the ratio of the
maximum county-level index value in the state to the state-level index
(*Max/State*). While computing these two statistics, it was necessary
for me to ignore the index values of 571 counties, for confidentiality
reasons.[^14] The measures reported in Table 1 therefore may (will)
understate the true degree of heterogeneity observed in the state.[^15]
Nonetheless, the values presented in Table 1 are fairly correlated with
their *true* values (calculated without these suppressions): For
*Max-Min* the pairwise and Spearman's rank correlations are 0.6528 and
0.5354, respectively, and for *Max/State* those two correlations are
0.9027 and 0.8960, respectively. A state's rank (highest value = 1) is
also shown in Table 1 for each of these two measures.[^16]

We see that the state with the highest *Max-Min* is Oklahoma, where the
difference in the maximum and minimum county-level index value is over 8
points. In terms of *Max/State*, the state with the highest value is
Kansas, where the environmental compliance cost index for the most
stringently regulated county is almost 20 times the state-wide index.
Meanwhile, Delaware ranks lowest in terms of both *Max-Min* and
*Max/State*, followed closely by Rhode Island and Connecticut. These
three states are our most geographically compact and have among the
fewest counties, which certainly may explain their relative homogeneity.

Among the top 10 manufacturing states (in terms of their employment),
California -- the largest manufacturing state by any measure -- has the
largest *Max/State* value, where the environmental compliance cost index
for the most stringently regulated county is nearly 9 times the
state-wide index. In terms of *Max-Min*, the largest manufacturing
states with the largest values are North Carolina, Michigan, Ohio, and
Texas, where the difference in the maximum and minimum county-level
index value is over 5 points. On the opposite end, New Jersey is the
largest manufacturing state having among the lowest values of both
*Max-Min* (a difference of about 1.4 points) and *Max/State* (a factor
of 3.4). To help further illustrate the matter, Figure 1 plots the
county- and state-level index values for California, Texas, and New
Jersey, respectively.

One particularly nice feature of these graphs is their depiction of the
confidence intervals around the state and county point estimates. This
makes obvious the fact that many of the counties toward the extrema are
clearly statistically different from their respective states, in terms
of the regulatory intensity and environmental compliance costs faced by
their manufacturing establishments.[^17] What is less obvious here is
whether the many counties with confidence intervals that overlap with
their state's are in fact statistically different from their state.

To test for this statistical difference, for county $m^{'}$ in state
$s^{'}$, note that the variance of the difference between the estimated
county and state index values is given by

$\text{var}({\hat{\varphi}}_{m^{'}} - {\hat{\varphi}}_{s^{'}}) = \text{var}({\hat{\varphi}}_{m^{'}}) + \text{var}({\hat{\varphi}}_{s^{'}}) - 2 \cdot \text{cov}({\hat{\varphi}}_{s^{'}},{\hat{\varphi}}_{m^{'}})$
(3)

where $\text{var}(\hat{\varphi})$ is the square of the estimated
standard error (*se*) associated with the respective index value
($\hat{\varphi}$). Here, ${\hat{\varphi}}_{m^{'}}$ and
${\hat{\varphi}}_{s^{'}}$ are equivalent regression coefficients from
two different models, estimated on the same sample, where one model
contains an additional set of explanatory variables (e.g., indicators
for all the other counties in the state). In cases such as this, Clogg
*et al.* (1995) -- as further refined in their reply to Allison (1995)
-- demonstrate that (3) becomes

$\text{var}({\hat{\varphi}}_{m^{'}} - {\hat{\varphi}}_{s^{'}}) = \text{var}({\hat{\varphi}}_{m^{'}}) + \text{var}({\hat{\varphi}}_{s^{'}}) - 2 \cdot \text{var}({\hat{\varphi}}_{s^{'}}) \cdot ({\hat{\sigma}}_{C}^{2}/{\hat{\sigma}}_{S}^{2})$
(4)

where ${\hat{\sigma}}_{C}^{2}$ and ${\hat{\sigma}}_{S}^{2}$ are the
estimated sum of squared errors from the county- and state-based
regression models, respectively. The corresponding 90% confidence
interval is therefore

$({\hat{\varphi}}_{m^{'}} - {\hat{\varphi}}_{s^{'}}) \pm 1\text{.}\text{645} \cdot \sqrt{\text{se}_{m^{'}}^{2} + \text{se}_{s^{'}}^{2} - 2 \cdot \text{se}_{s^{'}}^{2} \cdot ({\hat{\sigma}}_{C}^{2}/{\hat{\sigma}}_{S}^{2})}$
(5)

which is easily computed from standard regression output.

Table 2 summarizes the results of this statistical testing. Overall, I
find that 1,046 (41.6%) of the 2,513 counties in these 48 states have an
environmental compliance cost index statistically different from the
index of their respective state. These 1,046 counties contained 52% of
U.S. manufacturing employment and 53% of U.S. manufacturing
establishments in 2002.[^18] I find that 486 (18.6%) of the 2,513
counties have an environmental compliance cost index statistically
*higher* than the index of their respective state, while 578 (23.0%)
have an index statistically *lower* than the index of their state. These
two groups contained 20.4% and 31.3% of U.S. manufacturing employment,
respectively. Table 3 lists the largest of these counties (ranked by
their 2002 manufacturing employment) and the direction of their
difference vis-à-vis their state. Note that this list includes major
counties in New York City, Los Angeles, Chicago, San Francisco, Detroit,
Dallas, Houston, and other large cities.

Meanwhile, Tables 4a and 4b show the states with the highest and lowest
percentage of their counties that are statistically different from the
state. As it turns out, New Hampshire, Rhode Island, and Delaware
exhibit no heterogeneity whatsoever (from a statistical standpoint). At
the other extreme, two-thirds of New Jersey's counties are different
from its state-wide index. Earlier we noted that New Jersey had among
the smallest *Max-Min* and *Max/State*, which suggests that the
regulatory differences between New Jersey's counties may be relatively
narrow but are in fact statistically significant.

A potentially interesting question is whether there is any clustering of
"high" and "low" index values within a state. This could arise for a
number of reasons --- e.g., adjacent counties may share the same set of
state regulators and/or nearby counties may regulate themselves
similarly, to avoid inter-jurisdictional competition. It has also been
shown that environmental regulation may be more lax where exposure to
emissions is more likely to fall outside the state, such as in border
counties, and particularly those on a state's eastern edge (Helland and
Whitford 2003). To examine some of these issues, at least casually, I
present county maps for five states that figure prominently in Tables 3
and 4: California, Texas, Illinois, New York, and New Jersey. In
particular, in the maps of Figure 2, counties are grouped \[and shaded\]
by whether their index value is statistically smaller than the state's
index \[light grey\], statistically the same \[medium grey\], or
statistically larger than the state's index \[black\]. Counties with no
data or with an index value suppressed for confidentiality reasons are
also indicated \[stippling\].

In California, there is little evidence of any clustering, with the
exception perhaps of the above-average expenditure in the counties east
of San Francisco. In Texas, on the other hand, it certainly seems that
the counties along the gulf coast -- including those of the greater
Houston area -- tend to have significantly higher expenditures than
counties elsewhere in the state. Meanwhile, the counties of the Rio
Grande basin -- or at least the few that actually have data -- seem to
be significantly less regulated, by Texas standards. In Illinois,
Chicago's three main counties have significantly lower expenditures than
most counties elsewhere in the state, while there is a cluster of
counties southwest of Chicago (toward Peoria) with significantly higher
expenditures. In New York, New York City seems to be less regulated,
relative to the rest of the state, while New York's other large
metropolitan areas (Buffalo, Rochester, Albany, and Syracuse) all appear
to have significantly higher environmental compliance costs. And in New
Jersey, the counties near New York City are apparently the least
regulated while those on the western flank are significantly more
regulated. More analysis is needed to determine whether there are any
common themes across these (and all the other) states.

**4. Discussion**

The results presented in the previous section certainly indicate that
spatial heterogeneity in environmental compliance costs is real,
widespread, and significant. County-level variation is found to explain
14-18 times more of the variation in environmental compliance costs than
state-level variation alone. And in terms of variation within a state,
manufacturing establishments in the most stringently regulated county
are found to have environmental compliance costs that are many multiples
of those of manufacturers in other parts of the state, and the state as
a whole. Indeed, 42% of the counties (containing 52% of U.S.
manufacturing employment) have environmental compliance costs that are
statistically different from their states'. All told, there are only
three states with counties with homogenous environmental compliance
costs (in a statistical sense). These results would suggest that
important spatial variation is indeed lost in state-level studies of
environmental regulation.

The United States' states have long been used as a laboratory to explore
social and economic phenomena, including the impact of environmental
regulations on industrial location and industrial activity. The index
constructed in this paper could potentially improve such regulatory
analyses by expanding the laboratory to include U.S. counties. With this
index, one could in principle begin to (re-)explore the effects of
environmental regulation on industrial location, employment, output,
investment (including foreign direct investment), industrial emissions,
ambient pollution levels and so forth *at the county level*. An obvious
advantage of this index over the occasionally-used county-level NAAQS
non-attainment status is that it encompasses more than just six air
pollutants and is continuous (rather than dichotomous or categorical) in
nature. And that less than 8% of its variation is explained by
county-level factors commonly thought to affect local environmental
regulation (see Becker 2004 for list) highlights its usefulness and
uniqueness as a measure of local regulatory intensity. Subsequent work
will further explore, test, and refine the cost index introduced here in
this paper.

**References**

Allison, Paul D. "The Impact of Random Predictors on Comparisons of
Coefficients between Models: Comment on Clogg, Petkova, and Haritou,"
*American Journal of Sociology*, 100(5), 1294-1312, March 1995.

Bartik, Timothy J. "The Effects of Environmental Regulation on Business
Location in the United States," *Growth & Change*, 19(3), 22-44, Summer
1988.

Becker, Randy A. "Pollution Abatement Expenditure by U.S. Manufacturing
Plants: Do Community Characteristics Matter?" *Contributions to Economic
Analysis & Policy*, 3(2), #6, 1-21, 2004.

Becker, Randy A. "Air Pollution Abatement Costs under the Clean Air Act:
Evidence from the PACE Survey," *Journal of Environmental Economics and
Management*, 50(1), 144-169, July 2005.

Becker, Randy and Vernon Henderson. "Effects of Air Quality Regulations
on Polluting Industries," *Journal of Political Economy*, 108(2),
379-421, April 2000.

Becker, Randy A. and J. Vernon Henderson. "Costs of Air Quality
Regulation," in Carlo Carraro and Gilbert E. Metcalf (eds.), *Behavioral
and Distributional Effects of Environmental Policy*, Chicago: The
University of Chicago Press, 2001.

Berman, Eli and Linda T. M. Bui. "Environmental Regulation and
Productivity: Evidence from Oil Refineries," *The Review of Economics
and Statistics*, 83(3), 498-510, August 2001.

Brunnermeier, Smita B. and Arik Levinson. "Examining the Evidence on
Environmental Regulations and Industry Location," *Journal of
Environment and Development*, 13(1), 6-41, March 2004.

Clogg, Clifford C., Eva Petkova, and Adamantios Haritou. "Statistical
Methods for Comparing Regression Coefficients between Models," *American
Journal of Sociology*, 100(5), 1261-1293, March 1995.

Duffy-Deno, Kevin T. "Pollution Abatement Expenditures and Regional
Manufacturing Activity," *Journal of Regional Science*, 32(4), 419-436,
November 1992.

Friedman, Joseph, and Daniel A. Gerlowski, and Johnathan Silberman.
"What Attracts Foreign Multinational Corporations? Evidence from Branch
Plant Location in the United States," *Journal of Regional Science*,
32(4), 403-418, November 1992.

Gray, Wayne B. "Manufacturing Plant Location: Does State Pollution
Regulation Matter?" *NBER Working Paper Series*, #5880, January 1997.

Greenstone, Michael. "The Impacts of Environmental Regulations on
Industrial Activity: Evidence from the 1970 and 1977 Clean Air Act
Amendments and the Census of Manufactures," *Journal of Political
Economy*, 110(6), 1175-1219, December 2002.

Helland, Eric and Andrew B. Whitford. "Pollution Incidence and Political
Jurisdiction: Evidence from the TRI," *Journal of Environmental
Economics and Management*, 46(3), 403-424, November 2003.

Henderson, J. Vernon. "Effects of Air Quality Regulation," *American
Economic Review*, 86(4), 780-813, September 1996.

Kahn, Matthew E. "Particulate Pollution Trends in the United States,"
*Regional Science and Urban Economics*, 27(1), 87-107, February 1997.

Keller, Wolfgang and Arik Levinson. "Pollution Abatement Costs and
Foreign Direct Investment Inflows to U.S. States," *The Review of
Economics and Statistics*, 84(4), 691-703, November 2002.

Levinson, Arik. "Environmental Regulations and Manufacturers' Location
Choices: Evidence from the Census of Manufactures," *Journal of Public
Economics*, 62(1), 5-29, October 1996.

Levinson, Arik. "An Industry-Adjusted Index of State Environmental
Compliance Costs," in Carlo Carraro and Gilbert E. Metcalf (eds.),
*Behavioral and Distributional Effects of Environmental Policy*,
Chicago: The University of Chicago Press, 2001.

List, John A. and Catherine Y. Co. "The Effects of Environmental
Regulations on Foreign Direct Investment," *Journal of Environmental
Economics and Management*, 40(1), 1-20, July 2000.

List, John A. and Mitch Kunce. "Environmental Protection and Economic
Growth: What Do the Residuals Tell Us?" *Land Economics*, 76(2),
267-282, May 2000.

List, John A., Daniel L. Millimet, Per G. Fredriksson, and W. Warren
McHone. "Effects of Environmental Regulations on Manufacturing Plant
Births: Evidence from a Propensity Score Matching Estimator," *The
Review of Economics and Statistics*, 85(4), 944-952, November 2003.

McConnell, Virginia D. and Robert M. Schwab. "The Impact of
Environmental Regulation on Industry Location Decisions: The Motor
Vehicle Industry," *Land Economics*, 66(1), 67-81, February 1990.

U.S. Bureau of the Census. *Pollution Abatement Costs and Expenditures,
19\_\_*. Washington, DC: U.S. Government Printing Office, various years.

**[Table 1]{.underline}. Measures of Within-State Heterogeneity in
Environmental Compliance Costs**

> **State Max-Min (Rank) Max/State (Rank)**
>
> Alabama 4.412 (13) 11.810 (5)
>
> [Arizona 1.011 (45) 3.984 (36)]{.mark}
>
> Arkansas 3.473 (27) 4.360 (33)
>
> [California 3.061 (32) 8.740 (13)]{.mark}
>
> Colorado 3.083 (30) 8.885 (12)
>
> [Connecticut 0.638 (46) 1.733 (44)]{.mark}
>
> Delaware 0.156 (48) 1.150 (45)
>
> [Florida 3.682 (24) 5.826 (26)]{.mark}
>
> Georgia 3.919 (19) 7.903 (16)
>
> [Idaho 3.748 (22) 10.027 (10)]{.mark}
>
> Illinois 3.733 (23) 4.293 (34)
>
> [Indiana 3.776 (21) 4.501 (32)]{.mark}
>
> Iowa 4.078 (17) 4.749 (30)
>
> [Kansas 4.128 (16) 19.970 (1)]{.mark}
>
> Kentucky 2.982 (34) 2.871 (41)
>
> [Louisiana 5.122 (10) 2.989 (40)]{.mark}
>
> Maine 2.168 (38) 10.95 (8)
>
> [Maryland 2.201 (37) 2.509 (42)]{.mark}
>
> Massachusetts 1.453 (42) 3.176 (39)
>
> [Michigan 5.228 (7) 7.462 (18)]{.mark}
>
> Minnesota 3.397 (29) 16.380 (3)
>
> [Mississippi 5.508 (5) 8.480 (14)]{.mark}
>
> Missouri 4.229 (14) 8.393 (15)
>
> [Montana 3.819 (20) 11.331 (7)]{.mark}
>
> Nebraska 2.983 (33) 7.597 (17)
>
> [Nevada 1.580 (41) 6.453 (22)]{.mark}
>
> New Hampshire 1.251 (44) 3.682 (37)
>
> [New Jersey 1.355 (43) 3.404 (38)]{.mark}
>
> New Mexico 5.547 (4) - -
>
> [New York 3.077 (31) 7.386 (19)]{.mark}
>
> North Carolina 5.316 (6) 7.301 (20)
>
> [North Dakota 3.493 (26) - -]{.mark}
>
> Ohio 5.186 (8) 5.000 (28)
>
> [Oklahoma 8.093 (1) 12.433 (4)]{.mark}
>
> Oregon 4.203 (15) 10.875 (9)
>
> [Pennsylvania 4.003 (18) 4.868 (29)]{.mark}
>
> Rhode Island 0.398 (47) 1.968 (43)
>
> [South Carolina 2.797 (36) 4.262 (35)]{.mark}
>
> South Dakota 3.413 (28) - -
>
> [Tennessee 6.006 (3) 6.877 (21)]{.mark}
>
> Texas 5.131 (9) 5.386 (27)
>
> [Utah 1.762 (40) 11.381 (6)]{.mark}
>
> Vermont 2.830 (35) 5.850 (25)
>
> [Virginia 7.372 (2) 6.083 (24)]{.mark}
>
> Washington 4.949 (11) 19.933 (2)
>
> [West Virginia 4.801 (12) 4.654 (31)]{.mark}
>
> Wisconsin 3.584 (25) 6.306 (23)
>
> [Wyoming 1.944 (39) 9.577 (11)]{.mark}

**[Table 2]{.underline}. The Extent of Difference in County versus
State-wide Estimates of Environmental Compliance Costs**

**Percent of U.S. Percent of U.S.**

**manufacturing manufacturing**

**Number employment in 2002 establishments in 2002**

> Counties that are significantly **higher**
>
> than their state at 90% level 468 20.4% 18.2%
>
> Counties that are significantly **lower**
>
> than their state at 90% level 578 31.3% 34.8%
>
> Counties that are **not** significantly different
>
> from their state at 90% level 1,467 47.8% 45.8%
>
> Counties that are not in the sample 595 0.5% 1.2%

**[Table 3]{.underline}. Largest Manufacturing Counties that are
Statistically Different from Their State**

##  Manufacturing Index relative

## County employment in 2002 to state's index

> Los Angeles County, CA 523,293 Lower
>
> [Cook County, IL 270,190 Lower]{.mark}
>
> Orange County, CA 187,142 Lower
>
> [Santa Clara County, CA 160,734 Lower]{.mark}
>
> Harris County, TX 147,339 Higher
>
> [Dallas County, TX 132,968 Lower]{.mark}
>
> King County, WA 106,134 Lower
>
> [Cuyahoga County, OH 91,803 Lower]{.mark}
>
> Alameda County, CA 88,262 Higher
>
> [Hennepin County, MN 86,103 Lower]{.mark}
>
> Tarrant County, TX 83,010 Lower
>
> [Macomb County, MI 75,040 Lower]{.mark}
>
> Oakland County, MI 73,500 Lower
>
> [San Bernardino County, CA 66,352 Higher]{.mark}
>
> DuPage County, IL 66,165 Lower
>
> [Hamilton County, OH 60,975 Lower]{.mark}
>
> Monroe County, NY 59,260 Higher
>
> [Sedgwick County, KS 57,416 Lower]{.mark}
>
> Marion County, IN 57,373 Lower
>
> [Erie County, NY 56,473 Higher]{.mark}
>
> St. Louis County, MO 53,255 Lower
>
> [Riverside County, CA 52,885 Higher]{.mark}
>
> Dade County, FL 50,568 Lower
>
> [Lake County, IL 50,144 Lower]{.mark}
>
> New York County, NY 47,838 Lower

**[Table 4a]{.underline}. States with the *Highest* Percentage of Their
Counties Statistically Different from the State**

> 1\. New Jersey 66.7%
>
> 2\. New York 59.0%
>
> 3\. Oregon 57.6%
>
> 4\. Pennsylvania 54.5%
>
> 5\. California 53.7%
>
> 6\. Alabama 50.0%
>
> New Mexico 50.0%
>
> Vermont 50.0%
>
> 9\. Washington 48.6%
>
> 10\. Louisiana 48.1%

**[Table 4b]{.underline}. States with the *Lowest* Percentage of Their
Counties Statistically Different from the State**

> 48\. New Hampshire 0.0%
>
> Rhode Island 0.0%
>
> Delaware 0.0%
>
> 45\. Utah 11.1%
>
> 44\. Massachusetts 16.7%
>
> 43\. West Virginia 24.4%
>
> 42\. Connecticut 25.0%
>
> 41\. Colorado 31.0%
>
> 40\. Mississippi 32.5%
>
> 39\. Arkansas 33.3%
>
> South Carolina 33.3%
>
> Nevada 33.3%
>
> **[Figure 1]{.underline}.** **Heterogeneity in Environmental
> Compliance Costs: Three Examples**

![](media/image8.wmf){width="5.595138888888889in"
height="2.8131944444444446in"}![](media/image9.wmf){width="5.595138888888889in"
height="2.8743055555555554in"}![](media/image10.wmf){width="5.595138888888889in"
height="2.8743055555555554in"}

**[Figure 2]{.underline}. County versus State-wide Estimates of
Environmental Compliance Costs**

![](media/image11.wmf){width="6.4944444444444445in"
height="8.404166666666667in"}

**[Figure 2]{.underline} (continued).**

![](media/image12.wmf){width="6.4944444444444445in"
height="8.404166666666667in"}

**[Figure 2]{.underline} (continued).**

![](media/image13.wmf){width="6.4944444444444445in"
height="8.404166666666667in"}

![](media/image16.wmf){width="8.404166666666667in"
height="6.018055555555556in"}

**[Figure 2]{.underline} (continued).**

![](media/image18.wmf){width="6.502777777777778in"
height="8.272222222222222in"}

[^1]: The principal alternatives to such cost-based measures are various
    indexes and rankings produced by environmental organizations, which
    are often considered to be subjective in nature. See Levinson (2001)
    for a review and discussion.

[^2]: These survey data are confidential, collected and protected under
    Title 13 of the U.S. Code. Restricted access to these data can be
    arranged through the U.S. Census Bureau's Center for Economic
    Studies. See http://www.ces.census.gov/ for details.

[^3]: Though the collection of these data began with reference year
    1973, the establishment-level data from 1973-1978 and 1983 are
    currently unavailable. A survey for reference year 1987 was not
    conducted. The PACE survey was suspended over the past decade
    (1995-1998, 2000-2004), but annual collection has recently resumed,
    beginning with reference year 2005.

[^4]: In principle, one could use the separate operating costs on air,
    water, and solid waste abatement to construct media-specific
    indexes. I do not do so here in this paper.

[^5]: I have also performed the analyses that follow using a plant's
    output -- namely, value of shipments -- as the denominator.

[^6]: Alternatives to this include the PAOC intensity of the mean or
    median establishment in the set {*n´*, *t´*, *q´*} and/or could
    involve the weighting of observations.

[^7]: That is, the inter-quartile cutoffs are drawn so that each
    quartile contains one-fourth of the industry's employment, rather
    than one-fourth of its establishments. I conjecture that this
    grouping more closely approximates the manner in which regulators
    prioritize their scrutiny of plants within an industry. Because the
    PACE and ASM sample larger establishments more heavily, the
    distribution across these size quartiles in this sample is more
    uniform than it might be, with 18.8%, 26.9%, 29.8%, and 24.5% in the
    quartiles with the largest to smallest plants, respectively. Data on
    an industry's plant employment distribution is taken from the prior
    or contemporaneous CM.

[^8]: Demonstrating whether this is indeed the case is beyond the scope
    of this current paper. It should be noted that *any* use of plant
    vintage here is more than has been (or could be) done by any
    previous study.

[^9]: Because the same omitted category is used in both regressions
    (i.e., establishments in the "county" and "state" of Washington DC,
    observed in existence in the 1963 Census of Manufactures), the
    county- and state-level indexes are identically scaled.

[^10]: In particular, establishments are asked to report their
    expenditures in *thousands* of dollars. Therefore, with rounding, a
    response of zero reflects expenditures of less than \$500. The
    *cnreg* (censored normal regression) command in STATA is a
    generalization of the standard Tobit procedure that allows the
    censoring point to vary by observation. In this case, left-censoring
    occurs at *ln*(0.5/*EMP~i~*) for about 18% of the observations in
    this sample.

[^11]: An issue here is when the set {*n´*, *t´*, *q´*} contains just
    one establishment, particularly if the incidence of such cases is
    spatially concentrated. Also, if the set {*n´*, *t´*, *q´*} contains
    any extreme PAOC outliers, this will impact all counties with
    establishments in that same industry-year-size class. Future work
    will focus on the sensitivity of the results below to these issues.

[^12]: Regressions show that, other things being equal, establishments
    of older vintages have higher PAOC intensity.

[^13]: The index also includes potential geographic differences in
    prices related to pollution abatement, such as the salaries of
    environmental workers, cost of low-sulfur coal, price of
    electricity, fees for solid waste hauling and disposal, and so
    forth.

[^14]: This is in addition to the 600-plus counties that we do not
    observe at all in this database.

[^15]: Indeed, 36 of the 48 *true* minima were suppressed, as were 28 of
    the 48 *true* maxima.

[^16]: Max/State is undefined for three states -- New Mexico, North
    Dakota, and South Dakota -- because their state-level index happens
    to be negative.

[^17]: Note that this is true despite the fact that counties toward the
    extrema tend to be more imprecisely measured --- or rather, counties
    with more imprecise point estimates tend to define and/or fall
    toward the extrema. This is somewhat evident in the graphs, but
    regression analysis (not reported here) more clearly demonstrates
    this to be the case.

[^18]: Note that these states also have about 600 counties not in my
    sample and therefore without an index value. These counties
    accounted for just 0.5% of the manufacturing employment in these
    states.