How Will Climate Change Policies Affect Domestic...
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How Will Climate Change Policies Affect Domestic Manufacturing?
Joseph E. Aldy and William A. Pizer*
April 28, 2012
* Aldy is affiliated with Harvard University, Resources for the Future, and the National Bureau of
Economic Research. [email protected]; 617-496-7213; Harvard Kennedy School, 79 JFK
Street, Mailbox 58, Cambridge, MA 02138. Pizer is affiliated with Duke University, Resources for the
Future, and the National Bureau of Economic Research. [email protected]; 919-613-9286; Box
90311, Duke University, Durham, NC 27708. This research is supported by a grant from the Electric
Power Research Institute.
How Will Climate Change Policies Affect Domestic Manufacturing?
Joseph E. Aldy and William A. Pizer *
May 1, 2012 Draft
Abstract
The pollution haven hypothesis suggests that unilateral environmental regulation could cause adverse
“competitiveness” impacts on domestic manufacturers as they lose market share to foreign competitors
and relocate production activity – and emissions – to unregulated economies. This is particularly
troubling in the case of mitigating climate change, a global pollution externality, where there are no
localized environmental benefits and shifting emissions to unregulated economies undermines the
domestic policy rationale. Simulations have suggested this effect might shift between 5 to 20 percent of
regulated emission reductions to unregulated economies.
We instead use an empirical framework to examine this question, taking advantage of a state-by-
industry panel employment and price data over 1990-2009. We implement two identification
strategies: First, we instrument for electricity prices with global oil prices along with state-level monthly
heating- and cooling-degree-day data, while controlling for state × industry fixed effects. Second, we
employ a triple-differencing method that exploits differential changes in time across industries and
states, removing common trends in each state and industry.
Preliminary results suggest that employment faces about a -0.2 elasticity in the face of higher energy
prices. Based on recent estimates that climate change regulation would raise electricity prices by 8
percent, this suggests a 1.6 percent decline in manufacturing employment.
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How Will Climate Change Policies Affect Domestic Manufacturing?
Introduction
Any meaningful policy to mitigate U.S. greenhouse gas emissions will raise the costs of
production across the manufacturing sector. Regardless of the policy instrument – cap-and-trade, a
carbon tax, a national clean energy standard, or EPA regulation under the Clean Air Act – manufacturing
firms will face higher prices for electricity and possibly for direct combustion of fossil fuels on-site (Aldy
et al. 2010, Aldy 2012, Burtraw et al. 2011). Raising the cost of energy could adversely affect the
competitive position of some industries, especially the more energy-intensive and those located in
regions that generate carbon-intensive power.
We consider a number of interrelated questions about the potential impacts of a domestic
greenhouse gas mitigation policy on the U.S. manufacturing sector. What effect would a climate change
policy have on industry-level competitiveness? On production? On employment? What are the
winning and losing industries? How large are their gains and losses? What are the winning and losing
states? And how large are their gains and losses? What impact do other factors, such as transportation
costs and agglomeration economies, have on the competitiveness effects borne by various industries?
To address these questions, we employ a two-step analysis. First, we estimate the historic
relationships between energy prices and outcomes, such as employment and wages. The econometric
analysis takes advantage of detailed industry- by state-level outcome data from the Bureau of Labor
Statistics over 1990-2009. We focus on a decomposition of state-level economies into 53 manufacturing
industries. We integrate these data with industrial sector energy price data by state from the Energy
Information Administration and energy intensity data by industry from the Bureau of Economic Analysis’
2002 benchmark input-output tables. We employ both an instrument variables estimator and a triple-
difference estimator to estimate the impacts of energy prices on these industry outcome measures.
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Taking advantage of the states in our statistical analysis presents several advantages over a
more traditional multi-national assessment. First, state-level data are much more consistent and higher
quality than most international datasets. Second, the states are relatively similar to each other in a
variety of important ways that could otherwise affect such analysis. For example, all states face the
same federal income tax system, whereas countries do not experience similar tax codes. Third, the
state-level analysis also allows for specific evaluation of state and regional competitiveness effects,
which are of interest to the policy community in their own right. Finally, such an analysis ensures that
we have robust estimates of competitiveness effects in the United States, and not simply an average
effect across a variety of countries.
Second, we use these estimated price-employment relationships to simulate the effect of
greenhouse gas mitigation policy on industry-level and state-level employment. We draw from recent
modeling analyses that estimate energy price changes for a range of carbon prices, a national clean
energy standard, and EPA regulation of the power sector through performance standards under the
Clean Air Act (Energy Information Administration 2010, 2011; Burtraw et al. 2011). These energy price
changes are used with the estimated energy price-competitiveness relationships to predict the
manufacturing competitiveness impacts of various climate policy proposals. Our analyses focus on the
impacts of electricity price increases. This permits a reasonable comparison across these four kinds of
policy instruments, since a national clean energy standard and Clean Air Act performance standards
would apply only to the power sector. In addition, electricity expenditures represent a majority of
energy expenditures for about 88% of the manufacturing sector (Aldy and Pizer 2011).
Our preliminary estimates suggest an employment-electricity price elasticity of -0.2. Applied to
an estimated 8 percent price impact from recent cap-and-trade and carbon tax proposals, we would
expect an employment decline of 1.6 percent. However, there are a number of reasons to expect the
actual “competitiveness” effect to be smaller. This elasticity is based on shifting production across
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states; shifting production across countries is more costly. This elasticity does not differentiate between
employment declines owing to higher local energy prices when other jurisdictions remain the same,
versus higher energy prices in all jurisdictions. It is the difference between these effects – the shifting of
production to other unregulated, jurisdictions – that is the real competitiveness effect. Further work
will examine these issues in more detail, examine the impact on individual industries, explore additional
outcome measures such as value added and revenue, consider additional regulatory policies such as a
clean electricity standard, and refine the estimation procedure.
The next section synthesizes the literature on the relationship between environmental
regulations, energy costs, and manufacturing activity. The third section presents our empirical
framework for estimating the historical energy price-competitiveness relationships. The fourth section
briefly describes the data we employed to estimate the empirical models. The fifth section presents our
preliminary results and the simulations of the various greenhouse gas mitigation policies. The sixth
section concludes with policy implications and next steps for research.
Energy Prices, Regulations, and Employment
Three literatures bear on this question of how energy price increases from greenhouse gas
mitigation policies would impact the manufacturing sector. A quantitative, but non-empirical literature
has used detailed, applied general equilibrium models to simulate effects of mitigation policies focusing
on emission leakage (IPCC, 2001). Early analyses found emission leakage ranging from zero to 70
percent, but later analyses found a narrower range of 5 to 20 percent. That is, the ratio of emission
increases outside those countries pursuing emission reductions to the reductions achieved inside those
countries, is 5 to 20 percent.
More relevant to our empirical approach and focus on economic activity, but less specific to
climate change mitigation, has been work on the pollution haven hypothesis and oil price shocks. The
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common thesis across these literatures is that the higher costs of production (from the regulation in the
former and the price shock in the latter literature) adversely impacts output, employment, and other
measures of economic activity.
The pollution haven hypothesis literature, comprising extensive theoretical and empirical
research, has explored the question of whether environmental regulations induce firms to relocate to
other countries with less-stringent regulations and, as a result, result in lower domestic employment
and production (Jaffe et al. 1995). The empirical evidence of an adverse impact of regulations on
manufacturing activity, employment, and competitiveness impacts is mixed. The relocation of
manufacturing to other countries as a result of environmental regulations is modest (Ederington et al.
2005), and appears to be mitigated by a variety of factors, such as the availability of relevant labor,
material, and capital in other nations (Antweiler et al. 2001), transportation costs (Ederington et al.
2005), irreversible investments in fixed capital stock (Ederington et al. 2005), and agglomeration
economies (Jeppesen et al. 2002). A variety of other macroeconomic and policy factors likely play a
larger role in the decisions about the geographic location of manufacturing activity and the evolution of
trade. For example, Levinson and Taylor (2008) find that about 10 percent of the increase in U.S. net
imports with Canada and Mexico can be attributed to increasing pollution abatement costs in the U.S.
manufacturing sector. In some pollution-intensive industries, researchers have found precisely
estimated zero impacts of environmental regulations on employment, suggesting little competitiveness
impact or labor substitution effects that counter adverse output effects (Morgenstern et al. 2002).
In contrast, several studies have found more pronounced impacts in evaluations of
heterogeneity in environmental regulations across the U.S. states. Henderson (1996) and Greenstone
(2002) estimate significant changes in employment and effective firm relocation between states and
counties facing more stringent regulations (e.g., non-attainment designations for national ambient air
quality standards) than those bearing less onerous environmental rules. Kahn and Mansur (2010) focus
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on variation between adjacent counties and estimate more meaningful impacts on economic activity.
The states may serve as a useful model for evaluating the impacts of environmental regulations when
other factors that impact manufacturing location decisions in the global context – such as transportation
costs, tax policy, tariff policy, legal institutions, labor quality, etc. – are less important within the United
States.
The oil price shock literature has evaluated the impacts of the oil shocks of the 1970s as well as
oil price volatility to assess the impacts of energy prices on economic activity and employment
(Hamilton 2008). The evidence that higher energy prices adversely impact employment, dates back at
least to Hamilton’s (1983) paper on the relationship between oil prices and U.S. macroeconomic
performance. Davis and Haltiwanger (2001) investigated the impact on industry-specific job creation
and destruction as oil prices rise and fall, and find that capital intensity and energy intensity are
associated with larger oil price-induced changes in employment by manufacturing industry. More
recent research has questioned the extent to which oil price increases impact the macroeconomy (e.g.,
Blanchard and Gali 2009, Barsky and Kilian 2004), while others maintain that the most recent recession’s
timing and depth was affected by the 2008 run-up in oil prices (Hamilton 2009).
In recent work, Aldy and Pizer (2011) employ national-level data to estimate the impact of
idiosyncratic industry-specific electricity price shocks on production and net imports for manufacturing
industries. This draws from the latter two literatures, and attempts to simulate the impacts of
regulatory interventions on energy prices and hence manufacturing activity. They find that more
energy-intensive industries experience larger percentage reductions in production and percentage
increases in net imports than less energy-intensive industries in response to an increase in electricity
prices. Based on this empirical relationship, they simulate the impact of a $15/ton CO2 price, and find
that energy-intensive industries, such as chemicals, steel, aluminum, bulk glass, plastics, and pulp and
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paper, would experience reductions in production of 3-4 percent, with about one-third of this decline
reflecting an increase in net imports.
Empirical Model
Our basic challenge is to identify the effect of energy prices on employment and other outcome
measures while removing confounding influences. With outcome measures observed over different
industries, states, and time, and prices observed over different states and time, the relationship of
interest can be written:
ln���� = ��� + ���� ln��� + ���� (1)
Here, i indexes over industry, s over state, and t over time. We explicitly recognize even in this simplest
version that the price-outcome elasticity may vary over industry in a way that remains to be specified.
We also specify state-industry fixed effects in this simplest model: we are not trying to explain how or
why the general pattern of industry location varies over states. Still, the underlying problem is that a
variety of influences embedded in the error ϵist, particularly changes over time at the state level, could
affect both prices and outcomes and bias our results.
We take two approaches to identification: a triple difference (TD) estimator, which controls for
flexible industry- and state-level time trends, and an instrumental variables (IV) estimator, which uses
global oil prices and random fluctuation in annual weather patterns to identify exogenous price shocks.
In the TD approach we specify
ln���� = ��� + �� + �� + ���� ln��� + ���� (2)
To see how this becomes a triple-difference, consider a simpler case with two industries (beverages and
aluminum), two states (California and Iowa), and two years (1990 and 2009). We can difference out the
industry-state fixed effects by constructing time differences. That is,
ln���(����) = ��� + �(����) + �(����) + ���� ln��(����) + ���(����) (3)
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minus
ln���(����) = ��� + �(����) + �(����) + ���� ln��(����) + ���(����) (4)
equals
�ln��� = �� + �� + ����� ln�� + ���� (5)
This is the first difference. Then we difference out state-time effects by constructing industry
differences:
�ln�(bev)� = �(bev) + �� + ��bev�� ln�� + ��(bev)� (6)
minus
�ln�alum� = �(alum) + �� + ��alum�� ln�� + ��(alum)� (7)
equals
Δ�ln�(bev)� − Δ�ln�(alum)� = Δ�(bev) − Δ�(alum) + (��bev� − ��alum�)Δ� ln�� + Δ�,��� (8)
or
�ΔB − ΔA�� = Δ�(bev) − Δ�(alum) + Δ ���Δ ln�� + Δ�,��� (9)
where we have condensed notation slightly in the last expression. This is the second difference. Finally,
we difference out industry-time effects by constructing state differences:
�ΔB − ΔA��� = Δ�(bev) − Δ�(alum) + Δ ���Δ ln��� + Δ�,���� (10)
minus
�ΔB − ΔA��� = Δ�(bev) − Δ�(alum) + Δ ���Δ ln��� + Δ�,���� (11)
equals
�ΔB − ΔA��� − �ΔB − ΔA��� = Δ ���(Δ ln��� − Δ ln���) + Δ�,�,�� (12)
Thus, ignoring sampling error, we estimate
Δ ��� =
�ΔB − ΔA��� − �ΔB − ΔA���
Δ ln��� − Δ ln��� (13)
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That is, we take the differential change between beverages and aluminum in California and Iowa, and
compare it to the differential price change in California and Iowa. This estimates the differential price
response of beverages compared to aluminum. More generally, with data for multiple states, industries,
and years, we estimate the differential price response of each industry relative to a mean price response
across industries, averaged over states and years.
There are three important features to note about the triple difference approach. First, it
addresses the potential for confounding state-time effects (the δst’s) by looking at differences among
industries within a state. While helpful in removing a likely source of endogeniety – for example, an
increase in local economic activity that both increases employment and raises electricity prices – it also
removes a potential source of variation.
The related, second important feature to note is that the mean price response is not directly
estimated, only the response relative for each industry compared to the industry average. This follows
from the fact that we do not have industry-specific energy prices. As we remove the state-time effects
by taking the difference across industries in Equation (8), this would also remove the price response
unless the coefficients β(i) differ. We can work around this by specifying that β(i)=β ei where ei is the
energy intensity of industry i. That is, we expect the responsiveness of an industry with zero energy use
to be zero, and, for industries with non-zero energy use, the responsiveness to be proportional to the
energy share. With this assumption, differences among industries are used to fit a line through the
origin that defines absolute rather than relative elasticities for each industry.
The third important feature is that we remove any confounding industry-time effects (the γit‘s)
by looking at differences across states for each industry. While this again helps remove a potentially
confounding effect – for example, declines in heavy manufacturing producing energy price declines in
regions with those industries – it also again removes a potential source of variation.
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The second, IV approach uses both changes in global oil prices and random fluctuation in annual
weather to instrument for energy prices. Global oil prices are generally exogenous to local industrial
demand but correlated with electricity prices due to the cost of fuel oil and distillate. Similarly, random
weather fluctuations should be exogenous to industrial production and employment decisions but, as
unusually cold winters and hot summers lead to increased competition for energy, influence electricity
prices. In order to construct our instrumental variables estimator, we follow a two-step approach where
we first estimate a model
ln��� = �� + � + ����� + ���� + ��� (14)
where πs are state-specific effects, θt are time-specific effects, zst are our annual weather variables for
each state, and wt is the oil price.
In general, zst includes 24 variables reflecting the number of heating- and cooling-degree days
(HDD and CDD) for each of the twelve months. Heating degree days for a given month are the sum over
each day of the month of 65 minus the average daily temperature (using zero if the temperature is
above 65). Cooling degree days are the same calculation, except the sum of the average daily
temperature minus 75. Thus, for example, “HDD_JANst” is the number of heating degree days in
January, measured for each state and year.
Using the estimated parameters from (14), we then construct predicted energy prices,
ln���� = ��� + �� + ������ + ����� (15)
which are then used to estimate (1). Because these predicted energy prices vary only due to oil prices
and in-state weather variation, we would expect them to be unrelated to any confounding state-level
economic variation.
Data
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For outcome measures we focus on two series: average annual employment and annual total
wages collected by the Bureau of Labor Statistics over 1990 to 2009 (future work will look out value
added and revenue measures from the Bureau of Economic Analysis). In order to be able to relate these
industries to energy intensity estimates, we match them to benchmark input-output classifications used
by the BEA. The original BLS data is available at a level of detail that defines 473 manufacturing
industries; we collapse that to 53 industries. The data cover all 50 states and the District of Columbia.
We exclude petroleum refining from our 53 sectors in all of our analysis (leaving us with 52
industries). As an energy supply sector, we can expect it to behave in fundamentally different ways
from sectors that use energy to produce other products. Petroleum refining also uses energy as a
feedstock – more than 80 percent of its costs are energy, partly feedstock, partly energy use. Finally,
even ignoring feedstock use, it is more energy intensive by a factor of two than any other industry.
While there are other potentially problematic sectors that remain to be considered, and might be
accommodated in other ways, for the current analysis we simply remove petroleum refining and treat
other industries as comparable.1
This outcome data is merged with state-level price data from the Energy Information
Administration (EIA). The EIA collects data on state-level prices for a wide range of energy products:
coal, distillate fuel, gasoline, kerosene, natural gas, electricity, etc. It also tracks separate prices for four
or five sectors of users: residential, commercial, industrial, transportation, and (where relevant) electric
power. In this analysis, we focus on electricity prices for the industrial sector users.
We focus on electricity prices because it is the overwhelming source of energy for the
manufacturing sector (>80%). Moreover, it is generally related to the local price of other fuels. But
most importantly, the most frequent target of climate change regulation is the power sector: This was
1 Other potentially problematic sectors include chemicals, which also use energy products as a feedstock, and pulp and paper mills, which often cogenerate electricity from various byproducts (such as black liquor). However, the scale of these problems tends to be considerably smaller than that of petroleum refining.
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the focus in the EU (along with a handful of other energy-related industries), it was the focus in several
of the legislative proposals in the 112th Congress, and it is the focus of the administration’s current
proposal for a clean electricity standard. While we may eventually want to expand our consideration to
other fuels, understanding the impact of electricity only regulation and price impact is an important
starting point.
To specify the function β(i) for the triple difference approach, we assume β(i)=ψei where ei is
energy intensity. Energy intensity is measured using the 2002 benchmark input-output tables from the
Bureau of Economic Analysis. Energy is defined as inputs of oil and gas extraction (211000), coal mining
(212100), electric power generation, transmission, and distribution (221100), natural gas distribution
(221200), and petroleum refining (324110). Energy intensity is calculated as the share of these inputs at
producer prices in total costs for a given sector i. For the IV (and OLS) approach, we assume β(i)=β is
fixed.
For the IV approach, we use oil prices and state-by-month heating degree day and cooling
degree day data as a set of instruments for electricity prices. Oil prices are measured as the average
daily closing price on the New York Mercantile Exchange for West Texas Intermediary (WTI) crude. Our
oil price instrument is allowed to vary in its effect by state, allowing us to distinguish aggregate
economic trends from state-specific oil-price dependence. Data on state-level, monthly heating and
cooling degree days are from the National Oceanic and Atmospheric Administration (series HCS 5-1 and
5-2; this data is only available for the lower 48 states).
Summary statistics for the data is presented in Table 1. The first line highlights that less than
one-tenth (51/732) of the state-level (versus national) price variation arises from within-state variability
based on sum-of-square calculations. Given an overall standard deviation (of logged prices) of 0.61, the
standard deviation within states is around 16%. This is an important benchmark as ultimately we want
to explore the impact of climate change policies that might raise energy prices by around 10%. The
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second row and third rows provide information on the outcome variables. Almost 95% of the variation
in employment and wages arises from state-industry fixed effects, with about 5% arising from within
state-industry variation (with a small amount due to aggregate year effects). The fourth row shows that
the average energy intensity, excluding petroleum refining, is 2.3%. The last two lines provide
information about the instruments (to save space, we only report statistics for HDD in January).
Preliminary Results
Our initial results for estimating β in Equation (1) are reported in Table 2. For the OLS and IV
estimators, we assume β(i)=β. Results are presented separately for the weather instruments, the oil
price instrument, and both instruments together. For the triple difference estimator, we assume
β(i)=ψei where ei is the energy intensity of sector i. We then report ���̅ in the table. We show results for
both 1990-2009 and separately for 1990-1999 and 2000-2009, for both total annual wages and average
annual employment.
The only results that are consistent across both sub-periods periods are the triple difference
estimates that are positive but indistinguishable from zero (in the 1990s where the effects are
statistically significant, they are still extremely small). This suggests that the variation left after triple
differencing may not be particularly important. Future work will look at simpler difference-in-difference
models that remove either state or industry trends, but not both.
Among the remaining estimates, the 1990s consistently show a strong positive relationship
across all 4 estimates while the 2000s show a consistent negative relationship across all 4 estimates. A
significant difference between the 1990s and the 2000s is perhaps not surprising as the 1990s were a
period of significant deregulation of power markets in the United States. The 1992 Energy Policy Act
extended the 1978 Public Utility Regulatory Policies Act (PURPA) to allow open-access to the electricity
grid for all generators. Order 888 from the Federal Energy Regulatory Commission (FERC) in the summer
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of 1996 led to wholesale power competition throughout the United States (Brennan et al 2002; EIA
2000). Progress towards retail competition continued through the late 1990s but stagnated in the wake
of the 2001 California energy crisis (EIA 2010b).
This change in regulation raises the question of whether contracts for industrial customers were
substantially altered, whether the process of deregulation itself may be influencing the estimation, or
whether something entirely unrelated is occurring. For example, if customers in the 1990s faced a block
structure for power and paid more per unit for higher use, that could explain the positive relationship.
Or, if state-level deregulation went forward at moments when their economies were doing well, and
correlated with (short-term) price increases, that could also explain the positive relationship.
In contrast, the negative elasticity across the 2000’s is consistent across the OLS and the various
IV estimates, at about 0.2. For our purposes, the most recent behavior is the most relevant in any case.
However, it will be important to understand what is driving the changing relationship over time in order
to ensure such changes are not expected to continue and/or what assumptions are relevant.
Variation Across Industries
To be completed (all models can be estimated with various functions for β as a function of industry; may
be possible to identify particularly vulnerable industries).
Carbon Pricing Simulation
We can use these statistically-estimated relationships to simulate the effects of a $15 per ton
CO2 price from a U.S. climate change policy. Based on the Energy Information Administration (2008)
modeling of an economy-wide cap-and-trade program, such an allowance price would increase
industrial sector electricity prices by about 8 percent, which is approximately equal to a one standard
deviation increase in energy prices in our sample. We pick this price based on similar allowance prices
expected at the start of cap-and-trade programs proposed in recent legislation, including EPA’s (2009)
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estimate of a $13 per ton CO2 price under the Waxman-Markey Bill (H.R. 2454, 111th Congress), EPA’s
(2010) estimate of a $17 per ton CO2 price under the American Power Act (draft legislation from
Senators Kerry and Lieberman) as well as the first year carbon tax of $15 per ton CO2 in a 2009
Republican-sponsored carbon tax bill (H.R. 2380, 111th Congress).
Applying our estimated -0.2 elasticity to an estimated 8 percent price impact from recent
regulatory proposals, we would expect an employment decline of 1.6 percent. However, there are a
number of reasons to expect the actual “competitiveness” effect to be smaller. This elasticity is based
on shifting production across states; shifting production across countries is more costly. This elasticity
does not differentiate between employment declines owing to higher local energy prices when other
jurisdictions remain the same, versus higher energy prices in all jurisdictions. It is the difference
between these effects – the shifting of production to other unregulated, jurisdictions – that is the real
competitiveness effect.
Whether this effect of 1.6 percent should be viewed as large or small is unclear. One question is
the relative size of the “true competitiveness” effect – the consequence of inaction in other jurisdictions
compared to the consequences if all jurisdictions pursue similar policies. If the consequence of action in
all jurisdictions were, say, a 1.2 percent domestic effect, versus a 1.6 percent effect from U.S.-only
action, we might say this is quite large. One-quarter of the domestic effect would be associated with
leakage of employment, and presumably emissions, to other jurisdictions (or at least inaction in those
jurisdictions). However, compared to overall variability in employment over time, 1.6 percent is
relatively small. For example, employment in our manufacturing sample fell from 13.8 million to 9.9
million from 2000 to 2009. Further work should contemplate this question.
(Additional work will focus on variation of impacts across industry and states, as well as
alternate policies, such as a clean electricity standard and regulation through the Clean Air Act.)
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Conclusion and Next Steps
Concerns about the impacts of regulation on domestic manufacturing activity continue to be an
important theme in political debates surrounding policies to address climate change, particularly when
other key trade partners are unlikely to pursue similar policies in the near term. There is also an
important environmental question of whether domestic emission reductions might “leak” into other
unregulated jurisdictions; for climate change, this would undermine any environmental benefits. The
scope for these effects depends on both the energy intensity of manufacturing and the ability of
production to shift jurisdictions, as well as the scale of the regulation. This is largely an empirical
question.
We estimate these effects using a 20-year panel of employment and wage data differentiated by
state and industry, coupled with state-level energy prices. Our preliminary results suggest that the
elasticity of employment and wages with respect to electricity prices is about -0.2. Coupled with an
expected 8 percent rise in electricity prices associated with recent cap-and-trade proposals, this
elasticity predicts a 1.6 percent decline in employment.
This result is sensitive to the period of analysis: Including the 1990s leads to equally positive
estimates. However, electricity deregulation may be adversely affecting our estimation in that period.
There are also reasons to believe our -0.2 elasticity is an over-estimate for national-level impacts.
Shifting production internationally is more costly than shifting domestically (the basis of our state-level
estimation). Further, we have not distinguished overall manufacturing impacts from domestic
regulation from those arising associated with domestic regulation absent foreign regulation.
As noted throughout, these are preliminary results. Our intention is to pursue a number of
further steps to complete the project. This includes:
1. Refining our estimation procedure. We need to consider difference-in-difference estimates to
complement the triple-difference approach. We also need to do a number of statistical checks
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on our IV approach – for example, testing for weak instruments and/or the exogeneity of prices
(Stock and Yago, 2005; Kleibergen and Paap, 2006). Our weather data – monthly heating and
cooling degree days by state – could also be combined in different ways. For example, we could
combine the data into fewer variables and allow behavior to vary by state.
2. Including additional covariates. We have not fully utilized energy intensity differences among
industries in our model. We also have data on trade and measures of “footloose-ness” that we
can include to help explain differences among industries.
3. Focusing on subsets of states and industries. We know that many states do not contribute to
manufacturing and many industries do not have significant energy use. Additional work will
look at both the sensitivity of our results to various subsamples and weighting, as well as how
estimates vary across industries and states. This analysis could also explore in more detail
deregulation in different states, and how that might be influencing our results.
4. Considering other outcome variables. We have additional data on Gross State Product (which
includes capital as well as wages) that we have not yet explored as well as regional input-output
tables. We expect this to complement our initial focus on employment.
5. Additional simulations. We intend to consider energy price increases from other climate
policies, such as proposals for a clean electricity standard and use of existing authorities under
the Clean Air Act. We also expect to construct disaggregated estimates by state and industry.
Ultimately, we hope that these results will inform the debate over climate change policy design as it re-
emerges in coming years.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
17
References
Aldy, Joseph E. 2012. “Promoting Clean Energy in the American Power Sector: A Proposal for a National
Clean Energy Standard.” Environmental Law Reporter 42: 10131-10149.
Aldy, Joseph E., Alan J. Krupnick, Richard G. Newell, Ian W.H. Parry, and William A. Pizer. 2010.
“Designing Climate Mitigation Policy.” Journal of Economic Literature 48(4): 903-934.
Aldy, Joseph E. and William A. Pizer. 2011. The Competitiveness Impacts of Climate Change Mitigation
Policies. NBER Working Paper 17705.
Antweiler, Werner, Brian R. Copeland, and M. Scott Taylor. 2001. “Is Free Trade Good for the
Environment?” American Economic Review 91(4): 877-908.
Barsky, Robert B. and Lutz Kilian. 2004. “Oil and the Macroeconomy Since the 1970s.” Journal of
Economic Perspectives 18(4): 115-134.
Blanchard, Olivier J. and Jordi Gali. 2009. “The Macroeconomic Effects of Oil Shocks: Why are the 2000s
So Different from the 1970s?” International Dimensions of Monetary Policy. Jordi Gali and Mark
Gertler, eds. Chicago: University of Chicago Press.
Brennan, Timothy, Karen Palmer, and Salvadore Martinez. 2002. Implementing Electricity
Restructuring: Policies, Potholes, and Prospects. Environmental and Resource Economics 22: 99–132.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
18
Bureau of Economic Analysis. n.d. Gross State Product and Personal Income Data. Internet:
http://www.bea.gov/iTable/iTable.cfm?ReqID=70&step=1&isuri=1&acrdn=1.
Bureau of Labor Statistics. n.d. Quarterly Census of Employment and Wages. Beta version. Internet:
ftp://ftp.bls.gov/pub/special.requests/cew/beta/.
Burtraw, Dallas, Anthony Paul, and Matt Woerman. 2011. Retail Electricity Price Savings from
Compliance Flexibility in GHG Standards for Stationary Sources. RFF Discussion Paper 11-30. July.
Ederington, J., A. Levinson, J. Minier. 2005. “Footloose and Pollution-Free.” Review of Economics and
Statistics 87(1): 92-99.
Energy Information Administration. n.d. State Energy Data System. Internet:
http://205.254.135.7/state/seds/.
Energy Information Administration. 2010a. Energy Market and Economic Impacts of the American
Power Act of 2010. SR-OIAF/2010-01. Washington, DC: Department of Energy.
Energy Information Administration. 2010b. Status of Electricity Restructuring by State.
http://www.eia.gov/cneaf/electricity/page/restructuring/restructure_elect.html
Energy Information Administration. 2000. The Changing Structure of the Electric Power Industry 2000:
An Update.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
19
Energy Information Administration. 2008. “Energy Market and Economic Impacts of S.2191, the
Lieberman-Warner Climate Security Act of 2007.” SR-OIAF/2008-01. Washington, DC.
Environmental Protection Agency. 2009. EPA Analysis of the American Clean Energy and Security Act of
2009, H.R. 2454, 111th Congress. Washington, DC: EPA, June 23.
http://www.epa.gov/climatechange/economics/pdfs/HR2454_Analysis.pdf
Environmental Protection Agency. 2010. EPA Analysis of the American Power Act in the 111th
Congress. Washington, DC: EPA, June 14.
http://www.epa.gov/climatechange/economics/pdfs/EPA_APA_Analysis_6-14-10.pdf
Greenstone, Michael. 2002. “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.
Grossman, Gene M. and Alan B. Krueger. 1991. Environmental Impacts of a North American Free Trade
Agreement. NBER Working Paper 3914. November.
Hamiton, James D. 1983. “Oil and the Macroeconomy Since World War II.” Journal of Political Economy
91(2): 228-248.
Hamiton, James D. 2008. “Oil and the Macroeconomy.” Palgrave Dictionary of Economics. Steven
Durlauf and Lwrence Blume, eds. Palgrave McMillan Ltd.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
20
Hamilton, James D. 2009. “Causes and Consequences of the Oil Shock of 2007-08.” NBER Working
Paper 15002. Cambridge, MA: National Bureau of Economic Research.
Henderson, J. Vernon. 1996. “Effects of Air Quality Regulation.” American Economic Review 86: 789-
813.
Interagency Competitiveness Analysis Team. 2009. The Effects of H.R. 2454 on International
Competitiveness and Emission Leakage in Energy-Intensive, Trade-Exposed Industries. An interagency
report responding to a request from Senators Bayh, Spector, Stabenow, McCaskill, and Brown.
Washington, DC: U.S. Government.
http://www.epa.gov/climatechange/economics/pdfs/InteragencyReport_Competitiveness-
EmissionLeakage.pdf
Intergovernmental Panel on Climate Change (IPCC). (2001). IPCC Third Assessment Report: Climate
Change 2001: Working Group III: Mitigation. Geneva: IPCC.
Jaffe, Adam B., Steven R. Peterson, Paul R. Portney, and Robert N. Stavins. 1995. “Environmental
Regulation and the Competitiveness of U.S. Manufacturing: What Does the Evidence Tell Us?” Journal of
Economic Literature 33(1): 132-163.
Jeppesen, Tim, John A. List, and Henk Folmer. 2002. “Environmental Regulations and New Plant
Location Decisions: Evidence from a Meta-Analysis.” Journal of Regional Science 42(1): 19-49.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
21
Kahn, Matthew E. and Erin T. Mansur. 2010. “How do energy prices, and labor, and environmental
regulations affect local manufacturing employment dynamics? A regression discontinuity approach.
NBER working paper 16538.
Kleibergen, F. and Paap, R. 2006. Generalized Reduced Rank Tests Using the Singular Value
Decomposition. Journal of Econometrics, Vol. 133, pp. 97-126.
Levinson, Arik and M. Scott Taylor. 2008. “Unmasking the Pollution Haven Effect.” International
Economic Review 49(1): 223-254.
Morgenstern, R., W. Pizer, and J.-S. Shih. 2002. Jobs versus the Environment: Is There a Trade-Off?
Journal of Environmental Economics and Management 43(3), p. 412-436.
NOAA. n.d. Heating and Cooling Degree Day Data. Various updates of Historical Climatological Series 5-
1 and 5-2. Washington, DC: National Oceanic and Atmospheric Administration. Internet:
http://www.ncdc.noaa.gov/oa/documentlibrary/hcs/hcs.html.
Stock, J.H. and Yogo, M. 2005. Testing for Weak Instruments in Linear IV Regression. In D.W.K. Andrews
and J.H. Stock, eds. Identification and Inference for Econometric Models: Essays in Honor of Thomas
Rothenberg. Cambridge: Cambridge University Press.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
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Figures and Tables
Table 1: Summary Statistics
Average Sum of squares by fixed effects:
n/year- Total Model Residual
mean s.d. N state ind state-(ind) year
Ln(Price) 2.42 0.61 2000 1 - 732 509 172 51
Ln(Wage) 18.0 1.92 37,974 37.2 35.8 140,692 132,666 1,732 7,469
Ln(Empl) 7.53 1.82 37,974 37.2 35.8 125,564 117,845 202 6,371
Enrgy int (%) 2.3 2.8 52 - -
HDD_jan
(000)
1.04 0.37 960 1 - 514 426 38 50
Oil price 41.9 27.9 20
*Wage and employment data are not balanced, so state, industry, and year are not independent (and
sum of squares will not add). “Average n/year-state” indicates the average number of industries
observed over all state-year combinations (maximum of 52); “average n/year-ind” indicates the average
number of states observed over all industry-year combinations (maximum of 51, including the District of
Columbia). Note all data ignores petroleum refining as a sector for analysis.
Table 2: Average Elasticity estimates (β in Equation (1))*
OLS IV – HDD/CDD IV – Oil Price IV – HDD/CDD/
Oil Price
Triple
Difference*
1990-2009
Wage 0.02
(0.06)
0.72**
(0.10)
-0.02
(0.05)
-0.03
(0.05)
0.04
(0.03)
Employment 0.01
(0.05)
0.63**
(0.09)
-0.06
(0.05)
-0.07
(0.05)
0.03
(0.03)
1990-1999
Wage 0.13
(0.08)
0.23
(0.14)
0.20**
(0.10)
0.23**
(0.08)
0.03**
(0.01)
Employment 0.15**
(0.07)
0.22
(0.13)
0.22**
(0.09)
0.23**
(0.08)
0.02**
(0.01)
2000-2009
Wage -0.18**
(0.05)
-0.23**
(0.11)
-0.21**
(0.05)
-0.22**
(0.05)
0.05
(0.05)
Employment -0.18**
(0.05)
-0.18
(0.12)
-0.20**
(0.06)
-0.22**
(0.05)
0.06
(0.05)
*The reported estimate for the Triple difference model assumes βi = ψei, where ei is the energy intensity
of industry i. The reported elasticity equals ���̅ where e̅ is the average energy intensity across industries
in 2002, or 2.3%.
**Significant at the 5% level.
May 1, 2012 Draft; Comments Welcome; Do Not Cite
23
Table 3: Summary Data by Industry
Code Definition
Avg
states
Energy
intens
Avg
empl
s.d.
empl
Avg
wages
s.d.
wages
3110 Food manufacturing 51 2.01 9.30 1.54 19.48 1.70 3121 Beverage manufacturing 47 1.24 7.12 1.56 17.53 1.73
3122 Tobacco manufacturing 9 0.65 6.71 1.80 17.33 2.01
3130 Textile mills 36 3.38 6.91 2.28 17.13 2.45
3140 Textile product mills 47 1.28 6.79 1.86 16.78 1.98
3150 Apparel manufacturing 36 1.27 7.60 2.18 17.49 2.20
3160 Leather and allied product manufacturing 36 1.16 5.91 1.60 15.98 1.71
3210 Wood product manufacturing 49 1.84 8.38 1.68 18.59 1.75
3221 Pulp, paper, and paperboard mills 15 7.81 7.87 0.75 18.78 0.78
3222 Converted paper product manufacturing 39 1.63 8.35 1.27 18.89 1.31
3230 Printing and related support activities 51 1.53 8.79 1.40 19.14 1.51
3251 Basic chemical manufacturing 42 12.91 7.05 1.48 17.95 1.56
3252 Resin, rubber, and artificial fibers manufacturing 27 6.77 7.38 1.37 18.25 1.44
3253 Agricultural chemical manufacturing 39 13.56 6.01 1.49 16.62 1.71
3254 Pharmaceutical and medicine manufacturing 43 0.78 7.64 1.83 18.52 2.02
3255 Paint, coating, and adhesive manufacturing 33 3.46 7.16 1.20 17.85 1.30
3256 Soap, cleaning compound, and toiletry manufacturing 42 2.14 6.79 1.86 17.35 2.04
3259 Other chemical product and preparation manufacturing 41 4.41 6.83 1.43 17.46 1.53
3260 Plastics and rubber products manufacturing 46 2.52 8.78 1.74 19.15 1.82
3270 Nonmetallic mineral product manufacturing 51 3.76 8.29 1.35 18.73 1.41
331A Iron and steel mills and manufacturing from purchased steel 35 7.54 7.23 1.57 17.93 1.69
331B Nonferrous metal production and processing 29 4.20 7.51 1.34 18.14 1.38
3315 Foundries 35 4.15 7.22 1.71 17.60 1.87
332A Ordnance and accessories manufacturing 30 1.57 5.34 1.54 15.73 1.73
332B Other fabricated metal product manufacturing 50 2.05 8.59 1.90 18.98 2.00
3321 Forging and stamping 37 2.32 6.93 1.59 17.38 1.69
3322 Cutlery and handtool manufacturing 36 1.36 6.53 1.47 16.94 1.56
3323 Architectural and structural metals manufacturing 50 0.91 8.15 1.54 18.55 1.60
3324 Boiler, tank, and shipping container manufacturing 34 1.64 7.27 1.22 17.84 1.32
3331 Agriculture, construction, and mining machinery manufacturing 41 1.15 7.18 1.72 17.72 1.84
3332 Industrial machinery manufacturing 40 0.90 7.31 1.37 17.95 1.49
3333 Commercial and service industry machinery manufacturing 38 1.46 7.09 1.35 17.65 1.47
3334 HVAC and commercial refrigeration equipment manufacturing 40 0.73 7.77 1.37 18.22 1.43
3335 Metalworking machinery manufacturing 43 1.31 7.47 1.66 17.99 1.77
3336 Engine, turbine, and power transmission equipment manufacturing 32 0.70 6.96 1.49 17.62 1.58
3339 Other general purpose machinery manufacturing 42 0.88 7.79 1.66 18.38 1.73
334A Audio, video, and communications equipment manufacturing 36 0.44 7.64 1.71 18.35 1.87
3341 Computer and peripheral equipment manufacturing 30 0.39 7.55 1.70 18.53 1.87
3344 Semiconductor and other electronic component manufacturing 47 1.36 8.18 1.81 18.72 2.04
3345 Electronic instrument manufacturing 44 0.69 8.08 1.73 18.82 1.89
3346 Manufacturing and reproducing magnetic and optical media 26 1.46 6.23 1.61 16.96 1.79
3351 Electric lighting equipment manufacturing 35 0.83 6.50 1.63 16.86 1.77
3352 Household appliance manufacturing 19 0.68 6.55 1.81 16.99 1.84
3353 Electrical equipment manufacturing 41 0.71 7.56 1.67 18.12 1.70
3359 Other electrical equipment and component manufacturing 28 1.28 7.09 1.72 17.60 1.84
336A Motor vehicle body, trailer, and parts manufacturing 44 0.89 8.07 2.12 18.44 2.28
336B Other transportation equipment manufacturing 38 0.63 7.27 1.79 17.68 1.94
3361 Motor vehicle manufacturing 14 0.43 6.61 2.04 17.30 2.34
3364 Aerospace product and parts manufacturing 39 0.79 7.68 2.07 18.43 2.25
3370 Furniture and related product manufacturing 49 0.98 8.40 1.64 18.58 1.69
3391 Medical equipment and supplies manufacturing 50 0.59 7.66 1.72 18.05 1.92
3399 Other miscellaneous manufacturing 50 0.86 8.03 1.50 18.29 1.61