how will climate change policies affect domestic...

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.


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.

May 1, 2012 Draft; Comments Welcome; Do Not Cite

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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

Δ�ln�� = Δ�� + Δ�� + ��Δ� ln�� + Δ�� (5)

This is the first difference. Then we difference out state-time effects by constructing industry

differences:

Δ�ln�(bev)� = Δ�(bev) + Δ�� + ��bev�Δ� ln�� + Δ��(bev)� (6)

minus

Δ�ln�alum� = Δ�(alum) + Δ�� + ��alum�Δ� ln�� + Δ��(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:


minus


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.


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22

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.


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

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