Abstract
Pricing carbon emissions from an individual jurisdiction may harm the competitiveness of local firms, causing the leakage of emissions and economic activity to other regions. Past research concentrates on national carbon prices, but the impacts of subnational carbon prices could be more severe due to the openness of regional economies. We specify a flexible model to capture competition between a plant in a state with electric sector carbon pricing and plants in other states or countries without such pricing. Treating energy prices as a proxy for carbon prices, we estimate model parameters using confidential plant-level Census data, 1982–2011. We simulate the effects on manufacturing output and employment of carbon prices covering the Regional Greenhouse Gas Initiative (RGGI) in the Northeast and Mid-Atlantic regions. A carbon price of $10 per metric ton on electricity output reduces employment in the regulated region by 2.1 percent, and raises employment in nearby states by 0.8 percent, although these estimates do not account for revenue recycling in the RGGI region that could mitigate these employment changes. The effects on output are broadly similar. National employment falls just 0.1 percent, suggesting that domestic plants in other states as opposed to foreign facilities gain the most jobs from state or regional carbon pricing.
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Notes
For example, as of late 2018, Oregon was considering pricing carbon and linking its program with California’s, and many states in the Mid-Atlantic and Northeast were also considering the expansion of RGGI.
In the U.S., electricity accounts for less than 2 percent of total manufacturing costs. However, for aluminum, chemicals, cement, and certain other energy intensive industries the cost share is considerably higher-suggesting proportionately larger negative effects of higher electricity prices. While some plants combust fuels directly, virtually all facilities consume electricity.
Aldy and Pizer (2015) use national-level data to estimate the effects of energy prices on manufacturing employment. They use their results to infer the effects of a hypothetical national carbon price, finding that a carbon tax of $15 per ton would increase net imports by up to 0.8 percent for the most energy intensive industries. Because they use national-level data, their results reflect only competition among US and international manufacturing plants. Although Kahn and Mansur (2013) estimate the effect of electricity prices on employment by comparing adjacent counties, their analysis does not directly translate to a statewide carbon price, which would affect energy prices at the state and not the county level. The general equilibrium literature (e.g., Boehringer, Fischer, and Rosendahl 2010; Fischer and Fox 2012; and Adkins et al. 2012) lacks the geographic resolution necessary to address state-level competitiveness issues.
For example, Aldy and Pizer (2015).
In particular, states can use tax revenue (or in the case of a cap-and-trade program, allocate emissions credits rather than auction them) to compensate firms and reduce the likelihood of employment and output losses and the risk of emissions leakage. For example, California allocates emissions credits to certain industries based on their energy intensity and exposure to international competition. In addition, states can use tax revenue or revenues from allowance auctions to subsidize energy efficiency investments at energy consuming businesses, potentially reducing competitiveness effects. In 2017, almost two-thirds of the RGGI auction revenue supported energy efficiency and clean and renewable investments, mostly focused on the business and residential sectors.
Ganapati, Shapiro, and Walker (2017) estimate the pass-through of energy prices to marginal costs and output prices. Our assumption on pass-through regards the pass-through of the carbon price to energy prices, and not output prices for the manufacturing plants. Fabra and Reguant (2014), among others, provide evidence on full pass-through of a carbon price to energy prices.
In principle, we could add plant fixed effects to control for time-invariant unobservables at the plant level. Unfortunately, after including plant fixed effects there is insufficient remaining energy price variation to identify the coefficients.
https://www.nber.org/data/nberces.html. At the time of our analysis, these data were only available through 2011.
The ratio could be measured with error, which would bias estimated coefficients. We have performed data quality checks on both the expenditure and quantity variables used to calculate prices.
We also explored using the share of each industry’s shipments going under 250 miles, between 250 and 500 miles, and over 500 miles to calculate weighted average neighbor energy prices that would be industry specific. The results using the alternative distances are similar to those reported in the paper.
The starting value of 150 in 1987 was chosen so that the demand index numbers would remain positive throughout the sample for all industries.
We use a fixed set of cost shares to define the groups, rather than allowing industries to shift from one group to another over time, to focus the estimation within each group on the variation in energy prices over the complete 1981–2011 period. Using the 1992 data to compute industry cost shares also reduces concerns that the cost shares and group assignments are endogenous to energy prices. In any case, industry energy cost shares are highly correlated over time.
These plant-based sample shares differ from the industry shares mentioned in the previous sentence, since industries have differing numbers of plants.
The effect of the carbon price on electricity prices is broadly consistent with estimates reported in Linn and Muehlenbachs (2018), who estimate the effect of fuels prices on wholesale electricity prices using data from the 2000s. For states with regulated retail prices, we are assuming that regulators allow the firm to pass the carbon price through to regulated retail electricity prices.
In principle, a carbon price in RGGI could affect electricity prices outside the region, particularly if transmission lines connect the regions. The carbon price raises the cost of producing electricity in RGGI, which could increase generation from outside the region, causing marginal costs and electricity prices to increase. Shawhan et al. (2014) suggest that for a carbon price of $10 per ton of CO2, electricity price changes outside of RGGI are about 1 percent as large as electricity price changes inside of RGGI.. Fell and Maniloff (2018) find empirical evidence of leakage despite the low carbon price that has persisted in the market, although they do not examine the effect of RGGI on electricity prices. For simplicity, the simulations include the assumption that RGGI does not affect electricity prices outside the region. The results in Shawhan et al. (2014) suggest that this assumption likely has little effect on the results. Shawhan et al. (2014) estimate that a carbon price of $10 per ton of carbon dioxide raises electricity prices by roughly 0.4 cents per kilowatt hour. We assume an electricity price increase of 0.6 cents per kilowatt hour, suggesting that we may overstate the employment and output effects by roughly one-third.
To illustrate the role of the indirect effects, we have computed employment and output changes including only the direct effects of energy prices. The results show only small differences for the less energy intensive industries in groups 1–4, but we see substantially larger impacts for the high-energy groups 5–8 when indirect effects are included.
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Any views expressed are those of the authors and not those of the U.S. Census Bureau. The Census Bureau's Disclosure Review Board and Disclosure Avoidance Officers have reviewed this information product for unauthorized disclosure of confidential information and have approved the disclosure avoidance practices applied to this release. This research was performed at a Federal Statistical Research Data Center under FSRDC Project Number 1191. (CBDRB-FY22-P1191-R9226;R7965;R7059;R6673).
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Casey, B., Gray, W.B., Linn, J. et al. How Does State-Level Carbon Pricing in the United States Affect Industrial Competitiveness?. Environ Resource Econ 83, 831–860 (2022). https://doi.org/10.1007/s10640-022-00711-z
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DOI: https://doi.org/10.1007/s10640-022-00711-z