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Prolonging coal’s sunset: Local demand for local supply
Regional Science and Urban Economics 81 (2020) 103487
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Prolonging coal’s sunset: Local demand for local supply☆ Jonathan Eyer a, Matthew E. Kahn b, * a b
University of Southern California, USA Johns Hopkins University, USA
A B S T R A C T
The share of U.S electricity generated by coal has fallen from nearly 50% to 33%. This transition offers social environmental benefits but spatially concentrated costs as coal miners and their local communities have suffered. Coal states have responded to shifting demand conditions by introducing incentives for local power plants to purchase coal from local mines. We document that power plants in areas with mining activity are more likely to be coal-fired and to purchase more coal from mines which they share a political boundary even after controlling for the distance from power plants to mines. While politically-motivated coal purchases do result in improved conditions in coal-mining counties in some regions of the country, these benefits are likely to be small compared to the additional carbon costs.
1. Introduction To mitigate the global challenge of climate change, nations must burn less coal. In recent years, the share of U.S electricity generated by coal has fallen from nearly 50% to 33%. The U.S reduction in coal use for generating power is especially notable because it has occurred without the U.S imposing carbon pricing or a carbon tax (Cragg et al., 2013). The substitution away from coal is mainly due to adoption of fracking technology and some states sharply ratcheting up their renewable portfolio standards (Burtraw et al., 2012; Venkatesh et al., 2012). While environmentalists cheer for coal’s sunset, there are interest groups with strong incentives to protect this declining industry. Reduced power plant demand for coal imposes spatially-concentrated costs borne by traditional coal mining communities in states such as West Virginia, Ohio, Pennsylvania, Kentucky, and Wyoming, and the low skill workers who engage in mining and providing services in mining areas. There were 430 coal mines in the United States that shipped coal to the electricity sector in 2014. These mines are located in rural areas where the population is typically white and not college educated. These coal mining jobs tend to pay well and workers in these regions have few alternative job prospects. Their local economies experience slower economic growth and lower productivity than the rest of the nation (Islam et al., 2015; Bollinger et al., 2011). The durable housing stock in such areas means that local home prices could fall sharply if coal demand declines (Glaeser and Gyourko, 2005). In this case, if demand for coal labor is reduced displaced miners who own local homes lose both their mining jobs and their housing wealth. ☆
Local politicians in coal regions recognize these effects and thus have strong incentives to engage in protecting local coal interests. In this paper, we document that power plants are more likely to use coal to generate power if there is a coal mine sharing their state or congressional district and are more likely to purchase coal from a mine with which they share a political jurisdiction relative to a similarly distant mine that is in another jurisdiction. This finding is robust to flexibly controlling for the distance between mines and power plants using higher-order distance polynomials or fixed effects for neighboring counties on opposite sides of state lines. This purchasing behavior suggests that politicians are supporting coal consumption even when alternative energy suppliers are more cost effective. This raises two important concerns. First, this results in a transfer from typically urban electricity consumers to rural coal producers and creates deadweight loss. More concerning, it acts as an implicit subsidy to coal producers at the expense of other energy producers. Economically efficient policies aimed at addressing climate change rely on targeting prices or quantities so that the marginal cost of emissions equals the marginal benefit. Then, the energy mix will shift towards those resources with the lowest marginal emissions costs. Local subsidies for coal use help to prolong the coal sector’s “sunset”. 2. The economics of within political border trade Power plants operate in a political and regulatory environment that encourages them to consider the sourcing of coal inputs. Elected officials such as a mining state’s governor, Congressmen and local officials have
We thank the editor, reviewers and seminar participants at USC Law, Arizona State, PERC, University of Illinois, and Case Western University for useful comments. * Corresponding author. E-mail address: [email protected] (M.E. Kahn). https://doi.org/10.1016/j.regsciurbeco.2019.103487 Received 8 October 2019; Accepted 24 October 2019 Available online 6 November 2019 0166-0462/© 2019 Published by Elsevier B.V.
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by rail and the transportation costs are directly tied to distance. These costs are substantial. In 2014 transportation costs accounted for 45% of the total cost of coal that was delivered via rail.5 While coal can also be transported via barge or truck, truck transportation is prohibitively expensive and only power plants that are located along waterways can use barges. As a result, power plants have a strong incentive to purchase coal from closer mines and will bid less aggressively for coal that is from mines that are further away. Thus, we expect that a “gravity model” of bilateral trade between mines and coal fired power plants will have significant explanatory power (Lee and Swagel, 1997). For each power plant, we calculate the Euclidean distance to the coal mines with which it trades. There are two peaks in the distribution of coal shipment distances (See Fig. 2). The first peak is at short distances and represents local trading. Around 33% of total coal shipments travel less than 100 miles. The second peak occurs at around 700–1000 miles and represents plants purchasing low-sulfur coal from Wyoming. There are, of course, several mechanisms that may cause Euclidean distance to imperfectly predict the likelihood of trading. Preonas (2017) finds that rail companies exert market power and a rail company could increase the cost of transportation for coal shipped between a captured plant and mine pair, making more distant trading partners more appealing. Still, the markup on the transportation cost should not be large enough to make shipping from a close, captured mine more expensive than shipping from a distant, uncaptured mine because then the rail company would lower its rates to retain the sale. Similarly, Hughes and Lange (2018) find that as changes in natural gas prices affect the rents available to coal-fired power plants that rail operators change their prices. Again, while this behavior will shift rents from coal plants to railroads it should not change a power plant’s decision about from which mine to purchase. Cicala (2014) also notes the change in procurement patterns that resulted following deregulation of some power plants in the 1990s. If distance were a sufficient statistic for predicting whether a plant and mine would trade, the change in regulatory status should have left trading partners unchanged. There might also be non-linearities in shipping costs caused by rivers or mountains that make a trading partner in one direction more costly than a trading partner in a different direction. The EIA reports average rail shipping costs between states and prices are not uniformly higher when the rail line would cross a natural barrier. In 2008, for example, average shipping costs from West Virginia, to Virginia (across the Blue Ridge Mountains), Ohio (across the Ohio River), and Pennsylvania (no major natural impediments), were $22.79/ton, $21.91/ton, and $20.58/ton, respectively. While a power plant’s distance to a given mine is an important determinant of trade, there are other mine attributes that affect a power plant’s demand. Coal mines differ with respect to the sulfur content of their coal, for example. A power plant that primary purchases low sulfur coal from the Powder River Basin would have to install costly sulfur reduction technologies if it wanted to switch to coal with a higher sulfur composition. Given that this technology adoption represents a fixed cost, power plants have an incentive to “lock in” and purchase a majority of their coal from a given mine or location. Economic studies of coal trading have emphasized asset specificity such as transport infrastructure, sulfur scrubbers, or ash disposal (see Joskow, 1985, 1987; Preonas, 2017). While transportation infrastructure characteristics such as conveyer belts result in linkages between particular mine-plant pairings, most power plant infrastructure choices offer a plant a range of mine partners selling coal with similar characteristics. For example, a power plant in a region with sulfur dioxide regulations that has not installed scrubbers must buy low sulfur coal. While that plant may be currently purchasing its coal from a particular low-sulfur mine, it could ostensible switch to another low sulfur mine. Because the physical characteristics of coal (e.g., thermal content, sulfur content) vary relatively smoothly throughout space,
an incentive to help their constituents. Miners and the members of their communities are typically low-skilled people with long time roots to the area. Local elected officials in coal states are aware that their constituents face significant dislocation costs and seek to protect them from longlasting negative income shocks by stabilizing demand for their constituents’ output. Moreover, these officials have a set of mechanisms through which they could reward power plants that purchase in-state coal or punish those that do not.1 Cicala (2014) finds that electricity deregulation shifted coal procurement patterns, lowering total costs and reducing the fraction of coal that was purchased from in-state producers. Since he does not explore the bilateral trading flows between individual coal mines and coal fired power plants, his research design does not use a trade and gravity style model to consider the set of geographic alternatives that each power plant faces. We build on his approach to explicitly control for the distance between mines and power plants to consider the role of different types of jurisdictional borders (i.e states and Congressional Districts). Elected officials in coal areas have strong incentives to take actions that increase the demand for coal. Mining is a high paying job for low skill workers. Indeed, the average wage for all U.S. coal miners was $83,600 in 2015 according to the National Mining Association. The BLS reports that the average wage across all industries, by contrast, was around $50,000. Average coal mining wages were around 60% higher than the average wage in coal mining states. Coal states provide large financial incentives to encourage local coal purchases (Mairs, 2001). Maryland and Virginia offer a $3 per ton tax credit for utilities buying in-state coal (Bowen and Deskins, 2015).Oklahoma offers credits of $5 per ton to both coal mines and power plants, effectively contributing $10 to every ton of Oklahoman coal that is burned for electricity generation in the state.2,3 While these incentives are costly to the states, states are precluded from legally mandating that in-state power plants purchase local coal under the Commerce Clause of the Constitution. Oklahoma enacted a law aimed at requiring power plants to purchase at least 10% of their coal from Oklahoman mines but this law were eventually struck down by the Supreme Court.4 3. The spatial geography of coal trading Fig. 1 maps the 354 coal fired power plants in the United States and the 430 coal mines in the U.S that either purchased coal in 2014 or shipped coal to the electricity sector in 2014. Several clear geographic patterns emerge. Coal mining is concentrated in Appalachia (Kentucky, West Virginia, Ohio and Pennsylvania), in southern Illinois and Indiana, and in Wyoming. While the majority of mines are in the Appalachia region, the largest mines are in Wyoming, which has relatively low-quality but easily accessible coal deposits with low sulfur content. Coal power plants exist throughout the country, but are most prevalent in the Midwest, Mid Atlantic, and South. According to the EIA, most coal in the United States (70%) is shipped
1 Governors, for example, can choose whether or not to opt into a carbontrading scheme, institute renewable portfolio standards, or to appoint public utility regulators who will support increased electricity prices. A congressperson might press the EPA to loosen environmental regulations on the temperature of discharged cooling water or support DOE Secretary Perry’s efforts to reward coal plants for base-load grid reliability. A local official may ease permitting or local environmental pressure on a coal plant. In each case, the local power plant has an incentive to ensure that they are on friendly terms with elected officials. 2 The Oklahoma tax credits operate separately for mines and power plants and do not require bi-lateral trade between Oklahoma mines and plants. See https: //iec.ok.gov/sites/g/files/gmc216/f/Coal%20Incentives_Draft_09.29.17.pdf. 3 According to the EIA’s statistics on the delivered price of coal, this is approximately 3–4% of the average delivered price in Virginia and Maryland and nearly 1/3 of the delivered price in Oklahoma. 4 See Wyoming v. Oklahoma, 502 U.S. 437 (1992).
5 This information is contained in the EIA 923. See Table 1 of https ://www.eia.gov/coal/transportationrates/index.php.
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Fig. 1. Coal Mines and Plants in 2014.
Fig. 2. The Quantity Weighted Distribution of the Distance between Coal Mines and Power Plants.
in the absence of mine-mouth transportation infrastructure, plants are “locked in” to a mining region but may choose their supplier from a set of mines within the region which meet the physical requirements of their plant. Table 1 displays for each major coal mining state the share of its mined coal tonnage that is sold to an instate power plant based on EIA data on fuel deliveries. It also presents the share of the state’s power plants coal purchases that come from local coal. The average state power plant receives 24% of its total coal consumption from in-state mine. The average across only the states that produce coal is 48%. The states that receive the lowest-percentage of total coal purchases from in-state mines were Kansas (20), Maryland (24), Missouri (29), Oklahoma (40) and Tennessee (47). In each of these states, coal is the dominant source of electricity generation but coal mining is a relatively small industry. West Virginia’s purchases a surprisingly large amount of its coal from out of state (45%) given the state’s reputation as a coal mining economy.
Table 1 Coal consumption and deliveries by source and destination.
4. Power plant and mine data In this study, our main unit of analysis will be a coal mine-plant-year. The EIA collects data on fuel deliveries to the plants in the power sector. In our data set, there are 1186 coal mines, 469 power plants. We first compute the Euclidean distance between each mine and each power plant in our dataset. Finally, we calculate the total annual quantity of coal that is shipped for each county and the total annual quantity of coal that is received for each power plant and drop plant-coal county-year
3
State Name
Buy In State Consumption Share
Sell In State Delivery Share
Alabama Arizona California Colorado Illinois Indiana Kansas Kentucky Louisiana Maryland Mississippi Missouri Montana New Mexico New York North Dakota Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming
0.4 0.4 0 0.58 0.22 0.49 0.01 0.52 0.24 0.11 0.23 0.01 0.95 1 0 0.98 0.65 0.02 0 0.68 0.03 0.48 0.87 0.5 0.55 0.55 1
0.96 0.72 – 0.49 0.31 0.81 0.53 0.3 1 0.26 1 0.65 0.34 0.63 – 0.98 0.38 0.95 – 0.57 0.33 1 0.76 0.32 – 0.34 0.08
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and a function of every other mine’s price. This would lead to a large demand system. Local incentives to purchase same jurisdiction coal would introduce a wedge between the price the buyer pays and the price the mine collects.10 Since we do not observe the price that each mine offers each electric utility for its coal, we pursue a reduced form approach to measure “border effects”. We also do not know the exact subsidies each coal mining state offers. Even if we could observe all of this information, we would face an endogeneity challenge of modelling each state’s choice of optimal subsidies. We examine the effects of political boundaries on the trading patterns of pairs of power plants and coal mines. Our identification strategy relies on the assumption that our distance and geographic controls capture any affect associated with transportation costs between power plants and mines. Our econometric strategy combines the standard trade gravity model with the Holmes (1998) borders approach. The distinctive feature of our econometric framework is the vector of dummy variables indicating if the origin mine and potential destination power plant share a common political jurisdiction. Power plants and mines that are within the same jurisdiction are, of course, relatively close to each other and have lower transportation costs than power plants that are far away. By explicitly controlling for the distance between power plants and mines, our political jurisdiction variable compares the likelihood of buying coal from an in-state mine relative to a similarly distance out-of-state mine. We also note that there are factors that would shift shipping costs that may be correlated to political boundaries. For example, mountain ranges and rivers often generate state borders and these features may increase the cost of shipping coal via train. On the other hand, if plants and mines are on the same boundary river, it may be less costly to ship via barge to the other side of the river than to ship coal overland to a trading partner within the state. We explore the bilateral trade between each coal fired power plant and each open coal mine in the nation. The likelihood that plant i purchases coal from mine j in year t is governed by the latent variable
observations in which the plant received no coal or the coal county shipped no coal. Characteristics of the delivered coal are also reported for each transaction. These characteristics include the delivered cost of the coal, as well as the ash, sulfur, and heat content for the fuels. They also report characteristics of the trade such as whether or not the trade occurred pursuant to a contract and the duration of any contracts. We link each plant to a latitude and longitude using the EIA 860 and matching on EIA Plant ID. Similarly, we link each mine to a latitude and longitude using the Mine Safety & Health Administration (MSHA) data and matching on the MSHA which is reported in the EIA fuel purchases data. We then overlay state and congressional boundary shape files onto our geospatial data on power plant and coal mine location and create indicator variables for whether a power plant – coal mine combination are in the same state or congressional district. We also incorporate information that may shift the cost of shipping coal in a way that is discontinuous in Euclidean distance. We link each mine and plant to the maximum elevation in its county using data from Fishback et al. (2006) and calculate the difference in elevation between each pair. Similarly, we use data from Fishback et al. (2006) on whether a county contains a major river and generate a dummy variable for whether both the mine and power plant county contain at least one major river.6 Finally, we manually generate a dummy variable for whether plants and mines in opposite states lay on either side of a major river that defines a state boundary.7 We make two key restrictions to our dataset. In our data set, the closest mine and plant that are not in the same state are 10 miles apart while the most distant mine and plant that are in the same state are 335 miles apart. We further limit our sample to only the set of mine-plant pairs that are between 50 and 150 miles from each other. This restriction helps ensure that each trading pair could potentially be either instate or out-of-state conditional on the geographic distance between the two. Fig. 3 shows the density of distances between power plants and mines for same-state and cross-state pairs. While in-state pairings are more common at shorter distances and out-of-state pairings are more common at greater distances, there is general overlap in the distributions.8 Second, we account for heterogeneity in allowable coal characteristics by restricting trading pairs based on the reported sulfur dioxide and ash levels in the coal that they have purchased or sold. For each plant we calculate the average sulfur dioxide content and the average ash content of the coal that it has purchased. Similarly, for each mine we calculate the maximum sulfur dioxide and ash content of the coal that it has sold. We then restrict mines to only plants that purchase coal with average sulfur dioxide and ash content that falls in the mine’s range. This precludes plants that have locked-in to coal of a certain type (e.g., plants without scrubbers that we observe only purchasing low-sulfur coal cannot purchase high-sulfur coal).9
Yijt ¼ α þ g distanceij þ ηt þ β1 * Borderij þ β2 * Controlsijt þ Uijt
(1)
In equation (1), the key explanatory variables of interest are the vector of border dummies, Borderij. We include dummies indicating whether the mine and the plant are located in the same state or the same Congressional District. A key point to note is that we flexibly model the role of distance on trade. This gðdistanceij Þ polynomial and distance bins that we report below allow us to flexibly control for proxies for transportation costs. The Controlsijt variable is a matrix of power plant or mine characteristics that would affect the overall frequency with which a plant or mine engage in trade. ηt is a matrix of year-specific fixed effects. We estimate the probability that a mine and a power plant trade in a given year using a logistic regression defined as Tradeijt ¼ 1 if Yijt > 0.
5. Empirical approach
6. A border pairs test
In a standard differentiated products market setting, each coal seller sells a different variety of coal. In such a demand system, a given power plant’s demand for coal from mine j will be a function of mine j’s price
While the above specifications rely on flexible non-linear controls for distance, it is possible that the physical distance between a plant and a mine could still have an effect on the likelihood of trading in a way that would bias our results. For example, if the impact of Euclidean distance on the likelihood of trade varies throughout the country our distance controls will capture only the average effect. To address this concern, we estimate the effect of the political boundary on coal trading in a border
6 Note that this is whether they both contain any river rather than whether they contain the same river. 7 As major rivers that provide state boundaries we include the Colorado, Mississippi, Ohio, and Missouri rivers. The dummy variable applies to all plants and mines that are on opposite sides of one of these rivers that creates state boundaries. For example, the Mississippi River separates plants and mines in Mississippi and Arkansas. 8 The primary regression results are robust to limiting the sample to smaller and larger distance ranges. 9 We also considered performing this procedure based on heat content but the EIA’s data on heat content is inconsistently reported so it is difficult to perform this restriction with confidence.
10 Preonas (2017) documents similar wedges in coal shipping costs due to localized market power in the rail sector that increases delivered prices for coal power plants facing monopolist railroads.
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Fig. 3. The Distribution of Distances Between Coal Mines and Power Plants for Same-State and Cross-State Pairs.
pairs framework in the spirit of Black (1999) and Holmes (1998).11 Following Dube et al. (2010), we rely on controls for contiguous counties that are in different states to better control for unobserved characteristics of power plant locations that could bias our estimates of the cross-state effect. Our approach relies on the typical continuity assumption that characteristics on either side of a boundary are the same. Under this assumption, the effect of the border can be treated as randomly assigned. In our case, we assume that power plants in counties on either side of a state border are otherwise comparable and then estimate the causal effect of crossing the border on the probability that a plant buys coal from a mine. Following Dube et al. (2010), we limit our sample to only counties along state borders that are adjacent to a county in another state that also has a coal-fired power plant. There are 38 counties that both have a coal-fired power plant and are adjacent to a county in a different state that also has a coal fired power plant and a total of 25 unique county boundary pairs (some counties appear in more than one pair). There are 52 power plants in these 38 counties. We then create a set of adjacent-power plant county by mining county fixed effects for each mining county on either side of the state border. For example, there power plants in Apache County, Arizona and in neighboring McKinley County, New Mexico. There are six coal mines in two counties in New Mexico and one coal mine in Arizona. We assign a fixed effect for each of the three Arizona and New Mexico mining counties. This fixed effect captures the distance between the mine and the power plant county pair, so that our identification is driven by the difference in trading behavior between each mining and power plant on either side of the state border. There is variation in intra-jurisdictional status within each mine and county pair fixed effect by construction. For each mine and county pair combination, we observe at least one power plant in the same state as the mine and at least one power plant that is not in the same state as the mine. Fig. 4 shows the distribution of distances between mines and power plants for same-state and cross-state pairs for this restricted sample.
In order to implement this strategy, we modify equation (1) to reflect the addition of these bordering county fixed effects as Yijcpt ¼ δpc þ γ t þ β1 *Borderij þ β2 *Controlsijt þ Uijcpt
(2)
Note that this new regression differs from equation (1) because we now restrict the set of observations we include in the regression and we also include a set of δpc bordering county-by-mining county fixed effects where c denotes the county of mine j. We again estimate the probability that a mine and a power plant trade in a given year as determined by Tradeijcpt ¼ 1 if Yijcpt > 0.
6.1. Results on coal trading Across each distance specification, we consistently find a positive and statistically significant effect of being within state lines on the probability that a power plant will purchase coal from a mine. Table 2 presents these results. A power plant is 1.1 percentage points more likely to purchase coal from an in-state mine than from an out-of-state mine that is the same distance away. This effect is quite large in context. Across our entire sample, the probability that a plant-mine combination engages in a trade in a given year is about 1.8 percent. The probability that a power plant and a mine trade is increasing in both power plant purchases and in mine shipments. This indicates that plants that buy a lot of coal tend to purchase from more mines than plants that buy a relatively small amount of coal. Similarly, mines that produce a lot of coal sell to more power plants than small mines. These effects persist when we remove each plant’s largest trading partner from its set of potential partners. These results are presented in Table 3. The effect of being in the same state is slightly smaller than the results when we include the largest trading partner (See Table 2). By removing a mine’s largest trading partner, we preclude Joskow-style “lock in” from driving our results under the assumption that, if there is relationship-specific capital, each plant “locks in” with only one mine. Note that, a necessary condition for a mine to trade with a power plant is for the power plant to be coal fired and for it to exist. The above results take the presence of the power plant and mines as given. Given our assumption that the location of power plants is exogenous, the political boundary effect will be underestimated because our results are conditional on the existence of a power plant. There may also be an
11 This approach addresses the potential concern is that our cross-political boundary results might merely be the effect of non-linear distance effects that are not captured by our distance and geography controls. See Fig. 3 for the distribution of distances for this sample.
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Fig. 4. The distribution of distances between coal mines and power plants for same-state and cross-state pairs – county pair fixed effect specification.
Table 2 Effect of state and congressional boundaries on probability of trade between plants and mines.
Mine Production Plant Purchases Elevation Difference River State Boundary Rivers in County Same Congressional District Same State Distance Control Year FE Observations AIC
(1)
(2)
(3)
0.011*** (0.003) 0.101*** (0.001) 0.001*** (0.0003) 0.004 (0.005) 0.005 (0.005) 0.002 (0.007) 0.033*** (0.006) 10-Mile Bins X 31673 12049
0.011*** (0.003) 0.102*** (0.010) 0.001*** (0.0003) 0.004 (0.005) 0.005 (0.005) 0.002 (0.007) 0.033*** (0.006) Cubic Polynomial X 31673 12056
0.011*** (0.003) 0.101*** (0.010) 0.001*** (0.0003) 0.004 (0.005) 0.005 (0.005) 0.002 (0.007) 0.033*** (0.006) Quartic Polynomial X 31673 12058
Table 3 Effect of state and congressional boundaries on probability of trade between plants and mines excluding primary trading partner.
Mine Production Plant Purchases Same State Distance Control Year FE Observations AIC
(1)
(2)
(3)
0.004*** (0.001) 0.039*** (0.001) 0.012*** (0.002) 10-Mile Bins X 255984 52883
0.004*** (0.001) 0.040*** (0.001) 0.012*** (0.002) Cubic Polynomial X 255984 52809
0.004*** (0.001) 0.040*** (0.001) 0.012*** (0.002) Quartic Polynomial X 255984 52684
Note: Coefficients correspond to marginal effects of a logistic regression estimating the probability that a power-plant purchased coal from a given mine. For each mine-year, the mine’s largest purchaser is removed from the set of potential trading partners. Total Mine Production and Total Plant Purchases are expressed in tens of millions of tons. Elevation Difference is expressed in hundreds of feet. See Equation (1) Standard errors are clustered at the plant-mine level. ***: p < 0.01, **: p < 0.05, *: p < 0.10.
Note: Coefficients correspond to marginal effects of a logistic regression estimating the probability that a power-plant purchased coal from a given mine. Total Mine Production and Total Plant Purchases are expressed in tens of millions of tons. Elevation Difference is expressed in hundreds of feet. See Equation (1) Standard errors are clustered at the plant-mine level. ***: p < 0.01, **: p < 0.05, *: p < 0.10.
limiting each pair of adjacent-county power plants to only being able to purchase coal from mines in either state of the adjacent counties. For example, the power plants in Mobile County, Alabama and Jackson County, Mississippi would have as potential trading partners the coal mines in Alabama and Mississippi but we drop the power plant-mine observations in which the mines are in other states. In this specification, the border variable captures the relative difference in the probability of a trade between each mine in Alabama and the plants in Mobile, County Alabama and Jackson County, Mississippi. Our neighboring county-pair fixed effect captures unobserved characteristics of the Mobile/Jackson area and the remaining difference in probability is assigned to the border effect. We find that plants are approximately 3 percentage point more likely to purchase coal from in-state mines that from mines in a different state, although there is no statistically significant difference in trading propensity at the congressional district boundary.
extensive margin effect in which political pressure from local mines causes a plant to open and purchase from the nearby mine rather than an already open plant merely choosing the mine as its partner. If political pressure results in a power plant opening near a mine that this would induce the possibility of intra-jurisdictional trading that would not have been possible without the politically-motivated power plant placement. Similarly, if choices about power plant closures are motivated in part by a desire to protect mining jobs then we would continue to observe intrajurisdictional trading options that would not exist if the plant was closed in the absence of political pressure. Again, these impacts would result in an underestimate of our results because political pressure would be driving the likelihood that a pair is observed as potentially trading in our sample.
6.3. The social costs of “local border effects” Assuming the conservative estimate of a $40 per ton social cost of carbon suggested by the IWG and the EPA’s emission factor of 2.86 tons of CO2 per ton of coal, each of the 153 coal mining counties would be associated with an average of around $1.37 million in carbon costs. Aggregating across all of the coal mining counties in the sample, this
6.2. Border pairs results Our border pair models presented in Table 4 report further evidence of political boundary effects in coal purchasing. We restrict our sample by 6
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the durability of housing capital and the built up social networks established in mining areas, its residents face both migration costs and asset losses if the demand for coal mining declines. Such individuals face a fundamental job retraining challenge that middle-aged workers who have worked in mines will have trouble transitioning to other jobs. Local officials in coal areas are well aware that many of their constituents depend on the continuing viability of the coal industry. Local officials internalize the benefits of coal’s prolonged sunset but they ignore the social environmental costs associated with such implicit subsidies. Our examination of the sunset of the coal industry revisits recent research that has examined how rural communities have gained from the fracking boom (Feyrer et al. 2017, Allcott and Keniston, 2018). We have introduced a detection approach to measure “excess” within border transactions. Our empirical research design exploits the fact that coal mines and power plants vary with respect to their geographic location. Some lie within the same political jurisdiction while others do not. This variation allows us to use a flexible distance polynomial between pairs of power plants and coal mines to recover key border effects. We document an increased likelihood of trade when potential partners are within the same state.
Table 4 Effect of state and congressional boundaries on trades between plants and counties - mining county - plant pair FE.
Mine Production (Tens of Millions of Tons) Plant Purchases (Tens of Millions of Tons) Elevation Difference River State Boundary Rivers in County Same Congressional District Same State Plant-Mine County FE Year FE Observations R2
(1)
(2)
(3)
0.280*** (0.051) 0.289*** (0.051) 0.003*** (0.001) 0.005 (0.013) 0.039*** (0.001) 0.0.005 (0.025) 0.027*** (0.010) X X 7937 0.212
0.280*** (0.058) 0.287*** (0.051) 0.003*** (0.001) 0.005 (0.012) 0.040*** (0.013) 0.005 (0.024) 0.028*** (0.010) X X 7937 0.211
0.278*** (0.058) 0.287*** (0.051) 0.002*** (0.001) 0.005 (0.013) 0.040*** (0.012) 0.005 (0.024) 0.029*** (0.010) X X 7937 0.211
Note: Coefficients correspond to marginal effects of a logistic regression estimating the probability that a power-plant purchased coal from a given mine. Total Mine Production and Total Plant Purchases are expressed in tens of millions of tons. Elevation Difference is expressed in hundreds of feet. Plant-Mine County FE are a set of fixed effects corresponding to each pair of neighboring counties in different states and each mine in either state. See Equation (2). Standard errors are clustered at the plant-mine level. ***: p < 0.01, **: p < 0.05, *: p < 0.10.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi .org/10.1016/j.regsciurbeco.2019.103487. References
results in a total of $287 million in social carbon costs due to intrajurisdictional purchasing. Note that these social carbon costs should be viewed as an upper bound on the environmental impacts of localized coal purchasing. These results implicitly assume that if an intra-jurisdiction mine were removed from a power plant’s choice set that it would not substitute to coal from an out-of-jurisdiction mine and that a mine without access to an intrajurisdictional power plant would reduce its total output. While we attempt to control for this substitutability by including distance in our coal trading regressions, as the degree of substitution between intrajurisdictional partners and extra-jurisdictional ones increases the social costs of localized trading will decline. If coal sourcing is completely fungible, and a closure of an intrajurisdictional coal mine will result in increased purchases of coal from extra-jurisdictional mines, we would expect that plant-level coal purchases would be invariant to the number of intra-jurisdictional coal mines. If, however, total power plant coal consumption falls when the number of intra-jurisdictional mines falls, there is evidence of at least some global effect of intra-jurisdictional purchasing on global emissions. There are also important geographic aspects of the economic and financial implications of same state purchasing that are not captured in our models. First, pollutants like sulfur dioxide and particulate matter travel spatially so the communities containing the power plants purchasing this coal would be generating externalities on other regions based on their support of the local mines. Similarly, there are geographic economic spillovers that we do not observe. Increased local coal mining employment will increase local wages but there will also be general equilibrium impacts as these miners spend their wages. We also have not examined the impacts of this behavior on inter-state electricity transmissions or aggregate coal prices. If coal mining states are aggressively bidding for local coal they may be crowding out purchasers in other states or shipping low-cost electricity to consumers in other jurisdictions.
Allcott, H., Keniston, D., 2018. Dutch disease or agglomeration? The local economic effects of natural resource booms in modern America. Rev. Econ. Stud. 85 (2), 695–731. Black, Sandra E., 1999. Do better schools matter? Parental valuation of elementary education. Q. J. Econ. 114 (2), 577–599. Bollinger, Christopher, Ziliak, James P., Troske, Kenneth R., 2011. Down from the mountain: skill upgrading and wages in Appalachia. J. Labor Econ. 29 (4), 819–857. Bowen, Eric, Deskins, John, 2015. Government Incentives to Promote Demand for West Virginia Coal. West Virginia University. http://www.be.wvu.edu/bber/pd fs/BBER-2015-01.pdf. Burtraw, Dallas, Palmer, Karen, Paul, Anthony, Woerman, Matt, 2012. Secular trends, environmental regulations, and electricity markets. Electr. J. 25 (6), 35–47. Cicala, Steve, 2014. When does regulation distort costs? Lessons from fuel procurement in us electricity generation. Am. Econ. Rev. 105 (1), 411–444. Cragg, Michael I., Zhou, Yuyu, Gurney, Kevin, Kahn, Matthew E., 2013. Carbon geography: the political economy of congressional support for legislation intended to mitigate greenhouse gas production. Econ. Inq. 51 (2), 1640–1650. Dube, Arindrajit, Lester, T William, Reich, Michael, 2010. Minimum wage effects across state borders: estimates using contiguous counties. Rev. Econ. Stat. 92 (4). MIT Press: 945–64. Feyrer, James, Mansur, Erin T., Sacerdote, Bruce, 2017. Geographic dispersion of economic shocks: evidence from the fracking revolution. Am. Econ. Rev. 107 (4), 1313–1334. American Economic Association. Fishback, P.V., Horrace, W.C., Kantor, S., 2006. The impact of new deal expenditures on mobility during the great depression. Explor. Econ. Hist. 43 (2), 179–222. Glaeser, Edward L., Gyourko, Joseph, 2005. Urban decline and durable housing. J. Political Econ. 113 (2), 345–375. Holmes, Thomas J., 1998. The effect of state policies on the location of manufacturing: evidence from state borders. J. Political Econ. 106 (4), 667–705. Hughes, J.E., Lange, I.A., 2018. Who (else) Benefits from Electricity Deregulation? Coal Prices, Natural Gas, and Price Discrimination. CESIFO Working Paper, No. 7374. Center for Economic Studies and Ifo Institute (CESifo), Munich. Islam, T.M., Minier, Jenny, Ziliak, James P., 2015. On persistent poverty in a rich country. South. Econ. J. 81 (3), 653–678. Joskow, Paul L., 1985. Vertical integration and long-term contracts: the case of coalburning electric generating plants. J. Law Econ. Organ. 1 (1), 33–80. Joskow, Paul L., 1987 Mar 1. Contract duration and relationship-specific investments: empirical evidence from coal markets. Am. Econ. Rev. 168–185. Lee, Jong-Wha, Swagel, Phillip, 1997. Trade barriers and trade flows across countries and industries. Rev. Econ. Stat. 79 (3), 372–382. Mairs, John A., 2001. State Tax Credits Promoting Local Coal Use, 21. Energy & Min. L. Inst ch 12. Preonas, L., 2017 Nov 6. Market power in coal shipping and implications for US climate policy. ioj 2, q2. Venkatesh, Aranya, Jaramillo, Paulina, Griffin, W. Michael, Matthews, H. Scott, 2012. Implications of changing natural gas prices in the United States electricity sector for SO2, NOx, and life cycle GHG emissions. Environ. Res. Lett. 7 (3), 034018.
7. Conclusion States that specialize in mining have incentives to promote the growth (or at least slow the decline) of one of their key industries. Given
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Prolonging coal’s sunset: Local demand for local supply☆ Jonathan Eyer a, Matthew E. Kahn b, * a b
University of Southern California, USA Johns Hopkins University, USA
A B S T R A C T
The share of U.S electricity generated by coal has fallen from nearly 50% to 33%. This transition offers social environmental benefits but spatially concentrated costs as coal miners and their local communities have suffered. Coal states have responded to shifting demand conditions by introducing incentives for local power plants to purchase coal from local mines. We document that power plants in areas with mining activity are more likely to be coal-fired and to purchase more coal from mines which they share a political boundary even after controlling for the distance from power plants to mines. While politically-motivated coal purchases do result in improved conditions in coal-mining counties in some regions of the country, these benefits are likely to be small compared to the additional carbon costs.
1. Introduction To mitigate the global challenge of climate change, nations must burn less coal. In recent years, the share of U.S electricity generated by coal has fallen from nearly 50% to 33%. The U.S reduction in coal use for generating power is especially notable because it has occurred without the U.S imposing carbon pricing or a carbon tax (Cragg et al., 2013). The substitution away from coal is mainly due to adoption of fracking technology and some states sharply ratcheting up their renewable portfolio standards (Burtraw et al., 2012; Venkatesh et al., 2012). While environmentalists cheer for coal’s sunset, there are interest groups with strong incentives to protect this declining industry. Reduced power plant demand for coal imposes spatially-concentrated costs borne by traditional coal mining communities in states such as West Virginia, Ohio, Pennsylvania, Kentucky, and Wyoming, and the low skill workers who engage in mining and providing services in mining areas. There were 430 coal mines in the United States that shipped coal to the electricity sector in 2014. These mines are located in rural areas where the population is typically white and not college educated. These coal mining jobs tend to pay well and workers in these regions have few alternative job prospects. Their local economies experience slower economic growth and lower productivity than the rest of the nation (Islam et al., 2015; Bollinger et al., 2011). The durable housing stock in such areas means that local home prices could fall sharply if coal demand declines (Glaeser and Gyourko, 2005). In this case, if demand for coal labor is reduced displaced miners who own local homes lose both their mining jobs and their housing wealth. ☆
Local politicians in coal regions recognize these effects and thus have strong incentives to engage in protecting local coal interests. In this paper, we document that power plants are more likely to use coal to generate power if there is a coal mine sharing their state or congressional district and are more likely to purchase coal from a mine with which they share a political jurisdiction relative to a similarly distant mine that is in another jurisdiction. This finding is robust to flexibly controlling for the distance between mines and power plants using higher-order distance polynomials or fixed effects for neighboring counties on opposite sides of state lines. This purchasing behavior suggests that politicians are supporting coal consumption even when alternative energy suppliers are more cost effective. This raises two important concerns. First, this results in a transfer from typically urban electricity consumers to rural coal producers and creates deadweight loss. More concerning, it acts as an implicit subsidy to coal producers at the expense of other energy producers. Economically efficient policies aimed at addressing climate change rely on targeting prices or quantities so that the marginal cost of emissions equals the marginal benefit. Then, the energy mix will shift towards those resources with the lowest marginal emissions costs. Local subsidies for coal use help to prolong the coal sector’s “sunset”. 2. The economics of within political border trade Power plants operate in a political and regulatory environment that encourages them to consider the sourcing of coal inputs. Elected officials such as a mining state’s governor, Congressmen and local officials have
We thank the editor, reviewers and seminar participants at USC Law, Arizona State, PERC, University of Illinois, and Case Western University for useful comments. * Corresponding author. E-mail address: [email protected] (M.E. Kahn). https://doi.org/10.1016/j.regsciurbeco.2019.103487 Received 8 October 2019; Accepted 24 October 2019 Available online 6 November 2019 0166-0462/© 2019 Published by Elsevier B.V.
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by rail and the transportation costs are directly tied to distance. These costs are substantial. In 2014 transportation costs accounted for 45% of the total cost of coal that was delivered via rail.5 While coal can also be transported via barge or truck, truck transportation is prohibitively expensive and only power plants that are located along waterways can use barges. As a result, power plants have a strong incentive to purchase coal from closer mines and will bid less aggressively for coal that is from mines that are further away. Thus, we expect that a “gravity model” of bilateral trade between mines and coal fired power plants will have significant explanatory power (Lee and Swagel, 1997). For each power plant, we calculate the Euclidean distance to the coal mines with which it trades. There are two peaks in the distribution of coal shipment distances (See Fig. 2). The first peak is at short distances and represents local trading. Around 33% of total coal shipments travel less than 100 miles. The second peak occurs at around 700–1000 miles and represents plants purchasing low-sulfur coal from Wyoming. There are, of course, several mechanisms that may cause Euclidean distance to imperfectly predict the likelihood of trading. Preonas (2017) finds that rail companies exert market power and a rail company could increase the cost of transportation for coal shipped between a captured plant and mine pair, making more distant trading partners more appealing. Still, the markup on the transportation cost should not be large enough to make shipping from a close, captured mine more expensive than shipping from a distant, uncaptured mine because then the rail company would lower its rates to retain the sale. Similarly, Hughes and Lange (2018) find that as changes in natural gas prices affect the rents available to coal-fired power plants that rail operators change their prices. Again, while this behavior will shift rents from coal plants to railroads it should not change a power plant’s decision about from which mine to purchase. Cicala (2014) also notes the change in procurement patterns that resulted following deregulation of some power plants in the 1990s. If distance were a sufficient statistic for predicting whether a plant and mine would trade, the change in regulatory status should have left trading partners unchanged. There might also be non-linearities in shipping costs caused by rivers or mountains that make a trading partner in one direction more costly than a trading partner in a different direction. The EIA reports average rail shipping costs between states and prices are not uniformly higher when the rail line would cross a natural barrier. In 2008, for example, average shipping costs from West Virginia, to Virginia (across the Blue Ridge Mountains), Ohio (across the Ohio River), and Pennsylvania (no major natural impediments), were $22.79/ton, $21.91/ton, and $20.58/ton, respectively. While a power plant’s distance to a given mine is an important determinant of trade, there are other mine attributes that affect a power plant’s demand. Coal mines differ with respect to the sulfur content of their coal, for example. A power plant that primary purchases low sulfur coal from the Powder River Basin would have to install costly sulfur reduction technologies if it wanted to switch to coal with a higher sulfur composition. Given that this technology adoption represents a fixed cost, power plants have an incentive to “lock in” and purchase a majority of their coal from a given mine or location. Economic studies of coal trading have emphasized asset specificity such as transport infrastructure, sulfur scrubbers, or ash disposal (see Joskow, 1985, 1987; Preonas, 2017). While transportation infrastructure characteristics such as conveyer belts result in linkages between particular mine-plant pairings, most power plant infrastructure choices offer a plant a range of mine partners selling coal with similar characteristics. For example, a power plant in a region with sulfur dioxide regulations that has not installed scrubbers must buy low sulfur coal. While that plant may be currently purchasing its coal from a particular low-sulfur mine, it could ostensible switch to another low sulfur mine. Because the physical characteristics of coal (e.g., thermal content, sulfur content) vary relatively smoothly throughout space,
an incentive to help their constituents. Miners and the members of their communities are typically low-skilled people with long time roots to the area. Local elected officials in coal states are aware that their constituents face significant dislocation costs and seek to protect them from longlasting negative income shocks by stabilizing demand for their constituents’ output. Moreover, these officials have a set of mechanisms through which they could reward power plants that purchase in-state coal or punish those that do not.1 Cicala (2014) finds that electricity deregulation shifted coal procurement patterns, lowering total costs and reducing the fraction of coal that was purchased from in-state producers. Since he does not explore the bilateral trading flows between individual coal mines and coal fired power plants, his research design does not use a trade and gravity style model to consider the set of geographic alternatives that each power plant faces. We build on his approach to explicitly control for the distance between mines and power plants to consider the role of different types of jurisdictional borders (i.e states and Congressional Districts). Elected officials in coal areas have strong incentives to take actions that increase the demand for coal. Mining is a high paying job for low skill workers. Indeed, the average wage for all U.S. coal miners was $83,600 in 2015 according to the National Mining Association. The BLS reports that the average wage across all industries, by contrast, was around $50,000. Average coal mining wages were around 60% higher than the average wage in coal mining states. Coal states provide large financial incentives to encourage local coal purchases (Mairs, 2001). Maryland and Virginia offer a $3 per ton tax credit for utilities buying in-state coal (Bowen and Deskins, 2015).Oklahoma offers credits of $5 per ton to both coal mines and power plants, effectively contributing $10 to every ton of Oklahoman coal that is burned for electricity generation in the state.2,3 While these incentives are costly to the states, states are precluded from legally mandating that in-state power plants purchase local coal under the Commerce Clause of the Constitution. Oklahoma enacted a law aimed at requiring power plants to purchase at least 10% of their coal from Oklahoman mines but this law were eventually struck down by the Supreme Court.4 3. The spatial geography of coal trading Fig. 1 maps the 354 coal fired power plants in the United States and the 430 coal mines in the U.S that either purchased coal in 2014 or shipped coal to the electricity sector in 2014. Several clear geographic patterns emerge. Coal mining is concentrated in Appalachia (Kentucky, West Virginia, Ohio and Pennsylvania), in southern Illinois and Indiana, and in Wyoming. While the majority of mines are in the Appalachia region, the largest mines are in Wyoming, which has relatively low-quality but easily accessible coal deposits with low sulfur content. Coal power plants exist throughout the country, but are most prevalent in the Midwest, Mid Atlantic, and South. According to the EIA, most coal in the United States (70%) is shipped
1 Governors, for example, can choose whether or not to opt into a carbontrading scheme, institute renewable portfolio standards, or to appoint public utility regulators who will support increased electricity prices. A congressperson might press the EPA to loosen environmental regulations on the temperature of discharged cooling water or support DOE Secretary Perry’s efforts to reward coal plants for base-load grid reliability. A local official may ease permitting or local environmental pressure on a coal plant. In each case, the local power plant has an incentive to ensure that they are on friendly terms with elected officials. 2 The Oklahoma tax credits operate separately for mines and power plants and do not require bi-lateral trade between Oklahoma mines and plants. See https: //iec.ok.gov/sites/g/files/gmc216/f/Coal%20Incentives_Draft_09.29.17.pdf. 3 According to the EIA’s statistics on the delivered price of coal, this is approximately 3–4% of the average delivered price in Virginia and Maryland and nearly 1/3 of the delivered price in Oklahoma. 4 See Wyoming v. Oklahoma, 502 U.S. 437 (1992).
5 This information is contained in the EIA 923. See Table 1 of https ://www.eia.gov/coal/transportationrates/index.php.
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Fig. 1. Coal Mines and Plants in 2014.
Fig. 2. The Quantity Weighted Distribution of the Distance between Coal Mines and Power Plants.
in the absence of mine-mouth transportation infrastructure, plants are “locked in” to a mining region but may choose their supplier from a set of mines within the region which meet the physical requirements of their plant. Table 1 displays for each major coal mining state the share of its mined coal tonnage that is sold to an instate power plant based on EIA data on fuel deliveries. It also presents the share of the state’s power plants coal purchases that come from local coal. The average state power plant receives 24% of its total coal consumption from in-state mine. The average across only the states that produce coal is 48%. The states that receive the lowest-percentage of total coal purchases from in-state mines were Kansas (20), Maryland (24), Missouri (29), Oklahoma (40) and Tennessee (47). In each of these states, coal is the dominant source of electricity generation but coal mining is a relatively small industry. West Virginia’s purchases a surprisingly large amount of its coal from out of state (45%) given the state’s reputation as a coal mining economy.
Table 1 Coal consumption and deliveries by source and destination.
4. Power plant and mine data In this study, our main unit of analysis will be a coal mine-plant-year. The EIA collects data on fuel deliveries to the plants in the power sector. In our data set, there are 1186 coal mines, 469 power plants. We first compute the Euclidean distance between each mine and each power plant in our dataset. Finally, we calculate the total annual quantity of coal that is shipped for each county and the total annual quantity of coal that is received for each power plant and drop plant-coal county-year
3
State Name
Buy In State Consumption Share
Sell In State Delivery Share
Alabama Arizona California Colorado Illinois Indiana Kansas Kentucky Louisiana Maryland Mississippi Missouri Montana New Mexico New York North Dakota Ohio Oklahoma Oregon Pennsylvania Tennessee Texas Utah Virginia Washington West Virginia Wyoming
0.4 0.4 0 0.58 0.22 0.49 0.01 0.52 0.24 0.11 0.23 0.01 0.95 1 0 0.98 0.65 0.02 0 0.68 0.03 0.48 0.87 0.5 0.55 0.55 1
0.96 0.72 – 0.49 0.31 0.81 0.53 0.3 1 0.26 1 0.65 0.34 0.63 – 0.98 0.38 0.95 – 0.57 0.33 1 0.76 0.32 – 0.34 0.08
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and a function of every other mine’s price. This would lead to a large demand system. Local incentives to purchase same jurisdiction coal would introduce a wedge between the price the buyer pays and the price the mine collects.10 Since we do not observe the price that each mine offers each electric utility for its coal, we pursue a reduced form approach to measure “border effects”. We also do not know the exact subsidies each coal mining state offers. Even if we could observe all of this information, we would face an endogeneity challenge of modelling each state’s choice of optimal subsidies. We examine the effects of political boundaries on the trading patterns of pairs of power plants and coal mines. Our identification strategy relies on the assumption that our distance and geographic controls capture any affect associated with transportation costs between power plants and mines. Our econometric strategy combines the standard trade gravity model with the Holmes (1998) borders approach. The distinctive feature of our econometric framework is the vector of dummy variables indicating if the origin mine and potential destination power plant share a common political jurisdiction. Power plants and mines that are within the same jurisdiction are, of course, relatively close to each other and have lower transportation costs than power plants that are far away. By explicitly controlling for the distance between power plants and mines, our political jurisdiction variable compares the likelihood of buying coal from an in-state mine relative to a similarly distance out-of-state mine. We also note that there are factors that would shift shipping costs that may be correlated to political boundaries. For example, mountain ranges and rivers often generate state borders and these features may increase the cost of shipping coal via train. On the other hand, if plants and mines are on the same boundary river, it may be less costly to ship via barge to the other side of the river than to ship coal overland to a trading partner within the state. We explore the bilateral trade between each coal fired power plant and each open coal mine in the nation. The likelihood that plant i purchases coal from mine j in year t is governed by the latent variable
observations in which the plant received no coal or the coal county shipped no coal. Characteristics of the delivered coal are also reported for each transaction. These characteristics include the delivered cost of the coal, as well as the ash, sulfur, and heat content for the fuels. They also report characteristics of the trade such as whether or not the trade occurred pursuant to a contract and the duration of any contracts. We link each plant to a latitude and longitude using the EIA 860 and matching on EIA Plant ID. Similarly, we link each mine to a latitude and longitude using the Mine Safety & Health Administration (MSHA) data and matching on the MSHA which is reported in the EIA fuel purchases data. We then overlay state and congressional boundary shape files onto our geospatial data on power plant and coal mine location and create indicator variables for whether a power plant – coal mine combination are in the same state or congressional district. We also incorporate information that may shift the cost of shipping coal in a way that is discontinuous in Euclidean distance. We link each mine and plant to the maximum elevation in its county using data from Fishback et al. (2006) and calculate the difference in elevation between each pair. Similarly, we use data from Fishback et al. (2006) on whether a county contains a major river and generate a dummy variable for whether both the mine and power plant county contain at least one major river.6 Finally, we manually generate a dummy variable for whether plants and mines in opposite states lay on either side of a major river that defines a state boundary.7 We make two key restrictions to our dataset. In our data set, the closest mine and plant that are not in the same state are 10 miles apart while the most distant mine and plant that are in the same state are 335 miles apart. We further limit our sample to only the set of mine-plant pairs that are between 50 and 150 miles from each other. This restriction helps ensure that each trading pair could potentially be either instate or out-of-state conditional on the geographic distance between the two. Fig. 3 shows the density of distances between power plants and mines for same-state and cross-state pairs. While in-state pairings are more common at shorter distances and out-of-state pairings are more common at greater distances, there is general overlap in the distributions.8 Second, we account for heterogeneity in allowable coal characteristics by restricting trading pairs based on the reported sulfur dioxide and ash levels in the coal that they have purchased or sold. For each plant we calculate the average sulfur dioxide content and the average ash content of the coal that it has purchased. Similarly, for each mine we calculate the maximum sulfur dioxide and ash content of the coal that it has sold. We then restrict mines to only plants that purchase coal with average sulfur dioxide and ash content that falls in the mine’s range. This precludes plants that have locked-in to coal of a certain type (e.g., plants without scrubbers that we observe only purchasing low-sulfur coal cannot purchase high-sulfur coal).9
Yijt ¼ α þ g distanceij þ ηt þ β1 * Borderij þ β2 * Controlsijt þ Uijt
(1)
In equation (1), the key explanatory variables of interest are the vector of border dummies, Borderij. We include dummies indicating whether the mine and the plant are located in the same state or the same Congressional District. A key point to note is that we flexibly model the role of distance on trade. This gðdistanceij Þ polynomial and distance bins that we report below allow us to flexibly control for proxies for transportation costs. The Controlsijt variable is a matrix of power plant or mine characteristics that would affect the overall frequency with which a plant or mine engage in trade. ηt is a matrix of year-specific fixed effects. We estimate the probability that a mine and a power plant trade in a given year using a logistic regression defined as Tradeijt ¼ 1 if Yijt > 0.
5. Empirical approach
6. A border pairs test
In a standard differentiated products market setting, each coal seller sells a different variety of coal. In such a demand system, a given power plant’s demand for coal from mine j will be a function of mine j’s price
While the above specifications rely on flexible non-linear controls for distance, it is possible that the physical distance between a plant and a mine could still have an effect on the likelihood of trading in a way that would bias our results. For example, if the impact of Euclidean distance on the likelihood of trade varies throughout the country our distance controls will capture only the average effect. To address this concern, we estimate the effect of the political boundary on coal trading in a border
6 Note that this is whether they both contain any river rather than whether they contain the same river. 7 As major rivers that provide state boundaries we include the Colorado, Mississippi, Ohio, and Missouri rivers. The dummy variable applies to all plants and mines that are on opposite sides of one of these rivers that creates state boundaries. For example, the Mississippi River separates plants and mines in Mississippi and Arkansas. 8 The primary regression results are robust to limiting the sample to smaller and larger distance ranges. 9 We also considered performing this procedure based on heat content but the EIA’s data on heat content is inconsistently reported so it is difficult to perform this restriction with confidence.
10 Preonas (2017) documents similar wedges in coal shipping costs due to localized market power in the rail sector that increases delivered prices for coal power plants facing monopolist railroads.
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Fig. 3. The Distribution of Distances Between Coal Mines and Power Plants for Same-State and Cross-State Pairs.
pairs framework in the spirit of Black (1999) and Holmes (1998).11 Following Dube et al. (2010), we rely on controls for contiguous counties that are in different states to better control for unobserved characteristics of power plant locations that could bias our estimates of the cross-state effect. Our approach relies on the typical continuity assumption that characteristics on either side of a boundary are the same. Under this assumption, the effect of the border can be treated as randomly assigned. In our case, we assume that power plants in counties on either side of a state border are otherwise comparable and then estimate the causal effect of crossing the border on the probability that a plant buys coal from a mine. Following Dube et al. (2010), we limit our sample to only counties along state borders that are adjacent to a county in another state that also has a coal-fired power plant. There are 38 counties that both have a coal-fired power plant and are adjacent to a county in a different state that also has a coal fired power plant and a total of 25 unique county boundary pairs (some counties appear in more than one pair). There are 52 power plants in these 38 counties. We then create a set of adjacent-power plant county by mining county fixed effects for each mining county on either side of the state border. For example, there power plants in Apache County, Arizona and in neighboring McKinley County, New Mexico. There are six coal mines in two counties in New Mexico and one coal mine in Arizona. We assign a fixed effect for each of the three Arizona and New Mexico mining counties. This fixed effect captures the distance between the mine and the power plant county pair, so that our identification is driven by the difference in trading behavior between each mining and power plant on either side of the state border. There is variation in intra-jurisdictional status within each mine and county pair fixed effect by construction. For each mine and county pair combination, we observe at least one power plant in the same state as the mine and at least one power plant that is not in the same state as the mine. Fig. 4 shows the distribution of distances between mines and power plants for same-state and cross-state pairs for this restricted sample.
In order to implement this strategy, we modify equation (1) to reflect the addition of these bordering county fixed effects as Yijcpt ¼ δpc þ γ t þ β1 *Borderij þ β2 *Controlsijt þ Uijcpt
(2)
Note that this new regression differs from equation (1) because we now restrict the set of observations we include in the regression and we also include a set of δpc bordering county-by-mining county fixed effects where c denotes the county of mine j. We again estimate the probability that a mine and a power plant trade in a given year as determined by Tradeijcpt ¼ 1 if Yijcpt > 0.
6.1. Results on coal trading Across each distance specification, we consistently find a positive and statistically significant effect of being within state lines on the probability that a power plant will purchase coal from a mine. Table 2 presents these results. A power plant is 1.1 percentage points more likely to purchase coal from an in-state mine than from an out-of-state mine that is the same distance away. This effect is quite large in context. Across our entire sample, the probability that a plant-mine combination engages in a trade in a given year is about 1.8 percent. The probability that a power plant and a mine trade is increasing in both power plant purchases and in mine shipments. This indicates that plants that buy a lot of coal tend to purchase from more mines than plants that buy a relatively small amount of coal. Similarly, mines that produce a lot of coal sell to more power plants than small mines. These effects persist when we remove each plant’s largest trading partner from its set of potential partners. These results are presented in Table 3. The effect of being in the same state is slightly smaller than the results when we include the largest trading partner (See Table 2). By removing a mine’s largest trading partner, we preclude Joskow-style “lock in” from driving our results under the assumption that, if there is relationship-specific capital, each plant “locks in” with only one mine. Note that, a necessary condition for a mine to trade with a power plant is for the power plant to be coal fired and for it to exist. The above results take the presence of the power plant and mines as given. Given our assumption that the location of power plants is exogenous, the political boundary effect will be underestimated because our results are conditional on the existence of a power plant. There may also be an
11 This approach addresses the potential concern is that our cross-political boundary results might merely be the effect of non-linear distance effects that are not captured by our distance and geography controls. See Fig. 3 for the distribution of distances for this sample.
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Fig. 4. The distribution of distances between coal mines and power plants for same-state and cross-state pairs – county pair fixed effect specification.
Table 2 Effect of state and congressional boundaries on probability of trade between plants and mines.
Mine Production Plant Purchases Elevation Difference River State Boundary Rivers in County Same Congressional District Same State Distance Control Year FE Observations AIC
(1)
(2)
(3)
0.011*** (0.003) 0.101*** (0.001) 0.001*** (0.0003) 0.004 (0.005) 0.005 (0.005) 0.002 (0.007) 0.033*** (0.006) 10-Mile Bins X 31673 12049
0.011*** (0.003) 0.102*** (0.010) 0.001*** (0.0003) 0.004 (0.005) 0.005 (0.005) 0.002 (0.007) 0.033*** (0.006) Cubic Polynomial X 31673 12056
0.011*** (0.003) 0.101*** (0.010) 0.001*** (0.0003) 0.004 (0.005) 0.005 (0.005) 0.002 (0.007) 0.033*** (0.006) Quartic Polynomial X 31673 12058
Table 3 Effect of state and congressional boundaries on probability of trade between plants and mines excluding primary trading partner.
Mine Production Plant Purchases Same State Distance Control Year FE Observations AIC
(1)
(2)
(3)
0.004*** (0.001) 0.039*** (0.001) 0.012*** (0.002) 10-Mile Bins X 255984 52883
0.004*** (0.001) 0.040*** (0.001) 0.012*** (0.002) Cubic Polynomial X 255984 52809
0.004*** (0.001) 0.040*** (0.001) 0.012*** (0.002) Quartic Polynomial X 255984 52684
Note: Coefficients correspond to marginal effects of a logistic regression estimating the probability that a power-plant purchased coal from a given mine. For each mine-year, the mine’s largest purchaser is removed from the set of potential trading partners. Total Mine Production and Total Plant Purchases are expressed in tens of millions of tons. Elevation Difference is expressed in hundreds of feet. See Equation (1) Standard errors are clustered at the plant-mine level. ***: p < 0.01, **: p < 0.05, *: p < 0.10.
Note: Coefficients correspond to marginal effects of a logistic regression estimating the probability that a power-plant purchased coal from a given mine. Total Mine Production and Total Plant Purchases are expressed in tens of millions of tons. Elevation Difference is expressed in hundreds of feet. See Equation (1) Standard errors are clustered at the plant-mine level. ***: p < 0.01, **: p < 0.05, *: p < 0.10.
limiting each pair of adjacent-county power plants to only being able to purchase coal from mines in either state of the adjacent counties. For example, the power plants in Mobile County, Alabama and Jackson County, Mississippi would have as potential trading partners the coal mines in Alabama and Mississippi but we drop the power plant-mine observations in which the mines are in other states. In this specification, the border variable captures the relative difference in the probability of a trade between each mine in Alabama and the plants in Mobile, County Alabama and Jackson County, Mississippi. Our neighboring county-pair fixed effect captures unobserved characteristics of the Mobile/Jackson area and the remaining difference in probability is assigned to the border effect. We find that plants are approximately 3 percentage point more likely to purchase coal from in-state mines that from mines in a different state, although there is no statistically significant difference in trading propensity at the congressional district boundary.
extensive margin effect in which political pressure from local mines causes a plant to open and purchase from the nearby mine rather than an already open plant merely choosing the mine as its partner. If political pressure results in a power plant opening near a mine that this would induce the possibility of intra-jurisdictional trading that would not have been possible without the politically-motivated power plant placement. Similarly, if choices about power plant closures are motivated in part by a desire to protect mining jobs then we would continue to observe intrajurisdictional trading options that would not exist if the plant was closed in the absence of political pressure. Again, these impacts would result in an underestimate of our results because political pressure would be driving the likelihood that a pair is observed as potentially trading in our sample.
6.3. The social costs of “local border effects” Assuming the conservative estimate of a $40 per ton social cost of carbon suggested by the IWG and the EPA’s emission factor of 2.86 tons of CO2 per ton of coal, each of the 153 coal mining counties would be associated with an average of around $1.37 million in carbon costs. Aggregating across all of the coal mining counties in the sample, this
6.2. Border pairs results Our border pair models presented in Table 4 report further evidence of political boundary effects in coal purchasing. We restrict our sample by 6
J. Eyer, M.E. Kahn
Regional Science and Urban Economics 81 (2020) 103487
the durability of housing capital and the built up social networks established in mining areas, its residents face both migration costs and asset losses if the demand for coal mining declines. Such individuals face a fundamental job retraining challenge that middle-aged workers who have worked in mines will have trouble transitioning to other jobs. Local officials in coal areas are well aware that many of their constituents depend on the continuing viability of the coal industry. Local officials internalize the benefits of coal’s prolonged sunset but they ignore the social environmental costs associated with such implicit subsidies. Our examination of the sunset of the coal industry revisits recent research that has examined how rural communities have gained from the fracking boom (Feyrer et al. 2017, Allcott and Keniston, 2018). We have introduced a detection approach to measure “excess” within border transactions. Our empirical research design exploits the fact that coal mines and power plants vary with respect to their geographic location. Some lie within the same political jurisdiction while others do not. This variation allows us to use a flexible distance polynomial between pairs of power plants and coal mines to recover key border effects. We document an increased likelihood of trade when potential partners are within the same state.
Table 4 Effect of state and congressional boundaries on trades between plants and counties - mining county - plant pair FE.
Mine Production (Tens of Millions of Tons) Plant Purchases (Tens of Millions of Tons) Elevation Difference River State Boundary Rivers in County Same Congressional District Same State Plant-Mine County FE Year FE Observations R2
(1)
(2)
(3)
0.280*** (0.051) 0.289*** (0.051) 0.003*** (0.001) 0.005 (0.013) 0.039*** (0.001) 0.0.005 (0.025) 0.027*** (0.010) X X 7937 0.212
0.280*** (0.058) 0.287*** (0.051) 0.003*** (0.001) 0.005 (0.012) 0.040*** (0.013) 0.005 (0.024) 0.028*** (0.010) X X 7937 0.211
0.278*** (0.058) 0.287*** (0.051) 0.002*** (0.001) 0.005 (0.013) 0.040*** (0.012) 0.005 (0.024) 0.029*** (0.010) X X 7937 0.211
Note: Coefficients correspond to marginal effects of a logistic regression estimating the probability that a power-plant purchased coal from a given mine. Total Mine Production and Total Plant Purchases are expressed in tens of millions of tons. Elevation Difference is expressed in hundreds of feet. Plant-Mine County FE are a set of fixed effects corresponding to each pair of neighboring counties in different states and each mine in either state. See Equation (2). Standard errors are clustered at the plant-mine level. ***: p < 0.01, **: p < 0.05, *: p < 0.10.
Appendix A. Supplementary data Supplementary data to this article can be found online at https://doi .org/10.1016/j.regsciurbeco.2019.103487. References
results in a total of $287 million in social carbon costs due to intrajurisdictional purchasing. Note that these social carbon costs should be viewed as an upper bound on the environmental impacts of localized coal purchasing. These results implicitly assume that if an intra-jurisdiction mine were removed from a power plant’s choice set that it would not substitute to coal from an out-of-jurisdiction mine and that a mine without access to an intrajurisdictional power plant would reduce its total output. While we attempt to control for this substitutability by including distance in our coal trading regressions, as the degree of substitution between intrajurisdictional partners and extra-jurisdictional ones increases the social costs of localized trading will decline. If coal sourcing is completely fungible, and a closure of an intrajurisdictional coal mine will result in increased purchases of coal from extra-jurisdictional mines, we would expect that plant-level coal purchases would be invariant to the number of intra-jurisdictional coal mines. If, however, total power plant coal consumption falls when the number of intra-jurisdictional mines falls, there is evidence of at least some global effect of intra-jurisdictional purchasing on global emissions. There are also important geographic aspects of the economic and financial implications of same state purchasing that are not captured in our models. First, pollutants like sulfur dioxide and particulate matter travel spatially so the communities containing the power plants purchasing this coal would be generating externalities on other regions based on their support of the local mines. Similarly, there are geographic economic spillovers that we do not observe. Increased local coal mining employment will increase local wages but there will also be general equilibrium impacts as these miners spend their wages. We also have not examined the impacts of this behavior on inter-state electricity transmissions or aggregate coal prices. If coal mining states are aggressively bidding for local coal they may be crowding out purchasers in other states or shipping low-cost electricity to consumers in other jurisdictions.
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7. Conclusion States that specialize in mining have incentives to promote the growth (or at least slow the decline) of one of their key industries. Given
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