An Empirical Examination of the Electric Utilities Industry December 19, 2011
The Puzzle Why do price-regulated firms purchase input coal through both contract Figure and 1(a): spot Contract transactions, versus paying Spot acoal marked Price: premium Mean for contract coal over spot By coal? Date, 1983-1999 The contract and spot prices are obtained from monthly FERC Form 423 data on transactions between plant and mine. These prices are quantity-weighted means over all plants in the sample for each month. The contract prices used are the coal prices as delivered, including transport costs. The definition of contract is an agreement to purchase input coal, with repeated deliveries, lasting greater than one year.
Why is this interesting? In economics, we typically think of the regulator s objective as inducing firms to procure input coal as if they are (expected) cost minimizing. Considering how the legal and political structure of regulation in practice deviates from this economic intuition has important policy implications. Also, this distortion away from expected cost minimization is quite large; I find the percentage difference in total costs of coal procurement between the observed and cost minimization scenarios to be 18.47%
This Paper This paper posits firm-level as-if risk aversion brought about by the regulatory structure in place as an explanation for this contracting behavior. Therefore, this paper sets out to Posit a regulatory-based explanation for firms behaving as if they are risk-averse Document the existence of contracting behavior consistent with this as-if risk-aversion Quantify the magnitude of this behavior, and the extent of its deviation from expected cost minimization. Consider how these effects differ in a later time period primarily characterized by more intense competition from unregulated generators. What is a Contract?
This Paper This paper posits firm-level as-if risk aversion brought about by the regulatory structure in place as an explanation for this contracting behavior. Therefore, this paper sets out to Posit a regulatory-based explanation for firms behaving as if they are risk-averse Document the existence of contracting behavior consistent with this as-if risk-aversion Quantify the magnitude of this behavior, and the extent of its deviation from expected cost minimization. Consider how these effects differ in a later time period primarily characterized by more intense competition from unregulated generators. What is a Contract?
This Paper This paper posits firm-level as-if risk aversion brought about by the regulatory structure in place as an explanation for this contracting behavior. Therefore, this paper sets out to Posit a regulatory-based explanation for firms behaving as if they are risk-averse Document the existence of contracting behavior consistent with this as-if risk-aversion Quantify the magnitude of this behavior, and the extent of its deviation from expected cost minimization. Consider how these effects differ in a later time period primarily characterized by more intense competition from unregulated generators. What is a Contract?
This Paper This paper posits firm-level as-if risk aversion brought about by the regulatory structure in place as an explanation for this contracting behavior. Therefore, this paper sets out to Posit a regulatory-based explanation for firms behaving as if they are risk-averse Document the existence of contracting behavior consistent with this as-if risk-aversion Quantify the magnitude of this behavior, and the extent of its deviation from expected cost minimization. Consider how these effects differ in a later time period primarily characterized by more intense competition from unregulated generators. What is a Contract?
This Paper This paper posits firm-level as-if risk aversion brought about by the regulatory structure in place as an explanation for this contracting behavior. Therefore, this paper sets out to Posit a regulatory-based explanation for firms behaving as if they are risk-averse Document the existence of contracting behavior consistent with this as-if risk-aversion Quantify the magnitude of this behavior, and the extent of its deviation from expected cost minimization. Consider how these effects differ in a later time period primarily characterized by more intense competition from unregulated generators. What is a Contract?
This Paper This paper posits firm-level as-if risk aversion brought about by the regulatory structure in place as an explanation for this contracting behavior. Therefore, this paper sets out to Posit a regulatory-based explanation for firms behaving as if they are risk-averse Document the existence of contracting behavior consistent with this as-if risk-aversion Quantify the magnitude of this behavior, and the extent of its deviation from expected cost minimization. Consider how these effects differ in a later time period primarily characterized by more intense competition from unregulated generators. What is a Contract?
Main Findings I show descriptively that predictions consistent with risk-aversion, and not readily explainable through other theories, hold in the data Through estimation of a static model of contract versus spot purchases where firms trade off mean and variance of total costs, I find a significant elasticity governing this tradeoff. I find that both the descriptive and structural results diminish in magnitude, but do not disappear, in the post 1992 period characterized by increased competition from unregulated generators
Brief Overview of in the 1980s All electric utilities were under rate-of-return regulation. In theory, this entails a regulator actively attempting to set output electricity price such that the firm has an opportunity to recover prudently incurred costs. Also, the majority of the plants in my sample were under some form of fuel adjustment clause, which allows firms to pass through all or a portion of fuel costs into the output price without a corresponding formal review.
How does the structure induce as-if behavior? Prudence (and less frequently exorbinance ) bounds on realized profit induce higher order profit moments into the firm s objective function If a firm asking for a rate increase incurs costs that are deemed to be significantly higher than what a reasonable manager acting in the same circumstances would have incurred, these costs may be flagged as imprudent and so not passed through to consumers via the output price. More rarely, if firms faces very low costs relative to the output price set, a consumer or environmental group may intervene through asking the state commission to initiate a rate case. Relevant regulatory literature Graphical Intuition
Stylized Motivating of Inducing As-if Risk Aversion Let TC N(µ, σ 2 (µ)), where σ 2 (µ) is decreasing in µ Consider the case of perfect cost passthrough, subject to prudence constraint. Therefore, the firm gets: R(TC realized ) = TC realized if TC TC TC otherwise In words, the case of perfect passthrough is where the firm earns zero profits for any cost realization lower that TC, and earns TC TC realized for any cost realizations higher than TC In this setup, I show that the following two optimization problems are approximately equivalent: max min µ M E[R(TC) TC] = µ M E[TC TC > TC] TC min φ µ M (µ + σ } (1 Φ)
Stylized Motivating of Inducing As-if Risk Aversion Let TC N(µ, σ 2 (µ)), where σ 2 (µ) is decreasing in µ Consider the case of perfect cost passthrough, subject to prudence constraint. Therefore, the firm gets: R(TC realized ) = TC realized if TC TC TC otherwise In words, the case of perfect passthrough is where the firm earns zero profits for any cost realization lower that TC, and earns TC TC realized for any cost realizations higher than TC In this setup, I show that the following two optimization problems are approximately equivalent: max min µ M E[R(TC) TC] = µ M E[TC TC > TC] TC min φ µ M (µ + σ } (1 Φ)
Stylized Motivating of Inducing As-if Risk Aversion Let TC N(µ, σ 2 (µ)), where σ 2 (µ) is decreasing in µ Consider the case of perfect cost passthrough, subject to prudence constraint. Therefore, the firm gets: R(TC realized ) = TC realized if TC TC TC otherwise In words, the case of perfect passthrough is where the firm earns zero profits for any cost realization lower that TC, and earns TC TC realized for any cost realizations higher than TC In this setup, I show that the following two optimization problems are approximately equivalent: max min µ M E[R(TC) TC] = µ M E[TC TC > TC] TC min φ µ M (µ + σ } (1 Φ)
How did Change in the 1990s? For my study, the most important legislation to consider is the Energy Policy Act of 1992 (EPACT), which opened up the national transmission grids to wholesale, unregulated suppliers of electricity. FERC Order 888 set out to implement this vast restructuring of the electricity industry, and the majority of the changes were completed by 1998 Also, the 1990 Amendments to the Clean Air Act made the environmental impacts of burning coal much more salient. These legislative changes may be more pertinent for how we expect transaction cost predictions to differ after these Amendments.
How did Change in the 1990s? For my study, the most important legislation to consider is the Energy Policy Act of 1992 (EPACT), which opened up the national transmission grids to wholesale, unregulated suppliers of electricity. FERC Order 888 set out to implement this vast restructuring of the electricity industry, and the majority of the changes were completed by 1998 Also, the 1990 Amendments to the Clean Air Act made the environmental impacts of burning coal much more salient. These legislative changes may be more pertinent for how we expect transaction cost predictions to differ after these Amendments.
Section Outline I examine contracting behavior in a descriptive framework, drawing from the transaction-cost based approach implemented in Joskow (1987). I show within this framework that so-called risk aversion covariates have additional explanatory power beyond these transaction cost covariates for my 1979-1992 sample of contracts Further, I show that these descriptive findings diminish, but do not disappear, in my post 1992 sample (1993-1998).
Joskow (1987) Contract Duration Regression, with Risk-aversion covariates, 1979-1992 Table 3: Duration Results with Risk-aversion covariates, 1979-1992 (1) (2) VARIABLES Log(duration) Log(duration) Spot Price -0.000754 NA (0.00132) Sd(Spot Price) 0.00898** NA (0.00375) Consumption -3.13e-05-7.77e-05** (3.99e-05) (3.93e-05) Sd(consumption) 6.12e-06*** 5.31e-06** (2.22e-06) (2.10e-06) Inventory 1.75e-05 4.12e-05*** (1.29e-05) (1.46e-05) Midwest Indicator 0.458*** 0.443*** (0.0866) (0.0714) West Indicator 0.631*** 0.630*** (0.240) (0.184) Minemouth Plant Indicator -0.566*** 0.307 (0.139) (0.704) Log(Contract Quantity) 0.0685*** 0.0708*** (0.0207) (0.0191) Observations 881 1,037 R-squared 0.206 0.202 Contract year signed fixed effects are included Unit of observation is a contract signed between plant and mine, at the year of signing The spot price (sd of spot price) are calculated from average (std. deviation) of monthly transaction-level data over the year of the contract signing. Inventory and consumption are summed from monthly plant-level data, using the within-year standard deviation for std(con) The spot price data come from monthly, transaction-level data from FERC Form 423. The inventory and consumption data are from FERC Form 759. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1
Contract Proportion Regression using VAR model with GARCH errors, 1983-1992 Table 6: Quantity Regression using VAR(1) model with GARCH errors, Comparing 1983-1992 versus 1993-1998 1983-1992 1993-1998 1983-1992 1993-1998 VARIABLES Contract proportion Contract proportion Contract proportion Contract proportion Contract Premium -0.000626** -0.00177*** -0.000965*** -0.000427 (0.000314) (0.000282) (0.000288) (0.000351) Var(Spot Price) 0.000757*** 0.000217** 0.00230 0.00154 (0.000130) (8.66e-05) (0.0310) (0.00360) Var(consumption) 8.28e-11*** 1.23e-10*** 2.34e-10-5.87e-09 (1.68e-11) (2.37e-11) (1.55e-09) (8.90e-09) Midwest or West Plant 0.0980*** 0.0581*** -0.0177-4.181 Date Fixed Effects? Plant Fixed Effects? (0.0130) (0.0105) (0.0152) (6.193) Y Y N N N N Y Y Observations 1,065 1,349 1,065 1,349 R-squared 0.193 0.096 0.452 0.398 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Unit of Observation is Plant-month of sample, using a subsample of plant that transact on both contract and spot markets in all periods Contract Premium = contract price E[spot price], and these prices are from monthly FERC Form 423 data on transactions. The variances for consumption and spot price are computed using a VAR(1) model with GARCH errors, and I divided Contract Quantity, Stock, and Consumption scaled to be in 10 billion BTU Spot Price scaled to be in $/1millionBTU In the 1983-1992 sample, there are no West cost plants; there is only one West coast plant in the 1993-1998 sample
Overall s from the Section The overall descriptive findings for both duration and contract quantity suggest that as-if risk aversion plays an important role in explaining contract behavior, in addition to the traditional transaction cost explanation. These findings are diminished, but do not disappear, in the descriptive analysis on post 1992 data These results indicate further investigation of the role of risk aversion in fuel procurement behavior. Duration Regression Comparison with 1993-1998 Quantity Regression with Full Sample
Description I consider a static model where a plant with preferences over mean and variance of total costs can purchase a fixed amount of coal from either contract or spot transactions This plant faces spot price risk, as well as demand risk, noting that these two are likely correlated The primary goal of this modeling effort is to assign magnitudes to the plant-level distortion from the expected cost minimization benchmark.
Timing The plant first chooses contract quantity, facing uncertain demand and spot price (but knowing the contract price) Demand and spot price are realized The plant purchases its residual demand from the spot market
Assumptions The information set at the beginning of time t is {p C s } t s=1, {p S s } t 1 s=1, and {Y s} t s=1 Revenue is fixed by regulation, and so does not enter the mean or variance of the plant s objective function. The plant takes contract and spot prices, as well as electricity demanded as given; it cannot affect these magnitudes through its behavior.
Setup and Results In each period t, the plant maximizes: max c t 0 U(E t[ p C t c t p S t (Y t c t )], Var t [ p C t c t p S t (Y t c t )]) Solving this problem: p C t = E t [p S t ] + 2λ(c t Var t [p S t ] + Cov t [p S t, p S t Y t ]) However, note that λ as defined is not unitless: Define ɛ i U2Var[ pc t ct ps t (Yt ct)] U 1E[ p C t ct ps t (Yt ct) The above is an elasticity governing the plant s trade-off between mean and variance of total costs
First-Stage VAR with GARCH errors I use within-plant, time-series variation in order to obtain the price and demand moments for the above equation However, some plants do not transact on both the spot and contract markets in every period Therefore, I simply estimate for the subsample of plants transacting via both spot and contract transactions. I estimate this first-stage model separately for the periods 1987-1992 and 1993-1999 I do not estimate for 1983-1992 both due to sample considerations and due to the 1986 Oil Price Collapse.
First-Stage VAR with GARCH errors I use within-plant, time-series variation in order to obtain the price and demand moments for the above equation However, some plants do not transact on both the spot and contract markets in every period Therefore, I simply estimate for the subsample of plants transacting via both spot and contract transactions. I estimate this first-stage model separately for the periods 1987-1992 and 1993-1999 I do not estimate for 1983-1992 both due to sample considerations and due to the 1986 Oil Price Collapse.
First-Stage VAR with GARCH errors I use within-plant, time-series variation in order to obtain the price and demand moments for the above equation However, some plants do not transact on both the spot and contract markets in every period Therefore, I simply estimate for the subsample of plants transacting via both spot and contract transactions. I estimate this first-stage model separately for the periods 1987-1992 and 1993-1999 I do not estimate for 1983-1992 both due to sample considerations and due to the 1986 Oil Price Collapse.
Counterfactual of Expected Cost Minimization First, I simply construct the counterfactual where firms are only interested in minimizing expected total costs. I can compare this counterfactual to the observed expected total costs. I compute a finite difference elasticity based on the actual versus counterfactual costs I show that MRS C > MRS A ( ɛ C > ɛ A ) In words, under utility maximization, the cost-minimizing bundle contains too much variance relative to the optimum. The firm is willing to trade for relatively more mean in total costs for a one unit decrease in the variance.
Counterfactual of Expected Cost Minimization First, I simply construct the counterfactual where firms are only interested in minimizing expected total costs. I can compare this counterfactual to the observed expected total costs. I compute a finite difference elasticity based on the actual versus counterfactual costs I show that MRS C > MRS A ( ɛ C > ɛ A ) In words, under utility maximization, the cost-minimizing bundle contains too much variance relative to the optimum. The firm is willing to trade for relatively more mean in total costs for a one unit decrease in the variance.
Full Table of Results Table 7: Statistics relating to Contract Timing using Complete Price Data plants 1987-1992 1993-1999 Variable Mean Std. Dev. Mean Std. Dev Elasticity Mean Actual Costs ($) Mean Cost Minimization Counterfactual Costs ($) Variance of Actual Costs ($) (%change in mean counterfactual actual cost)/(%change in variance of counterfactual actual cost) 2.4814 (0.3199477 ) 1.846105 0.234661 (0.1327879) 0.117092 8165423 6748991 10500000 6150797 6657315 5096335 9602914 5422879 3782655E6 5504428E6 4651340E6 4254596E6-6.70847 (0.4770017 ) 15.56663 Note that the standard error of the elasticity is 0.895 for 1987-1992 and 0.549 for 1992-1999 0.858144 (0.2725793) 9.291715 Unit of observation is plant-date Elasticity is calculated as (U 2 *vartc)/(u 1 *meantc) = λvartc/meantc, where λ varies by plant, and costs vary by plant-date The counterfactual profits correspond to a case where λ=0 (%change in mean counterfactual actual costs) = (counter_mean_costs actual_mean_costs)/actual_mean_costs (%change in mean counterfactual actual costs) = (counter_variance_costs actual_variance_costs)/actual_variance_costs
Overall s from the Section Without estimation of the fuel procurement model, we can still compare the expected cost minimization counterfactual to the expected total costs in actuality. I find the difference in these (expected) costs to be 18.47/ From the fuel procurement model, the elasticity governing the tradeoff between mean and variance of total costs is significant both economically and statistically, at ɛ = 2.48 Both of these results are diminished in magnitude when you re-run the analysis for 1993-1999.
From the descriptive analysis, I demonstrate that firm risk-aversion provides additional explanatory power to the more traditional transaction-cost explanations for contracting behavior The static model with mean-variance preferences over total cost provides an economic magnitude for the above result, indicating that the tradeoff firms make between mean and variance of costs is non-trivial (ɛ = 2.48) These results are diminished, but do not disappear, when the analysis is performed on data from after 1992.
Future Similar as-if risk -aversion is potentially observed in the coal inventory behavior of these plants. Are there important dynamic considerations in the behavior of these regulated plants? I plan on more explicitly examining the interaction between regulator and regulated; what types of strategic behavior do we expect from the utility (ex: cost-padding prior to a regulatory meeting), and to what extent is the regulator inhibited by asymmetric information (and how does this disadvantage change over time within a given relationship?)
Thanks for your time
PPI for Coal Graph, 1983-2000
PPI for Coal Graph, 1983-2010
Contract versus Spot Prices Difference, Mean over plants for each date, 1983-1999 Figure 1(b): Contract Minus Spot Coal Price Difference: Mean By Date, 1983-1999 The contract and spot prices are obtained from monthly FERC Form 423 data on transactions between plant and mine. These prices are quantityweighted means over all plants in the sample for each month. The contract prices used are the coal prices as delivered, including transport costs (spot prices also include transport charges). The definition of contract is an agreement to purchase input coal lasting greater than one year.
Contract versus Spot Prices, Mean over plants for each date, 1983-2010 Figure A2(b): Contract versus Spot Coal Price: Mean By Date for 1983-2008 The contract and spot prices are obtained from monthly FERC Form 423 data on transactions between plant and mine. These prices are quantity-weighted means over all plants in the sample for each month. The contract prices used are the coal prices as delivered, including transport costs. The definition of contract is an agreement to purchase input coal, with repeated deliveries, lasting greater than one year.
Aggregate Contract versus Spot Quantities, Sum over plants for 1983-2000 Figure 2(a): Contract versus Spot Coal Quantities: Sum By Date, 1983-1999 The contract and spot quantities are obtained from monthly FERC Form 423 data on transactions between plant and mine. The definition of contract is an agreement to purchase input coal, with repeated deliveries, lasting greater than one year.
Aggregate Contract Proportion, 1983-2000 Figure 2(b): Proportion of Coal Purchased Via Contract: By Date, 1983-2001 The contract and spot quantities are obtained from monthly FERC Form 423 data on transactions between plant and mine. The definition of contract is an agreement to purchase input coal, with repeated deliveries, lasting greater than one year.
Summary Stats for Contract Dataset, 1979-1992 Table 1: Summary Statistics for Contract Dataset, 1979-1992 Name Description Mean Std. Dev N Quantity Contracted Total distance Quantity shipped by contract in year of signing (in 1000 short tons) 182.5077 352.2114 1046 Total distance from mine to plant 471.0264 437.1319 841 Indicator of whether the plant is located adjacent to a mine 0.001912 0.043706 1046 Minemouth Indicator Duration Contract Duration (in years) 4.683556 5.07289 1046 Inventory Coal inventory for year of contract signing (in 1000 short tons) 4702.398 4411.472 1037 Consumption Coal consumption for year of contract signing (in 1000 short tons) 1945.598 1777.126 1037 Spot Price Average Spot price, gross of transport (in $/100 million BTU) 145.6685 28.93943 920 Sd(consumption) Standard deviation, within the year the contract was signed, from monthly data 34475.17 25262.2 1037 Sd(Spot Price) Standard deviation, within the year the contract was signed, from monthly data 8.021855 8.220636 885 Midwest Indicator if plant is located in the Midwest 0.24761 0.43183 1046 West Indicator Indicator if plant is located in the West 0.034417 0.182385 1046 The unit of observation for this table is a contract between mine and plant at the time of signing. The spot price is averaged over the year of signing from monthly FERC Form 423 data on transactions,whereas consumption and inventory variables are summed over the year of signing from monthly plant-level data obtain from EIA Form 759
Summary Stats for Contract Dataset, 1993-1998 Table A1: Summary Statistics for Contract Dataset, 1993-1998 Name Description Mean Std. Dev N Quantity Contracted Total distance Minemouth Indicator Quantity shipped by contract in year of signing (in 1000 short tons) 201.0422 365.5236 1538 Total distance from mine to plant 483.6135 406.3353 1291 Indicator of whether the plant is located adjacent to a mine 0.0013 0.036049 1538 Duration Contract Duration (in years) 4.084525 4.418937 1538 Inventory Coal inventory for year of contract signing (in 1000 short tons) 4311.659 4154.036 1501 Consumption Coal consumption for year of contract signing (in 1000 short tons) 2043.263 1824.509 1501 Spot Price Average Spot price, gross of transport (in $/100 million BTU) 139.5133 29.31748 1376 Sd(consumption) Standard deviation, within the year the contract was signed, from monthly transaction data 35441.85 26041.41 1501 Sd(Spot Price) Standard deviation, within the year the contract was signed, from monthly transaction data 10.33279 10.57199 1314 Midwest Indicator if plant is located in the Midwest 0.277633 0.447977 1538 West Indicator Indicator if plant is located in the West 0.042913 0.202727 1538 The unit of observation for this table is a contract between mine and plant at the time of signing. The spot price is averaged over the year of signing from monthly FERC Form 423 data on transactions,whereas consumption and inventory variables are summed over the year of signing from monthly plant-level data and obtained from EIA Form 759
Summary Stats for Quantity Dataset, 1979-1992 Table A2: Summary Statistics for Quantity Regressions, 1983-1992 Name Description Mean Std. Dev N Quantity Contracted Consumption Profile Inventory Profile Contract Premium Sd(consumption) Sd(Spot Price) Midwest Indicator West Indicator Monthly Contract quantity delivered (in 10 billion BTUs) 315.0425 318.0562 39995 The mean over the entire sample of consumption for the plant (in 10 billion BTUs) 356.2306 318.9048 39821 The mean over the entire sample inventory for the plant (in 10 billion BTUs) 903.4971 853.2557 39821 Contract price Spot price (in $/100 million BTU) 29.16394 29.48679 15785 Standard deviation over the year of the observation (in 10 billion BTUs) 82.01099 67.27587 39599 Standard deviation over the year of the observation (in $/100 million BTU) 6.821382 8.26906 22388 Indicator if the plant corresponding to the observation is from the Midwest 0.288961 0.453286 39995 Indicator if the plant corresponding to the observation is from the West 0.070234 0.255544 39995 The unit of observation for this table is a plant in a given month. Consumption and inventory profiles are means over the entire sample taken from monthly plant-level data obtain from EIA Form 759. The remaining variables are taken from monthly transaction level data from FERC Form 423.
Summary Stats for Dataset, 1987-1992 Table A3: Summary Statistics for sample, 1987-1992 Name Description Mean Std. Dev N Contract Proportion Amount Purchased Contact Price Expected Spot Price Var(Amount Purchased) Var(Spot Price) Midwest or West Indicator Monthly Contract quantity delivered (in 10 billion BTUs) 0.5934726 0.1800457 1065 The mean over the entire sample of consumption for the plant (in 10 billion BTUs) 57115.58 43556.85 1065 The mean over the entire sample inventory for the plant (in 10 billion BTUs) 152.5111 29.24083 1065 E[Spot price] (in $/100 million BTU) 121.5453 30.37961 1065 Variance from the VAR+GARCH model (in $/100 million BTU squared) 266000000 386000000 1065 Variance from the VAR+GARCH model (in $/100 million BTU squared) 39.41961 44.3839 1065 Indicator if the plant corresponding to the observation is from the Midwest 0.2638498 0.4409259 1065 The unit of observation for this table is a plant in a given month. Contract proportion, total quantity purchased, and the prices are taken from monthly transaction level data from FERC Form 423. The variance variables are derived from a VAR model with GARCH(1,1) errors (see paper for the exact specification)
Summary Stats for Dataset, 1993-1998 Table A4: Summary Statistics for sample, 1993-1998 Name Description Mean Std. Dev N Contract Proportion Amount Purchased Contract Price Expected Spot Price Var(Amount Purchased) Var(Spot Price) Midwest or West Indicator Monthly Contract quantity delivered (in 10 billion BTUs) 0.5757836 0.181688 1162 The mean over the entire sample of consumption for the plant (in 10 billion BTUs) 77990.61 37265.66 1162 The mean over the entire sample inventory for the plant (in 10 billion BTUs) 140.739 32.90481 1162 E[Spot price] (in $/100 million BTU) 125.2757 24.46201 1162 Variance from the VAR+GARCH model (in $/100 million BTU squared) 292000000 217000000 1162 Variance from the VAR+GARCH model (in $/100 million BTU squared) 37.96364 45.74745 1162 Indicator if the plant corresponding to the observation is from the Midwest 0.3571429 0.4793637 1162 The unit of observation for this table is a plant in a given month. Contract proportion, total quantity purchased, and the prices are taken from monthly transaction level data from FERC Form 423. The variance variables are derived from a VAR model with GARCH(1,1) errors (see paper for the exact specification)
Joskow (1987) Contract Duration Regression, using data from Table 1979-1992 2: Estimation of Contract Duration Regressions from Joskow 1987 (1) (2) (3) VARIABLES Duration Duration Log(Duration) Quantity Contracted 0.00376*** NA NA (0.00114) (Quantity Contracted)^2-4.04e-07 NA NA (4.50e-07) Minemouth Plant Indicator 11.21 11.05 0.285 (9.562) (9.623) (0.790) Midwest Plant Indicator 2.615*** 2.822*** 0.455*** (0.394) (0.401) (0.0686) West Plant Indicator 4.742*** 4.875*** 0.629*** (1.403) (1.377) (0.186) Log(Quantity Contracted) NA 0.354*** 0.0822*** (0.114) (0.0196) Constant 5.018*** 4.258*** 0.931*** (0.518) (0.648) (0.118) Contract Year Signed Fixed Effects? Y Y Y Observations 1,047 1,047 1,047 R-squared 0.222 0.198 0.164 Unit of observation is a contract signed between plant and mine, at the year of signing. These data come from the Coal Rate Transportation Data Base. Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Back to 1983-1992 Duration Regression
Joskow (1987) Contract Duration Regression, with Risk-aversion covariates, 1979-1998 Table 5: Duration Regression comparing 1983-1992 to 1993-1998 Risk Aversion Covariate Results (1) (2) VARIABLES Log(duration) Log(duration) Sd(Spot Price)*(year<=1992) 0.0122*** NA (0.00406) Sd(Spot Price) * (year>1992) 0.00328 NA (0.00307) Sd(Consumption)*(year<=1992) 5.00e-06** 4.06e-06* (2.19e-06) (2.07e-06) Sd(Consumption)*(year>1992) 5.25e-06*** 6.21e-06*** (1.86e-06) (1.73e-06) Inventory*(year<=1992) 1.94e-05 3.49e-05*** (1.20e-05) (1.26e-05) Inventory*(year>1992) -2.90e-06 2.29e-06 (1.35e-05) (1.31e-05) Observations 1,299 1,501 R-squared 0.186 0.183 Additional covariates are log(tons_shipped), minemouth indicator, plant consumption, region indicators and contract year signed fixed effects Unit of observation is a contract signed between plant and mine, at the year of signing The spot price (sd of spot price) are calculated from average (std. deviation) of monthly transaction-level data over the year of the contract signing. Inventory and consumption are summed from monthly plant-level data, using the within-year standard deviation for std(con) Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Contract year signed fixed effects included Back to 1983-1992 Duration Regression
Contract Quantity Regression, with Risk-aversion covariates, Table 4: Quantity 1983-1992 Regression with Risk-aversion covariates, 1983-1992 (1) (2) (3) (4) VARIABLES Contract quantity Contract Quantity Contract quantity Contract quantity Contract Premium Sd(Spot Price) Sd(consumption) Inventory Profile Consumption Profile Midwest West Date Fixed Effects? Plant Fixed Effects? Observations R-squared -0.0932* -0.323*** NA NA (0.0498) (0.0592) 0.934*** 0.262** NA NA (0.153) (0.125) 0.0121-0.348*** 0.142*** -0.0980* (0.0475) (0.0629) (0.0347) (0.0520) 0.0575*** 0.0250** 0.00939*** -0.0118 (0.00551) (0.0125) (0.00340) (0.00936) 0.709*** 0.922*** 0.852*** 1.067*** (0.0146) (0.0412) (0.00919) (0.0594) -2.927 9.390** 16.81*** -5.850* (2.872) (4.525) (1.791) (3.218) -19.29*** 43.46*** 46.56*** 0.182 (4.518) (8.049) (2.993) (4.911) Y N Y N N Y N Y 13,250 13,250 32,558 32,558 0.796 0.870 0.794 0.853 Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 Unit of Observation is Plant-month of sample Contract Premium = contract price - spot price, and these prices are from monthly FERC Form 423 data on transactions. The inventory and consumption variables are means for each firm over the entire sample. The standard deviations for consumption and spot price are taken within year, over months from the corresponding datasets Contract Quantity, Stock, and Consumption scaled to be in 10 billion BTU Spot Price scaled to be in $/1millionBTU Back
What is a contract? The definition of a contract in the data is any coal purchase agreement between firm and mine lasting in excess of one year. These contracts specify repeated delivery of coal, and typically allow for adjustments by the firm in delivered quantity of coal. The typical contract is base price plus escalation, where some base price is set at the time of signing, and formulaic adjustments to this price are made based on market and cost conditions. Back
Relevant Literature on Constraints Joskow (1974) describes a passive regulator for whom the nominal output price is salient. In this framework: Realized profits below a level Π L correspond with a utility-initiated rate review, though realized profits below Π L may be deemed to be imprudently incurred, and so will be incurred by the firm. Realized profits above a certain level Π H trigger a consumer/environmental group initiated rate review, and may subsequently be confiscated by the regulator. Schmidt (1980) argues that some FACs in practice exercise partial passthrough, where 100 percent of the cost decreases are automatically passed through, yet less than 100 percent of the cost increases are passed through to the consumer. Back
Relevant Literature on Constraints Joskow (1974) describes a passive regulator for whom the nominal output price is salient. In this framework: Realized profits below a level Π L correspond with a utility-initiated rate review, though realized profits below Π L may be deemed to be imprudently incurred, and so will be incurred by the firm. Realized profits above a certain level Π H trigger a consumer/environmental group initiated rate review, and may subsequently be confiscated by the regulator. Schmidt (1980) argues that some FACs in practice exercise partial passthrough, where 100 percent of the cost decreases are automatically passed through, yet less than 100 percent of the cost increases are passed through to the consumer. Back
Graphical Intuition of Bounds Figure B1: Normal Distribution of Profits, with Bounds pdf Prudency Bound 0 Exorbitance Bound Profits The green portions of the graph are ranges of profits where the firm keeps that realization of profits; the yellow portions are ranges of profits where the regulator steps in and sets realized profit to zero. Back