# The Economic Effects of COVID-19 on the Producers of Ethanol, Corn, Gasoline, and Oil

Andrew Schmitz, Charles B. Moss and Troy G. Schmitz

# Abstract

The COVID-19 crisis created large economic losses for corn, ethanol, gasoline, and oil producers and refineries both in the United States and worldwide. We extend the theory used by Schmitz, A., C. B. Moss, and T. G. Schmitz. 2007. “Ethanol: No Free Lunch.” Journal of Agricultural & Food Industrial Organization 5 (2): 1–28 as a basis for empirical estimation of the effect of COVID-19. We estimate, within a welfare economic cost-benefit framework that, at a minimum, the producer cost in the United States for these four sectors totals $176.8 billion for 2020. For U.S. oil producers alone, the cost was$151 billion. When world oil is added, the costs are much higher, at $1055.8 billion. The total oil producer cost is$1.03 trillion, which is roughly 40 times the effect on U.S. corn, ethanol, and gasoline producers, and refineries. If the assumed unemployment effects from COVID-19 are taken into account, the total effect, including both producers and unemployed workers, is $212.2 billion, bringing the world total to$1266.9 billion.

## 1 Introduction

Sharply rising oil prices in the twenty-first century have incentivized the United States to seek energy self-sufficiency through increased domestic biofuel production. U.S. reliance on oil imports from the Middle East has been a major policy concern since the 1973 energy crisis. This concern led to the passage of the U.S. Energy Independence and Security Act of 2007, which has steadily increased the use of ethanol in the United States. In September 1995, ethanol represented 0.50% of total oil consumption in the United States; by August of 2008, ethanol use had risen to 3.36% of oil consumption; and by February of 2018, to 5.37% (Schmitz et al. 2020).

### Figure 2:

U.S. gasoline prices, 2010–2020.

### Table 3:

Levels of corn production and ethanol demand.

Item Value
Log change in ethanol price −0.667
Log change in corn consumption for ethanol −0.473
Corn consumption of ethanol in 2019:Q2 (million bushels) 1381.400
New corn consumption for ethanol (million bushels) 728.298
Reduction in ethanol demand (million bushels) 2.324
Share of ethanol to total corn use 0.307
Log change in corn consumption holding other uses constant −0.145
1. Source: Corn consumption data – USDA feed grains database, remaining values – Authors’ computations.

### Table 4:

Impact of reduced ethanol demand on U.S. corn prices, equilibrium demand, and producer surplus.

Demand elasticity Supply elasticity Equilibrium corn price (dollars/bushel) Equilibrium corn quantity (billion/bushels) Change in corn price (dollars/bushel) Percent change in price (dollars/bushel) Change in producer surplus (billion USD)
−0.250 0.250 2.196 14.792 −1.004 −0.314 −15.457
−0.375 0.250 2.396 15.026 −0.804 −0.251 −12.490
−0.250 0.375 2.396 14.562 −0.804 −0.251 −12.270
−0.375 0.375 2.530 14.792 −0.670 −0.209 −10.305
1. Source: Authors’ computations.

Our predicted decline in corn used for ethanol is somewhat larger than that predicted by Irwin and Hubbs (2020). In addition, our estimate of the impact of COVID-19 on corn prices is larger than that estimated by Hart et al. (2020). This difference is due to the methodology; we use supply and demand elasticities where Hart et al. (2020) use changes in futures prices to estimate the effect of COVID-19 on corn prices. Our overall estimate of the effect of COVID-19 on corn prices in the United States is similar to Beghin and Timalsina (2020) who predict that corn prices have fallen from $3.74/bushel in December 2019 to$2.94/bushel in May of 2020, which represents a 24.1% decline. This result is less than the 0.209% decline, which is our minimum estimate, but larger than the 25.1% reduction, which is our mid-range estimate of the effect of COVID-19 on corn prices.

### 4.2 U.S. Ethanol Complex

In 2007, we published a paper “Ethanol: No Free Lunch” in honor of Professor Bruce Gardner that gave a theoretical framework and empirical results on the effect of the U.S. Energy Policy that promotes the production and use of corn ethanol. We concluded that

The sharp rise in energy prices in the 1980s triggered a strong interest in the production of ethanol as an additional energy component. Economists are divided as to the payoffs from ethanol-derived corn in part because of the complex interrelationship between energy produced from ethanol and energy from fossil fuels. Using a welfare economic framework, we calculate that there can be treasury savings from ethanol using tax credits as these subsidies can be smaller than direct payments to corn farmers, which are essentially eliminated from the expansion of ethanol. Also, to the extent that ethanol dampens fuel prices there can be a net welfare gain from ethanol production in the presence of ethanol subsidies (Schmitz, Moss, and Schmitz 2007).

This paper has a different focus in that there is no benefit-cost assessment of the U.S. ethanol program. Rather the focus is on the producer effect of COVID-19 on the U.S. ethanol-corn-gasoline-oil complex, taking into account the many changes that have occurred in this complex.

U.S. production, consumption, and trade of ethanol has changed over the years (Figure 13). Prior to 2020, U.S. ethanol production expanded significantly, with the largest increase occurring between 2006 and 2010 (introductory period of the U.S. ethanol program). Between 2007 and 2019, ethanol production roughly doubled, and then collapsed in 2020 due to COVID-19. As a result, over 70 ethanol plants in the United States have significantly reduced operations and their labor force because of decreased demand due to COVID-19.

### Figure 13:

U.S. production, consumption, and trade of ethanol, 2000–2018.

Source: U.S. Energy Information Administration (USEIA) 2019.

Prior to COVID-19, the United States produced approximately 16 billion gallons of ethanol per year, with five companies producing 45% of the total U.S. ethanol production: Poet, Archer Daniels Midland, Valero Energy Corporation, Green Plains Renewable Energy, and Flint Hills Resources (FarmProgress.com 2016). COVID-19 has had a major impact on ethanol production, such that U.S. ethanol sales in 2020 could fall by more than $10 billion (Colombini 2020). The theory underlying the effects of the crisis on U.S. ethanol producers is extended in Figure 14. The supply prior to COVID-19 is S 1 and demand is D 1. Prices and quantities are given by p 0 and q 0. In the presence of COVID-19, demand shifts from D 1 to D 2, causing the price to fall to p 1 and quantity to fall to q 1. The loss to ethanol producers is (p 0 p 1 ab). ### Figure 14: COVID-19 and U.S. ethanol production. Because of the interaction between ethanol and corn prices, a fall in corn prices causes the supply of ethanol to shift from S 1 to S 2. As a result, while COVID-19 has no effect on ethanol production, the price of ethanol falls from p 0 to p 2. Also, producers are unaffected by COVID-19: (p 0 cb) = (p 2 di) . While inputs may be perfectly elastic in supply – except for the fixed factor that gives rise to the economic rent portion of the model – what happens if they are not? COVID-19 triggered high global unemployment levels. Our model, like the one in Figure 14, should account for this by calculating the size of (aghb) and taking a percentage of this amount to account for unemployment due to COVID-19. For example, the loss of ( k g h j ) implies the total loss from COVID-19, given S 1, is { ( p 0 p 1 a b ) + ( k g h j ) } . In our empirical analysis, we simplify the theoretical base. The supply and demand for ethanol are S G and D E (Figure 15a), where prior to COVID-19, the price of ethanol is p 1 and the quantity produced is q 1. The corresponding quantity of labor employed in production is q L and the wage rate is p L (Figure 15b), where prior to COVID-19, labor supply is S L and demand is D L . With COVID-19, the demand for ethanol shifts from D E to D . The price of ethanol falls to p 2 and output falls to q 2. The price of labor falls to p W and quantity demand falls to q W . As a result, producers lose (p 1 p 2 ba) and labor costs fall by (p L p W cq W q L d). ### Figure 15: Ethanol production and labor inputs. In the analysis, ethanol producers reduce their variable costs by (abq 2 q 1) because of the ethanol price drop. If part of this cost is labor, this creates a situation of unemployment if labor is sector-specific and immobile. Therefore, at least a percentage of (abq 2 q 1) is an economic cost due to COVID-19 (e.g., economic loss of (abfg) . In this case, economic damage from COVID-19 is {(p 1 p 2 ba)+(abfg)} . If all of the variable inputs become unemployed (or unused), the total cost of COVID-19 is (p 1 p 2 bq 2 q 1 a) . But note that this is equal to the change in lost total revenue from a fall in ethanol production. ### 4.3 Ethanol Empirics According to the Renewable Fuels Association (RFA 2020, p. 2), “the impact on the ethanol industry has been swift and sharp. Deeply negative operating margins and falling consumption have led to dramatic cuts in ethanol production. For the week ended April 10, ethanol production was 44% below the same time in 2019, hitting the lowest level since the EIA began reporting statistics in 2010… Approximately 70 ethanol facilities with an annual production capacity of 6.1 billion gallons have been fully idled, and nearly 70 more plants have reduced their operating rates by a combined 1.9 billion gallons annualized.” Taheripour and Mintert (2020), also note that “ethanol production could be expected to fall by approximately 3 billion gallons in 2020 for supply and demand to balance—a severe cutback of nearly 20%” (RFA 2020, p. 3). Falling oil and gasoline prices due to COVID-19 negatively affect the demand for ethanol and thus the corn market in the United States. The overall consumption of fuel ethanol reached a maximum of 1095 thousand barrels per day in January 2019, but fell roughly 30% in early 2020. Figure 16 gives the theoretical basis for our analysis, along with ethanol prices and quantities used in estimating the impact of COVID-19 on the ethanol sector. Because we do not consider the impact of COVID-19 on the distiller’s grain industry, our estimates understate the cost of COVID-19 (see Moss, Schmitz, and Schmitz 2014). Due to COVID-19, the demand for ethanol shifts from D to D′. The supply of ethanol is S (derived from the actual price and quantity data). It is price inelastic. The ethanol price falls from$1.30/gallon to $0.80/gallon due to COVID-19. The quantity produced before and after COVID-19 is 16 billion gallons and 12 billion gallons, respectively. The analysis applies to 2019 as compared to 2020. Therefore, the price and quantity used for 2020 are essentially forecasts based on data from January 1, 2020 to June 1, 2020. ### Figure 16: Ethanol empirics. The results, given supply (e s <1), for a price drop from$1.30/gallon to $0.80/gallon, are given in Table 5. The producer loss due to COVID 19 is$7 billion. Total revenue falls by $11.2 billion. ### Table 5: Impact of COVID-19 on ethanol (zero unemployment effect) in U.S. billion dollars. Variable (Supply curve S) Loss (USD) Producers Producer surplus (abcd)$7.0 billion
Variable input cost (cefd) $2.6 billion Change in total revenue {(axyd) – (bxrc)}$11.2 billion

Suppose now that the producers’ reduction in variable input cost is associated with unemployment. In this case, a percentage of the workers laid off cannot find jobs elsewhere. In Table 6, given supply curve S, if unemployment is measured by (cefo) with a shutdown price of $0.40 per gallon, the total cost of COVID-19 is$8.6 billion. If the unemployment effect is measured by (ncok), the economic loss from COVID-19 is much smaller, falling to $405 million. ### Table 6: Impact of COVID-19 (unemployment effect) in U.S. billion dollars. Variable (Supply curve S) Loss (USD) Producers Shutdown price$0.40
Producer surplus (abcd) $7.0 billion Variable input cost (cefd)$2.6 billion
Unemployment effect 1 (cefo) $1.6 billion Unemployment effect 2 (ncok)$405 million
Total cost 1 {(abcd) + (cefo)} $8.6 billion Total cost 2 {(abcd) + (ncok)}$7.4 billion
Variable (Supply curve S′) Loss (USD)
Producers Shutdown price $0.40 Producer surplus (ahjd)$5.4 billion
Variable input cost (igfd) $5.5 billion Unemployment effect 1 (jlfk)$2.5 billion
Unemployment effect 2 (iokj) $608 million Total cost 1 {(abcd) + (jlfk)}$7.9 billion

The U.S. Energy Administration has data available on monthly crude oil production for January through March 2020, and average monthly WTI crude spot prices for January through May 2020. In order to obtain predictions for June through December 2020, we make the following simplifying assumptions regarding future monthly prices and production levels. First, rather than attempting to forecast prices for the rest of 2020, we assume that the price of crude oil from June through December will be equal to the five-month average WTI crude spot price of $36.48 per barrel (which is also approximately equal to the spot price of WTI crude on June 15, 2020). Second, we assume that the quantity of crude oil produced by each country in April and May 2020 is equal to the actual quantity produced during the corresponding months in 2019. Third, due to the announcement in June 2020 that OPEC (including Russia and Saudi Arabia) will cut back crude oil production by 30%, we assume that the quantity of crude oil produced in each country from June 2020 through December 2020 will be equal to 70% of what each country produced in 2019. #### 4.4.3 Impact of COVID-19 on World Oil (Positively Sloped Supply Curves) We consider two possible scenarios from which we obtain estimates of the welfare implications of COVID-19 for the crude oil market. In the first scenario, we assume the supply curves (S 1S 3 in Figure 19) for each country are linear and use the actual price and aggregate quantity for 2019 as one point on their inverse supply curve, and the predicted price and aggregate quantity for 2020 to obtain the second point on their inverse supply curves. Using these two points to derive the inverse aggregate supply curves for each country, we can then calculate producer surplus in 2019 and 2020 as the area above the inverse supply curve, bounded by the X-axis from below and the price from above (Just, Hueth, and Schmitz 2004). The estimated welfare impacts of COVID-19 for crude oil producers associated with Scenario 1 (using aggregate supply curves across 2019 and 2020) are provided in Table 7. The average yearly crude oil price is projected to drop by$20.51 per barrel (36%) due to COVID-19. Crude oil production is predicted to drop in the United States by 700 Mb (16%), Russia by 690 Mb (17%), and Saudi Arabia by 630 Mb (18%) in 2020 as compared to 2019 data. U.S. crude oil producers are projected to lose $117 billion, followed by Russia ($54 billion) and Saudi Arabia ($49 billion). ROW crude oil producers are projected to lose$485 billion. The total predicted loss under Scenario 1 from COVID-19 in 2020, compared to 2019, is $706 billion for all world crude oil producers combined. ### Table 7: Impacts of COVID-19 for crude oil producers (yearly aggregated supply curves). Variable United States Russia Saudi Arabia ROW Total Price ($/barrel) −21.0 −21.0 −21.0 −21.0 −21.0
Quantity (billion barrels) −700 −689 −630 −3182 −52.0
Producer Surplus (billon USD) −117 −54 −49 −485 −706
1. Source: Authors’ calculations.

#### 4.4.4 Impact of COVID-19 on World Oil (Both Inelastic and Positively Sloped Supply Curves)

In the second scenario, we assume the supply curve for each country in 2019 is perfectly inelastic. For 2020, the supply curve for each country is separated into two periods (January through May and June through December), representing before and after the decision by OPEC to cut oil production by 30%. We assume the supply curve for each country in 2020 is perfectly inelastic in Period 1, and unitarily elastic in Period 2. The price of crude oil from June 2020 through December 2020 is assumed to be equal to the five-month average WTI crude spot price of $36.48 per barrel. The quantity of crude oil produced by each country in April and May 2020 is assumed to be equal to the actual quantity produced during the corresponding months in 2019, and the quantity of crude oil produced in each country from June 2020 through December 2020 is predicted to be equal to 70% of what each country produced in 2019. The estimated welfare impacts of COVID-19 for crude oil producers associated with Scenario 2 (which separates the 2020 supply curves into two periods) are provided in Table 8. In Period 1, comparing 2019 to 2020, predicted losses for crude oil producers in each region are as follows: United States ($34b), Russia ($35b), Saudi Arabia ($33b), and world crude oil producers combined ($263b). In Period 2, predicted losses for crude oil producers in each region are as follows: United States ($151b), Russia ($135b), Saudi Arabia ($123b), and world crude oil producers combined ($768b). On aggregate, we predict that COVID-19 will result in losses to crude oil producers in the United States ($151b), Russia ($135b), Saudi Arabia ($123b), and ROW ($620b), so that the total loss to the global crude oil market in 2020, compared to 2019, is predicted to be approximately$1 trillion.

### Table 8:

Impacts of COVID-19 for crude oil producers (two periods).

Change from 2019 to 2020 (January–May)
United States Russia Saudi Arabia ROW Total
Price ($/barrel) −21.36 −21.36 −21.36 −21.36 −21.36 Quantity (million barrels) 98.56 6.37 −7.12 −18.79 79.03 Producer surplus (billion USD) −34.89 −34.62 −32.71 −160.50 −262.72 Change from 2019 to 2020 (June–December) United States Russia Saudi Arabia ROW Total Price ($/barrel) −19.90 −19.90 −19.90 −19.90 −19.90
Quantity (million barrels) −798.81 −695.45 −623.27 −3163 −5281
Producer Surplus (billion USD) −116.12 −101.10 −90.60 −459.88 −767.70
Change from 2019 to 2020 (total)
United States Russia Saudi Arabia ROW Total
Price ($/barrel) −20.51 −20.51 −20.51 −20.51 −20.51 Quantity (million barrels) −700.25 −689.08 −630.39 −3182 −5202 Producer surplus (billion USD) −151.02 −135.72 −123.31 −620.37 −1030 1. Source: Authors’ calculations. ## 5 Refineries The United States is the largest exporter of refined oil, with most of its refineries located in the Gulf Coast region (the largest refinery is in Port Arthur, Texas). The five top U.S. companies refine between 1.0 and 3.0 million barrels of oil per day (Table 9). Although more than 50% of U.S. oil refineries have closed since 1982 (301 in 1982 vs. 132 in 2020), production volume has increased (USEIA 2019). To estimate the effect of changes in policy or events (such as COVID-19) on the combination choice between crude oil and ethanol, we start with a general differential multiproduct model (Suh and Moss 2017). This formulation is based on changes in the first-order conditions of the firm as. (3) γ ¯ t g ¯ r t Δ ln ( y r t ) = r = 1 n α r s ( Δ ln ( p s t ) i = 1 m θ i s Δ ln ( w i t ) ) + ϕ r Δ ln ( z t ) + δ r D t f ¯ i t Δ ln ( x i t ) = γ ¯ t r = 1 n θ i r g ¯ r t Δ ln ( y r t ) + j = 1 m π i j Δ ln ( w j t ) + ϕ i Δ ln ( z t ) + δ i D t where γ ¯ t = R t R t 1 / C t C t 1 , R t is the firm’s revenue, C t is the cost, g ¯ r t is the average output revenue share (between periods t and t−1) for output r, y rt is the output level for output r, p rt is the output price for output r at time t, w it is the input price for input i at time t, z t is the level of a quasi-fixed variable at time t, D t is a dummy variable that is one after the volumetric ethanol excise tax credit was allowed to expire, f ¯ i t is the average input share (between periods t and (t−1) for input i, and x it is the level of input i used at time t. In this formulation Δ ln ( y i t ) = ln ( y i t ) ln ( y i , t 1 ) . ### Table 9: Five largest U.S. refining companies in 2020. Ranking Corporation Barrels/day Number of refineries 1 Marathon 3.0 million 16 2 Valero energy 2.1 million 13 3 Phillips 66 1.9 million 10 4 Exxon Mobil 1.7 million 5 5 Chevron 1.0 million 5 1. Source: United States Energy Information Administration (2020). To estimate this model, we used information from the United States Department of Energy (2020) to construct a dataset in thousands of barrels per month for blended gasoline, jet fuel, distillates, and residual oil. The price of crude oil is dollars per barrel while the price of ethanol is dollars per gallon. United States Bureau of Labor Statistics data are used for labor prices and quantities. Labor prices are wages per hour for all workers in the refinery sector (34,110). Quantity of labor is derived by multiplying hours worked per week times the number of workers in the sector. We use refining capacity as a quasi-fixed variable, with all prices deflated using the Personal Consumption Expenditures component of the Implicit Gross Domestic Product deflator from economic data at the Federal Reserve Bank of St. Louis. Equation (3) is estimated using maximum likelihood. Because the unconstrained estimation may violate concavity, we impose concavity by constraining the matrix of [α rs ] to be convex and restricting the minimum eigenvalue of the matrix to be positive. Similarly, we constrain the maximum of the [π ij ] matrix to be negative. For the estimated parameters of the multiproduct differential model, the demand for crude oil increases significantly when blended gasoline production increases, while the demand for ethanol does not increase significantly when blended gasoline production increases. From the output choice parameters, the amount of blended gasoline increases with an increase in the price of blended gasoline. However, there appears to be only one substitution relationship (i.e., only one of the α rs is negative). This may be the result of a fixed-proportion output structure. For example, there may be little that a refinery can do to change the proportion of products produced from a barrel of crude oil. The exception is the negative coefficient for the choice between jet fuel and distillates. Two elasticities are statistically significant. Specifically, the elasticity of crude oil production with respect to an increase in the price of blended gasoline is 0.05324, while the elasticity of ethanol demand with respect to an increase in the price of blended gasoline is 0.05712. However, the estimated demand elasticity of the quantity of crude oil demand is negative, but not statistically significant at any conventional confidence level. Similarly, the elasticity of demand for ethanol is negative, but not statistically significant. Further, the cross-price elasticity indicates that the quantity demanded of crude oil is rather inelastic with respect to a change in ethanol prices, while the price of ethanol is inelastic, but somewhat more responsive, with respect to changes in crude oil prices. Estimates on the impact of COVID-19 on the U.S. oil refinery sector are given in Figure 20. The short-run supply curve is S and the long-run supply curve is S . The analysis considers two periods. Period 1 (December 2019 through March 2020) is based on supply curve S. Period 2 (April 2020 through December 2020) is based on supply curve S ˜ . Given a drop in demand from D to D , producer loss for refineries is ( p 0 p 1 ba) in the first period and ( p 0 p 1 ca) in the second period. Total producer loss is {( p 0 p 1 ba) + ( p 0 p 1 ca)} . ### Figure 20: Effect of reduction in demand from COVID-19 on refineries. Due to COVID-19, the consumption of gasoline dropped from 2336 thousand gallons in December of 2019 to 1961 thousand gallons in March of 2020 (Table 10). Gasoline prices declined from$2.18/gallon in December 2019 to $1.85/gallon in March of 2020. The loss in producer surplus from the COVID-19 event is between$703.9 million and $763.1 million per month (Table 11). This loss increases under a different elasticity assumption to$763.1 million per month. The monthly produce surplus loss is $9.2 billion. ### Table 10: U.S. prices and quantities of conventional (blended) motor fuel. Variable Price dollars/gallon Equilibrium quantity Barrels/month 1000 gallons/month December 19, 2019 (Observed) 2.1816 55,627 2336 March 20, 2020 (Observed) 1.8540 46,684 1961 1. Source: United States Department of Energy, Petroleum & Other Liquid data (https://www.eia.gov/petroleum/data.php). ### Table 11: Impact of COVID on refinery surplus in the short and long run. Equilibrium quantity Loss in producer surplus 1000 barrels/month 1000 gallons/month Million dollars/month Billon dollars/year Stage I Stage I (Short-Run) Producer surplus ( p 0 p 1 ba) (derived from actual quantity price data) 46,684 1961 703.86 Producer Surplus ( p 0 p 1 ba) (based on estimated supply elasticity) 55,296 2322 763.11 Stage II Stage II (Long-Run) Producer Surplus ( p 0 p 1 ca) (unitary elasticity) 47,274 1985 707.92 Total producer surplus loss 8.5 8.7 In the second stage, the supply curve is unitarily elastic, the loss in producer surplus$707.9 million per month, or $8.5 billion per year. This gives rise to a total loss to refineries of between$16.9 and $17.6 billion. ## 6 Summary: COVID-19 Producer and Unemployment Effects The costs to U.S. producers of corn, ethanol, and oil, and refineries are given in Table 12. For the year 2020, the total cost is$176.8 billion. When the world oil producers are taken into account, the cost rises to $1055.8 billion. Also, it is necessary to include the unemployment effects from COVID-19 (see Section 4.2, U.S. Ethanol Complex). ### Table 12: Producer’s economic losses (conservative estimates) from COVID-19, ethanol–gasoline–oil complex, 2020 (billion USD). Producer loss (producer surplus) Billion USD Producer loss plus unemployment* Billion USD United States United States U.S. corn producers 10.3 U.S. corn producers 12.4 U.S. ethanol producers 7.0 U.S. ethanol producers 8.4 U.S. oil producers 151.0 U.S. oil producers 181.2 U.S. refineries 8.5 U.S. refineries 10.2 Total 176.8 Total 212.2 World World World oil producers 1030.0 World oil producers 1236.0 Total [corn producers, ethanol, oil, and refineries] 1055.8 Total [corn producers, ethanol, oil, and refineries] 1266.9 1. ∗ Based on 20% of producer surplus Source: Authors’ calculations. In Table 12, we assume that the unemployment effect due to COVID-19 is 20% of the producer surplus values. For the United States, now the cost of the virus is$212.2 billion. When world oil is included, the total loss (producers and unemployed workers) is $1266.9 billion. ## 7 Conclusions Our estimates focus on the impact of COVID-19 on producers using classical welfare economics, where the key measure of losses is producer surplus. Commonly though, many studies report estimates based on changes in total producer revenue. Generally, these estimates overstate economic losses. The economic cost from COVID-19 for oil producers is huge, exceeding$1 trillion. The cost to the U.S. oil producers alone is $151 billion. The total oil producer cost is$1.03 trillion, which is roughly 40 times the cost to U.S. corn, ethanol, and gasoline producers, and refineries of $26 billion. Therefore, for example, if our estimate of the cost is 20% too high for the ethanol producers, the total world picture changes very little. Corresponding author: Andrew Schmitz, University of Florida, Gainesville, USA, E-mail: # Acknowledgements The authors thank Carol Fountain, Abra Gibson, and an anonymous reviewer for comments. ### References Beghin, J. C., and S. Timalsina. 2020. The Impact of the COVID-19 Crisis on Nebraska’s Ethanol Industry. Lincoln, NB: Cornhusker Economics, University of Nebraska. May 27.Search in Google Scholar Colombini, K. 2020. RFA Analysis: Ethanol Industry Could See$10 Billion in Losses Due to COVID-19. RFA News Release. April 20.Search in Google Scholar

de Gorter, H., and D. R. Just. 2009. “The Economics of a Blend Mandate for Biofuels.” American Journal of Agricultural Economics 91 (3): 738–50. https://doi.org/10.1111/j.1467-8276.2009.01275.x.Search in Google Scholar

FarmProgress.com. 2016. Top Five Ethanol Producers. Farm Progress News Release. (December 2).Search in Google Scholar

Hart, C. E., D. J. Hayes, K. L. Jacobs, L. L. Schulz, and J. M. Crespi. 2020. The Impact of COVID-19 on Iowa’s Corn, Soybean, Ethanol, Pork, and Beef Sectors. Ames, IA: Center for Agricultural and Rural Development, Iowa State University.Search in Google Scholar

Irwin, S., and T. Hubbs. 2020. The Coronavirus and Ethanol Demand. Urbana-Champaign, IL: FarmDoc Daily, University of Illinois. March 26.Search in Google Scholar

Just, R. E., D. L. Hueth, and A. Schmitz. 2004. The Welfare Economics of Public Policy. Cheltenham, UK: Edward Elgar Publishing.Search in Google Scholar

Moss, C. B., A. Schmitz, and T. G. Schmitz. 2014. “Ethanol and Distiller’s Grain: Implications of the Multiproduct Firm on United States Bioenergy Policy.” In Modeling, Dynamics, and Optimization and Bioeconomics, edited by Pinto, A. A., and Zilberman, D., 497–510. Cham, Switzerland: Springer.10.1007/978-3-319-04849-9_29Search in Google Scholar

Renewable Fuels Associations (RFA). 2020. The Economic Impact of COVID-19 on the Ethanol Industry. Ellisville, MO: Renewable Fuels Association (April 20).Search in Google Scholar

Schmitz, A., C. B. Moss, and T. G. Schmitz. 2007. “Ethanol: No Free Lunch.” Journal of Agricultural & Food Industrial Organization 5 (2): 1–28. https://doi.org/10.2202/1542-0485.1186.Search in Google Scholar

Schmitz, A., C. B. Moss, T. G. Schmitz, G. C. van Kooten, and H. C. Schmitz. 2020. (forthcoming) “The Economics of Biofuels.” In Food and Agricultural Policies: Trade, Agribusiness, and Rent-Seeking Behaviour, edited by Schmitz, A., Moss, C. B., Schmitz, T. G., van Kooten, G. C., and Schmitz, H. C., 3rd ed. Toronto: University of Toronto Press.Search in Google Scholar

Statista. 2020. Top 10 U.S. oil Companies Based on Revenue in 2019. Statista.com.Search in Google Scholar

Suh, D.-H., and C. B. Moss. 2017. “Decomposition of Corn Price Effects: Implications for Feed Grain Demand and Livestock Supply.” Agricultural Economics 48 (4): 491–500. https://doi.org/10.1111/agec.12350.Search in Google Scholar

Taheripour, F., H. Cui, and W. E. Tyner. 2019. “The Economics of Biofuels.” In The Routledge Handbook of Agricultural Economics, edited by Cramer, G. L., Paudel, K. P., and Schmitz, A., 637–657. London: Routledge.10.4324/9781315623351-34Search in Google Scholar

Taheripour, F., and J. Mintert. 2020. Impact of COVID-19 on the Biofuels Industry and Implications for Corn and Soybean Markets. Lafayette, IN: Center for Commercial Agriculture, Purdue University.Search in Google Scholar

Taheripour, F., and W. E. Tyner. 2014. “Welfare Assessment of the Renewable Fuel Standard: Economic Efficiency, Rebound Effect, and Policy Interactions in a General Equilibrium Framework.” In Modeling, Dynamics, and Optimization and Bioeconomics I, edited by Pinto, A. A., and Zilberman, D., 613–632. Cham, Switzerland: Springer.10.1007/978-3-319-04849-9_36Search in Google Scholar

United States Department of Energy. 2020. Key Federal Legislation. Washington, DC: United States Department of Energy.Search in Google Scholar

United States Energy Information Administration (USEIA). 2019. U.S. Refineries. Washington, DC: USEIA.Search in Google Scholar

United States Energy Information Administration (USEIA). 2020a. Petroleum and Other Liquids – Monthly Petroleum and Other Liquids Production. Washington, DC: USEIA. https://www.eia.gov/international/data/world/petroleum-and-other-liquids/monthly-petroleum-and-other-liquids-production.Search in Google Scholar

United States Energy Information Administration (USEIA). 2020b. Petroleum and Other Liquids – Spot Prices (Crude Oil in Dollars Per Barrel, Products in Dollars Per Gallon) – Monthly. Washington, DC: USEIA. https://www.eia.gov/dnav/pet/hist/LeafHandler.ashx?n=PET&s=RWTC&f=M.Search in Google Scholar

Voegete, E. 2020. Anderson Restarts 2 Ethanol Plants. Ethanol Producer Magazine. May.Search in Google Scholar

Worldoil.com. 2020. World oil Analysis: Research Shows Current oil Price Collapse Near Record Proportions. World Oil News Release. March 31.Search in Google Scholar