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Technological innovation and the conduct of innovating firms are key weapons in the fight against hunger and the pursuit of food security around the world. Agricultural biotechnology seems uniquely equipped, if not destined, to spearhead the effort to combat malnutrition and hunger around the world by conferring significant agronomic benefits to producers and by having the ability to enhance both the resistance of plants to environmental stresses and the quality and nutritional value of food. Research that was recently published by the Center of Agricultural and Food Industrial Organization-Policy Research Group at the University of Nebraska Lincoln analyzes the conduct of innovating firms in hunger-stricken countries where, based on the Food and Agricultural Organization of the United Nations, more than 800 million people have been facing malnutrition and hunger.

Recognizing that malnutrition and hunger can be reduced through access to increased quantities of nutritious food offered at affordable prices, the research analyzes the output/pricing strategies of innovating agri-food companies in hunger-stricken areas of the world. To do so, the research develops an empirically relevant multi-market framework of heterogeneous consumers and an imperfectly competitive innovating firm that seeks to maximize profits. To analyze the profit-maximizing strategies of the innovating firm in different regions of the world, the research considers the innovating firm’s behavior in two regions – a hunger-stricken country/region (HSC) that can benefit from genetically modified (GM) technology developed by the innovating firm, and the rest of the world where the innovation is marketed.

While most of the literature on innovator strategies regarding the management of intellectual property rights (IPRs) assumes that innovators desire the exercise of market power conferred by their IPRs (and the subsequent innovation rents that their monopoly position over their innovation confers), a key result of this study is that there could be cases that the innovating firms find it economically optimal to offer their innovation to HSCs for free. Intriguingly, this result holds even when the innovation is purely rival and it is consistent with observed innovating firm behavior, like Monsanto’s recent donation of its DroughtGardTM tolerant maize technology to Water Efficient Maize for Africa a private-public partnership aimed at developing maize varieties tolerant to drought for certain African countries.

Specifically, the analysis shows that under standard assumptions about the relationship between the hunger-stricken country/region and the rest of the world, the profit-maximizing innovating firm finds it optimal to price discriminate and exercise its market power in each region. The optimal strategy of the innovating firm changes, however, when its GM technology can increase the supply of, and consumer access to nutritious food in hunger-stricken areas of the world, and consumers in the rest of the world care about this technology-enabled reduction in malnutrition and hunger. Recent poll and survey findings suggest that consumers in developed countries express greater support for genetic engineering when the benefits it can confer as a solution to global food shortages or extreme weather conditions become salient.

To the extent that the association of the GM technology with malnutrition and hunger reduction in food-insecure areas of the world can lessen the consumer aversion to the GM technology in the rest of the world, it will also change the profit-maximizing strategy of the innovating firm. In particular, when the increased consumer access to nutritious food in hunger-stricken areas reduces consumer aversion towards the GM technology in the rest of the world, the innovating firm will find it optimal to reduce its price and increase the adoption of its technology and the subsequent consumer access to nutritious food in these hunger-stricken areas. The greater the benefits the firm realizes due to the increased goodwill in the rest of the world, the greater the reduction in its price in the hunger-stricken areas. When the innovator’s benefits from the reduced consumer aversion to GM technology are relatively high (i.e., when they exceed the losses due to reduced prices in the hunger-stricken areas for all relevant prices), the firm will find it optimal to offer its GM technology in the hunger-stricken areas for free. Such a strategy increases the adoption of technology and consumer access to nutritious food in the hunger-stricken areas and, through this, it enhances the firm’s goodwill and the benefits it enjoys in the rest of the world. For the benefits from the firm’s prosocial business practices to be maximized, it is important that the impact of the GM technology in hunger-stricken areas of the world is broadly and effectively communicated.

Given the conflict of interest, the innovating firm should probably not be the sole (or even the main) source of this information. Instead, trusted third parties with an interest in such humanitarian endeavors need to be identified and utilized to communicate the benefits of the technology to the public. This is particularly important for places like the European Union where the very strong (and, quite often, very vocal) consumer opposition to GM technologies has shaped both the adoption of the technology as well as its regulatory treatment in the continent and beyond. The identification of trusted information sources and the development of properly designed messages can maximize the consumption externality/goodwill of the innovating firm and, with it, the benefits of the strategy analyzed in this study.

Of course, a necessary condition for these benefits to be realized is allowing the entry of relevant GM innovations into the markets of interest. Such an entry would, in many cases, necessitate the careful revision/easement of regulatory requirements that have been acting as barriers to entry and commercialization of important hunger-reducing GM technologies. The development of technologies that offer solutions to food-related challenges and their donation or provision at low prices to hunger-stricken areas of the world can increase goodwill towards GM technologies and lead to the regulatory changes that are necessary for their adoption.

In 2018 Nebraska farmers planted 9.7 million acres of corn, the most of any crop in the state. The primary uses for corn in the state are cattle feed and ethanol production. Nebraska currently has 25 ethanol plants producing around 2 billion gallons of ethanol annually. This capacity consumes approximately 40 percent of Nebraska’s annual corn production.

Ethanol became widely produced in the state after the introduction of the Renewable Fuels Standard (RFS) in 2005, which mandates that a percentage of renewable fuels, mainly ethanol, be blended into transportation fuels. This article explores the changes in corn basis since the implementation of the RFS for five locations across Nebraska.

Changes in basis are important to Nebraska corn farmers’ financial wellbeing. Changes in the average basis value directly impact the farmer’s bottom line. The more negative the average basis value is, the less revenue the farmer is receiving. Furthermore, more volatile basis values result in greater basis risk.

Data

Basis is the difference between the cash price and the futures price. Basis is essentially the fee that grain buyers charge farmers for handling their grain. Many factors influence basis values, including the local supply and demand, transportation costs, quality of the grain, and the cost of doing business. The basis values used for this analysis were calculated using the United States Department of Agriculture’s Agricultural Marketing Service (USDA AMS) Cash Grain Bids report for Nebraska ( WH_GR111). Reports were collected Thursday of each week. Locations shown in this discussion must have had cash prices consistently reported since 1993, and are no closer than 50 miles from one another. The locations that have met these criteria are Beatrice, Greenwood, Grand Island, Lexington and Superior as shown in Figure 1. To obtain the basis, the cash price for each location was subtracted from the closing price of the nearby futures contract for that day. If there were missing observations, these values were interpolated using a simple average of the previous and subsequent basis values around the gap.

Map showing basis reporting locations and ethanol plants
Figure 1. Nebraska Ethanol Facilities and Reported Basis Locations

Analysis

Two periods of basis values were selected for comparison: (1) February 25, 1993 to August 4, 2005 and (2) August 11, 2005 to December 28, 2017. These two periods are divided by the RFS mandate, which was implemented August 8, 2005. Many changes to the corn market occurred during the span of these data. This analysis does not separate factors such as the increase in acreage, genetic advancements, or additional uses for corn that have influenced its demand or supply since 1993. Thus, the analysis will focus on the long-term adjustments in basis values rather than pinpointing the specific causes of these changes.

The summary statistics and coefficient of variation are reported for each location and period in Table 1. The summary statistics show that in all five locations, the average basis value was $0.05 to $0.09 per bushel lower from August 11, 2005 to December 28, 2017 than it was from February 25, 1993 to August 4, 2005.

Table 1: Summary Statistics: Basis for Selected Nebraska Cities
FEBRUARY 25, 1993 TO AUGUST 4, 2005 AUGUST 11, 2005 TO DECEMBER 28, 2017
Obs. Avg. Std. Dev. Min. Max Coef. Var. % Obs. Avg. Std. Dev. Min. Max Coef. Var. %
BEATRICE 650 -0.21 0.207 -0.94 1.34 98 645 -0.3 0.236 -0.68 1.5 80
GRAND ISLAND 650 -0.18 0.163 -0.5 1.09 90 645 -0.26 0.234 -0.73 1.5 91
GREENWOOD 650 -0.24 0.175 -0.6 1.29 73 645 -0.33 0.241 -0.75 1.45 74
LEXINGTON 650 -0.15 0.184 -0.53 1.59 119 645 -0.21 0.269 -0.71 1.76 130
SUPERIOR 650 -0.16 0.175 -0.58 1.2 113 645 -0.21 0.229 -0.58 1.6 107

This lower average basis value indicates that farmers have experienced a larger discount from the futures market price after the implementation of the RFS. This may seem counter-intuitive to farmers, as an increased demand brought about by the expansion of ethanol production would strengthen the corn basis or make it less negative. However, a recent study of North Dakota corn basis values by Fausti et al. (2017) would suggest that the increased corn production during the latter period of this study would outweigh the demand created by increased ethanol production.

The second portion of this analysis measures the differences in basis volatility between the two periods. There are two specific measures of volatility that can be discussed from the summary statistics. The first measure of volatility is the standard deviation (Std. Dev). Normally, the higher the standard deviation, the greater the basis volatility. All five locations experienced standard deviations from $0.03 to $0.08 per bushel larger in the second period. This means that the normal range of basis values for each location would be the average basis ± the standard deviation. For example, the normal basis range for Beatrice before RFS would have been $0.00 to -$0.44 per bushel. After the RFS, the normal basis range for Beatrice is -$0.06 to -$0.54 per bushel.

The second measure of volatility is the coefficient of variation (Coef. Var.). It is a measure of relative volatility and is expressed as a percentage. To calculate coefficient of variation, divide the standard deviation by the mean. The higher the coefficient of variation, the greater the price volatility. The coefficient of variation does not have a consistent result across all five locations. The coefficient of variation was smaller for Beatrice and Superior but was almost equal in Grand Island and Greenwood, and was much larger in Lexington.

Research by McNew and Griffith (2005) found that the farther one is from an ethanol facility, the lower the impact that facility will have on the price. This may hold true for the reported locations in this analysis. The two reported locations where the coefficient of variation improved, Beatrice and Superior, had the fewest number of ethanol facilities in a 50-mile radius. Grand Island and Greenwood experienced a slight increase in volatility. Grand Island has nine facilities with a 280 million bushel crush capacity in a 50-mile radius, and Greenwood has three facilities with a 114 million bushel crush capacity. Lexington has three plants in a 50-mile radius, one of which is located in Lexington itself.

This analysis shows that basis values have changed between the two periods of this study. Structural changes in the market have decreased the average basis value at the reported locations $0.03 to $0.08 per bushel. Basis has also become more volatile, but the amount of variability depends on the relative distance of reported location to an ethanol facility. Overall, these results indicate that farmers who are close to an ethanol facility have greater basis risk.

Increases in basis volatility can influence the effectiveness of a farmer’s hedging strategy. Imagine a corn farmer who takes a short position in the futures market during the growing season for grain he or she plans to deliver at harvest. When farmers place a hedge in the futures market, they do so assuming a specific basis value for harvest. The hedge locks in the futures prices, but leaves the farmer vulnerable to changes in the basis value. This vulnerability is referred to as “basis risk.” The larger the volatility measure is, the more basis risk a farmer has. However, greater volatility does not always imply a more negative outcome for the farmer. The basis at harvest could be stronger (less negative) than the basis value they had assumed when they placed the hedge. This stronger basis would result in a higher net price received. Farmers need to adjust their hedging strategies to account for lower average basis values, and a wider range of basis possibilities in order to account for the structural changes that have taken place in the corn market.