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Associate Professor of Economics
Montana State University
Dept of Agricultural Economics & Economics
My musings about news, publications, and general concepts in agricultural markets. The blog focuses primarily on issues relating to grain and cattle markets in the northern Great Plains, but other topics that I find interesting are also included. Your ideas for blog posts are always welcome and encouraged. Please shoot me an email or fill out a feedback/suggestion form.
I was recently asked to give a talk about what the future of agricultural research looks like. The previous 60-100 years have focused primarily on getting more food for less effort: efficiency. Furthermore, the primary model of doing and delivering research information has been top-down: researchers perform a project and then simply relay the information to producers.
I don't think that this is the most successful model going forward. As consumer income continues to grow (both in the United States and globally), there is a greater demand for food with particular characteristics. Whether it's how a food is produced, what inputs and how much of those inputs were used, or what features a product has to benefit specific lifestyle choices, consumers are more and more choosing with their wallets. In order to capture these potential premiums, producers must respond by growing foods that match those demands. However, how does one do so in a cost-effective manner?
That was the main question that I posed to myself and attempt to partially comment on in the infographic below
Each year on February 2, the groundhog Punxsutawney Phil comes out to predict whether the United States should expect an early spring. Such knowledge could be invaluable to agricultural producers, allowing them to better gauge the length of their growing season and perhaps get an early start on planting. To commodity market investors, this knowledge would also be useful because they would be able to immediately incorporate into futures prices the potential impacts of the changes in producers' behaviors.
Naturally, I became curious whether this lovable marmot's powers lie not only in the ability to predict an early end to winter (by the way, his accuracy has only been 37% since 1887), but also to move agricultural commodity markets. To test this hypothesis, I used daily futures price data between 1959 and 2014 for corn, soybeans, and soft red winter wheat (all commodities traded on the Chicago Mercantile Exchange). My idea was to see whether there was a statistically significant difference between prices of those commodities on February 2 (when Punxsutawney Phil makes his prediction) and, just in case all of the potential market effects did not get incorporated into the price of each commodity within the same day, I also included February 3. In cases when February 2 was a weekend, I used the first business day to measure any potential effects.
Using these data, I needed to estimate a reasonable econometric model that could somewhat accurately represent the daily price behaviors of these three commodities. I did not want to get too fancy, so I simply decided to estimate an ARIMA model for each commodity. Just in case you're interested in the gory details, I first-differenced each price series and used the SCAN (Tsay and Tiao, 1985) and minimum BIC search methods to identify the best ARIMA specifications; these turned out to be ARIMA(1,1,2) for corn, ARIMA(1,1,1) for soybeans, and ARIMA(3,1,0) for wheat. Additionally, in each ARIMA estimation, I used an indicator variable for days after an "early spring" prediction to determine whether markets had any reaction to this meteorological news event.
Alas, in all cases, the venerable ground squirrel's ability to sway the markets was even less successful than his ability to predict the start of a spring season. On the other hand, to borrow a line from the film Groundhog Day (1993), perhaps "this is one time where [academic research] really fails to capture the true excitement of a large squirrel predicting the weather."
I recently presented at a producers' meeting organized by the Montana State University's Northwestern Agricultural Research Center. The goal was to provide a quick overview of the tools and resources available to Montana farmers to develop their own costs of production budgets. As I've done in the past, I'm posting the infographic version of the presentation here.
A few days ago, I paid $1.78 for a gallon of gas. That made me really pleased, and that feeling is probably shared by the majority of the population (except those who are in the oil industry). The low gasoline prices reflect the continued slumps in the oil market. Today, U.S. oil futures price was approximately $33 per barrel. This is the lowest price since March 2004.
In Russia, where the economy has become highly linked to oil sales, continuing drops in oil prices mean continuing economic declines. This has led to the tumbling of the ruble (the Russian currency) relative to other countries' currencies and, coupled with the Russian government's restrictions on many food imports, has resulted in the precipitous rise of food in Russia.
As a result, the Russian government is about to decide whether to eliminate or reduce its current export tariff. The export tariff significantly reduced the flow of wheat out of Russia, a major global wheat producer that can sway the global wheat price, has contributed to the most recent drops in wheat prices. However, as reported by Reuters, Russian officials are holding a meeting on January 29 to decide whether to maintain or even strengthen the tariff in an effort to maintain a sufficient domestic wheat supply.
The continuation or increase in Russian export restrictions would continue to restrict the availability of wheat for importing countries, which would potentially lead to a rebound in wheat prices.
The nearby March 2016 winter wheat futures contract is currently trading at $4.68 per bushel. This is the lowest prices fell rapidly in 2010 after the run up in prices in 2008 and 2009. This most recent price decline is observed across all agricultural commodities (and many non-agricultural commodities), and according to price forecast put out by the Food and Agricultural Policy Research Institute, crop prices are expected to remain low in the 2015-16 and the 2016-17 marketing years, before beginning to rise in the 2017-18 marketing year.
Economic theory indicates that producers who face decreasing expected prices would respond by reducing their quantity supplies. In practice, we would observe this rational behavior by seeing a reduction in the seeded acreage.
The latest winter wheat seeding report published by the USDA National Agricultural Statistical Service shows that this is exactly what has occurred over the past three years.
2016 Winter wheat seedings, in 1,000
Data source: USDA National Agricultural Statistical Service, Winter Wheat Seeding Report, January 12, 2016
Across the United States, the 2016 winter wheat acreage is down by 7% since from 2015 and 14% since 2014. The 2016 planted acreage is 36.61 million, which is the lowest acreage seeded since 2010, when 36.57 million acres were planted. The next lowest occurred all the way back in 1970, when there were 37.62 million acres were planted in the United States.
U.S. wheat farmers are clearly smart economists.
I recently put together a presentation of some recent research about the impacts of shuttle-loading grain-handling facilities on the basis bids that we observe at these locations. Because these facilities are higher capacity, can load grain at a faster speed, and can obtain rail cars at lower costs, they have the opportunity to pass these cost savings to farmers in the form of higher prices bids for grain. However, does this actually happen? And if it does, what are the marketing landscapes that make this pass-through more likely?
I will be presenting to the REAL Montana group this Thursday about some general trends and issues in the U.S. and Montana's agricultural landscapes as well as a brief discussion about international trade in the agricultural industry. Here's the infographic of the presentation.
This upcoming Tuesday, I will be presenting at the 2015 Beeronomics conference, which brings together economics researchers and industry participants who are interested in researcher the economics of beer markets. I will be presenting a paper about the effects of state-level beer excise taxes on differences in the size of states' craft beer markets.
As I was putting together the presentation, I thought that I rarely see researchers' presentations be available online, and rarer yet do I see available presentations be in a format that can be accessible to audiences who did not actually attend the talk. In large part, this may be because most presentations are made in the "slide" format, with few graphics, and often fairly ambiguous keywords or short phrases that are made clear during the actual presentation, but which are difficult to understand when simply looking at the slides.
In an attempt to overcome these challenges, I decided to try a new presentation creation framework: infographics. In theory, this format provides an aesthetically engaging, graphics-based informational paradigm that can be used not only during presentations to a live audience, but will also be accessible to others who did not have a chance to attend the talk. Here is my first attempt at this.
The rise in the use of video games for education purposes is unmistakable. And why not? They are fun, increasingly realistic, and require players to be engaged, stimulating their creativity, reaction, and a multitude of emotions. A 2013 EDUCAUSE report noted that there are over 1 billion people worldwide who play at least one hour of video games every day. With the increasing proliferation of electronic devices, it is unlikely that this number will not continue to rise.
As access to technology that supports games continues to become cheaper and more available to much of the U.S. population, educators at all levels have increasingly become open to and enthused about using games for educational purposes. For example, this EdTech Magazine piece describes some of the reasons that this movement is happening, a Chronicle of Higher Ed article shares some ways that students learn better from games, and this reading list for teaching with games in the classroom is a great overview of places to start when thinking about games in the classroom. The primary explanation is that games engage the audience, an increasingly relevant problem in many classrooms.
You might be wondering what all of this has to do with agricultural extension. You might also be thinking that farmers have much more important things to do than play games. And that county extension agents, university extension specialists, and university professors who make educational presentations to agricultural producers are there to deliver information, not invite audience members for an all-day Madden NFL 16 tournament. Or should they?
Well, perhaps I can concede that the Madden NFL tournament would probably not be very educational, but there is an increasing number of games that could be relevant and provide important insights that a 30-minute PowerPoint presentation may be much less effective in making. For example, this recent story about a tractor simulation game got me thinking about this whole idea in the first place. The game allows users to operate tractors that use different quality tractor hydraulic fluids. The participants are able to quickly assess differences in tractor performance and gauge whether increasing their expenses for a higher-quality fluid would be worth it. Could this information have been presented using PowerPoint slides with lots of convoluted graphs and tables with tiny font that only audience members in the first row could see? Sure. Would the impact and insights have been the same? Perhaps not.
Here's another example: Farming Simulator 16. The game provides the ability to work in a modern day farming environment with high-end graphics, equipment that you'll find on a typical farm, and the uncertainty that farmers face each year. Check out one of the game's trailers:
Here's an example of questions that extension and outreach educators can answer by engaging audiences in playing along: What's the best re-allocation strategy? What's the best hedging strategy? What are the possible returns if you acquire crop insurance? What are the risk and expected returns of using a new cropping system? What are the cost-benefit trade-offs of acquiring new equipment? Want a slightly lower-tech approach? The The Farming Game is a board game alternative (in app form) to simulating farming conditions.
Engaged learners get more out of an educational experience. Games have been shown to be an effective tool in traditional classrooms to engage students and increase their educational attainment. The agricultural extension learning environment is a natural next step. Even farmers like to play games.
Beginning on August 11, the Chinese central bank has devalued the country's currency, the renminbi, by 4.4%. For a country that has refused to change its currency's valuation for decades, this move represents a major shift in international monetary policy. Previously, the country pegged the currency to the U.S. dollar with very little room for additional fluctuations.
In general, this currency depreciation has been viewed by many as a sign of a slowdown in the Chinese economy. As the U.S. dollar has strengthened relative to other major currencies in late 2014 and 2015, the Chinese renminbi also gained strength. This made it difficult for others to purchase the currency and prevented Chinese exporters to sell in the global marketplace. Devaluing the currency may be one approach that the Chinese government is attempting to spur the country's slowing economy, by providing its manufacturers to be more competitive in trade (perhaps especially in the face of the Trans-Pacific Partnership free trade agreement that is currently being negotiated).
While the devaluation could benefit the Chinese economy, there could be potentially adverse effects for U.S. agricultural exporters who have benefited from the strength of the Chinese currency. Consider a classic economic model (below) for understanding trade impacts when there are different currencies and relative currency values are characterized by exchange rates.
Effects of Chinese Currency Devaluation
Notes: P = price; Q = quantity; RMB = renminbi; USD = US dollars; ES = excess supply; ED = excess demand
In this model, the original conditions (before Chinese devaluation) are shown by the black lines. The United States exports goods and China imports those goods. U.S. exporters want to receive US dollars for their goods (not Chinese renminbi, which would be useless in the United States). Therefore, Chinese importers must first convert their currency into the U.S. currency. This is illustrated in the "Trade market, in RMB" graph. In that panel, we see the excess demand for a good in RMB and the excess supply of goods (from the United States) after converting the USD currency into RMB.
The RMB devaluation essentially means that Chinese exporters need more renminbi to purchase US dollars. That is, the price of importing has gone up. As a result, there is a movement back along the Chinese excess demand (ED) curve to a new equilibrium marked by the intersection of the red excess supply (ES) curve and the old black ED curve. At this new intersection, the price of trade with U.S. exporters is higher and, consequently, the quantity of good traded goes down. In the United States, exporting producers now face a lower price (because Chinese importers are buying less of the good).
If you've skipped the economics lesson, let me get to the potential implication to U.S. agricultural producers. Because Chinese importers can now exchange their currency for fewer dollars, they are likely to import less of U.S. agricultural products, including agricutlural commodities and processed food goods. This is expected to lower the prices that U.S. producers receive and also reduce the amount of goods that they can export. Multiplying a lower price by smaller quantity implies lower revenues.
What impact could this have? Considering that China is the largest international market for U.S. food and agricultural products, the impact could be significant. For Montana, China also represents the major export market. For example, in 2013, Montana's top exports to China included $169 million worth of crop production, $33 million in chemicals, $30 million in metals, and $11 in minerals and ores.
What can dampen this effect? A fall in the international value of the US dollar. A cheaper dollar would reduce the price for importers of U.S. agricultural commodities and other goods, increasing the demand for those goods and, consequently, the price received and sales by U.S. producers.
I have been hearing a lot lately about the increase in the production of cattle. Yes, it's true. As I've written before, lower corn and soybean prices have resulted in large positive profit margins for feedlot operators, resulting in increased demand for cattle and a move toward rebuilding herds. And, yes, the markets have observed an increase in cattle inventory and decrease in slaughter numbers. The July 2015 USDA report on cattle showed that all cattle cattle and calf inventories were up 2% since last July, the first time the markets have seen an increase in inventories in nine years. Moreover, the 2015 calf crop is expected to be 1% higher than it was in 2014.
A skeptic (that's me) would say that this is simply a market rebound effect from the numerous years of historically high feed costs, tough weather conditions, and continued reductions in pastureland. However, today, I read a much more optimistic view of the situation. A Rabobank Food and Agribusiness Research (FAR) and Advisory group's report describes a continued expansion of the U.S. cowherd. While this in itself is unsurprising, the report also states that the expansion will occur in locations where corn and soybeans have taken over the agricultural landscape---the Dakotas and the Corn Belt.
This last part was particularly surprising to me. First, consider what has occurred in the central Plains and Corn Belt regions during the past 20 years. As shown below, every state in this experienced substantial increases in the acres allocated to corn and soybean production, which was a rational market response to increases in corn and soybean prices during much of the 2000s. Where did these acres come from? To some extent, they were the more marginal land that prior to conversion had cattle.
Change in Corn and Soybean Acres Planted, 1995-2015
Data source: USDA National Agricultural Statistical Service
Change in All Cattle Inventory, 1995-2015
Data source: USDA National Agricultural Statistical Service
Change in All Cattle Inventory and Corn and Soybean Acres, 1995-2015
Data source: USDA National Agricultural Statistical Service
Clearly, there has been a ubiquitous shift away from cattle and toward crop production. Can this land go back into cattle production? Sure. But this will depend on the relative cattle-to-crop prices that a producer faces when making the decision. In my opinion, the relative price of cattle would have to be quite a bit higher than that observed in 2011, the year that the Rabobank report predicts to be the benchmark for what to expect in cattle inventory increases. That is, because there are significantly higher costs to introducing (or even re-introducing) a cattle operation than the costs of transitioning land into cropping, producers would need to see substantially higher cattle prices and expect substantially higher returns from cattle (relative to those from crops) to return land (even marginal land) into cattle production. In other words, taking down fences is a lot cheaper then putting fences up.
So, even with $3.00-$4.00/bu corn and cattle prices around $1.50-$1.75/lb, I am skeptical that we will see a major return of cattle production into corn and soybean country.
Dr. Gary Brester, a professor in the Department of Agricultural Economics and Economics at Montana State University, puts together a monthly newsletter to participants of the Beginning Farmers and Ranchers Program in Montana. The newsletter provides an excellent overview of the previous month's grain market activity and educational insights about hedging price risk using futures and options markets.
Dr. Gary Brester, a professor in the Department of Agricultural Economics and Economics at Montana State University, puts together a monthly newsletter to participants of the Beginning Farmers and Ranchers Program in Montana. The newsletter provides an excellent overview of the previous month's grain market activity and educational insights about hedging price risk using futures and options markets.
I recently read an Agriculture.com article describing political contributions made by the agricultural sector in the 2014 election. Immediately, two things popped into my head. First, there were almost no graphics that would allow for an easy way to visualize the information. Second, given the upcoming 2016 election and that industries, companies, and individuals are not very likely to change their contribution behaviors, I wanted to look into the crystal ball of what kind of contributions from the agriculture sector are likely to occur in 2016.
All federal-level contributions data are available through Open Secrets, part of the Center for Responsive Politics. The website is dedicated to making available information about campaign contributions, monetary expenses by members of the U.S. government, and lobbying expenditures. My data radar went into overdrive when I saw that they make available raw data describing contribution amounts and contribution sources: so many possibilities!
This is when I decided to make a multi-part blog series that digs down into the data and provides visualizations that help paint a picture about the agricultural industry's role in political contributions. In today's entry, I want to provide a zoomed-out view of the political contribution situation, and with each new entry, dig deeper and examine more detailed information.
First, I wanted to look at the overall contributions and number of contributors by broadly defined agricultural industries. These data show that by far the greatest contributing sector is the crops production and processing group, which had nearly 26,000 contributors giving nearly $22 million in political campaign contributions. This group includes the big grain crops organizations and crops handling facilities, such as elevators and processors. The next largest contributing sector is the food processing and sales sector, which includes food stores, food manufacturers, and processing facilities that supply end-use products. This group contributed $11.6 million, although this came from only approximately 6,100 contributors. Overall, agricultural sectors contributed $69.7 million to campaigns in the 2014 election cycle.
Digging a bit deeper into the overall contributions, I broke up each sector into different groups. There were too many groups to provide a reasonable visualization of the data, but here is a table of contributions by specific groups within the agricultural industry. Within the largest contributing sector, crop production and processing, nearly half of all contributions came from groups associated with processing the crops. Groups and individuals representing major cash grains, including corn, soybeans, and wheat, contributed $1.1 million, the vegatable, fruit, and tree nut ssector contributed $2.4 million, and the sugar sector contributed nearly $6 million. The large contributions of the latter may have been associated with the U.S. government's 2014 negotiations with Mexico over the World Trade Organizations (WTO) sugar trade dispute.
Last for today, here is a visualization of contributions from agricultural sectors to specific groups. I wanted to see whether contributions had particular political leanings and how much of the contributions went to political action committees (PACs), which could then make their monetary allocations to candidates that each PAC supports. Of the total contributions from the agricultural industry, 44.5% was made to Republican candidates, 30% was made to PACs, and only 11% went to Democrats. Out of the four largest contributors (crop production and processing, food processing and sales, agricultural services, and livestock), all but the food processing sector had nearly equal contributions directly to Republican candidates and to PACs. The food processing and sales sector contributed almost three times as much money directly to Republican candidates than it did to PACs.
What does this all mean? On the surface, there aren't many surprises here. There is a lot of money spent by the agricultural sector on the 2014 election cycle, much of the money came from crop production and processing, and the majority of the funds were contributed to Republican candidates. In part 2, I will look deeper into who contributed during the 2014 election cycle and in part 3 I will further explore the recipients of those contributions.
In the May 21, 2015 blog post, I wrote about the creative destruction of agricultural jobs. That is, the idea that although technological advances and using more capital equipment to replace labor initially led to significant reductions in the number of jobs within the agricultural sector, there is a rapidly growing research/science based industry that will support food production.
In the June 1, 2015 post on the Farm Press Blog, David Bennett notes that the recent news about increases in jobs within the agricultural sector "...may mean that [farm] kids will be coming home." That is, that there will be job growth in the production agriculture sector. This is not likely to be the case.
Here is a graph of some of the occupational outlooks for several fields in the agricultural sector. These are data that I collected from the U.S. Bureau of Labor Statistics' Occupational Outlook Handbook. The data show that for production agriculture professions, either in running an operation or as farm labor, is going to continue to decrease (by between 3% and 19%). However, the research-based support industries will have a significant increase by 4-11%. So, the kids are probably not going to be the ones growing the wheat, but they're going to be the ones developing innovative methods to grow the wheat better.
One of the things I enjoy is looking at data in different ways. In other words, I am a data visualization geek. Today, a Tuesday, is when the USDA posts their weekly crop progress report, which describes various production conditions such as crop planting, emergence progress, and quality conditions, among others. One of the more annoying aspects of these reports is that they provide limited insights about how crop production progress has changed over the course of the growing year. Another limitation (at least for me) is that the information is provided in tabular form, which is fairly difficult to visualize and make quick inferences.
I wanted to develop a better method. I downloaded hard red winter wheat conditions data from the USDA National Agricultural Statistical Service and was able to develop a map-based characterization of changes in wheat quality over the 2014/15 growing period. Here's what this looks like. The time-lapsed condition visualization shows the decreases in quality conditions in early Spring (during the time when there was little precipitation and soil moisture) and then improvements in late Spring (after significant rainfall). This is arguably a much easier way to get a general sense of the information available to market participants and provide some insights into the price patterns observed in the winter wheat market.
If you ask a New Yorker or Baltimorian what they it's like being an agricultural producer, you might get a response that reflects the state of agricultural production as it was in the 1940s. A family operation with 100 acres of land, 10 head of cattle, 8 children, and a single tractor. Or, you might get a response that almost all of the agricultural land in the United States is owned by three or four huge (probably also evil) farming corporations that manipulate food prices and production to simply maximize their own profits without any consideration for the greater social good. Of course, neither of these descriptions characterizes reality, although the reality might be somewhere in between.
The U.S. agricultural sector has experienced tremendous changes in the 20th century. Tremendous technological improvements and adoption of those technologies led to the classic labor-capital trade-off behavior and a concurrent improvement in output. To characterize the changes to a Baltimorian, for instance, would be to say that instead of having a family of 10 work 100 acres of land, a family of 3 can now work 5,000 acres.
Here's another way to consider how the agricultural industry has changed. I obtained data from the USDA Economic Research Service about implicit quantities of farm outputs and inputs between 1948 and 2011. Then, I plotted the livestock and crop output data and the labor (number of farm workers), capial (machinery), and land inputs. The plots make quite evident the upward trends in output and the downward trends in inputs. For example, between 1948 and 2011, output in livestock and related products increased by 129% and crop output increased by 163%. These tremendous increases occurred at the same time that land use for agricultural production decreased by 27% and labor decreased by 78%. The initial reduction in labor was replaced by a higher use of equipment inputs (which increased by 174% between 1948 and 1981). After the early-1980s, equipment inputs began to decline\d, likely as a result of equipment becoming larger and more efficient. For example, between 1950 and 1980, farm workers were replaced by machinery, which was not very efficient but was more so than a person. After the 1980s, the not very efficient machinery was replaced by equipment that was much more cost-effective (e.g., upgrading from a 10 foot combine header to a 40 foot combine header).
What was one of the results of these technological advances? Farms became bigger (taking advantage of economies of scale), the demand for labor decreased, older farmers could continue running their operations, and children who grew up on farms were less likely to return. It seemed as though this was just another case of technology crowding out individuals from an industry. And then, this happened:
"A field with 25K more jobs than grads each year" (CNBC News, May 20, 2015)
"Land of opportunity: Jobs in agribusiness plentiful" (Chesterfield Observer, May 20, 2015)
"The best field for new college graduates: agriculture" (Alexandria Echo Press, May 20, 2015)
What happened? How can an industry that's consolidating farmland and reducing labor inputs have such a deficit of qualified personnel? It appears that agriculture has fallen prey to the idea of "creative destruction," which generally describes the process of developing a "new" system only after destroying the existing system. The automation of the automobile assembly is frequently cited as an example of creative destruction. Robotic systems have replaced large portions of car assembly lines around the world, resulting in an entire industry of car assembly workers becoming unemployed. However, this automation spurred the development of a new industry for building, maintaining, and repairing the robotic systems.
The same creative destruction story is playing out in agriculture. The increasing scale and efficiency of machinery and the consolidation of farms has significantly reduced the role of traditional farm and ranch labor. However, who is going to continue advancing these technologies? Who is going to collect and analyze the huge amounts of data that all of the new machinery is generating? Who will manage the incredibly complex global infrastructure of moving food products from Glasgow, Montana to Tokyo, Japan? This is the emerging modern agricultural industry whose role is to support the revolutionized world of ag.
Recently, I've been seeing a number of discussions arguing that hedge funds have aggressively shorted (sold futures contracts) wheat markets. This has essentially created a low price bubble (my terminology). Similarly to the high price bubble that occurred in the housing market in the late-2000s, wheat markets are disproportionately skewed toward the short side, implying that the low prices that have recently been observed in wheat markets (for example, here are recent futures price charts for hard red winter wheat and hard red spring wheat) may be too low to accurately reflect market fundamentals. If there really is a low price bubble, we would expect wheat prices to increase when the bubble finally bursts.
These discussions made me curious about (a) whether there really does appear to be a low price bubble and (b) the size of this potential bubble. To try to answer these questions, I gathered Commitment of Traders (COT) data, which are published by the U.S. Commodity Futures Trading Commission (CFTC). These data show the number of contracts and position (long or short) that are held by producers, merchants, processors, and other users who "predominantly engages in the production, processing, packing or handling of a physical commodity and uses the futures markets to manage or hedge risks associated with those activities." The data also show contract positions held by swap dealers and money managers, who are market participants that are typically considered to be speculators in commodity markets.
I decided to look at a relatively simple but arguably quite instructive measure that indicates the proportion (relative to the total open positions) of long and short positions held by the second set of market participants: the speculators. The idea here is that participants that use futures markets for hedging price risk (e.g., producers, merchants, processors, and other commodity users) are unlikely to substantially "gamble" in the market and, therefore, unlikely to create a significant bubble (although I acknowledge that this can be argued and there is anecdotal evidence of price hedgers taking speculative positions).
After calculating the proportion and plotting the data for daily positions between January 3, 2006 and May 5, 2015, there does appear to be some evidence to indicate a potential low price bubble. Let's start with a less convincing indicator, the proportion of long positions held by speculators. The figure shows that in the past several months, the proportion of long positions has certainly decreased for all three wheat classes for which futures contracts are traded, but the current number of long positions does not appear to be substantially different from the long run average (and certainly not as low as it has been in the past).
However, there seems to be significantly more evidence of a low price bubble when considering the proportion of short positions held by speculators for hard red and soft red winter wheat contracts. In the hard red winter wheat market, the 64% of short positions held by speculators is 20 percentage points higher than previous peaks, which were observed only five times since 2006. It is also 40 percentage points (or 204%) higher than the historical average number of short positions held by speculators. In the soft red winter wheat market, speculators hold 71% of short positions, which is only second to the 73% observed in January 2014.
So is there a low price bubble? It certainly appears that there is at least suggestive evidence of one in the winter wheat markets, although little evidence (based on speculators' short positions) in the spring wheat market. If (or likely, when) the bubble bursts, wheat prices are likely to bounce back (and they have begun to do so; see the May 15, 2015 blog entry).
On May 05, 2015, both hard red winter and hard red spring wheat harvest period futures prices were at a five-year low. News of moisture in the central Great Plains, Russia's bumper crop, and reduction in U.S. export demand drove markets downward as hedgers overwhelmed the market by going big on short positions. Only ten days later, the wheat markets' upside "great potential" is apparently being realized as hard red spring wheat prices are trading at $5.75 per bushel (8.04% higher than the May 5th low) and winter wheat prices have rebounded to $5.42 per bushel (a 10.02% jump from its May 5th low).
What happened? It was likely the case of "too much of a good thing." The global production expectations were somewhat inhibit by the announcement that the world is an El Nino year. This raised concern about increased droughts in Australia, poor production conditions in Asia, and increased precipitation and cooler temperatures in the U.S. central Great Plains. While it is relatively easy to infer why droughts in Australia and Asia would increase prices (i.e., lower Australian supplies and higher Asian import demand), it may not be immediately evident why and how this affects price-increasing fears in the United States.
As I have mentioned in previous posts, rain is good until it isn't. High precipitation amounts have contributed to increased incidents of various wheat rust diseases in the central United States. This has increased concerns about the quality and production levels of winter wheat. This concern would result in an upward market readjustment, possibly one that we are currently observing.
Strategie Grains, a private French agricultural consulting firm, published a report that significantly reduced European Union export wheat expectations due to the increased output of Russian wheat. The European Union, and especially France, compete with Russia for buyers in northern Africa and the Middle East, and an increase in the Russian wheat harvest would make the export market more competitive. The situation could be exacerbated depending on Russia's timing to eliminate its trade tariffs on wheat exports, which were installed by Russian President Vladimir Putin as a countermeasure to the U.S. and European economic sanctions resulting from Russia's activity in Crimea and eastern Ukraine.
Russia's impact could have significant implications that last for longer than just a single year. Decreased competitiveness in the export market would imply increased inventories of wheat in the European Union. This would have additional ripple effects to the United States, where farmers similarly compete on the global market and prices reflect this competition. In the USDA's most recent World Agricultural Supply and Demand Estimates, the agency projects the European Union to export 34.5 million metric tons of wheat, with inventories (stocks) rising from 10.07 million metric tons to 14.72 million metric tons. The United States is also expected to increase its inventories by over 20%.
Higher inventories are likely to dampen wheat price levels. Moreover, the increasing global wheat inventories would likely maintain lower price levels over a longer time horizon. One potential saving grace could be the U.S. cattle market's move to rebuild herds that have been significantly reduced over the past 4-5 years. If domestic demand for feed remains steady or increases, U.S. wheat farmers could have a potential price advantage relative to other wheat producing regions.
The U.S. Wheat Quality Council organizes an annual tour of Colorado, Kansas, Nebraska, and Oklahoma winter wheat fields by crop scouts who analyze and provide forecasts about the yield potential at wheat farms across the state. In 2015, over 90 scouts visited 659 fields.
After a very low production year in 2014, the recent precipitation in the central Great Plains is expected to boost yields in 2015. The Wheat Tour results indicate an average 35.9 bushels per acre yield across Kansas, Nebraska, and Oklahoma, which represents 4.6 bushel acre increase over the average yield in 2014 across those states. While this production increase, coupled with high U.S. and global wheat inventories (among other reasons), has reduced wheat prices to a five-year low, the expected total output of 288.5 million bushels is below the 298 million bushel expected output that was predicted by industry experts prior to the tour.
Markets responded bullishly to the unofficial news of the wheat tour results (the official report has not yet been published by the Wheat Quality Council). Hard red winter wheat July futures are trading at $5.08 per bushel at the time of writing, nearly 30 cents per bushel higher than the $4.90 per bushel low observed on May 5, 2015. Similarly, hard red spring wheat September futures are trading at $5.47 per bushel, 11 cents per bushel higher than the May 5 closing price.
I followed the wheat tour using reports posted on Twitter. One of my key observations was the substantial disparity in the reports. Some fields were reported to have yields of 15-20 bushels per acre, while others were in the 60-70 range. This regional variability may play a key role in markets' ability to correctly establish prices due to the significant uncertainty in production information. Adding to this uncertainty was the number of stripe rust incidents that I noticed from the posted photos. This could lead to potential yield and quality issues.
The U.S. Department of Agriculture will release its first production and yield estimates of the season on May 12, 2015.
One of the main reasons that I became so interested in economics, and especially economics research, is the mind-blowing concept that one can make a reasonable prediction about people's behaviors just by analyzing data. The information-rich world of the Internet age has only made my addiction to data even stronger. Today, I want to share a really neat way to think about and visualize individuals' behavior in agricultural markets.
Consider, for example, a corn producer. Typically, you plant in mid-spring and market the corn in mid-fall. Prior to planting, you need to acquire fertilizer and after harvest, you might want to find a grain handling facility that offers you the highest price for your product. Twenty years ago, you might have made several phone calls to inquire about fertilizer and corn prices, and then made a purchasing or sales decision. Today, you might be as likely (if not more likely) to "Google" the information. This is where the really neat stuff begins!
In 2012, Google Inc. launched a service called Google Trends (various predecessors existed since 2008, but Google Trends represents the most current version). Google Trends tracks information about Google users' keyword search activity and makes highly aggregated statistics about this information available to the public. As an economist, these statistics are particularly interesting because they can provide insights about the demand for particular information and how this demand may be related to what is observed in markets.
Using the example above, consider search trends for the keyword "corn" and the keyword "fertilizer". Google Trends does not provide a measure of the actual search volumes, but rather a relative index of search volume, which ranges from 0 to 100. For example, if there were more searches in the month of April 2015 than there were in March 2015, then the index value for April would be higher than for March. In each figure, I plot the weekly search volume index between 2004 and 2015.
Each figure has a distinctive cyclical pattern. For corn, there is always a spike in the number of searches around late-September and early-October; that is, around harvest time. This corresponds to the period when producers are typically seeking information about where they can market their grain and which location can offer them the best price. Similarly, for fertilizer, search increases are observed in late-March and early-April, the period when many spring seeded crops are being planted and farmers purchase fertilizer for their operations. Incredibly, these patterns are very consistent across the 11-year period for which Google has maintained records of search terms. The spike in the fertilizer search volume in 2013 corresponds to the explosion of a Texas fertilizer factory, which prompted a much broader interest.
What can we learn from all of this? First, this helps very clearly verify the classic economic relationship between demand, supply, and prices. In the spring, when the demand for fertilizer sourcing and price product is in highest demand (as indicated by the demand for information about these aspects), fertilizer prices are highest. Similarly, in September and October, when the supply of corn is highest (as indicated by the demand for information about where to sell it and finding the best price), the price of corn is the lowest. Second, the data help provide insights about the potentially optimal times to buy and sell these products. Just look for the months when people are least interested!
Dr. Gary Brester, a professor in the Department of Agricultural Economics and Economics at Montana State University, puts together a monthly newsletter to participants of the Beginning Farmers and Ranchers Program in Montana. The newsletter provides an excellent overview of the previous month's grain market activity and educational insights about hedging price risk using futures and options markets.
Yesterday, Vietnam signed a free trade agreement with South Korea and is soon expected to sign similar agreements with Belarus, Kazakhstan, and Russia. These agreements are likely to be a boon to Vietnam's agricultural sector, which accounts for over 30% of the country's exports. Moreover, less expensive labor costs in the country are likely to make agricultural exports competitive on the world market.
The United States has been the major exporter of raw and processed food products to South Korea for over half a century, but Vietnam's free trade agreement with South Korea could have significant implications that may reduce United State's role as an important South Korean supplier. In 2014, the United States exported nearly $7 billion worth of agricultural products to the Asian country. In 2012-2014, grain exports accounted for 29% of that value ($1.76 billion) and meat products were approximately 18% ($1.07 billion). For Montana, South Korea is a particularly important trade partner. In 2013, the latest statistics prepared by the Montana Department of Commerce, exports to South Korea from Montana were valued at approximately $168 million.
While the United States and South Korea have enjoyed relatively amiable trade relationships in the past, the Vietnamese free trade agreement represents increasing global competition for the growing Korean middle-class consumer base. The Trans-Pacific Partnership (TPP), for which a "fast track" Trade Promotion Authority is currently being hotly debated in the U.S. Congress, may be necessary to potentially counteract Vietnam's entry as a competitor in the global trade market.
Yesterday, I had a phone conversation about, in part, recent developments in wheat markets. One of the discussion points was how representative was the U.S. Drought Monitor in providing information that can be used to understand production conditions. This tool is widely used by researchers, industry participants, and producers. However, during my conversation, it was noted that the production conditions suggested by the drought monitor may not be representative of the conditions experienced by farmers. For example, in the most recent drought monitor figure, most of Montana appears to be in normal conditions, with only a small part of the southern region designated as being Abnormally Dry. However, anecdotal evidence from Montana producers indicated that in some regions marked as normal were actually experiencing low moisture conditions.
This got me to wondering: while the drought monitor is useful in indicating longer-run weather conditions, but how representative is it of the shorter run? Perhaps a better indicator of agricultural production conditions could be provided by soil moisture measures. With this in mind, I started looking for tools that can help better understand these measures. There are three indicators that I found to be both useful and easily accessible: surface soil moisture, root zone soil moisture, and the evaporative stress index. All three measures are calculated with data that are collected by the National Oceanographic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA) using remote sensing, which typically includes satellite, radar, and/or aerial photography.
Here are the most recent surface soil moisture, root zone soil moisture, and evaporative stress index (ESI) figures. The surface soil and root zone soil moisture measures are presented in relative terms that compare current conditions to a long-term, 60-year average. For example, in Montana, the two moisture levels show that in the central, north central, and southwest regions of the state, surface soil moisture levels are nearly the same as the long-run average. However, in the northeastern, southwestern, and western parts of Montana, conditions are significantly below the average. The evaporative stress index shows anomalies (both high moisture, green, and low moisture, green) in rates of water use across locations. For Montana, the most recent ESI similarly shows that central and north central Montana are showing above-average moisture rates and the northeastern, eastern, and western regions are below-average.
These measures provide a complementary, perhaps more representative characterization of production conditions in the United States. Moreover, these tools are updated rapidly and offer more geographically precise moisture conditions. While the U.S. Drought Monitor is certainly an important instrument, it alone is unlikely to provide the type of information that can help better assess production conditions and, ultimately, economic markets.
Recently, I found a really neat data set compiled by the United States Geological Survey's (USGS) National Water-Quality Assessment (NAWQA) Program that provides county-level estimates of pesticide use for over 400 products. These data can provide interesting insights into the way that the agricultural sector has changed its behavior in pesticide use and drill down to fairly specific areas.
What I like most about spatial data that are available over time is the ability to develop dynamic maps that show trends across geographic spaces. I've put together two examples using the pesticide use data. Both examples look at trends in the use of glyphosate, which is a broad system herbicide designed to target broadleaf and grassy weeds. The herbicide was originally patented and marketed by Monsanto in 1973 under the brand name Roundup. In 2000, the Roundup patent expired and generic glyphosate products entered the market. Furthermore, Monsanto has introduced genetically modified crops that are resistant to glyphosate herbicide, which significantly reduces farmers' cost of managing weeds. For example, in 1996, Monsanto introduced Roundup Ready soybeans and in 1998, Roundup Ready Corn.
While the pesticide use data base provides information on many products, glyphosate is a herbicide used by the majority of commodity producers in the United States. First, I analyze changes in estimated glyphosate in the continental United States. Second, I consider only the northern U.S. states (Idaho, Montana, North Dakota, and Washington).
The figures provide several interesting insights. First, there appears to be a rapid adoption in use of glyphosate after the Monsanto patent in 2000. Furthermore, much of the heaviest adoption occurred in the corn belt states, which can at least partially be explained by farmers' increasing use of Roundup Ready crops. In the northern Great Plains and northwest states, the increase and cyclical nature of glyphosate use is likely also tied to increased no tillage management practices. According to the USDA Economic Research Service, the proportion of acres in no-till operations has increased from 20% to 40% for wheat between 2000 and 2010, from approximately 17% to 23% for corn, and from 35% to 45% for soybeans. In 2010, Montana had over 40% of its acres in no till practice and North Dakota had approximately 26%.
In mid-April, Bunge Ltd., a large U.S. grain trading company, teamed up with the Saudi Agricultural and Livestock Investment Company to a announce a planned purchase of the majority stake in the Canadian Wheat Board. The Canadian Wheat Board was the sole buyer and seller of Canadian grains in the world market until 2011, when the Conservative Party of Canada eliminated this power through the 2011 Marketing Freedom for Grain Farmers Act. The legislation ended Canadian farmers' obligation to market their grains only to the Canadian Wheat Board, and also ended the ability of only Canadian Wheat Board's to market Canadian grain internationally.
After having to relinquish its single-desk buying and selling power, the Canadian Wheat Board simply became one of the many market participants who had to compete to buy and sell Canadian wheat, although its lengthy presence as the marketer of Canadian wheat helped the Board maintain substantial market share. Now, the Wall Street Journal reports, the Canadian Wheat Board's majority share and, as Bunge and its partner hope the marketing share associated with having that majority share, is expected to be sold for $206.5 million around mid-2015. Furthermore, Bunge's CEO Soren Schroder noted that the company's plans are to focus Canadian grain movement toward export facilities on the west coast.
Unlike recent recent entry by Asian multinational corporations into large wheat production regions of the northern United States, Bunge's partnership with the Saudi Agricultural and Livestock Investment Company likely signals the growing demand for securing grain supplies from the Middle East. The Saudi Arabian population is estimated at over 31 million and continues to grow at approximately 3% annually. The increasing population, highly limited agricultural production capabilities due to water deficiency, and relatively high incomes resulting from oil sales are all factors that will continue to expand the demand for wheat in that country. Bunge's acquisition of the Canadian Wheat Board and its increasing presence in the Australian wheat export market (the company is completing its second Australian export facility in 2016) are signals that the company is taking a strong position to be competitive in the growing Middle East food market.
Favorable weather conditions in the midwest and higher production in major importing countries have contributed to a downward slide in wheat futures prices (see the hard red winter wheat price chart and hard red spring wheat price chart). A recent report by Commerzbank noted that the rapid price decrease was due to a record number of short positions in the soft red and hard red winter wheat futures. The widespread move to the short side of the market reflects investors' concerns for a greater than expected supplyin the United States (even with lower plantings) and decreased demand from major international buyers.
But is there reason to panic? Probably not. First, the most recent (April 27, 2015) USDA crop progress report shows that the recent precipitation events in the central Plains did not have an immediate impact on improving winter wheat quality. Winter wheat quality in Kansas, the largest producer of this wheat class, remained relatively stable with only 26% of wheat in good or excellent condition in both of the last two weeks. Moreover, this represents a slight decline from the 28% of wheat in good or excellent condition in Kansas three weeks ago. Similar condition persistence was observed in Nebraska and Oklahoma during the past three weeks. Montana, however, has had more favorable production conditions with 63% of its winter wheat rated as being in either good or excellent condition.
Second, continued worries about lack of moisture in top spring wheat producing states (Montana and North Dakota) has somewhat buoyed spring wheat prices. As of the April 28, 2015 USDA Weekly Weather and Crop Bulletin, the percentage of topsoil rated as having very short or short moisture levels was 37% in Montana and 33% in North Dakota. Lastly, factors that are favorable to production also come with the potential for adverse effects. The recent rains in the central Great Plains are certainly a boon to this year's winter wheat crop, but they have also sparked worry about wheat rust outbreak. While continued heavy precipitation and high wind conditions in some areas and low moisture situations may propogate production worries, they may also mitigate or reverse the recent downward wheat price trends.
In the midst of the 2013-14 grain marketing year, the northern Great Plains experienced a significant shock to the grain transportation portion of the marketing channel. An above-average production year, a cold and snowy winter, and rail line repairs were among the primary factors that led to northern Great Plains grain not being delivered in a timely manner to west coast export facilities. The major economic impacts occurred during the first two quarters of 2014. First, the lower supply of available rail cars resulted in large increases to rail car prices in secondary rail markets, where grain handling facilities can bid on cars to be delivered to their locations. For example, between 1997 and 2012, elevators paid on average approximately $50 above the original rail car price to reserve cars for their facilities. In the 2013-14 marketing year, the additional cost in secondary markets was above $750 per rail car. Some elevators paid over $3,000 per rail car for the right to obtain a car in the thin market.
As with nearly all costs, the individual or organization that pays the cost does not necessarily experience the full burden of that cost. In most cases, elevator facilities passed on a portion of their additional expenses to farmers through lower prices, thus reducing their own total costs in an attempt to reduce losses in their profit margins. To analyze these potential impacts, I performed an informal analysis of historical basis for Montana wheat. Specifically, I combined daily nearby basis data for 1998–2014 from six regions in Montana with state production and available stocks information to analyze potential deviations in basis values during the first five months of calendar year 2014. The results indicate that for hard red winter wheat (11% protein content), basis were $0.13–$0.24 per bushel weaker in April and May 2014 than historical basis averages in those months. For hard red spring wheat (13% protein content), basis were $0.12–$0.66 per bushel weaker in February through May 2014. These estimates provide suggestive empirical evidence that grain transportation constraints in early-2014 may have adversely affected local cash prices.
Naturally, I began wondering whether similar issues have been observed during the 2014-15 marketing year. To begin answering this question, I looked at the weekly carload reports published by the BNSF Railway Company, which serves the vast majority of Montana and other northern states' grain markets. The data contained in the reports provide relatively good news, which I have summarized in two graphics. First, I looked at the weekly numbers of BNSF cars that were used to transport grain between January 3, 2014 and April 18, 2015, and how these weekly number of cars compared to a relatively normal 2012-13 marketing year. The figure shows that between January and April 2014, there were 10%-25% fewer BNSF rail cars delivering grain relative to the same period in 2013. While there was some recovery between May and July 2014, the available rail car deficit was observed again during the beginning of the 2014-15 marketing period. This undoubtedly played a role in affecting northern Great Plains wheat prices.
The good news is what happened after approximately November 2014. The carload reports indicate that, on average, there were 25% more grain carloads being delivered in January-April 2015 relative to the same period in 2014. This good news is even more evident when considering the total number of carloads delivered between January 1 and the week of a published report. These data, summarized in this figure, show that the number of rail cars available to ship grain has returned to a relatively stable, historical level after experiencing significant declines in early 2014.
So, for northern Great Plains farmers, there is evidence that the transportation markets have returned to a relative normal state. However, there are several issues that are still necessary to keep in focus. First, part of the "solution" to the rail issues observed in 2013-14 was the decline in the demand for rail cars to transport oil out of eastern Montana and western North Dakota. The precipitous drop in oil prices (figure) and continued downward pressure has eased the transportation demand from the emerging U.S. oil markets. Second, higher U.S. and global wheat inventories have led farmers to reduce their 2014 winter wheat and 2015 spring wheat plantings, which is likely to reduce overall production and, thus, demand for rail cars during the 2015-16 marketing year. However, continued high production of small grains, corn, and soybeans in the central Great Plains and the Corn Belt may continue straining the U.S. rail infrastructure, which can have indirect economic impacts on northern Great Plains markets.
The Trans-Pacific Partnership (TPP) is a proposed agreement that began negotiations in 2005 and after an initial goal of launching in 2012, has picked up political steam in 2014 and 2015. The partnership is the cornernstone of President Obama's Administrations Asia-Pacific economic policy and is intended to significantly reduce trade barriers among North American nations and western South American countries and major Asia-Pacific importers, including Japan, Malaysia, Singapore, South Korea, Taiwan, and Vietnam. The 12 nations that comprise the TPP represent approximately 40% of the world's gross domestic product (GDP); that is, 40% of the world's economic output. The TPP is likely to have major implication for Montana's crop production industry, because over 80% of Montana's small grain are exported to the Asia-Pacific. Nearly the same proportion of pulse crops is exported from Montana, also primarily heading west.
Quite typical of many trade accords, the TPP has received substantial opposition from domestic groups who claim that the treaty will harm U.S. labor markets, adversely affect food safety, and create additional public health concerns, among other issues. This opposition made its way into the political realm, resulting in a significant slow down of the legislation's movement through the U.S. Congress. Specifically, while the TPP largely has the support of Republican lawmakers, Democrats are divided about the initiative due to the party's close ties to labor union organizations.
On April 22, 2015, the Senate Finance Committee approved by a 20 to 6 vote a "fast track" measure for the trade agreement. Then, on April 23, 2015, the House Ways and Means Committee voted 25 to 13 to approve a similar Trade Promotion Authority bill. The fast track provides guidelines for the the Obama administration to negotiate the partnership, which will then be presented to Congress for an up or down vote, without the possibility of changing the language of the agreement. Perhaps one of the underlying ideas for enacting the fast track measure is that few, if any, large, complex, multi-national deals would ever be enacted if each country's legislative bodies participated in negotiations. However, even though the fast track measure is a significant boon to the TPP's negotiation process, the partnership is far from being signed into law. Aside from the discontent about the measure by many of President Obama's Democratic allies (including House minority leader Nancy Pelosi) and other groups who support the Democratic party (such as trade and labor unions, environmentalists, and Latino organization), the Senate Finance Committee fast track measure set 150 negotiating objectives that will have to be satisfied by the TPP. However, there is strong support by businesses and business lobby groups as well as Republican leadership. Ironically, the loss of majority in both chambers of the U.S. Congress to the Republican party during the 2014 election may actually turn out to be rather helpful to President Obama's cornerstone trade program.
Today I presented my thoughts about the 2015 agriculture outlook at the Montana Farm Bureau's Council of Presidents Conference in Helena, MT. The 2015/16 marketing year for northern Great Plains small grains is slightly less optimistic than last year primarily due to higher U.S. inventories and global inventories of small grains. Both domestic and world production was higher than consumption last year, resulting in higher than expected supplies and, consequently, lower prices. However, continued uncertainty about below-average precipitation in the central Great Plains continues to buoy wheat prices. Unless water availability increases in the large hard red winter wheat producing regions, basis-adjusted wheat prices are expected to be between $5.00 and $5.50 per bushel for HRWW and between $5.25 and $5.75 per bushel for HRSW.
Feeder cattle prices are likely to remain relatively high for Montana producers, with basis-adjusted values ranging between $2.00 and $2.50 per pound for 550-600 pound steers. Lower corn prices have resulted in increased profits for Midwest feedlot operators, contributing to continued strong demand for feeder cattle. Moreover, drought conditions are expected to be minimal during 2015. These strong markets for feeder cattle and favorable production conditions are both very encouraging for Montana ranchers.
More details are provided in the PDF of the full presentation, which you can download below.
Anton Bekkerman joined the Department of Agricultural Economics and Economics at Montana State University as an Assistant Professor of Economics in 2009. He received a B.B.A. in Business Economics in 2005 from the Sellinger School of Business at Loyola University in Maryland, and advanced degrees in Economics from North Carolina State University.
Bekkerman's research interests include price analysis, agricultural and food marketing, production and agribusiness management, crop insurance, and applied econometrics with a concentration in spatial and temporal modeling. He is also involved in numerous inter and intradisciplinary research projects including cropping system analysis, economics of education, and the economics of public libraries.
Montana State University
Dept of Agricultural Economics & Economics
Montana State University
Dept of Agricultural Economics & Economics
SAS Institute, Inc.
Econometrics/Time Series Group
SAS Institute, Inc.
Technical Documentation Group
North Carolina State University
North Carolina State University
Business Economics, Major
Computer Science, Minor
Mathematical Science, Minor
Loyola University in Maryland
Below are my current research projects as well as a catalog of my published research, including work that has been published in peer-reviewed journals and outreach reports. If you see anything that interests you, please don't hesitate to get in touch with me to ask questions and provide feedback on my existing research and/or discuss potential future work.
Inconsistent discipline across schools can inequitably impact students' access to education by separating certain students from familiar learning environments, especially in misconduct cases that result in longer removal periods. We empirically estimate whether such inconsistencies are attributable to heterogeneity in student body demographic characteristics. The results indicate that a greater number of disciplines that remove students from school for an extended period of time are observed in schools with a higher proportion of black students, but no significant differential punishment effects are observed in schools with a higher Hispanic student population. Furthermore, results of decomposing the marginal effects into conditional and unconditional elasticities indicate that it is not the case that schools with predominantly white student bodies have the least severe punishments and schools with more minority students have the most severe punishments. Rather, schools with inconsistent disciplinary behavior have a proportion of the inconsistency attributable to the race of the student body.
This article assesses the role of simulation methods in econometrics pedagogy. Technological advances have increased researchers' abilities to use simulation methods and have contributed to a greater presence of simulation-based analysis in econometrics research. Simulations can also have an important role as pedagogical tools in econometrics education by providing a data-driven medium for difficult-to-grasp theoretical ideas to be empirically mimicked and the results to be visualized and interpreted accessibly. Three sample blueprints for implementing simulations to demonstrate foundational econometric principles provide a framework for gauging the effectiveness of simulation analysis as a pedagogical instrument.
Camelina (Camelina sativa) is a promising bioenergy crop, but a sustainable production system for this crop has not yet been well developed. There is also concern about competing land use between crop productions for bioenergy or food use. One approach to overcoming this concern and developing sustainable production systems for bioenergy crops is potentially replacing the fallow period in wheat-based cropping systems with bioenergy crops. The agronomic and economic benefits of growing camelina in rotation with winter wheat were evaluated in a replicated rotation study from 2008 to 2011 in the Northern Great Plains (NGP), focusing on the effects on wheat yield and overall profitability of the cropping system. Average winter wheat yields were 2401 and 1858 kg/ha following camelina and barley, respectively, representing a 13.2 and 32.8% winter wheat yield reduction compared to the fallow–winter wheat rotation (2766 kg/ha). Lower winter wheat yield in the alternative systems were offset by 907 kg/ha camelina and 1779 kg/ha barley yields. Economic analyses revealed that at existing market prices and production costs, the traditional fallow–winter wheat rotation provides greater net returns to growers due to substantially lower variable costs of the system. Scenario analyses that use more optimized, lower cost camelina production practices show that the net profits of camelina-wheat system could be closer to those in the fallow-wheat system. However, higher grain price and/or greater grain yield of camelina are essential to attract producers to include camelina in their cropping systems. Although the fallow–wheat system resulted in higher short-run net returns, the total biomass production and crop residue return to soil is much greater in camelina-wheat than fallow-wheat rotation, which is likely to improve soil quality and productivity in the long run.
No-till (NT) and Organic (ORG) farming systems each claim increased sustainability compared with conventional tilled systems. Our objective was to compare agro-economic productivity and soil nutrient status among diversified NT and ORG cropping systems in Montana. Five cropping systems were compared, including four NT systems and one organic system. Three NT systems were designed as 4-yr rotations, including a pulse [lentil (Lens culinaris Medik.) or pea (Pisum sativum L.)], an oilseed [canola (Brassica napus L.) or sunflower (Helianthus annuus L.)], and two cereal crops [corn (Zea mays L.), proso millet (Panicum miliaceum L.), or wheat (Triticum aestivum L.)] in alternate year broadleaf-cereal arrangements. No-till continuous wheat was also included. The organic system that included a green manure (pea), wheat, lentil, and barley (Hordeum vulgare L.) received no inputs. Winter wheat grain yields in the ORG system were equal or greater than those in the NT systems, even though 117 kg N ha-1 was applied to the NT winter wheat. After 4 yr, soil nitrate-N and Olsen phosphorus were 41 and 14% lower in the ORG system, whereas potentially mineralizable N was 23% higher in the ORG system. After 4 yr, per hectare total economic net returns were equal between NT and ORG systems, despite the inclusion of a 3-yr market transition period for ORG when grain prices were equal for both NT and ORG systems.
Growers in north central Montana ('Golden Triangle') have questions about risk management of cropping intensity and nitrogen (N) fertilizer strategies. A 10-yr study at Bozeman demonstrated economic resilience provided by variably-managed pea in rotation with wheat under contrasting available N fertility and uncertain wheat protein discount/premium schedules (Miller et al. 2015). A follow-on study was initiated in north central Montana to investigate whether this long-term response is also observed in a drier climates. Our objectives were to compare winter wheat yield and quality, agronomic efficiency of fertilizer use, and economic trade-offs in four alternative crop rotations managed with four different N fertility regimes. This study is planned to run for a minimum of six years at both sites. Here we report on the results.
The 2011 Marketing Freedom for Grain Farmers Act deregulated Canadian grain markets and removed the Canadian Wheat Board (CWB) as the sole buyer and seller of Canadian grain. We develop a rational expectations contract decision model that serves as the basis for an empirically informed simulation analysis of malt barley contracting opportunities between Canadian farmers and U.S. maltsters in the deregulated environment. Comparative statics and simulation results indicate that some new opportunities for contracting are possible, but the likelihood of favorable conditions for U.S. maltsters to contract with Canadian rather than U.S. farmers is low—between 9% and 35% over a range of possible selection rates. The effects on contracting of the termination of the Canadian grain transportation revenue cap policy and of the relaxation of criteria for the release of new spring wheat varieties are also investigated. While changes to grain transportation policies are not likely to significantly affect favorable conditions for contracting, reducing constraints on Canadian farmers' access to higher yielding wheat varieties could increase the returns from growing spring wheat but decrease the likelihood of contracting for malt barley with U.S. maltsters by an average of 5.3 percentage points.
This study empirically investigates the potentially unintended effects of state laws that seek to improve safety in U.S. public school by mandating standardized student punishment. We estimate the effects of exogenous state-level variation in the quantity and type of such mandates on disciplinary disparities across students who commit serious offenses. Estimation results indicate that more severe punishments are imposed in schools with higher proportions of black or Hispanic students, but such disparities are significantly dampened in states that mandate a higher number of guidelines for serious offenses. However, more guidelines for less severe misconduct tend to increase race-based disciplinary disparities and increase the severity of punishments administered for serious offenses. These outcomes extend the existing sentencing guidelines literature and provide empirical implications for considering marginal deterrence effects when crafting future policies.
We investigate the effects of exposure to coal power plant emissions on school absenteeism for children with asthma, a leading cause of health-related barriers to education. We combine responses from the 2007–2009 Behavioral Risk Factor Surveillance System survey with coal power plant emission data to estimate a zero negative binomial regression model of school absences and investigate misspecification bias associated with naive assumptions about emission dispersion and self-selection into treatment groups. The results show a robust, positive relationship (P<0.001) between increases in emission exposure and the likelihood of a school absence due to an asthma episode. Exposure to higher emission volumes is associated with a 1.92–4.81% higher likelihood of missing an additional school day. Furthermore, assuming uniform emission dispersion and not controlling for self-selection underestimates the effects by 2.72–4.27 times. Access to education and the ability to develop human capital through schooling is affected for children with respiratory illnesses who are exposed to emissions. Public policies for emission regulation are likely to remain relevant for lowering pediatric respiratory health risks and lower barriers to educational opportunities.
Annual legume green manure (LGM) cover crops may have potential in dryland wheat (Triticum aestivum L.) production areas where rotation with whole-year summer fallow is practiced. No-till cropland management enhances soil water conservation, possibly enabling cover cropping, but tillage may be necessary to stimulate mineralization of LGM N in time to affect crop yield. A 2-yr LGM-wheat crop sequence study was repeated three times in Montana, with mean annual precipitation of 356 mm. Spring-planted pea (Pisum sativum L.) and lentil (Lens culinaris Medik.) The LGM were terminated at first bloom with tillage or herbicide. Post-termination weed control also was accomplished with either tillage or herbicide in a factorial combination with the termination treatments, resulting in four management regimes. Fallow and non-N-fixing cover crop controls were included and subjected to the same management regimes. Spring wheat was grown the following year in subplots with four levels of N fertilizer. Wheat tiller density increased only when LGM was tilled at least once. Tillage also resulted in reduced soil water storage and wheat kernel weight in 1 yr. Effects on grain yield were usually neutral or positive, with pea more frequently having a positive effect than lentil, and interactions with tillage varying each year. Wheat grain protein was increased by pea LGM regardless of tillage, even when LGM did not affect wheat yield, indicating that LGM N supply is accelerated by tillage. Managing LGM in dryland environments involves a tradeoff of soil water for N supply, and tillage affects this balance.
The data-driven analysis used over 4,000 observations describing wheat stem sawfly (WSS) infestation, cutting, and parasitism outcomes from 1998 to 2011 across thirty-one locations in Montana and southern Canadian prairie provinces. WSS-related damages in Montana were estimated to be approximately $80.1 million in 2012, the most recent year for which production data were available. Nearly 9.7 million bushels did not reach consumers. Per-farm economic losses ranged between $15,000 and $20,000 for spring wheat farmers and $25,000 and $47,000 for winter wheat farmers in 2012 at an assumed 2,000 acre operation. In high impact areas, winter wheat producers were estimated to have forgone between $110,000 and $120,000 per farm. Management strategies that prevent high WSS infestation levels provide the greatest long-run economic benefits. Unlike existing recommendations to swath at relatively low infestation levels, the swathing management strategy was found to be economically cost-effective only when WSS infestation levels are high.
Proximity to information resources has repeatedly been shown to affect urban development. However, individuals' increased abilities to access information content electronically may have dampened urban areas' comparative advantage of proximity-driven knowledge flows. We investigate the effects of increased high-speed Internet access on the role of information proximity by modeling changes in the demands for locally-based information resources, exploiting variation in the use of US public libraries—the most common low-cost providers of locally accessible information content. Data describing a nearly comprehensive set of US public libraries during 2000–2008 provide empirical evidence of complementary growth in Internet access and the use of public library resources, suggesting that Internet access increases the value of locally accessible information content and overall information demand. Moreover, the complementarity is found to be largest in metropolitan areas, indicating that improved Internet access in locations with greatest proximity and information spillover effects are likely to experience more substantial economic impacts.
Growing global wheat supply uncertainties and unintended impacts of U.S. domestic policies may have contributed to Asian multinational agribusinesses' increased interests in securing long-run access to reliable sources of high-quality U.S. wheat. These interests have been manifest in their increased efforts to vertically integrate wheat procurement, handling, transportation, and exports, largely by constructing and acquiring efficient, high-capacity shuttle-loading facilities. Long-run implications of these changes could include the exit or change in the role and function of less efficient, smaller elevators and subsequent increases in market-power concentration by a few multinational agribusinesses. Significant economic ramifications to northern Great Plains grain producers, traditional wheat marketing structures, and land conservation efforts could follow. Furthermore, price impacts could spill over to other U.S. wheat markets because northern Great Plains production constitutes a large share of overall U.S. grain output.
When consumers have heterogeneous perceptions about product quality, traditional parametric methods may not provide accurate marginal valuation estimates of a product's characteristics. A quantile regression framework can be used to estimate valuations of product characteristics when quality perceptions are not homogeneous. Semi-parametric quantile regressions provide identification and quantification of heterogeneous marginal valuation effects across a conditional price distribution. Using purchase price data from a bull auction, we show that there are nonconstant marginal valuations of bull carcass and growth traits. Improved understanding of product characteristic valuations across differentiated market segments can help producers develop more cost-effective management strategies.
Uncertain and changing economic conditions can have substantial effects on price relationships in spatially separated, linked markets. Although numerous studies have analysed price relationships to characterize market linkage structures, most assume that the relationships and associated linkages are time invariant. This study extends the literature by modelling and estimating time-dependent market linkages that are conditional on changes in exogenous factors. The methodology is used to investigate price relationships in North Carolina (NC) corn and soya bean markets. Empirical results indicate that generalized market-linkage models provide a better representation of price relationships over time, improving the understanding of price discovery dynamics and marketing strategies.
Wind-borne diseases can spread rapidly and cause large losses. Producers may have little incentive to prevent disease spread because prevention may not be welfare-maximizing. This study proposes a market-based mitigation program that indemnifies producers against disease-related losses and provides an incentive to neighboring producers to take preventive action, which can substantially mitigate infestations, reduce the likelihood of catastrophic losses, and increase social welfare. An equilibrium displacement model simulates introduction of the program for U.S. soybeans. Simulations reveal that the market-based solution contributes to minor market distortions but also reduces social welfare losses and could succeed for other at-risk commodities.
The aim of this paper is to show how provisions of the Supplemental Revenue Assistance Payments (SURE) program impacts production practices, and empirically examine changes in crop insurance participation rates as a means of measuring producer responses to the program. The structure of the SURE program is described and a stylized theoretical model is used to show the SURE program's effects on farm-level crop insurance and production decisions. A county-level cross-sectional empirical specification with regional fixed effects is used to test the hypothesis that producers who are most likely to benefit from production practice re-optimization are more likely to participate in crop insurance. Results from empirical analyses of corn, soybean, and wheat production areas show that the SURE program has had substantial impacts on crop insurance participation by producers who are more likely to receive SURE indemnities and exploit moral hazard opportunities.
Extensive literature has shown that student attainment outcomes are affected by student-to-teacher ratios and overall teacher aptitude levels, but offers little information about which method offers the greatest student attainment return relative to associated costs. This study provides empirical evidence that staffing policies should consider the cost-effectiveness of teacher-hiring decisions when multiple education policies are effective.
The rotational effects and economic potential of incorporating fall-seeded pea (Pisum sativum L.) and lentil (Lens culinaris Medik) into conventional wheat (Triticum aestivum L.)-based cropping systems in the northern Great Plains are not well understood. Two 2-yr crop rotation experiments were conducted in central Montana to investigate how winter pea hay, lentil green manure, and lentil grain affects subsequent winter wheat yield and protein content, as well as the economic returns of the systems under no-till conditions. In Exp. 1, a winter pea hay–winter wheat (WP–WW) rotation was compared to fallow–winter wheat (FW–WW) and spring wheat–winter wheat (SW–WW) rotations. In Exp. 2, a winter lentil for green manure–winter wheat [WL(m)–WW] rotation was compared to a winter lentil grain–winter wheat [WL(g)–WW] rotation. Four different rates of N were applied to the winter and spring wheat. Winter wheat yield in the WP–WW rotation was 2193 kg ha–1, which was equivalent to the yield in the FW–WW rotation (2136 kg ha–1), and much greater than the SW–WW rotation (1155 kg ha–1). Averaged over all N rates, the WP–WW, FW–WW, and SW–WW systems had $196, $116, and $41 ha–1 net return, respectively. In Exp. 2, the WL(m)–WW rotation produced greater grain yield and protein content at lower N input levels, indicating a greater N benefit. Nevertheless, the WL(g)–WW system generated $213 ha–1 net profit while the WL(m)–WW system produced $92 ha–1. Therefore, the winter pea cover crop, used for livestock feed, improves the system profitability.
The purpose of this paper is to examine the potential gains in hedge ratio calculation for agricultural commodities by incorporating market linkages and prices of related commodities into the hedge ratio estimation process. A vector autoregressive multivariate generalized autoregressive conditional heteroskedasticity (VAR-MGARCH) model is used to construct a time-varying correlation matrix for commodity prices across linked markets and across linked commodities. The MGARCH model is estimated using a two-step approach, which allows for a large system of related prices to be estimated. In-sample and out-of-sample portfolio variance comparison among no hedge, bivariate GARCH, and MGARCH models indicates that hedge ratios estimated using the MGARCH approach reduce agricultural producers and commercial consumers' risks in futures market participation.
Cow-calf producers seek to reduce costs and increase profits by selecting bulls that produce more efficient offspring. Organizers of formal bull auctions usually produce catalogs for potential buyers that advertise bull performance measures and genetic characteristics, including EPD and simple performance measures (SPM). Buyers use this information to make decisions regarding bull purchases based on heritable bull traits. Residual feed intake (RFI) is a relatively new SPM of feed efficiency. The Midland Bull Test company (Columbus, MT) measures RFI in addition to other SPM during bull performance testing. The Midland Bull Test company records individual animal feed intake by using GrowSafe (Airdrie, Alberta, Canada) technology. Residual feed intake for each bull is calculated as the difference between actual and expected feed intake. The Midland Bull Test company included RFI along with EPD and other SPM in its 2008 and 2009 sale catalogs. A linear hedonic price model was used to quantify RFI values with various bull performance measures from the Midland Bull Test sale catalogs and associated bull sale prices. Analyses indicate that buyers were willing to pay more for bulls that were RFI efficient (P < 0.01). Although other performance measures (e.g., BW gain, birth weight, and age) were valued more highly (P < 0.01) by bull purchasers, an RFI SPM could eventually be valued to the extent that an RFI EPD might be developed.
Soybean rust is a highly mobile infectious disease and can be transmitted across short and long distances. Soybean rust is estimated to cause yield losses that can range between 1-25%. An analysis of spatio-temporal infection risks within the United States is performed through the use of a unique data set. Observations from over 35,000 field-level inspections between 2005 and 2007 are used to conduct a county-level analysis. Statistical inferences are derived by employing zero-inflated Poisson and negative binomial models. In addition, the model is adjusted to account for potential endogeneity between inspections and soybean rust finds. Past soybean rust finds and inspections in the county and in the surrounding counties, weather and overwintering conditions, and plant maturity groups and planting dates are all found to be significant factors determining soybean rust. These results are then used to accordingly price annual insurance contracts or indemnification programs that cover soybean rust damages.
Part of my outreach efforts is to make my research as accessible and useful as possible to the stakeholders who will most benefit from this research. Below you will find software (web applications) and code that attempt to bridge the gap between academic research and those for whom the research is most relevant.
The wheatbasis.montana.edu web application is a tool for forecasting wheat basis at over 70 grain elevators in Washington and Montana.
The Bozeman Public Library Geostatistics Project is a product of a collaborative effort among economists at Montana State University and the Bozeman public library. The project was supported by funding from the National Leadership Grants for Libraries program, administered by the Institute of Museum and Library Services.
The project's goal is to provide a data-driven approach for more precisely identifying trends, changes, and opportunities for growth in patrons' use of public library resources. This includes dividing a library system's service area into neighborhoods to obtain a more detailed understanding of geographical locations that may be underserved or where there is greatest potential for growth, having greater insights about periods when library services are in greatest demand and how these trends changed over time, and pinpointing information resources that are most and least important to a library's patrons.
Often, as was the case with the Bozeman public library (BPL), libraries already have much of the information necessary to gain a deeper understanding of their patrons' changing demands for information resources. The challenge was organizing the large quantity of data in a way that made these data easily accessible, allowed administrative decisions to be made quickly and accurately, and preserved patron privacy. The project team designed specialized software that confidentially identified and sorted patrons into their respective geographical neighborhoods and presented only neighborhood-level use statistics. While these aggregated statistics do not reveal information about any particular patron, they offer an immensely greater level of library use information than a single, annually-calculated value for the entire library system.
The success of the Bozeman Public Library Geostatistics Project indicates that there are significant benefits to public libraries from gaining a more precise geospatial understanding of their patrons' demands for information resources. The long-term objective is to investigate how these methods can be applied to both larger and smaller library systems in Montana and the United States. By providing library administrators technology to quickly and easily assess opportunities for their libraries, these community anchor institutions can become more effective and efficient distributors of knowledge in the 21st century.
This is code and associated paper for determining road distances (rather than as-the-crow-flies distances) among geographic locations. The code is implemented fully within SAS but calls Google Maps to acquire the necessary road distance information.
Many times, I simply don't have enough time to present everything that I think would be useful or interesting. Therefore, I will post "appendices" to presentations that will include all of the items that were not included in presentations. And, of course, I'll post the presentations as well.
Links to the session's presentations, in order of appearance.
Integrative Learning: An Overview (Anton Bekkerman, Montana State University)
Integrating Learning in the Classroom: Client Based Projects and Self-directed Learners (Lindsey M. Higgins, Cal Poly State University, San Luis Obispo)
Integrated Learning in Extension Programs (Mykel Taylor, Kansas State University)
Integrative Learning: A Discussion (Kerry K. Litzenberg, Texas A&M University)
These are resources that I have found to be useful in keeping up to date with resources, information, and techniques that help me think about transitioning my courses to an increasingly integrative framework.
21st Century Educational Technology and Learning
The Web 2.0 Connected Classroom
Faculty Focus: Higher Ed Teaching Strategies
Chronicle of Higher Education: ProfHacker (Teaching, tech, and productivity)
Discussion in the College Classroom: Getting Your Students Engaged and Participating in Person and Online (Jay R. Howard)
Teaching Naked: How Moving Technology Out of Your Classroom Will Improve Student Learning (Jose A. Bowen)
High-Impact Instruction: A Framework for Great Teaching (Jim Knight)
The Graphic Syllabus and the Outcomes Map: Communicating Your Course (Linda B. Nilson)
Integative Learning: Mapping the Terrain (Mary T. Huber and Pat Hutchings)
Fostering Integative Learning through Pedagogy (Richard A. Gale)
Problem sets
Solution sets
I am always excited to chat economics. I am typically in my office between 8 a.m. and 5:30 p.m. Mountain time. If my door is open, then I am in the office and welcome all drop-in visitors. However, if you would like to ensure that I will be able to meet, please contact me using the information to the right.


text:
(AP) Three men say they drove to 48 states in less than five days, ending their journey in the Four Corners area of southeastern Utah. The Four Corners destination allowed them to quickly hit four states _ Utah, Colorado, New Mexico and Arizona.
The men arrived there Thursday around 6:30 p.m., after driving for 106 hours and 43 minutes. They started in Vermont on Sunday.
Josh Keeler, Adam Gatherum and Joey Stocking rode 7,000 miles in a 2005 Toyota Scion. They say their longest stop was 15 minutes for gas and a bathroom break.
They spent more than $1,000 on gas.
On the Net: http://www.greatamericanroadtrip.us
Information from: The Salt Lake Tribune, http://www.sltrib.com
Copyright 2008 The Associated Press. All rights reserved. This material may not be published, broadcast, rewritten or redistributed.

subset: commoncrawl