Author: Michael Sykuta

Innovation trends in agriculture and their implications for M & A analysis

This is a repost from the Mergers in Ag-Biotech blog symposium over at Truth on the Market. If you’re interested in more perspectives on the topic, I encourage you to read the other posts there.  If you’d like to comment, please do so on the TOTM version so it’s part of the general discussion.

The US agriculture sector has been experiencing consolidation at all levels for decades, even as the global ag economy has been growing and becoming more diverse. Much of this consolidation has been driven by technological changes that created economies of scale, both at the farm level and beyond.

Likewise, the role of technology has changed the face of agriculture, particularly in the past 20 years since the commercial introduction of the first genetically modified (GMO) crops. However, biotechnology itself comprises only a portion of the technology change. The development of global positioning systems (GPS) and GPS-enabled equipment have created new opportunities for precision agriculture, whether for the application of crop inputs, crop management, or yield monitoring. The development of unmanned and autonomous vehicles and remote sensing technologies, particularly unmanned aerial vehicles (i.e. UAVs, or “drones”), have created new opportunities for field scouting, crop monitoring, and real-time field management. And currently, the development of Big Data analytics is promising to combine all of the different types of data associated with agricultural production in ways intended to improve the application of all the various technologies and to guide production decisions.

Now, with the pending mergers of several major agricultural input and life sciences companies, regulators are faced with a challenge: How to evaluate the competitive effects of such mergers in the face of such a complex and dynamic technology environment—particularly when these technologies are not independent of one another? What is the relevant market for considering competitive effects and what are the implications for technology development? And how does the nature of the technology itself implicate the economic efficiencies underlying these mergers?

Before going too far, it is important to note that while the three cases currently under review (i.e., ChemChina/Syngenta, Dow/DuPont, and Bayer/Monsanto) are frequently lumped together in discussions, the three present rather different competitive cases—particularly within the US. For instance, ChemChina’s acquisition of Syngenta will not, in itself, meaningfully change market concentration. However, financial backing from ChemChina may allow Syngenta to buy up the discards from other deals, such as the parts of DuPont that the EU Commission is requiring to be divested or the seed assets Bayer is reportedly looking to sell to preempt regulatory concerns, as well as other smaller competitors.

Dow-DuPont is perhaps the most head-to-head of the three mergers in terms of R&D and product lines. Both firms are in the top five in the US for pesticide manufacturing and for seeds. However, the Dow-DuPont merger is about much more than combining agricultural businesses. The Dow-DuPont deal specifically aims to create and spin-off three different companies specializing in agriculture, material science, and specialty products. Although agriculture may be the business line in which the companies most overlap, it represents just over 21% of the combined businesses’ annual revenues.

Bayer-Monsanto is yet a different sort of pairing. While both companies are among the top five in US pesticide manufacturing (with combined sales less than Syngenta and about equal to Dow without DuPont), Bayer is a relatively minor player in the seed industry. Likewise, Monsanto is focused almost exclusively on crop production and digital farming technologies, offering little overlap to Bayer’s human health or animal nutrition businesses.

Despite the differences in these deals, they tend to be lumped together and discussed almost exclusively in the context of pesticide manufacturing or crop protection more generally. In so doing, the discussion misses some important aspects of these deals that may mitigate traditional competitive concerns within the pesticide industry.

Mergers as the Key to Unlocking Innovation and Value

First, as the Dow-DuPont merger suggests, mergers may be the least-cost way of (re)organizing assets in ways that maximize value. This is especially true for R&D-intensive industries where intellectual property and innovation are at the core of competitive advantage. Absent the protection of common ownership, neither party would have an incentive to fully disclose the nature of its IP and innovation pipeline. In this case, merging interests increases the efficiency of information sharing so that managers can effectively evaluate and reorganize assets in ways that maximize innovation and return on investment.

Dow and DuPont each have a wide range of areas of application. Both groups of managers recognize that each of their business lines would be stronger as focused, independent entities; but also recognize that the individual elements of their portfolios would be stronger if combined with those of the other company. While the EU Commission argues that Dow-DuPont would reduce the incentive to innovate in the pesticide industry—a dubious claim in itself—the commission seems to ignore the potential increases in efficiency, innovation and ability to serve customer interests across all three of the proposed new businesses. At a minimum, gains in those industries should be weighed against any alleged losses in the agriculture industry.

This is not the first such agricultural and life sciences “reorganization through merger”. The current manifestation of Monsanto is the spin-off of a previous merger between Monsanto and Pharmacia & Upjohn in 2000 that created today’s Pharmacia. At the time of the Pharmacia transaction, Monsanto had portfolios in agricultural products, chemicals, and pharmaceuticals. After reorganizing assets within Pharmacia, three business lines were created: agricultural products (the current Monsanto), pharmaceuticals (now Pharmacia, a subsidiary of Pfizer), and chemicals (now Solutia, a subsidiary of Eastman Chemical Co.). Merging interests allowed Monsanto and Pharmacia & Upjohn to create more focused business lines that were better positioned to pursue innovations and serve customers in their respective industries.

In essence, Dow-DuPont is following the same playbook. Although such intentions have not been announced, Bayer’s broad product portfolio suggests a similar long-term play with Monsanto is likely.

Interconnected Technologies, Innovation, and the Margins of Competition

As noted above, regulatory scrutiny of these three mergers focuses on them in the context of pesticide or agricultural chemical manufacturing. However, innovation in the ag chemicals industry is intricately interwoven with developments in other areas of agricultural technology that have rather different competition and innovation dynamics. The current technological wave in agriculture involves the use of Big Data to create value using the myriad data now available through GPS-enabled precision farming equipment. Monsanto and DuPont, through its Pioneer subsidiary, are both players in this developing space, sometimes referred to as “digital farming”.

Digital farming services are intended to assist farmers’ production decision making and increase farm productivity. Using GPS-coded field maps that include assessments of soil conditions, combined with climate data for the particular field, farm input companies can recommend the types of rates of applications for soil conditioning pre-harvest, seed types for planting, and crop protection products during the growing season. Yield monitors at harvest provide outcomes data for feedback to refine and improve the algorithms that are used in subsequent growing seasons.

The integration of digital farming services with seed and chemical manufacturing offers obvious economic benefits for farmers and competitive benefits for service providers. Input manufacturers have incentive to conduct data analytics that individual farmers do not. Farmers have limited analytic resources and relatively small returns to investing in such resources, while input manufacturers have broad market potential for their analytic services. Moreover, by combining data from a broad cross-section of farms, digital farming service companies have access to the data necessary to identify generalizable correlations between farm plot characteristics, input use, and yield rates.

But the value of the information developed through these analytics is not unidirectional in its application and value creation. While input manufacturers may be able to help improve farmers’ operations given the current stock of products, feedback about crop traits and performance also enhances R&D for new product development by identifying potential product attributes with greater market potential. By combining product portfolios, agricultural companies can not only increase the value of their data-driven services for farmers, but more efficiently target R&D resources to their highest potential use.

The synergy between input manufacturing and digital farming notwithstanding, seed and chemical input companies are not the only players in the digital farming space. Equipment manufacturer John Deere was an early entrant in exploiting the information value of data collected by sensors on its equipment. Other remote sensing technology companies have incentive to develop data analytic tools to create value for their data-generating products. Even downstream companies, like ADM, have expressed interest in investing in digital farming assets that might provide new revenue streams with their farmer-suppliers as well as facilitate more efficient specialty crop and identity-preserved commodity-based value chains.

The development of digital farming is still in its early stages and is far from a sure bet for any particular player. Even Monsanto has pulled back from its initial foray into prescriptive digital farming (call FieldScripts). These competitive forces will affect the dynamics of competition at all stages of farm production, including seed and chemicals. Failure to account for those dynamics, and the potential competitive benefits input manufacturers may provide, could lead regulators to overestimate any concerns of competitive harm from the proposed mergers.

Conclusion

Farmers are concerned about the effects of these big-name tie-ups. Farmers may be rightly concerned, but for the wrong reasons. Ultimately, the role of the farmer continues to be diminished in the agricultural value chain. As precision agriculture tools and Big Data analytics reduce the value of idiosyncratic or tacit knowledge at the farm level, the managerial human capital of farmers becomes relatively less important in terms of value-added. It would be unwise to confuse farmers’ concerns regarding the competitive effects of the kinds of mergers we’re seeing now with the actual drivers of change in the agricultural value chain.

TOTM Blog Symposium on Ag and Biotech M&A

My friends at Truth on the Market are hosting a blog symposium later this week including yours truly. It should be an interesting set of perspectives:

Agricultural and Biotech Mergers: Implications for Antitrust Law and Economics in Innovative Industries

March 30 & 31, 2017

Earlier this week the European Commission cleared the merger of Dow and DuPont, subject to conditions including divestiture of DuPont’s “global R&D organisation.” As the Commission noted:

The Commission had concerns that the merger as notified would have reduced competition on price and choice in a number of markets for existing pesticides. Furthermore, the merger would have reduced innovation. Innovation, both to improve existing products and to develop new active ingredients, is a key element of competition between companies in the pest control industry, where only five players are globally active throughout the entire research & development (R&D) process.

In addition to the traditional focus on price effects, the merger’s presumed effect on innovation loomed large in the EC’s consideration of the Dow/DuPont merger — as it is sure to in its consideration of the other two pending mergers in the agricultural biotech and chemicals industries between Bayer and Monsanto and ChemChina and Syngenta. Innovation effects are sure to take center stage in the US reviews of the mergers, as well.

What is less clear is exactly how antitrust agencies evaluate — and how they should evaluate — mergers like these in rapidly evolving, high-tech industries.

These proposed mergers present a host of fascinating and important issues, many of which go to the core of modern merger enforcement — and antitrust law and economics more generally. Among other things, they raise issues of:

  • The incorporation of innovation effects in antitrust analysis;
  • The relationship between technological and organizational change;
  • The role of non-economic considerations in merger review;
  • The continued relevance (or irrelevance) of the Structure-Conduct-Performance paradigm;
  • Market definition in high-tech markets; and
  • The patent-antitrust interface

Beginning on March 30, Truth on the Market and the International Center for Law & Economics will host a blog symposium discussing how some of these issues apply to these mergers per se, as well as the state of antitrust law and economics in innovative-industry mergers more broadly.

As in the past (see examples of previous TOTM blog symposia here), we’ve lined up an outstanding and diverse group of scholars to discuss these issues:

  • Allen Gibby, Senior Fellow for Law & Economics, International Center for Law & Economics
  • Shubha Ghosh, Crandall Melvin Professor of Law and Director of the Technology Commercialization Law Program, Syracuse University College of Law
  • Ioannis Lianos,  Chair of Global Competition Law and Public Policy, Faculty of Laws, University College London
  • John E. Lopatka(tent.), A. Robert Noll Distinguished Professor of Law, Penn State Law
  • Geoffrey A. Manne, Executive Director, International Center for Law & Economics
  • Diana L. Moss, President, American Antitrust Institute
  • Nicolas Petit, Professor of Law, Faculty of Law, and Co-director, Liege Competition and Innovation Institute, University of Liege
  • Levi A. Russell, Assistant Professor, Agricultural & Applied Economics, University of Georgia
  • Joanna M. Shepherd, Professor of Law, Emory University School of Law
  • Michael Sykuta, Associate Professor, Agricultural and Applied Economics, and Director, Contracting Organizations Research Institute, University of Missouri

Initial contributions to the symposium will appear periodically on the 30th and 31st, and the discussion will continue with responsive posts (if any) next week. We hope to generate a lively discussion, and readers are invited to contribute their own thoughts in comments to the participants’ posts.

The symposium posts will be collected here.

We hope you’ll join us!

 

 

Medical Malpractice Data and Inquiries

The current issue of Journal of Empirical Legal Studies includes an interesting data resource and survey by Bernard Black, et al., titled Medical Liability Insurance Premia: 1990–2016 Dataset, with Literature Review and Summary Information. Having just talked briefly about med mal premia and healthcare regulation last week, I was interested to read through the review and description of some of the data and trends. The authors have compiled data from the Medical Liability Monitor, “the only national, longitudinal source of data on med mal insurance rates.”  But they don’t stop there.

We link the MLM data with several related datasets: county rural-urban codes (from 2013); annual county- and state-level data on population (from the Census Bureau); number of total and active, nonfederal physicians, with a breakdown by specialty (from the Area Health Resource File, originally from the American Medical Association); annual state-level data on paid med mal claims against physicians from the National Practitioner Data Bank (NPDB), available through 2015; and data on direct premiums written by med mal insurers from the National Association of Insurance Commissioners (NAIC), available through 2015. We also provide a literature review of papers using the MLM data and summary information on the association between med mal insurance premia and other relevant features of the med mal landscape.

The data appendix, public data, and STATA code book (for cleaning the dataset) are also available from SSRN here. The survey includes a summary of some research into possible explanations for and consequences of medical malpractice premia: effect of med mal risk on healthcare spending, effect of med mal reform on med mal premia, effect of med mal rates on C-section rates and physician supply, effect of med mal payouts on med mal premia.

Noticeably absent from the literature they summarize, which they claim are the principle prior studies using MLM data, is any attention to or focus on market structure issues. Doubly so since there has been a consistent drop in rates over the past 15 years that is generally unexplained in the cited literature. Now, I don’t specialize in health care industry research, but I do know that in the past 15 years there has been an ongoing trend of consolidation among both health insurance companies and medical providing companies (e.g., hospital networks, physician groups, both).  I could easily hypothesize a couple potential dynamics:

  • Increased consolidation among insurance companies may lead to contractual incentives (by way of contract rates and performance measures) that affect the expected cost of med mal insurance.
  • Increased consolidation among hospital networks and physician groups leads to more consistent or standardized practices across larger populations of patients/services, thereby reducing uncertainty or volatility of medical service provision/quality and, thereby, expected cost of med mal insurance.

I suspect there are several potential channels, but it would seem a potentially fruitful area of research–and now there is a more convenient data set with which to play.

Calling a Cost a Cost: NY Anti-Free Speech Edition

Seems the State of New York is going to the Supreme Court for another of its protectionist regulatory policies. Yesterday the US Supreme Court granted a petition to hear the case of Expressions Hair Design v. Schneiderman. As the WSJ explains, at issue is whether New York’s regulations concerning credit-versus-cash retail prices constitute a First Amendment speech violation.

The problem stems from the fact that the State of New York has attempted to have its cake and eat it to by ignoring economic rcredit-card-1520400_1280ealism and prohibiting retailers from calling a cost a cost. The State prohibits retailers from charging customers a fee for using a credit card, but allows retailers to give customers a discount if they use cash. A group of hair salons, led by Expressions Hair Design, sued the state for infringing on its right of free commercial speech. The salons won their initial case, which was reversed on appeal. Now SCOTUS will have an opportunity to weigh in.

The Cost of Using Credit
From an economic perspective, the issue is fairly simple. Credit card companies charge vendors a fee every time a consumer pays with plastic. How much depends on the credit card company, whether the transaction is run as debit or credit, and the amount of the transaction. But typically, the fee is around 2-4% of the amount of the purchase. This reduces the amount of revenue retailers receive when the customer uses plastic. Put another way, when customers choose to use plastic, it raises the retailer’s cost of doing business for that sale.

In a free economy, retailers could choose one of three options: 1) force the credit card user to pay the additional transaction fee, which raises the price at the point of sale, 2) charge the same price for all buyers, implicitly charging cash users more for the product to subsidize the costs of the plastic users, or 3) pass the transaction fee savings on to cash users by giving them a discount. The only economic difference between 1 and 3 is what the sticker price is relative to the price actually paid. In #1, credit card users pay more than the sticker price; in #3, cash users pay less than the sticker price. In #1, the credit card fee is made explicit by adding it on just for those consumers who use plastic. In #3, the sticker price includes (i.e., hides) the cost of using a credit card and by default is the price everyone pays unless they are aware of the cash discount. In either case (1 or 3), the retailer is price discriminating between cash and plastic users. Or the retailer could simply post two sets of prices, one for credit and one for cash, which would then beg the question of “why the difference?” And that is where the NY regulations become a problem.

The NY regulation prohibits retailers from choosing #1 but allows them to choose #3. In other words, the regulation allows retailers to price discriminate, but only if they present it as a discount for cash users rather than a surcharge for credit card users. In short, NY allows the exact same price discrimination between two sets of consumers, but restricts the speech of retailers in how they are allowed to describe that price difference. As Expressions Hair Design argues in their complaint, this places a burden on the business in how it is allowed to explain or justify what is otherwise a perfectly legal two-price pricing system since the regulations make it illegal for employees to explain that the difference between the cash price and the credit price is due to the cost of the credit transaction. It would be like passing a law prohibiting a restaurant from explaining the cost of its steaks went up relative to its pork chops because the price of beef rose.

Framing matters
Why would the State of New York prohibit credit card surcharges but not prohibit cash discounts? Consumers respond to price signals, so how those signals are presented matters. If consumers are charged an extra fee for using their credit card, it makes the cost (price) of using the credit card very obvious to the consumer and she is more likely to change her behavior by using cash instead. This would be bad for the banks that make a significant amount of money on credit card swipe fees. Not surprisingly, banks support laws prohibiting explicit credit card surcharges. However, as noted in #2 above, charging cash and plastic users the same forces cash users to subsidize the purchases of plastic users, which also tends to penalize lower income persons relative to wealthier shoppers. So allowing retailers the opportunity to provide cash discounts is socially superior to not allowing differential pricing. However, the NY’s prohibition on calling a cost a cost and explaining the price difference for what it is, is not only an infringement on speech, but unjustifiable as anything other than an attempt to mislead consumers and protect credit card issuers.

A win for the auto cartel, a loss for Missourians

The Missouri Auto Dealer Association (MADA) has been exercising its political muscle for at least a couple years to protect its antiquated state-supported cartel over new car sales. It seems they have finally succeeded in court where their lobbying efforts have failed. In an opinion  last week by Cole County Circuit Judge Daniel Green, the court ruled that Missouri state statutes governing automobile distribution prohibit Tesla from operating its own retail stores in the state.

The case, which the MADA filed against the Missouri Department of Revenue, contested the State’s issuance of two franchise dealer licenses to Tesla for Tesla to open its own “franchise” retail stores. Basically, Missouri statutes have implemented a circular argument that prohibits auto manufacturers from owning new vehicle dealerships. § 301.550.3 RSMo specifically limits new car dealers to being franchises, statutorily side-stepping the possibility of a non-franchise new car dealer. The court essentially argued (perhaps rightly) that Tesla’s self-dealing of the franchise to itself was merely a rhetorical ploy to circumvent this failure of the statutes to allow for non-franchise dealers. However, even if that side-step were permissible, § 407.826.1 RSMo specifically prohibits auto industry franchisors from “owning or operating a new motor vehicle dealership in this state.”

Judge Green’s opinion basically means the laws of the state of Missouri preclude the possibility of any auto manufacturer selling its cars in Missouri directly to consumers. While Tesla can continue to operate its two service centers in the state, it cannot make car sales there. Instead, the company must continue to sell to Missourians over the internet with a point-of-sale in another state. (So much for more sales jobs.)

I and others have written previously (here, here, and here) why bans on Tesla’s direct-to-consumer sales model are bad for consumers and for society in general. This most recent ruling in Missouri just highlights how fundamentally flawed the regulation of commerce can be. Missouri’s laws, to the extent they ever made sense, are rooted in an antiquated industry and technological setting. Advancements in information technology alone have undercut many, if not all, of the economic justifications for an auto manufacturer to use a franchised distribution system. Laws that were written to protect franchisees in a 1950s-era distribution system do nothing now but raise consumers’ costs and thwart technological and organizational innovation that make everyone better off. Everyone, that is, except the franchised auto dealer cartel that sees all too clearly how little value it now adds in the sale and distribution of new cars.

Hopefully Missouri’s legislature will have the gumption to fix the flaws in its statutes that limit all new car retailers to “franchises” and instead let auto manufacturers (or any other manufacturer) choose the model they find best for themselves and their customers.

 

How mergers affect innovation…maybe?

Justus Haucap and Joel Stiebale with the Düsseldorf Institute for Competition Economics (DICE) at the University of Düsseldorf have a recent paper analyzing the effects of mergers on innovation in the European pharmaceutical industry. The develop a model that suggests mergers reduce innovation not only in the merged firms, but among industry competitors as well. Their data bear this out, as explained in the abstract:

This papers analyses how horizontal mergers affect innovation activities of the merged entity and its non-merging competitors. We develop an oligopoly model with heterogeneous firms to derive empirically testable implications. Our model predicts that a merger is more likely to be profitable in an innovation intensive industry. For a high degree of firm heterogeneity, a merger reduces innovation of both the merged entity and non-merging competitors in an industry with high R&D intensity. Using data on horizontal mergers among pharmaceutical firms in Europe, we find that our empirical results are consistent with many predictions of the theoretical model. Our main result is that after a merger, patenting and R&D of the merged entity and its non-merging rivals declines substantially. The effects are concentrated in markets with high innovation intensity and a high degree of rm heterogeneity. The results are robust towards alternative specifications, using an instrumental variable strategy, and applying a propensity score matching estimator.

While I haven’t yet read the paper in detail, a cursory examination suggests they have ignored another possibility: mergers in high-intensity R&D industries could be a leading indicator of decreased innovation productivity (i.e., lower returns to investment in R&D). Consider that as research advances, the “low hanging fruit” are collected first before the more difficult (and lower return) investments are pursued. As companies in a high-intensity R&D industry exploit all of the low hanging fruit, particularly internally, one might expect mergers as a way of expanding the available set of lower-cost/higher-return R&D investment opportunities. Since firms are competing in the same science space, a slow-down in one firm is likely to be spuriously correlated with slowdowns throughout the industry.

“Affect” is a word of causation. To suggest that mergers cause a reduction in innovation is a strong statement–especially when paired with a merger policy implication. This may be something that bears more scrutiny since, as the authors note, the entire subject is one on which relatively little light has thus far been shed.

Flipping a Coin for Happiness

Steven Levitt of Freakonomics fame (and professor of economics at University of Chicago) has a new paper out on a not-so-new research project. In “Heads or Tails: The Impact of a Coin Toss on Major Life Decisions and Subsequent Happiness” (gated at NBER, a summary article is available here), Levitt finds that individuals who made important life decisions based on a coin flip were more likely to be happy two or six months afterward.  Based on these findings, Levitt suggests that individuals may be too cautious in making major decisions. The abstract reads:

Little is known about whether people make good choices when facing important decisions. This paper reports on a large-scale randomized field experiment in which research subjects having difficulty making a decision flipped a coin to help determine their choice. For important decisions (e.g. quitting a job or ending a relationship), those who make a change (regardless of the outcome of the coin toss) report being substantially happier two months and six months later. This correlation, however, need not reflect a causal impact. To assess causality, I use the outcome of a coin toss. Individuals who are told by the coin toss to make a change are much more likely to make a change and are happier six months later than those who were told by the coin to maintain the status quo. The results of this paper suggest that people may be excessively cautious when facing life-changing choices.

So if in doubt, maybe you should reach in your pocket for a coin. Or you could do it like this guy: