The Academic Literature on State Tax Cuts

by Levi Russell

State fiscal policy continues to be a popular issue. Some are criticizing right-leaning state governments for lowering taxes with the intention of boosting growth. These commentators point out that growth in these states has not skyrocketed. Others are criticizing left-leaning states for funding issues with their public pensions and financial problems associated with Affordable Care Act co-ops. These other commentators point out that these financial issues are not easy to solve and that a more conservative spending approach is probably warranted.

So, being an economist, I thought I'd look at the academic literature on the effects of state-level taxation on economic growth. I pulled the 5 most recent articles I could find on the subject from Google Scholar and looked at the results. Of the 5 articles (of which one examined Canadian provinces) I read, 4 showed a negative effect of state taxation on growth. One showed no effect on own-state growth, and a positive effect from other states' tax increases. I may have missed some other important analysis on this subject, but it seems to me that we can (at least provisionally) conclude that 1) it's not likely that lower taxes are harming growth at the state level and that 2) it's probably a good idea to find ways to fix over-spending rather than increase taxes.

Here are the articles I read. If I missed an important, recent paper, please link to it in the comments below!

Another look at tax policy and state economic growth: The long-run and short-run of it, Economics Letters, 2015, Bebonchu Atems (one of my former graduate school colleagues)

The Determinants of U.S. State Economic Growth: A Less Extreme Bounds Analysis, Economic Inquiry, 2008, W. Robert Reed

The Impact of Tax Cuts on Economic Growth: Evidence from the Canadian Provinces, National Tax Journal, 2012, Ergete Ferede and Bev Dahlby

Redistribution at the State and Local Level: Consequences for Economic Growth, Public Finance Review, 2010, Howard Chernick

The Robust Relationship between Taxes and U.S. State Income Growth, National Tax Journal, 2008, W. Robert Reed

Potpourri

The guys at Agricultural Economic Insights discuss trends in U.S. ag exports.

Jayson Lusk on changes in meat consumption.

John Cochrane offers an alternative explanation for recent slow growth.

In characteristic fashion, Don Boudreaux dispels the myth of beneficial natural disasters.

David Henderson responds to Noah Smith on blind ideology and the effects of economic freedom.

Another interesting article on regulation. 

Defend Trade Secrets Act 2016: Can It Help Protect Your Farm Data?

On May 11, 2016, President Obama signed the federal Defend Trade Secrets Act (the Act) into law. In case you are new to the discussion of trade secrets in terms of farm data, feel free to read some of our older blog posts here. First, it’s important to know that debate exists around the ownership and privacy rights surrounding farm data collected via the many technological tools used in agriculture today. Examples of this data include soil analysis, nutrient information, hybrid seed selection, plant populations, and yield data. Trade secret is arguably one of the tools which may protect farmers’ particular farm data considering, it does not fall squarely into any intellectual property definition. Why is that?

Trade secret law is different from state to state. Even so, many states have adopted the majority of the Uniform Trade Secrets Act (UTSA) provisions. To date, 47 states, and the District of Columbia, Puerto Rico, and the U.S. Virgin Islands have enacted UTSA. New York, North Carolina, and Massachusetts continue to apply their own definitions and standards. Since the majority of states have applied the UTSA as the trade secret standard, courts in those states will look to that definition to resolve conflict surrounding farm data.

The UTSA defines a trade secret as:

1. Information, including a formula, pattern, compilation, program, device, method, technique, or process;
2. Which derives independent economic value, actual or potential, from not being generally known to or readily ascertainable through appropriate means by other persons who might obtain economic value from its disclosure or use; and
3. Is the subject of efforts that are reasonable under the circumstances to maintain its secrecy.

When applied in a farm data context, these three aspects of the definition have more meaning. Consider this definition in the context of growing soybeans. With respect to Part 1: Is the manner and strategy in planting and harvesting soybeans a formula or pattern? We would argue yes, they are. With respect to Part 2: Does growing soybeans in this manner derive economic value? In good years, absolutely. Is the plan for growing and raising soybeans “generally not known or readily ascertainable” to other people in or outside of the industry? Possibly. This is one part of the definition where farm data does not exactly fit and the law must catch up with technology. However, when looking at whether the data are “readily ascertainable,” courts have recognized that where information is available by other means (like a phone book as opposed to a company’s customer list), the data are not protected by trade secret (USAChem, Inc. v. Goldstein, 512 F.2d 163 (2d Cir. 1975)). For example, if dairy cow genetics are available on a publicly available database, the genetic data would not be considered a trade secret, nor protected.

The third aspect of the trade secret definition deserves further consideration. With respect to Part 3: Farmers, landowners, and their advisors haven’t historically employed reasonable efforts to maintain secrecy of their data or practices. A farmer who has grown and harvested the same crops on the same property for several years and understands a particular piece of land better than others potentially has a good argument that his or her farm data are a trade secret, as long as he or she has taken reasonable steps to maintain its secrecy. When considering what reasonable steps are, courts will look to the actions of the farmer. This is done on a case-by-case basis and may be different depending on the data and farm operation.

So where does the Act fall? What the Act provides for is a federal cause of action instead of the patchwork state cause of action previously the only option for any misappropriation lawsuits. The Act does not, however, preempt or invalidate the trade secret laws from individual states but gives the plaintiff the option of using the federal law or the state law in pursuing a lawsuit.

Additional parts of the Act may be advantageous to farmers and farm data which are not typically present in the UTSA and other state specific laws.

1. The definition of trade secrets under the Act is broader, or more inclusive, than the UTSA. The Act incorporates the definition used under the Economic Espionage Act which includes “all forms and types of financial, business, scientific, technical, economic, or engineering information, including patterns, plans, compilations, program devices, formulas, designs, prototypes, methods, techniques, processes, procedures, programs, or codes, whether tangible or intangible, and whether or how stored, compiled, or memorialized physically, electronically, graphically, photographically, or in writing. ” As long as these items are kept secret, they will qualify as “trade secret” under the Act. This definition appears to incorporate farm data more easily as the definition includes “intangible” items (data) as well as techniques and processes which arguably includes planting patterns and the like.
2. There is a whistleblower protection provided under the Act which must be incorporated into employee contracts or non-disclosure agreements. What does this mean? Whistleblowers who disclose trade secrets to an attorney or government official are protected from retaliation and agreements must disclose this fact. Any company or farm which does not do this will be unable to recover punitive double damages (awarded to punish the defendant) or attorney’s fees in litigation.
3. Damages awarded under the Act will be slightly different than those at the state level and may include injunctive relief (stopping the other party from using the secret), damages, and the possibility of double damages for willful and malicious misappropriation of the trade secret. The willful and malicious standard will be decided on a case-by-case basis and depends on the unique situation of the parties. The Act authorizes damages for the actual loss of any and all unjust enrichment caused by the misappropriation. Reasonable royalty is not looked favorably upon but may be used where actual damages cannot be determined. For further discussion on damages under the UTSA for a comparison, see Dr. Terry Griffin’s analysis here.
4. Lastly, the Act applies to almost everyone, not just in cases where a trade relationship exists. What does that really mean? Instead of the lawsuit only arising out of a relationship between a farmer and his co-op or agriculture technology provider (ATP), the Act applies even where an employee may be talking business with someone outside of a formal relationship.

An attorney consulting with a client on a misappropriation case will carefully consider all these differences between a state law and the federal Defend Trade Secrets Act. Whether to pursue the lawsuit at the state level or the federal level will be decided by specific facts, and an experienced attorney will be able to guide the farmer as to which method is best for a particular situation. Being aware of the new legislation and the potential advantages for protection of farm data provided by the Act is important to the ongoing business of the farm.

This post is not to be considered legal advice but simply educational in nature. Please consult an attorney for your specific needs.

Ashley Ellixson, J.D., LL.M.

Guest Contributor

@Legally_Ashley

E-mail

Resources:

https://www.priorilegal.com/blog/6-things-general-counsel-need-to-know-about-the-defend-trade-secrets-act

https://www.congress.gov/bill/114th-congress/senate-bill/1890/text

http://www.agmanager.info/machinery/precision-agriculture/ownership-and-protections-farm-data

http://www.agmanager.info/machinery/precision-agriculture/value-farm-data-proving-damages-based-trade-secret-protections

Proving Damages from Loss of Yield Monitor Data: Forensic Economics and Farm Data Valuation Part 3

This is the final part of my 3-part series on valuing farm data in the event that data were lost. Part 1 provided the background information on the farm scenario and Part 2 described how consequential damages were estimated.

Estimating Speculative Damages
Speculative damages are damages claimed by a plaintiff for losses that may occur in the future, but are highly improbable. Here, I discuss four potential speculative damages with respect to 1) foregone opportunity to participate in ‘big data’ communities, 2) the increased risk of damaging field equipment, 3) inability to negotiate with landowners, and 4) lack of information to base improved drainage structures.

Speculative damages #1: big data systems

Farm data has an inherent value in ‘big data’ systems; and the loss of data reduced the value of the farm’s database for participation these systems. It has been speculated that farm data are worth several dollars per acre-year. Even moderate sized farms have substantial value of data; or several thousand dollars in damages from inability to participate in community analytics.

Speculative damages #2: risk to field equipment

The yield monitor was used to flag large stones and foundation pieces that need to be removed from the field to prevent equipment damage. Given that the stones and foundation pieces are not going to move on their own, there is opportunity to locate these in upcoming seasons; however there is increased risk that combine heads, planters, and tillage equipment may be damaged prior to removal. Farmers paid $14.30 per acre on average for rocks to be picked out of their fields; therefore we can assume that farmers value this service at least $14.30 or higher per acre in perpetuity. By applying this per acre value to all acres of the farm gives a conservative estimate of any repair costs from equipment damages and an adequate risk adjusted value. Even on small areas of 100 acres, damages of $1,430 are reasonable ($14.30 per ac x 100 acres = $1,430). However the $14.30 per acre expense has expectation that the rocks were removed in perpetuity and therefore the value accumulates over time such that the entire $14.30 per acre was not enjoyed in the first year. Therefore the value must be discounted.

Speculative damages #3: leasing arrangements with landowners

Some landowners expect scale tickets to prove yield in crop share agreements while other landowners rely upon yield monitor data or maps for proving yield and negotiating future rental rates or improvements. For flex-cash and flexing based on yield, the yield monitor has become a standard source of yield data for calculating current rents and negotiating future rental arrangements. Irreparable harm may result in the relationship between the farmer and landowners that could impact the farm acreage structure. A 3,000 acre farm typically leases half of the total farmland; and even a loss of 500 acres forces the farm to a 2,500 acre farm in a market fiercely competing for farmland. The 2,500 acre may now be over equipped and not able to support the fixed costs of current equipment complement; therefore now at financial risk.    

Speculative damages #4: drainage improvements

Yield monitor data are used to calculate yield loss due to unimproved drainage. Without adequate data, improved drainage structures were not installed in proper locations for at least another year. Yield losses were estimated for one year by comparing yields from with and without improved drainage structures. This is especially important when negotiating with landowners in their decision to make drainage improvements.

Limitations of these analyses

It can be argued that if the farmer valued the data on the yield monitor that they would have downloaded the data prior to the combine being serviced or at the very least downloaded data periodically such as weekly or after each field or on-farm experiment were harvested. This limitation provides credence to the recommendation of downloading data frequently and to make redundant backup copies. Therefore if data from several on-farm experiments existed on the yield monitor then that implies that data were not downloaded on a periodic basis, and could be interpreted as the farmer placing relatively low value on that data. Similar arguments can be made for other types of precision agricultural data. Newer technology that wirelessly transmits data to the cloud alleviates some of this concern and could indicate the farmer placing more value on the data if they actively pay for services that securely archive the data.

One area of analysis omitted here is the value foregone from lost data for other entities besides the farmer. Seed company representatives may have been relying upon that data as part of a larger on-farm research program. Local retailers often rely upon farm-level data to populate their data systems for community analysis; and would be at some level of disadvantage especially early on in the lifecycle of their system. Landowners often expect yield monitor data as part of their indication of yields and some use to  reminisce from nostalgic purposes or use as conversation piece with their friends.

Summary
Farm data has a value to farmers (and others), although specific values have not been estimated that can globally be applied across farming operations. The value of data lies in how that data are converted to information for farm management decision making. Using yield monitor data and other spatial technologies to conduct on-farm research has been a leading example of monetizing data. In this specific case the greatest value estimated was from consequential damages while speculative damages were the most difficult to estimate a reasonable value. In the near future it is expected that the value of farm data will increase for farmers and others if that data are combined across farms into a community, i.e. big data. Estimating consequential and speculative damages from foregone data is one of the first steps in valuation of farm data.

Terry Griffin, Ph.D.

@SpacePlowboy

twgriffin@ksu.edu

Proving Damages from Loss of Yield Monitor Data: Forensic Economics and Farm Data Valuation Part 2

This is part 2 in my 3-part series on valuing farm data in the event that data were lost. Part 1 provided the background information on the farm scenario.

Estimating Consequential Damages

Consequential damages, otherwise known as special damages, are damages that can be proven to have occurred due to the failure of one party to meet a contractual obligation (goes beyond the contract itself and into the actions garnished from the failure to fulfill). The analysis of consequential damages were separated into two distinct alternatives. The first alternative presented the foregone investment of implementing the on-farm experiment including the 1) differences in cost of the products being tested relative to the status quo product, 2) yield penalties from delaying planting, and 3) miscellaneous supplies such as consultants, flags, and measuring tapes. The second alternative builds upon the first alternative by estimating the foregone revenue stream of making a better decision such as choosing a superior product relative to the status quo. This second alternative is further described below.

To conduct the forensic economics for the consequential damages, I estimated the value of discounted stream of income that would have been realized if the data had not been destroyed (i.e. net present value or NPV). Two scenarios were evaluated. The first scenario evaluated the revenue stream if the data were available. The second scenario evaluated the revenue stream for when farm data were unavailable to the decision making process. The difference in revenue streams between these two scenarios is the forgone revenue.

To set the precedent of historical use of data in farm management decision making, the value of completed on-farm experiments from the last several years were estimated. A series of NPV analyses were conducted on several recent experiments to demonstrate a history of utilizing yield monitor data from on-farm experiments to make farm management decisions. This indicated that the farmer had a history of using yield monitor data for farm management purposes; and that the data had a substantial value to the farmer’s overall net farm income. For the on farm experiments that yield data were not available, the cost of conducting the research were calculated then a reasonable expected yearly revenue stream were estimated for the net present value analysis. These can be thought of as the cost of making the wrong decision.

One of the first decisions to be made is how many years that the results will be usable for the farm. Discussions with the farmer revealed how many years the results from the on-farm experiment typically were used. As a guideline, corn hybrid results are usable for only 1 or maybe 2 years given the relatively short market life of corn genetics. Other input products such as herbicides, fungicides, and insecticides have a longer market life than corn hybrids. Results may be usable for 1 to potentially 10 or more years. For fertilizer rates, the results may be useful for several additional years since the products typically do not have a defined market life. In any case, on-farm research results have a finite lifespan and the value of that data diminishes over time. The results from on-farm research may also be limited in time due to results becoming common knowledge to farmers once public research results are released and/or neighboring farmers providing anecdotal insights. Therefore, the revenue stream may be reduced to fewer years than even the market life of the product.

A key component of the benefit-cost analysis for the expert witness is to compare the ‘best’ decision from an on-farm experiment relative to the status quo practice, not the worst case practice scenario. As an extreme example, a corn hybrid that had an expected yield of 175 bushels per acre (bu per ac) should be compared against the most likely hybrid choice (say 170 bu per ac) and not the option of no seeds (i.e. 0 bu per acre). So the net benefit for a given year would be the price of corn times yield difference (175 bu minus 170 bu = 5 bu) minus difference in seed costs. A more relevant example may be testing two fungicides against no fungicide at all, i.e. the untreated control treatment, where Fungicide A resulted in 15 bu per acre more than the control of no fungicide and Fungicide B resulted in 12 bu per acre more than the control. The expert witness would not use a difference of 15 bu per ac but rather a difference of 3 bu per ac (15 bu – 12 bu = 3 bu per ac). Economists refer to these calculations as partial budget analysis.

One of the leading debates in forensic economics literature is the choice of discount rate used in the net present value analysis. Given that the pertinent farm data examples are shorter time periods relative to the class human lifespan examples, the chosen discount rate has relatively less importance to the outcome of the analyses. That being said, some farm data plaintiffs may prefer higher discount rates and others prefer smaller discount rates depending upon the length of the discounted revenues and relative size of annual returns.

Although there is substantial variability in commodity crop prices over time, a constant price were used for all years of the analysis. A long-run planning price for each crop was chosen for all analyses. An alternative was to assign two or three planning prices (low, expected, high) and perform the analysis at three different levels of commodity crop prices. This provides the decision maker with a set of analysis to choose from and is common practice in benefit-cost analyses. Consequential damages were estimated using the process described in this article. In the final blog of this series, speculative damages are described with respect to this scenario.   

Terry Griffin, Ph.D.

@SpacePlowboy

twgriffin@ksu.edu

Proving Damages from Loss of Yield Monitor Data: Forensic Economics and Farm Data Valuation Part 1

Previously, I described how damages could be proven in the event that farm data were misappropriated (i.e. when farm data disclosed such that it was used in a manner not consistent with agreements although the farmer still had complete access to the data). Working on that project with Ashley Ellixson reminded me of serving as an expert witness on  a case where farm data were destroyed such that the farmer no longer had access to that data (not misappropriated or disclosed but destroyed). In this article, I explain how the value of that data was estimated to the specific farmer.

Description of Farm Data Loss Scenario

The farmer desired to determine the value of the lost farm data in anticipation of building a case against the negligent party. Without disclosing details, the facts of the case were that the yield monitor on the combine harvester was destroyed while the combine was being serviced by a third-party service provider. The third-party service provider admitted fault and agreed to replace the yield monitor,  however they argued that the data on the yield monitor had no value and were not willing to compensate the farmer for the data. The farmer believed that the data did have substantial value and had intended to use that data in their farm management decision-making process. Therefore, the farmer argued that the service provider should compensate the farmer for the lost data. The farmer has a history of using yield monitor data for farm management decision making including on-farm experiments that tested products and rates of inputs under their management practices under environmental conditions of their fields.

Estimating Damages
To reiterate the importance that farmers place on their data, farmers’ willingness-to-pay for data sensors and collection tools (commonly referred to as precision agriculture) indicate farmers readily invest their financial resources in this technology. The substantial amounts of money invested to collect and store site-specific data indicate farmers at least perceive value in the data collected for farm management decision making purposes. Surveys and industry data support the idea that substantial proportions of farmers and even higher percentages of farmland are being harvested with with combines equipped with yield monitors capable of collecting site-specific data. In addition to direct investments in sensors and data management services, investments in human capital to management farm data are substantial.  

Given that the direct damages of the physical components were not contested, the remaining two types of damages including 1) consequential and 2) speculative are being examined. Consequential damages, otherwise known as special damages, are damages that can be proven to have occurred due to the failure of one party to meet a contractual obligation (goes beyond the contract itself and into the actions garnished from the failure to fulfill). Speculative damages are damages claimed by a plaintiff for losses that may occur in the future, but are highly improbable.

Consequential damages resulting from foregone revenue from lost data are considered in Part 2. In Part 3, speculative damages with respect to 1) foregone opportunity to participate in ‘big data’ communities, 2) the increased risk of damaging field equipment, 3) inability to negotiate with landowners, and 4) lack of information to base improved drainage structures are described.

Terry Griffin, Ph.D.

@SpacePlowboy

twgriffin@ksu.edu

The Legacies of Jim Buchanan and Elinor Ostrom

by Levi Russell

About a month ago I ran across a blog post entitled "How to start thinking like a public choice economist." Given my interest in this field of study, I read and enjoyed the post and watched the (short) video interviews. Another post entitled "Putting the action back in collective action" recently came out on the same site, so I thought I'd share them both.


If you're interested in the work of Jim Buchanan and Elinor Ostrom (Nobel Prizes in Economics in 1986 and 2009, respectively), I'd encourage you to check out the posts and videos.

100 Years of Zoning

by Levi Russell

In the past I've discussed zoning laws, referencing articles that compare present vs past policies and that explain the unusual case of Houston, TX. More recently I read an article on Bloomberg with a provocative title: "Zoning has had a Good 100 Years, and That's Plenty." The author's main point is that the costs of zoning laws (primarily their negative effects on the poor) outweigh the benefits. Below are some passages I particularly liked.

Over the past few years, zoning has been blamed, mainly by economists bearing substantial empirical evidence, for an ever-growing litany of ills. The charge that zoning is used to keep poor people and minorities out of wealthy suburbs has been around for decades. But recent research has also blamed it for increasing income segregation, reducing economic mobility and depressing economic growth nationwide.

One can never be certain about these things, but it’s quite possible that excessive land-use restrictions are among the major causes of our long national economic malaise.  Jason Furman, chairman of the White House’s Council of Economic Advisers, made this very point in a speech in November. Yet the platform adopted at the Democratic National Convention this week made no mention of either “land use” or “zoning,” while the Republican platform mentioned them only to condemn the current administration’s purported efforts “to undermine zoning laws in order to socially engineer every community in the country.”

 ...

Dartmouth College economist William Fischel, whose excellent book “Zoning Rules!” has been my most important source on this topic, favors a different explanation. In the decades before the automobile, industrial and residential development was to a large extent constrained by the location of rail and streetcar lines. After trucks and buses became common, though, industrial businesses could locate far from railways (and wharves) and apartment developers could build far from streetcar lines. Anxious homeowners -- and in some cases, merchants -- clamored for rules to keep people from building factories next door.

This does seem to have been one of the motivating factors in New York. According to David W. Dunlap’s New York Times column Monday on the zoning anniversary, “the merchants of Fifth Avenue were losing their retail customers and watching the value of their properties drain away, as big loft buildings for garment manufacturers muscled in around them.” Still, as America’s least auto-centric city, New York also focused its zoning rules on concerns -- skyscraper design, for example -- that were less of an issue elsewhere in the country. It was to be another zoning ordinance adopted six years later in Euclid, Ohio, that ended up fully establishing zoning as a national institution. 

 ...

Sutherland [a Supreme Court Justice in the 1920s and 1930s - LR] affirmed that cities had every right to zone land without compensating landowners or businesses that were harmed. He also said -- unprompted by the facts of the case -- some strangely nasty things about apartment buildings. A sample:

Very often the apartment house is a mere parasite, constructed in order to take advantage of the open spaces and attractive surroundings created by the residential character of the district.

I find it really hard to read that as anything but an affluent guy justifying the legal exclusion of less-affluent people from his neighborhood. There’s been an element of class discrimination to zoning from the early days -- sometimes mixed in with racial discrimination. Still, there have always been other, more-positive aspects, too. In Fischel’s words, “zoning probably makes for more efficient provision of local services and better neighborhoods than would be available without it.”

After about 1970, though, zoning’s negative economic effects began to grow. Before then, housing prices were more or less the same across the country. Since then, prices in the metropolitan areas of the Northeast and West Coast have risen much faster than in most of the rest of the nation -- in the process increasing inequality, thwarting residential mobility and slowing economic growth. Ever-tougher zoning rules and restrictions on growth appear to be a major cause. Fischel has a long list of explanations for this intensification of zoning that I won’t go into here, other than to mention the one that drives me the craziest -- the dressing-up of self-interested economic arguments in the language of environmentalism and morality.

Howard Baetjer on Regulators as Monopolists

by Levi Russell

Howard Baetjer (Towson University) has an interesting article arguing that, in many cases, regulators behave like monopolists. I've written on the subject of monopoly several times over the last year or so (this one and this one are particularly relevant) and I've personally thought a lot about the ideas Baetjer explores in his piece.

The whole thing is worth reading, but here are some really good paragraphs:

Among the most important services in society is assuring the quality and safety of goods and services. We want assurance, for example, that our taxi drivers are competent and their cars are safe, that our banks have adequate capital, that our medicines are safe and effective, and that our schools teach our children well.

And yet the government agencies that regulate the quality and safety of these are legal monopolies. Those they regulate are required to abide by the government agencies’ decisions; the regulated enterprises have no freedom to choose different quality-assurance services from some competing entity instead. Government regulatory agencies are thus not regulated by market forces and, accordingly, they are not directly accountable to the public they are supposed to serve. ... They are indirectly accountable to the public through the political process, but that process puts so much distance between the public and the government regulator that regulators are effectively left unregulated.

So, government regulators are unregulated monopolies.

 ...

To be clear, these regulatory agencies do not have monopolies in the strict sense that no other provider of quality assurance is allowed to operate. For example, some taxi companies may distinguish themselves by enforcing particularly high standards of cleanliness and punctuality; banks could join associations that certify their exceptionally large capital cushions; and name-brand drug manufacturers try to distinguish their products as better than generics. In all these cases, however, the government regulator is the only quality assurer to whose standards all the enterprises in the industry must by law conform. Additional requirements over and above what the government requires are allowed, but the government’s requirements are mandatory. In this sense government regulators have monopolies.