Adam Thierer's Blog, page 20

Best Practices for Public Policy Analysts

Over the years I have been asked to speak to colleagues and students I work with about best practices for preparing testimony, public interest comments, opeds, speeches, etc. A few years back, I jotted down some miscellaneous thoughts and used these notes whenever speaking on such matters. I did another session with some GMU econ students today and someone suggested I should publish these tips online somewhere.

So, for whatever it’s worth, here are a few ideas about how to improve your content and your own brand as a public policy analyst. The first list is just some general tips I’ve learned from others after 25 years in the world of public policy. Following that, I have also included a separate set of notes I use for presentations focused specifically on how to prepare effective editorials and legislative testimony. There are many common recommendations on both lists, but I thought I would just post them both here together.

CONTENT BEST PRACTICES: Never bury the lede & hammer your key point(s) repeatedly

Get your key point up-front. We live in a world of information overload and limited attention spans. No matter what it is you are producing (opeds, papers, speeches, testimony, and even books), it is vital to get the message up front. Do not be so arrogant as to assume people care about what you have to say or are willing to spend much time thinking about it. As you begin any project, write down your thesis or key takeaways and make sure it is in the first few lines of your publication or remarks. And then end by repeating that point to drive it home. Do this in all your writing and speaking. Make it a habit of mind.
Repeat, repeat, repeat! Never be ashamed to repeat what you’ve said before. Again, people are really busy and will have very limited time to devote you and your arguments. Just because you said something brilliant once doesn’t mean anyone heard you the first time around, or that they remember it. In fact, don’t be afraid to self-plagiarize a bit. If you spent a lot of time coming up with brilliant arguments and excellent messaging, there’s no need to reinvent the wheel. Reuse your key arguments and talking points again and again. Hammer them home.
Use lists . People love lists. They help focus their thinking. They love Top 3, Top 5, and Top 10 lists in particular. I begin almost every speech and testimony by saying, “There are 3 things I want you to remember about this issue,” and people always start jotting down whatever I say. It’s like magic! I also wrap up by briefly reiterating that same list of key takeaways/conclusions in case they missed them.
Repurpose your work and publish variations constantly . Use a modular “building blocks” approach to your policy outputs. Think of your work like Legos that can be stacked in many different ways. Every product you create is really multiple products that can be aggregated, disaggregated, and then re-aggregated in different ways and in different formats. (See options in graphic below and think about how your main message and talking points can be used across the entire range of outputs).

MARKETING BEST PRACTICES: Build your own brand & know how to target your audience

Don’t wait for others to promote you; promote yourself . Think of yourself as a brand that needs to be promoted and then figure out how to be your own advertising agency.
Do some of your own outreach. Every analyst should do some of their own outreach, particularly to the academy, contacts they have built up over the years, Cap Hill, Executive Branch, press, the academy, etc.  This can complement efforts by outreach and communications departments in your organization.
Have lists of people that you want to consistently push your work out to .  If you quote someone in a paper, journal article, book, or article, highlight it and send it to them.  This greatly increases the chances they will cite you and your work in the future.  
Know the “connectors” in your space (i.e., the people who know everybody in your circles and have a huge following) and get your work on their radar screen.
Stay active on social media platforms (e.g., blogging, Twitter, LinkedIn, FB, etc.), at least as much as you can tolerate before the jerks get you down. In particular, use social media to constantly remind people of relevant work you have done when you are at other events or even just listening to other speeches.
Use multimedia to communicate your message in creative ways beyond boring slide shows (e.g., YouTube, podcasts, or other video and audio services. Even animated videos can help).
Plan ahead and try to be first-mover out of the gate . There is a huge value in being first out with commentary when your topic hits the news; that value drops rapidly if you are second and third out of the gate.
“Tease” your own forthcoming work. While working on paper or new project, alert relevant parties it is coming; seek their input. Also consider doing a couple “teaser” blog posts or short essays alerting others that your paper is coming.
Post all your major publications on major document hosting sites such as SSRN and ResearchGate, among others.
Tag your work . Good SEO (search engine optimization) is vital to making your work easier to find. Use embedded keywords (take the 20 -30 most important keywords in your document and then paste them in the “properties” or “keywords” section of your Word documents, PDFs, SSRN uploads, and blog posts.)
Use anniversaries to your advantage . If there is a special day or anniversary coming up that you can hook your work to, take advantage of them.  

PERSONAL BEST PRACTICES: Get organized & de-clutter your life & brain

Develop talking points files for major issues you cover to help you remember your main points in an instant in case you get random media or policymaker calls and can’t remember everything you wrote 5 years ago on a topic.
Develop a good system of organizing your work. Keep hyperlinked lists of your major publications to easily repurpose elsewhere. Also, using Evernote (web page clipping service) combined with Dropbox (cloud-based document storage that syncs with all your computers & devices) can be a very useful way to organize your work and retrieve it quickly in the future. It helps to develop a sensible filing taxonomy to organize all your work.
Learn which communications to ignore . Do all those emails or social media messages have to be answered right away (or at all)? As important as it is for you to engage with others across multiple mediums, it is also important to figure out who and what can be safely ignored so that you can actually get some thinking and work done! (Ex: I only check work emails twice a day. Most stuff can wait.)
Find your “magic hour.” Different people work better during different parts of the day. For me, I get more quality writing done between 9 to 10 am each day than I do most of the rest of the day. Whatever your “magic hour” is, make it sacred and block out all other distractions to maximize your productivity when you are at peak output potential.
Develop your own style and voice. Examine the approaches others and learn from them, but don’t get too hung up on trying to perfectly mimic them. Develop your own approach that fits your comfort zone. And practice, practice, practice! Get some speech training in particular. Public speaking is difficult for many analysts.
Communicate with conviction but courtesy . It is easy to start screaming when you are passionate about policy issues. Restrain yourself. Treat people and their arguments (no matter how silly) with a certain degree of dignity. You will do a better job demolishing bad arguments with reason and empirical analysis than with sarcasm and shouting. You will also be respected as a better person for taking the high road, even by many of your intellectual enemies.

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3 Specific Tips for Crafting Good Opeds & Testimony
by Adam Thierer

Here are 3 simple rules to live by when crafting good opeds and congressional testimony. Before reading them, it is vital to never forget one simple truth: People are busy! We live in a world of information overload and limited attention spans. So:

Why should anyone care about your issue or argument relative to any other?
What key point should they remember about it?
How does it affect them or others they care about?

Keep those questions in mind at all times as you are preparing opeds or testimony. Accordingly:

Don’t bury the lede:

Tell your audience right up front the most important takeaway (or takeaways) from your article/testimony. Even consider telegraphing it with a line like, “The most important thing to remember about this issue is _______.”
Alternatively, make a short list. People love lists! (ex: “My message here today can be boiled down to three simple points: ______”) As soon as you say that line, watch how people grab a pen and start writing down what you say. [See an example here.]


Keep it simple / speak clearly: 

Use clear, jargon-free “family dinner table” language. Pretend you are writing a letter to your grandma and want to make sure she can understand what the hell you are talking about but without being condescending. You want to impress people with your intelligence, but you don’t want to overwhelm them with it.
Metaphors are particularly helpful and create lasting mental images. Fun example: “Giving money and power to government is like giving whiskey and car keys to teenage boys.” – P. J. O’Rourke.
Keep the narrative tightly focused on bolstering your 1-3 key points. Do not go off on wild tangents.
Make sure you reiterate your key point (or points) at the end. Remember, people are busy and their minds are cluttered (especially policymakers). Thus, they are only likely to remember one or two key themes. You have to hammer them home and dispense with much of the supporting evidence. (For testimony, put supporting evidence in an appendix. For opeds, very briefly summarize it. Better to focus on one big number or result as opposed to dozens of statistics.)


Honor word count / time limit:

Most opeds can only be about 700 words, and most testimony is capped at 5 minutes. Do not exceed those limits.
For opeds, edit and re-edit multiple times and then ask friends and colleagues to proof them to cut words and tighten language.
For testimony, rehearse your remarks out loud multiple times until you are 100% certain that you will not go over and get cut off before you are finished. In my experience, I can get out about 990 words in 5 minutes, but that is really pushing it and I am a very fast talker. Better to shoot for under 950 words and speak at a normal pace.

Technological Innovation, Economic Growth & Human Flourishing

Why should we really care about technological innovation? My Mercatus Center colleague James Broughel and I have just published a paper answering that question. In “Technological Innovation and Economic Growth: A Brief Report on the Evidence,” we summarize the extensive body of evidence that discusses the relationship between innovation, growth, and human prosperity. We note that while economists, political scientists, and historians don’t agree on much, there exists widespread consensus among them that there is a symbiotic relationship between the pace of innovation and the progress of civilization. Our 27-page paper documenting the academic evidence on this issue can be downloaded on SSRN or from the Mercatus website. Here’s the abstract:

Technological innovation is a fundamental driver of economic growth and human progress. Yet some critics want to deny the vast benefits that innovation has bestowed and continues to bestow on mankind. To inform policy discussions and address the technology critics’ concerns, this paper summarizes relevant literature documenting the impact of technological innovation on economic growth and, more broadly, on living standards and human well-being. The historical record is unambiguous regarding how ongoing innovation has improved the way we live; however, the short-term disruptive aspects of technological change are real and deserve attention as well. The paper concludes with an extended discussion about the relevance of these findings for shaping cultural attitudes toward technology and the role that public policy can play in fostering innovation, growth, and ongoing improvements in the quality of life of citizens.

An Esoteric Reading of LM Sacasas

After reading LM Sacasas’ recent piece on moral communities, I couldn’t help but wonder if the piece was written in the esoteric mode.

Let me explain by some meandering.

Now, I am surely going to butcher his argument, so take a read of it yourself, but there is a bit of an interesting call and response structure to the piece. He begins with commentary on “frequent deployment of the rhetorical we,” in discussions over the morality of technology. Then, channeling Langdon Winner, he notes approvingly that “What matters here is that this lovely ‘we’ suggests the presence of a moral community that may not, in fact, exist at all, at least not in any coherent, self-conscious form.”

He is right, the use of the rhetorical we helps to construct a community, which he thens deploys later in the piece. To see this in action,   

…The idea that technical forms are merely neutral has proven hard to shake. For a very long time, it has been a cornerstone principle of our thinking about technology and society. Or, more to the point, we have taken it for granted and have consequently done very little thinking about technology with regards to society.

I’ll note in passing that the liberal democratic structures of modern political culture and the development of technology are deeply intertwined, and they have both depended upon the presumption of their ostensible neutrality. I tempted to think that our present crisis is a function of a growing realization that neither our political structures nor our technologies are, in fact, merely neutral instruments.

Before becoming a policy analyst, I went to graduate school at the University of Illinois at Chicago and studied communication, which at the time was transitioning away from the influence of former dean Stanley Fish and becoming a new media study program. The staff was and still is excellent, but at the time it was deeply heterodox, including both old school rhetoricians and literary scholars as well as communication historians, and communication sociologists.

All of this background is to say that Sacasas’ charge that “we have taken it for granted and have consequently done very little thinking about technology with regards to society,” depends a lot on the kind of community you call your own and how you understand community.

My former community, communication scholars, has a long history of exploring these questions. Indeed, one of my favorite classes was an introductory survey course on democracy and technology. But Sacasas all too well knows that community. I don’t think he was intending to suggest those kind of counterpublics when suggesting community. As he notes, “There is no moral community or public space in which technological issues are topics for deliberation, debate, and shared action.” Here, he means moral community as it comes to us from Durkheim. Just as a reminder, moral community in this tradition generally references “those beings that you need to think ‘but is this right’ before you do something that could affect them.” In other words, questions over the morality of technology are not attended by the kinds of questions that constitute a moral community. I want to come back to this point later.

Where does this leave us? He further explains,

We are, at present, stuck in an unhelpful tendency to imagine that our only options with regard to how we govern technology are, on the one hand, individual choices and, on the other, regulation by the state. What’s worse, we’ve also tended to oppose these to one another. But this way of conceptualizing our situation is both a symptom of the deepest consequences of modern technology and part of the reason why it is so difficult to make any progress.

Technology operates at different scales and effective mechanisms of governance need to correspond to the challenges that arise at each scale. Mechanism of governance that makes sense at one end of the spectrum will be ineffective at the other end and vice versa.

Our problem is basically this: technologies that operate at the macro-level cannot be effectively governed by micro-level mechanisms, which basically amount to individual choices. At the macro-level, however, governance is limited by the degree to which we can arrive at public consensus, and the available tools of governance at the macro-level cannot address all of the ways technologies impact individuals. What is required is a cocktail of strategies that address the consequences of technology as they manifest themselves across the spectrum of scale.

In other words, Sacasas sets up a governance gap problem. There are micro-level solutions and macro-level solutions, but nothing in the middle that might emanate from a moral community. But, again, the fundamental criticism of this entire argument hinges on accepting the rhetorical we and the notion of a community. Or, to say it another way, a community must first be constructed for a governance gap to exist. If we don’t agree to the rhetorical construction of community, if there is no we, then there is no gap to fill. This is no small feat. Even Durkheim’s original understanding of moral community was a subjective understanding of the ethics of an imagined community.

But even separate from the construction problem, it is not clear to me that there isn’t already “a cocktail of strategies that address the consequences of technology as they manifest themselves across the spectrum of scale.” For example, Facebook changed its policy on breastfeeding photos after a group of mothers organized and pushed the #FreeTheNipple campaign. I cannot help but wonder if that is the kind of community driven strategy that Sacasas would want to promote.

That notoriously nebulous concept of civil society is worth invoking here. Organizations like EFF and EPIC and FreePress sue platforms and local governments, and help enact change. And what about all of the reports from journalists in the last decade? They have impacted both Facebook and Google, forcing them to change. Same with Apple and AT&T and Verizon. All of this is to say, I’m not exactly convinced a Habermasian vision of the world is the appropriate yardstick of critique.   

For air taxis, the government can literally make money out of thin air

Every week, it seems, there is a news story about another air taxi startup or test flight. Another signal of interest in industry is that at a House Transportation and Infrastructure hearing last week, Eric Fanning, the President and CEO of the Aerospace Industries Association, devoted most of his testimony to urging lawmaker action on air taxi (also called vertical takeoff and landing aircraft and, colloquially, flying cars) policy and infrastructure.

The technology is exciting but federal officials are interested in whether the air taxi industry will be a drain on taxpayers. Using government estimates of the air taxi industry and current tax rates for infrastructure-based industries like wireless and oil extraction, I estimate that the air taxi industry could deposit tens of billions of dollars into the US Treasury annually. Hopefully the hundreds of air taxi “vertiports” required are privately funded as well.

Air Taxi Market Size

In November, the Wall Street Journal published my op-ed introducing readers to the rapid development and promise of the air taxi industry. Around that time a Treasury official inquired as to the potential size of the air taxi market and government revenue. I wasn’t aware of any estimates at the time. Nevertheless, I estimated that the US market could one day reach $200 billion in revenue annually–about the size of the current US aviation market and the US wireless broadband market.

Other analyst and government estimates are now coming out, and I’m pleased to say that my estimates were on the conservative side. For instance, a NASA-funded study (.pdf) estimated that, at the upper limit, the US market could approach $500 billion annually. That would require tens of thousands of air taxis serving over 10 million passengers per day.

Experts at McKinsey, NASA, and JP Morgan Chase estimate that the global air taxi market could be anywhere from $615 billion to $3 trillion annually by 2040. Given the potential for this industry, other countries are moving quickly to commercialize air taxis. Given this race towards air taxi commercialization, German consultancy, Roland Berger, predicts there will be 3,000 commercial air taxis by 2025. The drone expert at the World Economic Forum believes Chinese companies are far ahead when it comes to autonomous air taxi service. That said, the operator of the Frankfurt airport announced a partnership with an eVTOL company recently, and the powerful Japanese trade and industry ministry has convened a 25-member private-public council to develop air taxis Japanese regulators intend to be the first country with urban air taxi service.

Private or Public Funding of Vertiports?

A key decision for federal lawmakers is whether the hundreds of vertiports in the US will be privately funded or will, like today’s airports, receive subsidies. A NASA study estimates that each major US city could support on average about 200 “vertiports.” That would be a major drain on taxpayers if publicly funded.

My working paper on the subject contemplates entirely private funding of urban vertiports and infrastructure. It also proposes that the government auction aerial corridors to air taxi operators. Private infrastructure and the auction of exclusive aerial corridors, in my view, is the safest and most fiscally responsible way to develop the American air taxi market.

However, the FAA and NASA’s plans are unclear on whether air taxi infrastructure will be funded by taxpayers or funded privately. There’s a good chance the FAA and NASA will import the norms and regulations for traditional aviation–open access airspace and public funding of shared airports–into the urban air mobility market. I think that would create an anticompetitive market and be an unnecessary drain on taxpayers.

Government Revenue From the Air Taxi Industry

How much government revenue could be generated by the air taxi industry? We can look to other assets that are auctioned by government for analogues: spectrum and offshore oil sites. There is no “spectrum tax,” but wireless taxes and fees function as a de facto tax on cellular spectrum. The Tax Foundation puts government (federal, state, and local) wireless taxes and fees at around 9% of wireless revenues. For oil leases on federal property, there is a government royalty amounting to about 12.5% of oil revenue.

Let’s assume that government taxes and fees will one day amount to about 10% of air taxi revenues. Supposing that the US air taxi market will one day be between my conservative estimate, $200 billion annually, and the NASA best-case estimate, $500 billion, the air taxi industry could one day generate about $20 billion to $50 billion in tax revenue annually. That’s a significant stream of tax revenue, and doesn’t include the auction revenues of aerial corridors. If spectrum auctions and offshore oil leases are the best comparison, the auction of aerial corridors could return another $100 billion to the US Treasury.

These are tentative estimates. Market size estimates vary widely, and much depends on whether a workable regulatory framework develops. In any case, like aviation 100 years ago, it’s an exciting area to watch.

The Limits of AI in Predicting Human Action

-Coauthored with Mercatus MA Fellow Walter Stover

Imagine visiting Amazon’s website to buy a Kindle. The product description shows a price of $120. You purchase it, only for a co-worker to tell you he bought the same device for just $100. What happened? Amazon’s algorithm predicted that you would be more willing to pay for the same device. Amazon and other companies before it, such as Orbitz, have experimented with dynamic pricing models that feed personal data collected on users to machine learning algorithms to try and predict how much different individuals are willing to pay. Instead of a fixed price point, now users could see different prices according to the profile that the company has built up of them. This has led the U.S. Federal Trade Commission, among other researchers, to explore fears that AI, in combination with big datasets, will harm consumer welfare through company manipulation of consumers to increase their profits.

The promise of personalized shopping and the threat of consumer exploitation, however, first supposes that AI will be able to predict our future preferences. By gathering data on our past purchases, our almost-purchases, our search histories, and more, some fear that advanced AI will build a detailed profile that it can then use to estimate our future preference for a certain good under particular circumstances. This will escalate until companies are able to anticipate our preferences, and pressure us at exactly the right moments to ‘persuade’ us into buying something we ordinarily would not.

Such a scenario cannot come to pass. No matter how much data companies can gather from individuals, and no matter how sophisticated AI becomes, the data to predict our future choices do not exist in a complete or capturable way. Treating consumer preferences as discoverable through enough sophisticated search technology ignores a critical distinction between information and knowledge. Information is objective, searchable, and gatherable. When we talk about ‘data’, we are usually referring to information: particular observations of specific actions, conditions or choices that we can see in the world. An individual’s salary, geographic location, and purchases are data with an objective, concrete existence that a company can gather and include in their algorithms.

Not all data, however, exist objectively. Individuals do not make choices based on preset, fixed rankings, but ‘color’ their decisions with subjective interpretation of the information available to them. When you purchase a Kindle, for instance, perhaps you are purchasing it because you travel frequently and can’t take a lot of physical books with you. This subjective plan is not directly available and recordable; only the actual purchase shows up as a data point. Machine learning algorithms make predictions based on second-hand, objective data that cannot perfectly reflect the subjective data or knowledge that the individual used to make their decision. Unlike information, knowledge is contextual and is generated from an individual’s interpretation of information against the background of conditions particular to their local time and place.

This does not make prediction impossible; if the actions and decisions of others held no useful information content, the price system as a whole would not function. AI can still assist companies with making predictions, but the contextual nature of knowledge simply restricts the kind of prediction it can make. In 1974, economist F.A. Hayek distinguished between pattern predictions about broad trends in systems and point predictions about what a particular individual or component of the system might do next. If we think about pattern and point predictions, we often think of the difference between the two as a technological problem. But the problem is not a technological one, but an epistemic one. As Don Lavoie put it in National Economic Planning:

“The knowledge relevant for economic decision-making exists in a dispersed form that cannot be fully extracted by any single agent in society. But such extraction is precisely what would be required if this knowledge were to be made usable.”

[Lavoie, Don. 1986. National Economic Planning: What is Left. Page 56]

Let’s assume for a second that AIs could possess not only all relevant information about an individual, but also that individual’s knowledge. Even if companies somehow could gather this knowledge, it would only be a snapshot at a moment in time. Infinite converging factors can affect one’s next decision to not purchase a soda, even if your past purchase history suggests you will. Maybe you went to the store that day with a stomach ache. Maybe your doctor just warned you about the perils of high fructose corn syrup so you forgo your purchase. Maybe an AI-driven price raise causes you to react by finding an alternative seller.

In other words, when you interact with the market—for instance, going to the store to buy groceries—you are participating in a discovery process about your own preferences or willingness to pay. Every decision emerges organically from an array of influences both internal and external that exist at that given moment. The best that any economic decision-maker can do, including Amazon, is to make pattern predictions using stale data that cannot predict these organic decisions and thus have no guarantee of persisting into the future. AI can be thought of as a technology that reduces the cost of pattern predictions by better collecting and interpreting the available data—but the data that would enable either humans or machines to make point predictions simply does not exist.

When we make grand claims about AI’s ability to price products as Uber does, we forget about the role of human action in consuming these services. As Will Rinehart argues, “prices convey information, which then allows for individual participants to act.” The point is that no matter how much information companies collect, and how sophisticated AI becomes, consumer preferences are not something determined ahead of time that exist concretely for the AI to discover. The data predicting these exact choices don’t exist, because the patterns of choices made by individuals are defined by the process of exchange and interaction itself. As long as the competitive forces that drive this process continue to exist, we need not fear dynamic pricing models will erode consumer welfare.

In short, choice is genuine and powerful; we don’t carry around a static schedule in our heads of what prices we are willing to pay for which goods under specific circumstances. Instead, we make choices based on our knowledge and unintentionally reveal our preferences, not just to others, but often ourselves as well. As economist James Buchanan states, market “participants do not know until they enter the process what their own choices will be.” Our preferences, such as they are, are continually created and updated in the process of interaction itself. People’s preferences are consequently moving targets, and cannot be accurately forecasted by AI based on data reflecting past choices.

What do these insights mean for discussions on protecting consumers from exploitative manipulation from companies such as Amazon? First, the epistemic obstacles faced by algorithms means that worst-case scenarios will not likely come about. Instead, the benefits of algorithmic dynamic pricing will outweigh the societal costs. For example, consumers benefit from the Google Chrome add-on Honey, which combs the web for the best coupons to apply when checking out any given product.

Policymakers should be wary of regulating companies to protect consumers against a threat that might not appear. If consumers choose to use platforms such as Amazon or Spotify that gather personal data, we should not automatically assume these algorithms will erode consumer welfare. If policymakers rush to protect consumers because we’re overestimating the forecasting capabilities of AI and underestimating the entrepreneurial capability of individuals in the market, they risk stifling the boon to consumers borne from technological innovations in AI. Policymakers should instead leave room to let individuals and firms work out the best tradeoff between privacy and tailored customer services.

The Kids Are Going To Be Alright

Catchy headlines like “Heavy Social Media Use Linked With Mental Health Issues In Teens” and “Have Smartphones Destroyed a Generation?” advance a common trope of generational decline. But a new paper in Nature uses a new and rigorous analytical method to understand the relationship between adolescent well-being and digital technology, finding a “negative but small [link], explaining at most 0.4% of the variation in well-being.”

What really sets apart the new paper from Amy Orben and Andy Przybylski is that it aims to capture a more complete picture of how variables interact. The problem that Orden and Przybylski tackle is endemic one in social science. Sussing out the causal relationship between two variables will always be confounded by other related variables in the dataset. So how do you choose the right combination of variables to test?

An analytical approach first developed by Simonsohn, Simmons and Nelson outlines a method for solving this problem. As Orben and Przybylski wrote, “Instead of reporting a handful of analyses in their paper, [researchers] report all results of all theoretically defensible analyses.” The result is a range of possible coefficients, which can then be plotted along a curve, a specification curve. Below is the specification curve from one of the datasets that Orben and Przybylski analyzed.

Amy Orben and Andrew Przybylski explain why this method is important to policy makers who are interested in the tech use question:

Although statistical significance is often used as an indicator that findings are practically significant, the paper moves beyond this surrogate to put its findings in a real-world context.  In one dataset, for example, the negative effect of wearing glasses on adolescent well-being is significantly higher than that of social media use. Yet policymakers are currently not contemplating pumping billions into interventions that aim to decrease the use of glasses.

Truthfully this is the first time I have encountered specification curve analysis and am quite impressed with its power. For more details, check out the OSF page, the writeup in Nature, and the full paper. Also, Michael Scharkow details how to apply SCA to variance and includes some R code.

State policy and air taxis

Air taxis and electric vertical takeoff and landing aircraft (eVTOLs) will receive significant regulator attention in 2019 as companies test these aircraft and move towards commercialization. I’m fairly bullish on the technology and its potential and I’m pleased to see state lawmakers and mayors, however, seem to be waking up to the massive possibilities of this industry.

A recent NASA-commissioned study estimates that in the best-case scenario, the U.S. air taxi market would be worth about $500 billion annually, which is nearly the size of the U.S. auto sector. This translates into about 1 million air taxis in the air and 11 million flights per day. Morgan Stanley researchers recently estimated that the global flying car market could be about $1.5 trillion annually by 2040.

You can quibble with the numbers, but it’s clear that aircraft companies and governments believe flying cars are no longer science fiction. Uber plans to offer commercial eVTOL flights in 2023, with testing beginning in 2020. Boeing plans testing later this year.

Federal and state lawmakers need to start preparing for the industry. In November, I published a paper and a Wall Street Journal op-ed proposing that the FAA demarcate and auction highways in the sky–exclusive aerial corridors–for air taxi flights, as a way to manage airspace congestion and preserve competition.

As I wrote in the Detroit News a few weeks ago, state lawmakers also need to start planning for air taxis. States don’t manage aircraft flights but they do manage zoning, property rights, and other areas where state policy can inhibit or encourage the air taxi industry. I mentioned in the op-ed that there are two things states can do in the near future.

Aerial Navigational Easement

First, a good policy is to grant small aircraft a navigational easement to low-altitude airspace. Trespass lawsuits from landowners could scare away companies and innovators who want to test passenger drone and air taxi flights.

About half of states created these aerial navigation easements in the 1920s and 1930s so that trespass lawsuits would not interfere with the new aviation industry. Per these state statutes, flights over property are allowed so long as they do not substantially interfere with the homeowner’s use and enjoyment of the land.

This 80-year old policy will see new relevance in the states this year. Last month, in Washington, a landowner sued a drone operator for aerial trespass. Washington, notably, does not provide for an aerial navigational easement in law.

Air Taxi Advisory Committee

Second, governors or legislatures should consider creating advisory committees for the air taxi industry. Air taxis will raise all sorts of novel state and local issues. A few come to mind:

Should municipal zoning laws for helipads and air taxi “vertiports” be liberalized?EVTOLs require substantial electrical grid improvements and distributed, powerful charging stations on rooftops and landing sites. Are state regulations standing in the way?Air taxis, like trains and autos, create significant noise and local nuisance laws could essentially preclude all air taxi testing and operation. What decibel levels are appropriate to balance industry and public acceptance? Should that be decided at the state or local level?

State advisory committees were created for another emerging technology sector–autonomous vehicles. Committees are composed of stakeholders, including public safety representatives, consumer groups, industry representatives, and academics. They can create policy recommendations for legislators and participate in hearings as air taxis come closer to commercialization.

For the air taxi industry to reach its potential, there needs to be collaboration between and foresight from state and federal lawmakers. Air taxi technology has moved far ahead of law, regulation, and public perception. Fortunately, I expect state and local officials to start examining there current laws and whether modernization is in order to stimulate this transportation sector.

Global Innovation Arbitrage: Export Controls Edition

Policy incentives matter and have a profound affect on the innovative capacity of a nation. If policymakers erect more obstacles to innovation, it will encourage entrepreneurs to look elsewhere when considering the most hospitable place to undertake their innovative activities. This is “global innovation arbitrage,” a topic we’ve discussed many times here in the past. I’ve defined it as, “the idea that innovators can, and will with increasingly regularity, move to those jurisdictions that provide a legal and regulatory environment more hospitable to entrepreneurial activity.” We see innovation arbitrage happening in high-tech fields as far-ranging as drones, driverless cars, and genetics,among others.

US policymakers might want to consider this danger before the nation loses its competitive advantage in various high-tech fields. Today’s most pressing example arrives in the form of potentially burdensome new export control regulations. In late 2018, the US Department of Commerce’s Bureau of Industry and Security announced a “Review of Controls for Certain Emerging Technologies,” which launched an inquiry about whether to greatly expand the list of technologies that would be subjected to America’s complex export control regulations. Most of the long list of technologies under consideration (such as artificial intelligence, robotics, 3D printing, and advanced computing technologies) were “dual-use” in nature, meaning that they have many peaceful applications.

Nonetheless, the Trump Administration is plowing forward with the inquiry following the passage last summer of the Export Control Reform Act of 2018, which required that the President formulate an interagency process to coordinate export control rules with the goal of creating, “a regular and robust process to identify the emerging and other types of critical technologies of concern, as defined in United States foreign direct investment laws, and regulate their release to foreign persons as warranted regardless of the nature of the underlying transaction.” As part of this process, the Commerce Department is to create a list “of foreign persons and end-uses that are determined to be a threat to the national security and foreign policy of the United States . . .  and to whom exports, reexports, and transfers of items are controlled.”

As Jennifer Skees and I wrote at the time, if restrictive export controls were imposed on a broad class of dual-use emerging technologies, it would likely undermine US innovation and competitiveness. More people are waking up to that reality, as well as the specter of global innovation arbitrage kicking in if such heavy-haded regulations are imposed.

Commenting on the impact that these new export controls might have, Cade Metz of the New York Times suggested this week that “[o]verly restrictive rules that prevent foreign nationals from working on certain technologies in the United States could also push researchers and companies into other countries.” Metz also quoted international trade lawyer Jason Waite of the firm Alston & Bird who said of the rules, “It might be easier for people to just do this stuff in Europe,” if controls were imposed by the US.

That, in a nutshell, is how global innovation arbitrage works in practice. Anti-innovation policies create incentives for entrepreneurs to behave more “evasively” and shop around for better places to engage in creative endevours. You can be certain that innovators and especially investors are watching these developments closely. When policymakers are debating the imposition of burdensome new rules, it sends a clear signal to markets about where to put their money. As venture capitalist Marc Andreessen explained back in 2014:

Think of it as a sort of “global arbitrage” around permissionless innovation — the freedom to create new technologies without having to ask the powers that be for their blessing. Entrepreneurs can take advantage of the difference between opportunities in different regions, where innovation in a particular domain of interest may be restricted in one region, allowed and encouraged in another, or completely legal in still another.

Investors like Andreessen will place their bets on technologies and innovators which have the best hope in thriving in such an open environment, wherever that may be on the planet. Let’s hope that continues to be the US. If burdensome exports control regulations are imposed on America’s best and brightest entrepreneurs, that will not likely be the case.

The Top 10 Most-Read Posts of 2018

Below are the top 10 posts on the Technology Liberation Front in 2018. Everything from privacy, to 5G, to tech monopolies, and net neutrality. Enjoy, and Happy New Year!

10. How Well-Intentioned Privacy Regulation Could Boost Market Power of Facebook & Google, April 25.

9. Nationalizing 5G networks? Why that’s a bad idea., January 29. (Republished at The Federalist.)

8. The Pacing Problem, the Collingridge Dilemma & Technological Determinism, August 16.

7. GDPR Compliance: The Price of Privacy Protections, July 9.

6. Evasive Entrepreneurialism and Technological Civil Disobedience: Basic Definitions, July 10.

5. No, “83% of Americans” do not support the 2015 net neutrality regulations, May 18.

4. The FCC can increase 5G deployment by empowering homeowners, July 26.

3. Doomed to fail: “net neutrality” state laws, February 20.

2. Should We Teach Children to Be Entrepreneurs, or How to Pay Licensing Fees?, Aug. 21.

1. The Week Facebook Became a Regulated Monopoly (and Achieved Its Greatest Victory in the Process), April 10.

If you’re worried about net neutrality, put your reputation on the line and make a prediction about the future

It is now been a year since network neutrality rules supported by Title II were officially repealed, marking the end of the Obama-era legislation. Writing in Wired, Klint Finley noted that, “The good news is that the internet isn’t drastically different than it was before. But that’s also the bad news: The net wasn’t always so neutral to begin with.”

At the time, many worried what would happen. Apple co-founder Steve Wozniak and former FCC Commissioner Michael Copps suggested that two worlds were possible. “Will consumers and citizens control their online experiences, or will a few gigantic gatekeepers take this dynamic technology down the road of centralized control, toll booths and constantly rising prices for consumers?”

Katrina Vanden Heuvel, editor & publisher of The Nation warned that, “A broadband carrier like AT&T, if it wanted, might even practice internet censorship akin to that of the Chinese state, blocking its critics and promoting its own agenda.”

Senator Ed Markey even addressed the issue of apocalyptic messaging: “Don’t be fooled by the voices that say this is all doom and gloom & that the ISPs would NEVER block or throttle content. Mark my words, without #NetNeutrality, these are not alarmist & hypothetical harms. They are real, & without #NetNeutrality they may become the new normal.”

Each of these statements is a testable prediction. And those that deeply care about the issue should be willing to make accurate predictions that can be tested at some near point in the future. What bothers me the most is that very few people are willing to bear reputational cost if they fail to correctly predict the future. To borrow a phrase Nassim Taleb, more people should have skin in the policy game.

Here is a set of questions to get the ball rolling. In three years from this week, we should be willing to come back to settle up and see who was right.

A large ISP, as defined by more than 1 million subscribers, will explicitly block political speech.  A large ISP will explicitly throttle an upstream content site.A large ISP will demand additional payment from an upstream content site, separate from transit negotiations.Beginning in January 2019, the Consumer Price Index for “Internet services and electronic information providers” (SEEE03) will begin to rise faster than the total CPI.

Why does this matter? Making nuanced predictions seems to diminish extreme views. A new paper from Barbara Mellers, Philip Tetlock, and Hal R. Arkes gives some context:  

People often express political opinions in starkly dichotomous terms, such as “Trump will either trigger a ruinous trade war or save U.S. factory workers from disaster.” This mode of communication promotes polarization into ideological in-groups and out-groups. We explore the power of an emerging methodology, forecasting tournaments, to encourage clashing factions to do something odd: to translate their beliefs into nuanced probability judgments and track accuracy over time and questions. In theory, tournaments advance the goals of “deliberative democracy” by incentivizing people to be flexible belief updaters whose views converge in response to facts, thus depolarizing unnecessarily polarized debates. We examine the hypothesis that, in the process of thinking critically about their beliefs, tournament participants become more moderate in their own political attitudes and those they attribute to the other side. We view tournaments as belonging to a broader class of psychological inductions that increase epistemic humility and that include asking people to explore alternative perspectives, probing the depth of their cause-effect understanding and holding them accountable to audiences with difficult-to-guess views.

The issue of network neutrality has become polarized. One way to mitigate that bifurcation is to put your reputation on the line and make a prediction about the future.