Socialism and data science


Perhaps the best argument for capitalism is that no central planner can adequately understand the entire economy, however good their intentions. This point is made by Hayek, as Cass Sunstein summarizes in his book Infotopia:

Hayek claims that the great advantage of prices is that they aggregate both the information and the tastes of numerous people, incorporating far more material than could possibly be assembled by any central planner or board… For Hayek, the key economics question is how to incorporate that unorganized and dispersed knowledge.  That problem cannot possibly be solved by any particular person or board.  Central planners cannot have access to all of the knowledge held by particular people.  Taken as a whole, the knowledge held by those people is far greater than that held by even the most well-chosen experts.

Free market advocates are prone to take this point too far, overlooking pervasive market failures and the well documented merits of the welfare state and mixed economies. Nonetheless, it remains a central argument for the use of markets to organize much of our economy.

But what if planners could actually aggregate and make sense of all that information?

There’s an interesting piece in The New Yorker on the history of this idea, the interplay between “big data” and socialism. The piece looks at Chile under Allende, and the quest to utilize computer modeling to aid central planning:

At the center of Project Cybersyn (for “cybernetics synergy”) was the Operations Room, where cybernetically sound decisions about the economy were to be made. Those seated in the op room would review critical highlights—helpfully summarized with up and down arrows—from a real-time feed of factory data from around the country… Four screens could show hundreds of pictures and figures at the touch of a button, delivering historical and statistical information about production—the Datafeed… In addition to the Datafeed, there was a screen that simulated the future state of the Chilean economy under various conditions. Before you set prices, established production quotas, or shifted petroleum allocations, you could see how your decision would play out.

As you can imagine, the modeling that was possible in the 70’s wasn’t all that sophisticated, and so it’s no surprise that the system didn’t overcome Hayek’s critique. But the example is a reminder that the efficacy of central planning isn’t necessarily static. With increasingly ubiquitous data collection and more and more advanced data analysis tools and even artificial intelligence, might planners one day rival the market’s ability to distribute scarce resources?

We’re not nearly there yet, however numerous pieces in recent weeks have detailed government’s increasing interest in data science as a tool for conducting policy. Cities like Chicago are using analytics to improve public health, by better targeting regulators’ interventions based on predictive models. More ambitiously, India now has a dashboard that logs attendance of government workers throughout the country. Perhaps the most aggressive is Singapore, which is collecting a frankly scary amount of data:

Across Singapore’s national ministries and departments today, armies of civil servants use scenario-based planning and big-data analysis from RAHS for a host of applications beyond fending off bombs and bugs. They use it to plan procurement cycles and budgets, make economic forecasts, inform immigration policy, study housing markets, and develop education plans for Singaporean schoolchildren — and they are looking to analyze Facebook posts, Twitter messages, and other social media in an attempt to “gauge the nation’s mood” about everything from government social programs to the potential for civil unrest.

There are any number of objections to raise here, starting with privacy. And The New Yorker piece makes clear that the fragile political economy in Chile mattered more than modeling limitations in limiting that particular experiment. Public choice critiques of planning, based on bureaucratic incentives, likely remain poignant even as technical barriers are removed.

And yet in an age where we are able to imagine automated offices, robotic managers, and markets ruled in real-time by algorithms, why not allow ourselves to consider, even briefly, what the same technologies could do for government? Not just to improve a rule here or a program there, but to perhaps revise what the optimal economic system looks like.

My favorite, if oversimplified ,description of the choice between markets and central planning comes from political scientist Charles Lindblom. As I’ve written of it previously:

In his 1977 book “Politics and Markets”, political scientist Charles Lindblom describes the “key difference” between markets and central planning as “the role of intellect in social organization” with “on the one side, a confident distinctive view of man using his intelligence in social organization [central planning]; on the other side, a skeptical view of his capacity [markets].”

If this is true, then the belief that ubiquitous data collection, cheap computing power, and machine intelligence are making us smarter over time should be mirrored by a belief that planning is becoming more plausible. Perhaps more techo-utopians ought to be aspiring socialists, too.

Image via

Our patent problems go way beyond trolls

UPDATE: More recent data documents the serious uptick in patent troll litigation. Likely still true that the patent problem goes way beyond trolls, but they are a problem nonetheless. Recent research is here.

I did a Google Hangout with two intellectual property experts this week, and wrote an article to go along with it. The jumping off point was Tesla’s patent sharing announcement, but really it ended up being broader than that, covering the problems with our patent system and the possibility for reform.

One thing it was not really about was patent trolls, and it occurs to me based on some of the reaction to the article that I should have made this more explicit.

Here’s the chart from the post showing the explosion of patent litigation in the U.S.:



(If you’re curious about that spike at the end, read the update at the bottom of my post.)

The consequence of this dramatic increase is that patents have the effect of making innovation less profitable, rather than more so, in all industries except pharma and chemicals. In other words, when you count up the benefits to innovators from excluding others from their invention, and then subtract the cost of litigation, you get a negative number.

There are many reasons for this, among them the fact that in industries like software the “boundaries” around patents aren’t clear. So you have a patent and I have a patent and neither of us are quite sure what either of the patents does and doesn’t cover. That leads to a lot of unnecessary litigation, and beyond that just a lot of uncertainty.

But it’s worth spelling out that while patent trolls are a problem — one that needs to be addressed — they are not the primary driver of this explosion in litigation. Much of my post borrows from James Bessen of BU, one of the experts I interviewed, who has done research on this question. Here’s what he says in his book Patent Failure:

We also considered the role of patent “trolls,” which we define narrowly as individual inventors who do not commercialize or manufacture their inventions. One story claims that the increasing availability of patent litigators willing to work on contingency fees has spurred lawsuits by such trolls, who might otherwise be unable to afford litigation. The share of lawsuits initiated by public firms has not declined, however, nor has the share of lawsuits involving patents awarded to independent inventors increased. This suggests that the increase in litigation cannot be mainly attributed to patent “trolls,” at least through 1999. Of course, if we use a broader definition of “troll” that includes all sorts of patentees who opportunistically take advantage of poor patent notice to assert patents against unsuspecting firms, then troll-like behavior might be a more important explanation. Indeed, if patent notice is poor, then all sorts of patent owners might quite reasonably assert patents more broadly than they deserve. But then it is more appropriate to attribute the surge in litigation to poor patent notice, not to trolls per se.

As indefensible as the business model of companies like Intellectual Ventures is, that pure troll model does not itself explain the rise in patent litigation.

I wish I’d made this point even in passing in my HBR piece this week. It’s easy to blame the trolls, as well we should. But our patent problems go well beyond them.

The promise of NYT Now

NYT now

There are few if any media outlets that can really go up against the big social networks and have a prayer of stealing away attention. The New York Times might be an exception.

When I first heard about NYT Now I didn’t think twice. It seemed like yet another addition to an already complicated, expensive offering. And its name suggested the reason I didn’t need it: speed is not the primary thing I’m looking for in consuming the Times’ content.

But a piece at Nieman Lab has me rethinking my skepticism:

NYT Now can be seen in part as an Empire Strikes Back play: It aims to take readership back from Twitter and Facebook.

In most cases, for most publications, this will be a losing battle. Still, I can’t help but feel that the moment is ripe for some modest progress here, and The New York Times might be the ones to do it.

I’m not the only one backing away from the social platforms, turned off by the chattering torrent therein. It seems harder every day to maintain a decent signal to noise ratio, which in theory is something the platforms themselves could change. They are developing better filters, and will continue to do so. But with their entire business strategies hinging on more eyeballs on more content for longer, they have a hard time actually making progress on this problem. Essentially, I want tools that make it easier for me to spend less time on Twitter and still find all that I want. That’s in conflict with Twitter’s business plan.

Of course, The Times wants eyeballs on its content for as long as possible, too. But the fact that its business model now includes a subscription component helps here. Once I’m paying for the content, the economics of providing a product I’m not obsessively checking constantly work better. So I’m hopeful that The Times might successfully offer a news feed app that works without being a hopelessly addictive time suck.

A big piece here is that NYT Now plans to include some stories from elsewhere, overcoming one of the biggest barrier to news apps in general, which is that no single publication can ever have all the content you want to read.

There’s also the price. My hesitation in paying for The Times is well documented, and can be summarized as:

1) As much as I like the Times, I don’t need it. And I’d rather be asked to support good journalism than forced to pay for it.

2) If I am going to pay money to support good journalism, I want to know that my money is going directly to that cause.

I wish The Times were structured more like The Guardian, with an endowment funding its efforts. But that’s not the case, and I’m more amenable to paying for it than I was a couple years ago, for various reasons. But at nearly $9/week, the price for the full digital subscription is still high for me.

My basic benchmark in terms of what feels reasonable is Netflix and Spotify: the $7-10/month range. Sure enough, NYT Now falls squarely in that range, at $2/week. That’s getting cheap enough that I might pay merely to support the paper’s mission.

The final reason I’m excited is that I’ve found Circa’s Android app more satisfying than I would have thought, in large part because it is sparing with its notifications. (It seems to only push out truly major news, as opposed to The Times, which pushes alerts about the Final Four.) The presentation in Circa is so clean and condensed that it for the first time has me inclined to see real value in the news app, above and beyond the content, where previously it has always seemed that an RSS reader or Twitter handles the app layer just fine.

For all these reasons, I could see NYT Now working for me (once it comes out for Android). It’s a relatively inexpensive way to support good journalism, and a less noisy way to stay on top of the news as opposed to social media. And they’ve finally learned the lesson that aggregation doesn’t dilute the brand. I may finally have found a news app I want to pay for.

(Note: here’s another Nieman review.)

iPhones and Income: Does Technology Change the Middle Class Stagnation Story?

Steve Jobs Announces the iPhone in 2007

One of the most common responses to my post on middle class incomes was to point out the role of technological progress. If the average American family went back in time to 1989, I wrote, they’d make just as much money but work longer fewer hours to do it. But, some responded, they wouldn’t have iPhones. That isn’t meant to sound trivial, and as someone optimistic about technology I don’t consider it to be. Improvements in technology are an important piece of any conversation about progress. But do they change the story about middle class incomes?

Yes and no.

Short version: All of the data I included adjusted for inflation, which accounts for certain kinds of technological progress but not others. Some new technologies – like the iPhone – aren’t currently captured in that data. Others are. If new technological inventions like the iPhone were able to be included in common inflation measures, the incomes of the middle class would indeed look at least a bit higher.

Here’s the long version, starting with a short overview of inflation.

Measures of inflation track the price of goods over time, and although it’s technically an oversimplification, you can think of such measures – like the Consumer Price Index (CPI) – as a proxy for the cost of living. If the stuff you need to get by costs, in total, $100 per week today, but next year that same stuff costs $200 per week, you’d need to be making twice as much money just to be keeping up. So if you hadn’t gotten any raise over the course of that year, an inflation-adjusted (“real”) accounting of your income would say that your income dropped 50%. Inflation-adjusted income measures account for how much stuff costs.

Prices don’t all change together of course, so the CPI uses a bunch of “baskets” of goods. Food is one part of that. Let’s say the price apples goes up, but the price of bananas goes down. If those changes average out, from the CPI’s perspective, “prices” haven’t changed. (If this happened, you might choose to buy only bananas for a while, in order to take advantage of the low prices. So this is an example of when the CPI starts to diverge from cost-of-living. That’s called the substitution effect and it’s one of the big challenges to measuring inflation, but it’s a bit outside the scope of this post.)

In theory, technological improvements should be captured in measures inflation. Say one of the things most people do is to send letters, documents, and other information to each other. It used to require going to Staples, buying envelopes, paying to print, then paying for postage, etc. Now you can just email them from a relatively inexpensive computer in your home. The price of sending all this stuff, one of your regular life activities, just got cheaper. Inflation is about measuring prices, so a measure of inflation should capture this price decrease. If the inflation measure captures it, it would mean that inflation-adjusted income (like I used in my previous post) would capture the impact of tech.

But in practice, measures of inflation have a really hard time capturing new technologies. To see when inflation does and doesn’t capture technology, let’s go back to the food example.

The kind of technological change that inflation is relatively well set up to track is the kind that results in decreased prices for an existing good. Say a farmer comes up with a new way to grow apples and the result is that the exact same kind of apple you’re used to buying suddenly costs half as much as it used to. The CPI will capture that decrease, and so inflation-adjusted income will reflect the improvement.

But say an agricultural scientist invents some new health shake, unlike any food out there on the market, which provides all your daily calories and nutrients. This counts as a “new good” and inflation measures don’t really have any way to account for it. In practice, if a bunch of people start buying the health shake, after a while the Bureau of Labor Statistics will decide to add it to the CPI and start tracking changes to its price going forward, but this misses the value of the new invention in two respects.

The first, and simpler, problem is that the BLS only updates the CPI’s “baskets” every four years. And for some technologies, prices can drop a lot over that amount of time. So imagine the health shake debuts at $100 per serving, but four years later, by the time the BLS gets around to counting it, it’s going for $20 per serving. That price decrease will be missed.

The second issue is a trickier. The very act of invention, if the new product is novel enough, is simply not accounted for at all in inflation statistics. Here’s how a report from The National Academies puts it:

Without an explicit decision to change the list of goods to be priced, standard indexing procedures will not pick up any of the effect of such newly introduced items on consumers’ living standards or costs…

…If significant numbers of new goods are continually invented and successfully marketed, an upward bias will be imparted to the overall price index, relative to an unqualified [Cost of Living Index]…

…Proponents of more traditional price index methodologies argue that it is a perversion of the language to argue that the effect of, say, the introduction of cell phones or the birth control pill is to reduce the price level, a result that comes from confusing the concept of a price level with that of the cost of living. Their position is tempered somewhat by the realization that, outside of price measurement, there is nowhere else in the national accounts for such product quality improvements to be included and, as Nordhaus (1998) and others have argued, real growth in the economy is thereby understated.

How would the introduction of a brand new good be translated into a change in price? The idea here is that sometimes a new good comes to market at a price lower than some consumers would have been willing to pay. Our magic shake example comes to market at $100 per serving; but perhaps some consumers would have been willing to pay $200 per serving for it, but just never got the chance because the technologies that make it possible hadn’t yet been invented. This difference represents value that inflation measures won’t catch. (An interesting note for innovation econ nerds: this is less likely to be a problem to the extent you see technological innovation as a demand or “pull” driven process. It’s really supply shocks that will cause big problems for inflation measures.) There are econometric techniques that some experts believe could be used to capture this value, but they are complex, controversial, and not yet in use.

To sum up, here’s how to think about it: when Amazon uses better software to make retail more efficient and therefore makes a bunch of consumer products cheaper, that’s captured in our most common measure of inflation. But when a radically new consumer product — like the iPhone — is introduced, some portion of the new value will go uncounted. If the iPhone gets cheaper over the first few years before it is incorporated into the CPI, that value will be lost. But once it is included, improvements in technology that make the iPhone cheaper will be captured.

The result is that inflation-adjusted income measures do fail to account for certain kinds of technological progress. How big is that bias? Best I can tell, we don’t really know. Some have suggested it is sizable, but there is no consensus.

So as for the response — sure, middle class incomes were the same a decade or two ago, for fewer hours worked, but now we have iPhones — it is on to something. It’s perfectly reasonable to point out that certain new tech products are available now and weren’t then, and that income data doesn’t fully capture that. But be careful with this argument. It’s not all new tech that goes un-captured. Lots of the behind-the-scenes increases in efficiency due to tech that result in lower consumer prices are captured, as is at least a portion of the continuing decreases in price for consumer tech products once they’ve been in the market for a while.

So it’s a good point, but a nuanced one.

UPDATE 2/5/14: Martin Wolf at FT nicely captures this in two sentences: “Its price was infinite. The fall from an infinite to a definite price is not reflected in the price indices.”

Don’t gamify healthcare — gamify health


There was a piece in Fortune earlier this month with which I strongly disagreed, on the subject of healthcare, technology, and “gamification”. The post centers around a health tech hackathon and, I think, in dismissing the promise of gamification, misses one of the most promising aspects of health IT. Here’s the gist:

Several months ago, I sat in on a case competition at Boston University’s School of Management. The event played out over two days, during which 15 teams of five students from B-schools all over the world — India, South Korea, Canada, but mostly the U.S. — pitched their ideas for a company, one that would revolutionize health care (the stated goal was particularly jargon filled: “to leverage information technology to transform global health care and create value”)…

Immediately, a theme emerged, and the theme was games. “How do we gamify health care?”… As the day wore on, one of the Merck representatives finally asked, in exasperation, “Why would you make a game out of taking a pill? This will never be fun,” which is true…

I happen to think this is a bit needlessly cynical with respect to drug adherence, but the point I want to make is different. The term “health IT” tends to conjure the thought of medical records and the efficiency of medicine more broadly. But one of the most promising areas in my mind, specifically with mobile technology, is in gamifying health.

If you look at what’s driving U.S. healthcare costs, a huge chunk is driven by diseases directly caused by poor health behaviors like smoking, overeating, and lack of exercise. As I put it in a post a little over a year ago:

Want to crack healthcare costs? Help at-risk individuals smoke less, drink less, exercise more and eat better.

This is where the potential for gamification lies. (If you don’t like the buzzword, call it behavior modification.) Think of it like this: using a doctor to treat the fact that you eat too much and don’t get enough exercise is a terribly inefficient health plan. You go in every few months, the doctor scolds you for not sticking to your diet and exercise regimen, you go home and don’t change.

The opportunity is to leverage the fact that we now all carry powerful computers connected to the internet with us at all times (in the form of smartphones) to nudge us toward better behavior. This is by no means easy! And for now it’s way worse than the alternative of relying on a mix of social support from family and friends along with willpower and attempts to form better habits. But is it out of the question to think that mobile technology can supplement those things?

Think about RunKeeper, the running app, or GymPact, the workout commitment app, in this context. They’re both, basically, turning fitness into a kind of game, and they’re both using different motivational levers to try and increase your likelihood of exercising. This kind of thing — the good behavior layer — is where the potential for gamification lies. Not in making it more fun to take your pills or to receive a medical diagnosis.

The area that excites me in terms of health technology isn’t revolutionizing medicine, as big a deal as that may be, but revolutionizing health.

When there is no one right tool for the job

“The problem with [a] spending freeze is you’re using a hatchet where you need a scalpel.” -Barack Obama, 2007 presidential debate

The implication of the metaphor above is that it’s important to use the right tool for the job; you wouldn’t attempt surgery with a hatchet, after all. It’s a good line, especially when talking about cutting. But, of course, you wouldn’t try to perform a surgery only using a scalpel either. The list of surgical instruments is long, and the procedures are complex enough that there is no single tool that can itself get the entire job done.

McDonald’s and Wages

This idea that any single tool is likely inadequate for a complicated job has been on my mind lately, following an interesting debate about the wage that McDonald’s pays its employees. In particular, Business Insider’s Henry Blodget and Josh Barro have nicely captured this debate in competing posts (and in the short video debate below).

Blodget says McDonald’s just needs to suck it up and freely choose to make less profit in order to pay its workers more. Barro makes a standard economic argument to the contrary, suggesting that should McDonald’s do so, they will suffer for it:

Nor is the enforcement of such a moral norm likely to be an effective way to advance the interest of workers… If McDonald’s decides to pay more than it must, it can be outcompeted by competitors who will feel no such obligation.

Instead, he says, better public policy is the answer. Even there, though, there are limits. A higher minimum wage would almost certainly be good policy, for instance, but raise it too high, Barro argues, and you will start discouraging the hiring of low-wage workers. (Here’s a primer I wrote on the minimum wage a while back.)

If you care about the plight of low-wage workers, this can all be a bit depressing. Simply demanding that companies treat their workers better seems problematic, and at least some relevant public policies have their limits.

The answer, it seems to me, is to rely on multiple tools simultaneously. To pressure companies to treat workers better, while also raising the minimum wage, expanding the earned income tax credit, pursuing sound macroeconomic policy, etc. Rather than relying on any single approach to solve the problem on its own, hope that a number of levers can each make a dent.

Using the Whole Toolbox

There’s nothing original about the idea that thorny problems are unlikely to be solved via a single mechanism, and no doubt this point has been made by many authors in many contexts. But I want to build off one such formulation, offered by Lawrence Lessig in his book Code 2.0. His point, visualized below, is that behavior is shaped by laws, social norms, the market, and by physical architecture. He goes on to suggest that software defines the “architecture” of our online behavior.



As I’ve been thinking about issues like the one above, I’ve kept coming back to Lessig’s drawing, but have mentally added several components. Here’s a very non-pretty articulation of what I’ve been thinking of:

Toolbox - Multi-lever thinkingTechnology: I’ve renamed “Architecture” here, to focus on the role that technology can play in solving a given problem.

Peer production: Though likely not relevant in the wages scenario, peer production – best articulated by Yochai Benkler – is a powerful new tool for thinking about a host of issues. It involves lage-scale collaborative production, often enabled by the internet and typically lacking hierarchical decision-making structures. Think Wikipedia, Linux, etc. I situated it between norms and technology to reflect its reliance on both.

Norms: Our commonly held knowledge, customs, expecations, etc.

Social enterprise: Situated between norms and markets, any commercial effort that is explicit in its desire to improve human well-being in ways that diverge from profit maximization. If a company decided to pay its workers more, for instance, not out of a belief that doing so would increase their share price, but because they felt it was the right thing to do, that would count. An enterprise specifically devoted to making a product or delivering a service because that product or service would help solve a certain social problem would also count.

Market: In my view, it’s wise to frequently ask, when confronted with a problem, could competitive markets solve this? And it’s wiser still never to assume the answer will be ‘yes’.

Market-based policy: Any policy that seeks to channel the benefits of markets. For example, “sin taxes” on things like booze, cigarettes, or soda seek to utilize market incentives to discourage unhealthy behavior. Cap-and-trade for climate change would be another example.

Law and policy: Any attempt to solve a problem through legislation or government rule-making.

Oftentimes, I end up approaching social problems primarily through the lens of policy. What’s the most efficient legislative fix to the issue? Or I might consider what role markets could play, or technology. But seldom do I consciously approach a problem with this entire toolbox in mind. It’s a goal of mine to do so more regularly.

I’m sure in some cases utilizing multiple tools could lead to some crowding out, where the use of one tool disrupts the use of another. But my guess is that in our messy world, we’re most often better off utilizing not just a hatchet, or even a scalpel, but the full range of tools at our disposal.



Medium vs. The New Yorker

new yorkerThe New Yorker is special. I grew up with it around and was a subscriber until about a year ago; lots of my friends and family are addicts. There’s a sense that if you make it through the magazine every week you’re up to speed on a sort of bare minimum of intellectual life. (I have one friend who literally reads every issue cover to cover, and so is over a year behind. Right how he’s probably reading an issue from early 2012.) You don’t have to devote hours a week to following the news because The New Yorker will filter it all for you and provide smart analysis, packaged alongside cultural coverage and fiction.

So editor Nicholas Thompson’s explanation of the brand’s recent success online made sense to me:

The Internet wants to read smart takes on what’s in the news right now.

Let the news addicts wade through every break in every story. The New Yorker will figure out what’s important and give you something intelligent to chew on. They’re just doing it more often and a bit quicker in recognition of the way the web operates.

But will it work?

Someone pointed out on Twitter that this is basically The Atlantic’s online strategy, and it’s worked very well there. I’m optimistic about both publications’ odds, given such strong brands. But if I were in charge of either, the competitor I’d be watching most closely is Medium.

While I and many others like to read smart news analysis online, it doesn’t all need to come from professional journalists. In venture capital, which I cover, it’s the VC’s who frequently write the best analysis. In economics, it’s often tenured professors whose blogs are indispensable. And so the idea of aggregating smart writing from a diverse contributor base is a powerful one. That’s what Medium is doing, combining a beautiful writing tool with the network of Twitter co-founders Evan Williams and Biz Stone to great effect.

Here’s how Williams recently described the project:

“The magazine is the analog for what we’re doing.” … “We’re not focused on news,” he said. “We’re focused on ideas and stories that have a longer shelf life, [whether it’s] short opinion pieces or long-form investigative journalism. We want that to thrive.”

Remember that with this model, not every post on Medium has to be New Yorker quality. The publish then filter model allows you to get a lot of solid contributors on board, writing mostly for free, then filter out the occasionally great stuff and push it out.

I buy that there’s room for a slower, more considered publication to thrive online, purposely contrasting itself to the cacophony of online news. But I have real doubts about it as a business. Amateurs will never fully replace pros – there are many indispensable VC and economics reporters – but there will be some crowding out. The New Yorker may survive on the strength of its brand and the superiority of its writers, but any publication that pursues the same strategy will have to compete – and indeed already is competing – against the amateurization of “smart take.”

How I’d Moderate a Presidential Debate on Economic Policy

Just a quick political vent here. I’ve got fairly strong views about economic policy, but even those with whom I don’t agree recognize a lot of our contemporary political debate on the subject is detached from reality. Here’s a quick outline of how I’d structure a sane debate on economic policy.

1) Remind everyone in the room what the federal government spends money on. I’d give anything to have the image on the right flashed on screen during the presidential debates. The bulk of what the federal government spends on is defense + transferring money from future to current retirees + pay for the health insurance of the elderly and the poor.

2) Unemployment: Differentiate short term vs. long term. Next up would be a discussion of our most pressing economic problem today: high unemployment. What is the candidate going to do to address our short term problems?

3) How would you lower healthcare costs? Future deficits are driven by rising healthcare costs. What’s your plan to lower healthcare costs across the system?

4) What’s Your Plan for Economic Growth? Ok, now we can talk long term. What’s your plan to stimulate economic growth? This is where we’d talk about tax policy, to the extent the candidates believed it was a key growth driver. This is also where candidates would address climate change.

5) Making the Economy Work for Everyone. For the last 30 years, the economic growth we’ve seen has mostly lined the pockets of the most wealthy among us. Yes, life has gotten better for most people, but gains have been highly unequal. Does your long term growth plan address this? Would the benefits of that growth be shared broadly? If not, what other policies would you introduce to create a shared prosperity?


How Much Journalism is Worth Saving?

Back in early 2010, shortly after launching this blog, I put pen to paper on some of the core ideas guiding my views on the future of news. Here’s one of them that I still believe is crucial and often ignored:

1) When I talk about how we will finance news/journalism I’m interested in only a very small subset of all journalism which I’d refer to as that which provides core civic knowledge.  In other words, the information that we feel is vital to a functioning democracy.*  By this measure, most of what we see in the newspapers is not an issue.  Go through a newspaper sometime and look.  We’re not talking about how to fund the sports section, the travel section, the style section, that article on some writer’s quest for the perfect espresso.  That is beyond the scope of what we, as a society, need to ensure exists going forward.

You can read the full post here.

Today, for the fun of it – yes, this is what I do for fun on a Sunday – I browsed the NYT homepage starting at the top and counted 25 headlines to see how many seemed to fit (based on headline alone, for time’s sake) my definition of civic journalism. I ended up with 12 out of 24, with one not classifiable based on headline (it was a Dowd column so we can be fairly sure it could go.)

That 12 included one story not civic  per se, but a breaking news story about an earthquake that seemed worth counting based on the idea of essential national news. You can count the top stories yourself and see what you come up with, though my screenshot below doesn’t capture all 25.

My point here is that most journalism isn’t worth saving, from a democratic perspective. Now, we might want to save it for other reasons. I’m a writer, so I’d like to have a job. I work for a startup that’s betting on the reinvention of media as a business. But whether writers have jobs and investors can get a decent return off of a content business are separate from the question of protecting capital J Journalism.

As for how to preserve that core civic journalism, I don’t have the perfect answer. (I lean toward a nonprofit model like ProPublica, and am additionally hopeful that universities will shoulder a lot of the burden.) But a lot of the discussions about how to save journalism – and about what new media experiments are worthwhile and not – become much clearer once you realize you’re really only trying to preserve a subset of existing media.

One thing that bugs me, perhaps because I work for a new media business, is that new entrants are often judged for the quality of their content without much attention to that of the incumbents. Business Insider gets panned for its slideshows or HuffPo for its celebrity gossip without any mention of the fact that the majority of traditional journalism served no civic purpose, but just existed as entertaining content.

That roughly 50% of the top stories in America’s top newspaper makes this clear. Now, I love the NYT and I’d rather read their non-essential stuff more than the non-essential stuff at various other outlets. But wanting to read a J-school grad reporting on New York’s nightlife lawyer isn’t in and of itself better than reading a Gawker writer mocking this that or the other.

The next time you have a conversation about saving journalism, or about the quality of a new media entrant, remember: the segment of media worth saving for the sake of democracy is only a very small slice of what has traditionally gone under the banner of journalism.

Lying liars and filter failure

There’s no such thing as information overload; only filter failure. That’s the wonderful Clay Shirky maxim. Now we can add to that the maxim: There’s no such thing as a Misinformation problem; only filter failure and attribute it to ex-Google CEO Eric Schmidt. Ok, he didn’t quite say that. But it was close. This is from an awesome Atlantic piece by Alexis Madrigal on Schmidt’s incurable techno-optimism:

“All of us grew up with an assumption that what we were seeing on television, especially in legitimate news, was edited and properly vetted. That’s no longer the case. Furthermore, you can anticipate very powerful forces will attempt to do misinformation campaigns to you for one business objective or another,” he said. “It will be worth it to them to spend millions of dollars to spend millions of dollars to create fake websites and so forth to convince you that something that is really bad for you is really good for you. Because they have a business interest to do so and the Internet allows that.”

Gosh, that sounds bleak! What possible way could we solve this problem? “We have to rank against it,” Schmidt said, that is to say, Google should notice disinformation and rank it lower than good information.

That might seem hopeless if you’re just thinking of it in the context of a search engine. Even if Google ranks something low it can still spread like wildfire on social media. But rankings can extend beyond search.

That’s what’s so cool about experiments like Dan Schultz’s Truth Goggles project. Once you have some ranking or some statement of authority, you can build it into the experience at any level of the tech stack that you want. If the New York Times knows the article your friend is sharing w/ you is crap, that does you no good if their statement saying as much is on your site and you never go there. But if NYT is your trusted source, they could be your browser (or at least a plugin) and have a bright bar up top of everything you read with a credibility ranking.

Of course, these are ridiculously hard problems at every step of the way. But I kinda love Schmidt’s optimism.