I re-crunched the CVR and NVCA data, including the new information for 1H2011 (which I annualized to make the numbers comparable).  Bottom line: there has been a slight recovery in the angel contribution and continued growth in the superangel segment.  But these increases  have been mostly offset by a decrease inVC seed activity.  (My collation of the data is available in this Excel file.) Here are updated version of the dollar volume charts.

This is about what I expected.  I think angels’ willingness to invest is driven primarily by the macro environment, which has been improving, albeit rather slowly.  I think LPs willingness to give VCs more dollars to invest is driven by both the macro environment and historical fund returns, which have been very poor.

Now I was a little surprised at the super angel situation.  I had expected a really dramatic expansion from super angels.  First, I searched for new super angels using TechCrunch, VentureBeat, and Google.  I only found two.  IMAF (focused on North Carolina) and Michael Arrington’s CrunchFund (no Web site as of this posting).  According to their SEC Form Ds, they are $13M and $16M respectively.

Second, I searched the SEC Edgar database for all the funds on the original list from Chubby Brain.  Other than Quest Venture Partners, I was able to locate filings for all the significant funds.  Jeff Clavier’s SoftTech VC and Ron Conway’s SV Angel both had decent increases, from $15M to $35M and $20M to $40M respectively.  But in my opinion, those two have reputations such that they could support much larger funds.  Equally strong were Lerer Ventures’ increase from $7M to $25M and Thrive Capital’s increase from $10M to $40M.

The big winner was Roger Ehrenberg ‘s IA Ventures with a jump from $25M to $100M!

But nobody else has appeared to raise a new fund.  Even with these increases, the total confirmed super angel dollars “only” rose from $253M to $440M.  That’s a lot, but not the $1B I would have guessed given the press coverage.  Also, a ~$200M boost spread over multiple years just isn’t that significant when you’re talking about a market that is $8.5B per year.

So I’ll stick to my guns.  No general seed bubble (at least for now).

We’re all meta-startups—startups that working to improve the process of launching startups.

Up until a few years ago, founding a tech startup usually followed the same haphazard process it had for decades.  Founders were pretty much on their own to thrash around and figure out how to test their innovations in the marketplace.  It’s how I did my first startup in 1993.  It’s how I did my last startup in 2004.  Same with Dave Lambert, my partner at RSCM, with his first startup in 1993 and his last in 2003.  It’s what we saw all our entrepreneurial friends do.

This “process” has a high bar for founders to clear and a low success rate, limiting innovation.  Now, the Internet increased the speed at which the haphazard process can execute.  So the situation has modestly improved over the last 15 years.  But what we want is fundamental improvement.  What we want is the equivalent of an Amazon, Google, or Facebook to change the rules of the startup game.

High-volume, high-speed incubators like Y Combinator (2005), TechStars (2006), and Founder Institute (2009) are a great leap forward in discovering a more systematic startup process.  They’re paving the way toward more startups, higher success rates, and dramatically more innovation.  Many of the improvements they pioneer should diffuse out into startup community.  So they’ll enable startup in general, not just the ones in their programs, to launch more smoothly.

Our role in this revolution is eliminating the huge seed stage funding roadblock.  Now, with all the press about angels and superangels funding startups in Silicon Valley and New York, you may think getting seed funding is easy.  But I’ve run the numbers.  Seed funding is actually down 40% from it’s peak in 2005.  I’m pretty sure the cost of doing a startup hasn’t dropped 40%.   And I’m quite sure that the number of quality founding teams hasn’t dropped 40%.

Startups succeed by challenging conventional wisdom.  As a meta-startup, we are no different.  Luckily, our job is easier than most tech startups’.  They have to challenge the conventional wisdom about the future of technology.  There just isn’t much data to go on.  We have to challenge the conventional wisdom about funding startups.  In our case, there’s a lot of data that existing investors are just ignoring:

• The seed stage generates very high returns.  There’s a good dataset of angel investments from the Kauffman Foundation, mostly from 1998 and later.  Depending on your selection criteria, returns are at least 30% and possibly over 40%.  Calculations here and here.
• You can’t pick winners at the seed stage.  This is precisely the kind of prediction tasks where “gut feel” or “expert judgement” performs poorly.  Human predictions almost never beat even a simple checklist.  Review of evidence here.
• Interviewing founding teams is a poor indicator of their ability.  There’s been an incredible amount of research trying to figure out how to predict who will be good at which job tasks.  Unstructured interviews are the worst predictor.  Highly structured interviews or matching of past experience to current requirements does better.  Review of evidence here.
• Grand slams aren’t necessary to achieve high returns.  You don’t have to do anything special to make sure you get the “best” deals.  First, nobody has shown they can identify such startups before they release a product.  Second, even if all you get is base hits, you’ll still have returns of about 30%.  Calculation here.

The conclusion from this evidence is pretty straightforward.  But it implies a seed-stage funding approach very different what we see today.  A classic opportunity for a startup (or meta-startup).

If the seed stage has high average returns but you can’t pick specific winners, basic financial theory says to use a portfolio approach.  You want enough investments to have a reasonably high confidence of achieving the average return.  I’ve done the calculation, and my conclusion is that you want to build a portfolio of hundreds of seed-stage investments.

That may sound like an unreasonable number.  But if you don’t have to spend hours doing multiple interviews of every founding team, you can dramatically streamline the process.  In fact, we have a demo version of our software that can produce a pre-money valuation from an online application in a few seconds.  After that, it’s a  matter of back-office automation.  Luckily, previous generations of startups have made such automation pretty easy.

Just think about how these conclusions should lead to a much better environment for seed stage startups.  Investors will actually decrease risk by making more investments.  The same process that allows them to efficiently building a large portfolio means they can give a much faster response to entrepreneurs.  Lower transaction costs mean startups will see more of the money investors put on the table.  As always, innovation is a win-win.  And in this case, we’re innovating in the process of funding innovation.  A meta-innovation, if you will.

Even better, the average person on the street wins too.  Because, as I’ve shown before here and here, increasing the rate of startup formation increases the rate of economic growth.  So if meta-startups can permanently increase this rate, the returns to society as a whole will quickly compound.

Regular readers already know that I’ve done a fair bit of Moneyball-type analysis using the available evidence for technology startups (see here, here, here, here, here, and here).  But I thought I’d take this opportunity to make the analogy explicit.

I’d like to start by pointing out two specific elements of Moneyball, one that relates directly to technology startups and one that relates only indirectly:

• Don’t trust your gut feel, directly related.  There’s a quote in the movie where Beane says, “Your gut makes mistakes and makes them all the time.”  This is as true of tech startups as it is of baseball prospects.  In fact, there’s been a lot of research on gut feel (known in academic circles as “expert clinical judgement”).  I gave a fairly detailed account of the research in this post, but here’s the summary.  Expert judgement never beats a statistical model built on a substantial data set.  It rarely even beats a simple checklist, and then only in cases where the expert sees thousands of examples and gets feedback on most of the outcomes.  Even when it comes to evaluating people, gut feel just doesn’t work.  Unstructured interviews are the worst predictor of job performance.
• Use a “player” rating algorithm, indirectly related.  In Moneyball, Beane advocates basing personnel decisions on statistical analyses of player performance.  Of course, the typical baseball player has hundreds to thousands of plate appearances, each recorded in minute detail.  A typical tech startup founder has 0-3 plate appearances, recorded at only the highest level.  Moreover, with startups, the top 10% of the startups account for about 80% of the all the returns.  I’m not a baseball stats guy, but I highly doubt the top 10% of players account for 80% of the offense in the Major Leagues.  So you’ve got much less data and much more variance with startups.  Any “player” rating system will therefore be much worse.

Despite the difficulty of constructing a founder rating algorithm, we can follow the general prescription of trying to find bargains.  Don’t invest in “pedigreed” founders, with startups in hot sectors, that have lots of “social proof”, located in the Bay Area.  Everyone wants to invest in those companies.  So, as we saw in Angel Gate, valuations in these deals go way up.  Instead, invest in a wide range of founders, in a wide range of sectors, before their startups have much social proof, across the entire US. Undoubtedly, these startups have a lower chance of succeeding. But the difference is more than made up for by lower valuations.  Therefore, achieving better returns is simply a matter of adequate diversification, as I’ve demonstrated before.

Now, to balance out the disadvantage in rating “players”, startup investors have an advantage over baseball managers.  The average return of pure seed stage angel deals is already plenty high, perhaps over 40% IRR in the US according to my calculation.  You don’t need to beat the market.  In fact, contrary to popular belief, you don’t even need to try and predict “homerun” startups.  I’ve shown you’d still crush top quartile VC returns even if you don’t get anything approaching a homerun.  Systematic base hits win the game.

But how do you pick seed stage startups?  Well, the good news from the research on gut feel is that experts are actually pretty good at identifying important variables and predicting whether they positively or negatively affect the outcome.  They just suck at combining lots of variables into an overall judgement.  So we went out and talked to angels and VCs.  Then, based on the the most commonly cited desirable characteristics, we built a simple checklist model for how to value seed-stage startups.

We’ve made the software that implements our model publicly available so anybody can try it out.  We’ve calibrated it against a modest number of deals.  I’ll be the first to admit that this model is currently fairly crude.  But the great thing about an explicit model is that you can systematically measure results and refine it over time.  The even better thing about an explicit model is you can automate it, so you can construct a big enough portfolio.

That’s how we’re doing Moneyball for tech startups.

My initial response was that we’ll get into those deals before they are monsters.  We don’t need the reputation of a name firm because the guys we want to fund don’t have any of the proof points name firms look for.  They’ll attract the big firms some time after they take our money.  Of course, this answer is open to debate.  Maybe there is some magical personal characteristics that allows the founders of Google, Facebook, and Groupon to get top-tier interest before having proof points.

So I went and looked at the data to answer the question, “What if we don’t get any homeruns at all?”  The answer was surprising.

I started with our formal backtest, which I produced using the general procedure described in a previous post.  It used the criteria of no follow-on and stage <= 2, as well as eliminating any company in a non-technology sector or capital-intensive one such as manufacturing and biotechnology.

Now, the AIPP data does not provide the valuation of the company at exit.  However, I figured that I could apply increasingly stringent criteria to weed out any homeruns:

1. The payout to the investor was < $5M.
2. The payout to the investor was < $2.5M
3. The payout to the investor was < $2.5M AND the payout multiple was < 25X.

It’s hard to imagine an investment in any big winner that wouldn’t hit at least the third threshold.  In fact, even scenarios (1) and (2) are actually pretty unfair to us because they exclude outcomes where we invest $100K for 20% of a startup, get diluted to 5-10%, and then the company has a modest $50M exit.  That’s actually our target investment!  But I wanted to be as conservative as possible.

The base case was 42% IRR and a 3.7x payout multiple.  The results for the three scenarios are:

1. 42% IRR, 2.7x multiple
2. 36% IRR, 2.4x multiple
3. 29% IRR, 2.1x multiple

Holy crap!  Even if you exclude anything that could be remotely considered a homerun, you’d still get a 29% IRR!

As you can see, the multiple goes down more quickly than the IRR. Large exits take longer than small exits so when you exclude the large exits, you get lower hold times, which helps maintain IRR.  But that also means you could turn around and reinvest your profits earlier.  So IRR is what you care about from an asset class perspective.

For comparison, the top-quartile VC funds currently have 10-year returns of less than 10% IRR, according to Cambridge Associates.  So investing in an index of non-homerun startups is better than investing in the funds that are the best at picking homeruns. (Of course, VC returns could pick up if you believe that the IPO and large acquisition market is going to finally make a comeback after 10 years.)

I’ve got to admit that the clarity of these results surprised even me.  So in the words of Adam Savage and Jamie Hyneman, “I think we’ve got to call this myth BUSTED.”

(Excel files: basecase, scenario 1, scenario 2, scenario 3)

Of course, Karl only looked at federal government revenues.  However, with my state budget OCD, I immediately saw the flaw in this analysis.  As one would expect from a public economics professor, he did a good job of controlling for inflation and population, but he forgot that the federal government isn’t the only one with it’s hands in our pockets.

Here are his graph of real per capita federal revenues and my graph of real per capita federal+state+local revenues.

Karl claims that the slope has changed since 2000 so that the rate of growth has significantly slowed or even turned negative.  But I claim that if you add in state and local revenues, the peak to peak trend seems relatively steady since about 1980.  (Note that Karl’s graph is of quarterly data and mine is of annual data, because the state and local series is only available yearly.)

Here are his graph and my graph of the 10 year growth rates:

The peak and trough from this last business cycle look very slightly lower, but my eyeball estimate is that it’s not significant.  Government spending has continued it’s inexorable rise.  All that’s happened is we’ve shifted the relative tax burden modestly from the federal level to the state+local level.

(Like Karl, I generated the data using the Federal Reserve Bank of St Louis’ FRED Graph tool.)

This insight coalesced when I was reading about Mark Satterthwaite, an economist at Northwestern’s Kellogg School of Management.  He’s famous for two important impossibility theorems: (1) the Myerson-Satterthwaite Theorem and (2) the Gibbard-Satterthwaite Theorem.

Informally, (1) says that there is no bargaining mechanism that can guarantee a buyer and seller will trade if there are potential gains from trade, while  (2) says that there is no voting mechanism for determining a single winner that can induce people to vote their true preferences.  In both cases, the reason for the impossibility is that people have incentives to hide their actual values to achieve a strategic advantage.

Add these to the Prisoner’s Dilemma and Arrow’s Impossibility Theorem on the list of fundamental barriers to cooperation (Holmstrom’s Theorem is another good one; it explains why you can’t get everyone in a firm to exert maxium effort).  By “fundamental”, I mean there is no general solution.  So the evolutionary process cannot just discover a mechanism that guarantees cooperation when it is efficient.  There will always be the opportunity for individuals to subvert the cooperative process to promote themselves, thus creating selection pressure against the cooperation mechanism.

(Note that there is a hack: make sure each individual has the same genes.  This is how multicellular and hive organisms get around the problem.  But the existence of cancer in the former case and the reduced genetic diversity in the latter case make them limited solutions.)

To achieve extensive cooperation in large groups, individuals need the ability to model the strategic situation, estimate the payoffs to various group members, and continuously assess what strategies other members may be playing. On top of that, there’s an arms race between deceiving and detecting deception.  It’s the old, “I know that you know that I know…” schtick.  The smarter you are, the further you can compute this series.

Bottom line: the impossibility theorems mean the only way to achieve cooperation is to have the machinery in place to make detailed case-by-case determinations.  We’ve talked about the Dunbar Number before: the maximum size of primate groups is determined in large part by a species’ average neocortical volume.   I claim you need to be smarter to process more complex strategic configurations and maintain models of more individuals’ goals.

If I’m right, there are two interesting implications.  First, politics will be with us forever.  No magical technology or philosophical enlightenment will eliminate it.  Second, if we ever encounter intelligent aliens, they’ll have politics too.  Nothing else about them may be recognizable, but they’ll have analogs of haggling over price and building political coalitions.

First, the history.  Ever since I can remember, at least back to high school, I have come down with more than my fair share of URIs.  This source says that adults average 2-4 colds per year.  I typically averaged 6-8.  Moreover, my URIs seemed more severe than other people’s.  This study on zinc lozenges says the average length of an untreated cold is 7.6 days.  I typically averaged 10-14 days.

Even worse, I developed a lot of secondary bacterial bronchitis and sinusitis, which meant a lot of antibiotics.  There were two periods, one in college and one in my late 20s, where I had 3 sinus infections per year for several years.  They’d put me on inhaled steroids, which would solve the problem for the six months I was on them plus another six months, after which the sinus infections would return.

Finally, about 2.5 years ago, my father suggested I try a nasal irrigation syringe.  I had tried a Neti pot previously without much luck, but the syringe seemed more usable and to generate better irrigation.  After about 2 weeks (and two instances of experiencing copious amounts of amazingly neon-colored discharge), my sinuses were clear for the first time in years.  I haven’t had a single sinus infection since.

Now, I still had the URIs.  They weren’t as bad because I didn’t have painful sinus pressure or develop sinus infections, but they still sucked.  This winter, I was on my normal trajectory of 3 colds between Halloween and New Year’s.  Then I went for my physical in January and my doctor said my serum vitamin D was very low: 17 ng/ml when the recommended range is 30-100.  So I started taking 1,000 IU of D-3 twice a day.

I haven’t had a severe URI since.  I think I’ve had a couple of colds, but their quality is completely different than in the past.  Hardly even worth mentioning compared to my previous experience.  Could be coincidence.  However, vitamin D is crucial to enabling the activation of your immune systems T-cells.  So an improved immune response makes sense.

Rinsing my sinuses with saline once or twice a day and taking a vitamin supplement twice a day are pretty simple interventions.  Probably cost 10-20 cents per day.  Extremely low risk of adverse reactions.  But if they only improve my experience to the average (and I seem to be doing better than average now), I can expect about 60 more days per year free of URI symptoms.  If I’d known about this 25 years ago, that would be a cumulative 4 years saved!

I could have started a whole other startup with the time I spent sick in bed or barely functional at work.

It seems like as our health diagnostic, tracking, and analytic technologies progress, we should be able to identify these situations where simple interventions can result in dramatic health improvements.  I imagine we could see a tremendous improvement in economic productivity if my experience is any barometer.

Rather, I think the increased attention that top startups attract these days induces availability bias.  Because Y Combinator and superangels generate pretty intense media coverage, people read more frequently about the few big investments in seed-stage startups.  They confuse the true frequency of high valuations with the amount of coverage.  Of course, they never read about all the other seed-stage startups that don’t get high valuations.

But if you look at the data on the aggregate amount of seed funding and the average deal size, I think it’s very hard to argue for a general seed-stage bubble.  At worst, there may be a very localized bubble centered around consumer Internet startups based in the Bay Area.

First, look at the amount of seed funding by angels over the last nine years, as reported by the Center for Venture Research.  I calculated the amount for each year by multiplying the reported total amount of funding by the reported percentage going to seed and early stage deals.  (Note: for some reason the CVR didn’t report the percentage in 2004, so I interpolated that data).

As you can see, the amount of seed funding by angels in 2009-20010 was down by half from its level in 2004-2006.  Hard to have a bubble when you’re only investing 50% of the dollars you were at the recent peak.  But perhaps it’s a pricing issue and angels are pumping more dollars into each startup.  While the CVR doesn’t break down the average investment amount at each stage, we can calculate the average investment amount across all stages and use it as a rough index for what is probably going on at the seed and early stage (the index of 100 corresponds to a $436K investment).

The amount invested in each startup in 2010 was down 35% from its 2006 peak.  Now, the investment amount is not the same as the valuation.  However, for a variety of reasons (anchoring on historical ownership, capitalization table management, and price  equilibrium for the marginal startup), I doubt angels have radically changed the percentage of a company they try to own.  So deal size shifts should be a good proxy for valuation shifts.

Now, you might think that VC moves in the seed stage market could be a factor.  Probably not, for two reasons.  First, VCs account for a much smaller share of the seed stage market.  Second, what gets counted as the seed stage in the VC data isn’t what most of us think of as seed stage investments.  Check out the seed dollar chart and the average seed investment data from the National Venture Capital Association.

Notice that amount of seed funding by VCs has remained flat for the last three years.  Moreover, angels invest dollars in the seed stage at a rate of 3:1 compared to VCs.  So VCs probably aren’t contributing to a widespread seed bubble.  But the story takes a strange twist if you look at the average size of VCs’ seed stage investments.

The size has increased since 2007.  But look at the absolute level!  $4M+ seed rounds?  I’m starting to think that “seed” does not mean the same thing to VCs as it does to angels and entrepreneurs.  Obviously, VCs cannot be affecting what I think of as the seed round very much.  However, they could be generating the impression of a bubble by enabling a few “mega-seed” deals.  VCs did 373 seed deals in 2010 while angels did around 20,000 (NVCA and CVR data, respectively).

The last factor we have to account for is the superangels.  Most of them are not members of the NVCA.  However, they probably aren’t counted by the CVR surveys of individual angels and angel groups either.  ChubbyBrain has a list of the superangels that seems pretty complete; I can’t think of anyone I consider a superangel who isn’t on it.  Of the 16, there are known fund sizes for 13.  Two of them (Felcis and and SoftTech VC) are members of the NVCA and thus included in that data.  The remaining 11 total $253M.

Now, there are probably some smaller, lesser known superangels not on this list.  However, many on the list will not invest all their dollars in a single year and some will invest dollars in follow-on rounds past the seed stage.  So I’m confident that $253M is a generous estimate of the superangel dollars that go into the seed stage each year.  That’s only about 3% of angels and VCs combined.

Just to really drive the point home, here’s a graph of all seed dollars, assuming superangels did $253M per year in 2009 and 2010.  Seed funding is down $5.4B or 40% from it’s peak in 2005! So I don’t believe there’s a bubble.

(The spreadsheet with all my data is here.)

Over the entire period, real per-capita revenues rose 22%.  On the graph, you can clearly see the 2001-2002 and 2008-2009 recessions. If we go peak to peak in the last business cycle, 2001 to 2007, the growth was 13%.  If we go trough to trough, 2002-2009, the growth was 9%.

Notice that even from the peak of the previous business cycle in 2001 to the trough of the current business cycle in 2009, real per-capita revenues were still up 6%.  So the recession is clearly not the proximal cause of the state and local budget problems.

State and local government agencies have more inflation-adjusted dollars per person today than they did at the peak of the boom in 2000.

Their revenues are consistently growing. The problem is that they can’t get their damned spending under control!

* State and local government tax revenue from this Census Bureau data.  2000-2009 population estimates from here.  1990-1999 from here.  Ironically, 2010 population data is not yet available so I couldn’t generate a per-capita datapoint for 2010.  I used the CPI-U series for inflation.  I did all the analysis in this spreadsheet file.

About a year ago, she came up with a solution to the “employee union problem”.  With the recent public employee union showdown in Wisconsin, I thought I should share it with you.  What’s particularly ironic is that she’s out in front with some pretty serious economists in thinking about issue.  For example, check out this Wall Street Journal article by Robert Barro, a Harvard professor and author of my undergraduate macroeconomics textbook.

Most people think that unions are simply workers exercising their rights to band together.  Actually, this isn’t the case.  As Barro points out, unions are a monopolies that are specially exempted from anti-trust law. Moreover, the government actually enforces their monopolies.

The key law here is the National Labor Relations Act (NLRA) as administered by the National Labor Relations Board (NLRB).  The NLRB protect the rights of employees to collectively “improve working terms and conditions” regardless of whether they are part of a union.  In addition, the NLRB can force all employees at a private firm to join a union, or at least pay union dues.  This process is called “certification”.  Once a union is certified, it has the presumptive right to negotiate on behalf of all employees at the firm, and collect dues from them, for one year.

After that, it is possible to “decertify” a union.  However, the union usually has enough time to consolidate its power and then use it to keep the rank and file in line.  When you have monopoly power, you have the opportunity to abuse it.  In fact, there’s a whole government agency devoted to investigating such abuses: the Office of Labor-Management Standards.  UnionFacts.com has helpfully collated the related crime statistics for 2001-2005.  Assuming that only a fraction of abusive behavior faces prosecution, these statistics are pretty sobering.

Of course, federal, state, and local government employees are exempt from the NLRA.  You might think this exemption is a good thing for limiting union power.  However, what it means in practice is that each level of government is free to offer special treatment to their employee’s unions without oversight from the NLRB.  As you can imagine, the politicians and public employee union leaders get nice and cozy.  The politicians give the unions a sweet deal and the unions give politicians their political support.  Everybody wins.  Except ordinary citizens.

Personally, my solution would have been to completely eliminate the government-enforced monopoly of unions.  However, I admit this blanket approach could swing power too far towards management in some industries.  My wife’s solution is better.  She says the unions can get monopolies, but only for a set period of time.  Say 3 or 5 years.

From an economics standpoint, this approach is really insightful.  First, it removes union leaders’ incentive to form a union just to accumulate power.  It will all go away pretty soon.  Second, it prevents originally well meaning union leaders from getting corrupted over time.  Pretty soon they’re ordinary workers again.  Third, it does provide help to those workers who feel management is truly abusing them.  They can form a union and get better treatment.  When the union’s existence terminates, they can still bargain collectively, just not exclusively.  If management tries to screw them again, they will have the example of how to work together.  An economist would call this “moving to a better equilibrium”.

I’ll admit this solution isn’t perfect.  Some management abuses will slip through the cracks.  But I’m pretty confident they’ll be less extensive than the current union abuses.  It’s also probably better than my original thought of banning unions altogether.  And there’s some small chance my wife’s approach would actually be politically feasible.  Nice work, Jane!