#researchreports

Economic Structure & GDP/Capita

“The first problem for the government in carrying out an industrial policy is that we actually know precious little about identifying, before the fact, a ‘winning’ industrial structure. There is not a set of economic criteria that determine what gives different countries preeminence in particular lines of business. Nor is it at all clear what the substantive criteria would be for deciding which older industries to protect or restructure.” Charles Schultze, chairman of the Council of Economic Advisors under US President Jimmy Carter

This essay is going to present some research I’ve done recently on industrial structure or what I like to call ‘economic structure.’ A country’s economic structure describes what industries or economic activities it is involved in. We are used to classifying a country’s economic activity as either agricultural, manufacturing or services. Knowing the percentage of economic output that is either agricultural, manufacturing or services we then know the overall economic structure of the country. Of course this is only to a ‘3-industry’ level of detail. You could classify a country’s economic activities much more precisely and in fact in future essays I hope to look at data where a country’s economic activity has been classified to a ’12-industry’ level of detail. The aim of this essay though is to investigate the relationship between a country’s ‘3-industry’ economic structure and its GDP/capita. If there is a strong correlation this would suggest that the path to economic development is largely the same for all countries because for every level of development (i.e. GDP/capita) there is a specific economic structure. If there is a weak correlation this suggests that there are many paths to economic development and that a country’s success is not just a function of what it does but also how well it does it.

3-industry analysis

I did a 3-industry analysis of all the countries in the world I could find data on excluding countries that have populations of less than a million people. The reasoning for excluding them was partly because I didn’t have any data for them but also because intuitively I felt like small countries could have bizarre economic structures which might be very misleading. For example a country of just a few hundred thousand people might have an economy that is largely based on tourism which clearly for much larger countries, with tens of millions of people, is not scalable.

What we find is that the R², a measure of how well the data fits our statistical model, is 0.35 which is not bad but not great. Removing the ‘oil countries’ though we find the R² improves significantly to 0.47.

One puzzling aspect of the results is why the p-values are so low. Even after removing agriculture, especially as for many of the developed countries agriculture is just a few % of GDP, multicollinearity should be a big problem. This is because lowering manufacturing’s % of GDP should (in some cases almost 1-1) increase services’ % of GDP. However regressing manufacturing on services I find an R² of just 0.09 suggesting that there is no multicollinearity problem.

However, further investigation suggests that the t-tests are not valid because the errors are not normally distributed. Eyeballing the pnorm and qnorm graphs we can see the errors are clearly not normal.

Taking a look at a 3-D graph though it becomes clear that it is actually agriculture that does most of the explanatory work. Below you can see a top-down view of the 3-D graph where the red line represents the 1-1 trade-off between services and manufacturing. The closer a data point is to the red line the smaller agriculture contribution to GDP is and the larger the combined contribution of services and manufacturing is.

Taking a side-on view we can see that when agriculture is a high percentage, more than 10% of GDP, i.e. left of the blue line GDP/capita is very low. If agriculture is less than 10% of GDP suddenly GDP/capita is much higher. In fact if you compare the average GDP/capita of countries with agriculture that is more than 10% of GDP/capita to countries where agriculture is less than 10% of GDP/capita you find a stark difference. The more agricultural economies have an average GDP/capita of just $4294.8 whereas the less agricultural economies have an average GDP/capita of $26261.7. And in fact a regression on just % of GDP that is agriculture has an R² of 0.43 only slightly lower than the manufacturing and services regression R² of 0.47. As you can see below, as GDP/capita rises countries tend to have a larger Services % as compared to Manufacturing % but the effect is not as extreme as one might expect.

I think the real puzzle to me is why there is this turning point at around 10% agriculture. It cannot just be that the less productive agriculture is being phased out in favour of the more productive manufacturing and services. There must be something more fundamental going on but what exactly I’m not sure. Nonetheless I think you could make a good argument that the countries in the left column which have relatively low GDP/capita given their low % of GDP that is agriculture are perhaps on the verge of a massive economic growth as they race up the curve and join the countries on the right column.

10% Agriculture

I want now to try and investigate what is happening when countries agriculture drops below 10% and explain why GDP/capita rockets up so much once this threshold is breached. Unfortunately the Serbian and Argentina websites are not in English and so I was unable to find the data I wanted. Thailand, Macedonia and Malaysia on the other hand have only very limited data sets available. This leaves us with Turkey, Belarus, Tunisia and China.

Part of the problem is I’m not sure what I’m looking for. I suppose it seems like manufacturing and services suddenly become much more productive when agriculture dips below 10%. So why is this? Is it coincidence? Is that at that point enough labour transfers across? Is it that old industries suddenly become more productive or new industries become less productive?

Next steps a) Investigate countries around the 10% boundary. Why the sudden increase in GDP/capita b) Investigate whether countries historic development in terms of the turning point around 10% agriculture matches the current distribution. c) Investigate 12-industry data to see whether there is one path to economic development or multiple. Does economic structure determine GDP/capita?

Investing in the London Housing Market

This essay is an exploratory look into the housing market, using London as the starting point. Housing is interesting because there is no obvious fundamental to tie the price to. This is in contrast to say stocks where the price of a company should, at least in practice, be tied to the current and discounted future expected profits. Of course like any market I would still expect housing to be internally consistent, where no house sells for much more or less than other similar houses in the same area. This internal consistency however, does not protect you from bubbles and the mass delusion that my house is not mispriced because look at what next door sold for! Clearly having some fundamental to tie house pricing to would be very useful as a way to spot bubbles. Ideally though we would like to be able to go further and use predictions about the fundamental to in turn predict future house pricing.

Most housing investors tend to have one of two strategies. The first is to buy an old house and renovate it and the second is to predict which areas are ‘on the up,’ which areas are about to be gentrified. This is more of an art than a science but classic clues are improved transport links, better shops and bars and good (but perhaps in need of work) housing stock. I even heard one investor who bought based on wherever a new Starbucks was about to be built. Looking at the data for house prices between 1995 and 2014 we find that there is no obvious relationship between the initial mean house price in 1995 and the amount the index has increased by 2014. Some cheaper areas were gentrified a lot other areas where not.

All of these strategies though are fundamentally unsatisfying because it still feels a lot like guesswork. My mum manages and invests in property for a living and it is interesting talking to her because she would much rather invest a million pounds on a flat in Chelsea (a comparatively expensive area) than say three houses in Lewisham (a comparatively cheap area). Her argument is that people will always want to live in Chelsea. Initially I thought this argument seemed a little naïve but actually it bears a striking resemblance to Warren Buffett’s approach to investing in stocks. Buffett argues that, just in the same way it is difficult to predict which area will be gentrified next, it is very difficult to predict which companies will succeed. In fact even if you are sure that say, the car or the plane (as they most certainly did) are going to change the world that does not make it any easier to pick the winners from the losers. So Buffett’s approach is to ask what will technology not change? Who is on top now and is likely to remain there? This strategy has led to safe and unsexy investments in companies like Coca Cola and Wrigley’s chewing gum rather than the roulette table of trying to predict the next Facebook or Google.

Nonetheless it seemed to me that imbedded in my Mum’s preference for Chelsea was the hidden assumption that rich people are going to get richer faster than poor people will. And this gave me the idea that the fundamental that could be used to tie down housing prices should be income and in particular how well different strata of the income distribution are doing and are expected to do in the future. Put simply if you expect there to be more inequality with the rich accelerating away from the poor then you would expect expensive areas like Chelsea to be a better investment. If you expect income inequality to go down with lower income people getting richer faster than higher income people a cheaper area like Lewisham may be a better investment.

Income and property prices

The first step was to investigate whether there was a relationship between property prices in an area and the incomes of people in that area. Intuitively it seems like there should be but there are also good reasons to think there may not be. Firstly property markets are not particularly liquid because people buy and sell houses relatively infrequently. Furthermore, housing’s relatively low running costs mean that once you own a property even if your income is relatively low you can continue to live there. Perhaps you bought your house with your low income decades before and then just been fortunate to have the value of the house rise a lot since. Similarly even if your income is much higher than your house suggests you may have already put down roots in an area, with all your friends and family nearby so even though you could afford a more expensive house you do not move. And finally you would expect there to be quite a lot of variation in how much of their income people would want to spend on housing versus other goods. What we find though is actually there is quite a strong relationship between mean house price and mean income across the boroughs of London.

Above is a regression of mean house prices in the 32 boroughs of London on the mean income of people in those boroughs. The house price data is from the Land Registry’s House Price Index and the income data from HMRC’s Survey of Personal Incomes. The House Price data is released on a monthly basis and so it was averaged out for the tax year to match the HMRC’s data. In this case the data is from 2011. What we find is a very high R² of 0.936 suggesting the model has a lot of explanatory power. The t-statistics are statistically significant with very small p-values. And in fact, this is not surprising because if you eye-ball the graph the data certainly looks fairly linear.

However it is important to do some robustness checks to make sure the assumptions of linear regression are being met, particularly to check whether the errors look random or not. Which as you can see from the rvf plot the residuals clearly do not.

I thought the lack of normality and randomness of the errors might be caused by non-normal mean income and mean house price variables. So I tried using ladder, gladder and qladder functions on Stata to suggest possible transformations.

First I found that mean income can be considerably improved by transforming it to 1/meanincome² where from ladder we can see that we get a χ² of just 0.69 which is pretty good.

This is also reflected in the gladder plot which shows visually the different transformations and how normal they look. As with ladder 1/square looks the best.

The 1/square transformation looks especially good in the qladder plot which is sensitive to deviations at the extremes.

However unfortunately we find that for mean house price no similarly good transformation exists.

1/square is again the best, but this time only the best of a bad bunch. The qladder plot in particular is not that good.

And in fact I found that if we regress the new variables 1/meanhouseprice² on 1/meanincome² in addition to losing explanatory power the rvfplot does not improve that much because the errors still do not look very random.

Use income as predictive variable of property prices?

Given that there is clearly some sort of relationship between property prices and income I thought it might be interesting to try and use income as a predictive variable for future property prices. In the above example I used income and property data from the same tax year but it would not be that surprising if perhaps there was a lag effect where income increases and then it takes a few years before that is converted into an increase in property prices.

To investigate this I regressed mean house prices for each year across all the different mean income years that I have in my dataset. Thus we take the mean income data for the tax year 2000 as the explanatory variable for 12 regressions for mean house price from the year 2000 to 2012.

What we find is roughly what you would expect which is that the mean income data for the year 2000 has more explanatory power for year 2000 mean house prices than the mean income data for the years following that. However, when we consider the other years we find that this pattern is not maintained.

Instead it seems like some years the mean income data has more explanatory power than other years, or perhaps more intuitively, some years the housing prices are more in sync with income distributions (2005 and 2005) and other years more out of sync with the income data (2000 and 2002).

Surprisingly housing data is more in sync with income for 2005 and 2006, the years just preceding the financial crisis. It seems a reasonable hypothesis that the housing market should be explained by incomes in that area and so years where there is a larger disconnect such as in 2000 and 2001 might suggest a good time to purchase for a potential investor. Having said, the variations in R² are pretty small and I have not done robustness checks as I would expect similar problems transforming the data as before.

One thing is clear, it seems unlikely that changes in London’s overall income distributions can be used to predict changes in future house prices. This is especially true as income data is only available two years afterwards (so for the tax year 2014 only 2012 data is currently available).

Borough by borough income and property price data

The next step I thought would be to try to see if there were any patterns in the borough by borough housing and income data.

What I noticed was that more expensive areas seemed to recover much quicker from the recession than cheaper areas. As an example Kensington & Chelsea and Islington seem to be already back to trend. This is in contrast to many cheaper areas where increases in income in recent years has not be converted into higher house prices. This led me to hypothesis that perhaps poorer people were being disproportionally credit constrained.

As you can see boroughs with mean house prices that were less than £350000 in 2007 by 2012 had suffered decreases in house price value. This is contrast to boroughs with mean house prices in 2007 above about £350000 where their prices increased. And in fact the seemingly linear relationship has a pretty good R² of 0.935. The question then is whether this constraint is a long term phenomena or a short-term one. If it is short-term it would make sense to invest in the cheaper areas whilst the house prices are temporarily lower because of short-term increases in lending standards.

However when I investigated the amount of leverage for the different housing boroughs I found that the least leveraged areas where the most expensive. Admittedly all the mean income data is pre-tax but given the tax rules vary from person to person I could not think of an intelligent way to estimate the average tax rate for each borough.

Conclusion

So in conclusion it seems like the best area to invest in is Kensington & Chelsea. There are several reasons for this.

Firstly in the last twenty years house prices have increased seven times in Kensington & Chelsea compared to just three, four or five times in other areas. Clearly to invest in an area simply because historically it has increased the most is unwise but on the other hand at least it gives a positive trend.

Secondly the leverage of residents in Kensington & Chelsea is the lowest of all the boroughs suggesting that the increase in prices is not a function of lax lending standards pre-recession. Having said that, I suspect many Kensington & Chelsea residents may own multiple properties so they might actually be more leveraged than I initially imagined. Also as previously mentioned, incomes are pre-tax so it is possible I’m underestimating the amount of leverage.

Thirdly increasing inequality particularly as the most wealthy’s incomes are increasing at a faster rate than everyone else’s is likely to lead to even greater increases in house prices in Kensington and Chelsea.

And finally there are very high numbers of foreign buyers, who tend to invest primarily in the prime areas of central London including Kensington and Chelsea. In fact, according to property experts Knight Frank as many as 28% of buyers of properties over £1 million do not live in the UK. Having said that it is possible that foreign demand will slow down with the introduction of a new capital gains tax for foreigners when they sell homes in the UK from April 2015.

The next step in the research will I think to try and investigate these factors further.

Incentive Systems And New Approaches To Venture Capital

Nothing can be more central to the healthy functioning of our economies than the investment in innovation of new products and services and the incentive systems that motivate them. For decades, Venture Capital (VC) has been at the heart of this story and yet as an asset class, especially outside the top 20 VC firms, returns have been weak. According to Cambridge Associates the 10 year returns on early stage funds was just 3.9%. Even later and expansion stage funds only achieved 9.3% which compared to the S&P 500 at 8.0%. This is despite the fact that VCs talk about a minimum respectable return for a VC fund being 20%. In fact according to a 2012 report called ‘We have met the enemy… and he is us’ by the Kauffman Foundation, itself a VC, ‘Returns data is very clear: it doesn’t make sense to invest in anything but a tiny group of ten or twenty top-performing VC funds.’ In aggregate the amount of VC money put to work each year is $25 billion but this is a tiny amount of money, less than 0.2% of US GDP. For comparison US capital market activity in 2013 was an astonishing $1.72 trillion and about 10% of US GDP! Despite this, according to the NVCA, that 0.2% has been crucial in creating companies that account for 21% of US GDP and 11% of private sector jobs.

The obvious problem is that if investing in VCs and the technologies of the future isn’t profitable than maybe the technologies of the future will not be created. Some are already arguing that this is the case. For example, billionaire entrepreneur and VC investor Peter Thiel has famously complained ‘we wanted flying cars and instead we got 140 characters.’

‘Well I think that there should be a broader scope of investments that venture capitalists consider in the Valley. I think there’s a little too much of people kinda focusing on IT or focusing on silicon or basically stuff that’s gone before. I think there needs to be a look at multiple, different industries. I think there’s probably too much capital chasing the obvious stuff.’ Elon Musk, Paypal, Tesla & SpaceX

The question is how can we do better? How can we incentivise more innovation? By zooming out and analysing venture capital as just one of many societal incentive systems that can motivate technological progress I shall try to show that there is an astonishing lack of diversity in VC firms and their investment approaches. With the broader incentive system context being laid out in the first half of the essay I shall use the second half to try to, as systematically as I can, suggest alternative approaches that Venture Capital investments (and other incentive systems) could take that would not only lead to more innovation and the obvious societal benefits of that but also better returns to investors.

WHERE DOES INNOVATION COME FROM IN A SOCIETY

‘Well I think I’ve been in the top 5% of my age cohort all my life in understanding the power of incentives, and all my life I’ve underestimated it. And never a year passes [without me] getting some surprise that pushes my limit a little farther.’ Charlie Munger, Berkshire Hathaway

The following tables try to break down the major institutions we have in the West (i.e. democratic welfare state capitalist societies) that can be expected to help our societies innovate. These major institutions are made up of companies, Government, non-profits & universities.

Sometimes it can be easy to get trapped into the norms of a given society or way of thinking about work but it is worth pointing out that the profit motive (whether for labour or capital) as the primary incentive for people to work and create value has been a relatively new phenomenon. Throughout most of human history slavery has been remarkably widespread and widely used. Slaves it is worth pointing out were driven not by the ambition of getting promoted or accumulating wealth but fear of physical punishment. In fact, it was genuinely believed that societies would not be able to function without slaves because who would do all the low-skilled difficult jobs? As an example, last year I visited the Terracotta warriors in Xi’an (西安) which are an awesome sight to behold. It took 720,000 workers, many of these highly skilled artisans, 37 years to complete the project. This was the burial site of the emperor and so its location or even its being built was a complete secret. And yet according to our guide, from a population of just twenty million almost three quarters of a million people were employed on the project, many dragged from their homes in the middle of the night with their families never hearing from them again.

Although for modern western eyes this is an appalling breach of peoples’ rights it is worth pointing there is a trade-off, a terrible one, but a trade-off nonetheless and I suspect it can be reasonably be argued that without the disregard for human life and freedom that Ancient Egypt and such societies had then great wonders like the Pyramids would not have been possible. In fact throughout most of human history even those agricultural workers who weren’t slaves their incentives to work were not financial, at least not in the modern sense, but instead just survival for one and one’s family. Religion has also been an incredibly powerful incentive system. To consider the great churches like Westminster Abbey compared to the resources and capabilities of their age is truly incredible. And certainly it is hard to imagine modern western countries ever being able to embark on ventures of the scale of the Crusades in the Middle Ages. It is worth pointing out its modern equivalent is truly impossible to comprehend as it would involve all the leaders of the free world not only financing repeated expeditions to Mars for the glory of God but actually having those leaders, Barack Obama included leaving the countries they were governing. Richard I, the Lion-heart of England as an example spent possibly less than six months of his reign actually in England!

One of the central themes of this essay then, is that for all the progress we have made in our societies in terms of human rights and democratic freedoms there has been a cost, namely our ability to embark on huge costly, long-term projects. Do I think slavery is worth bringing back in order to build some modern day equivalents of the Pyramids? No, of course not. But I do think history shows that our current incentive systems are just a few of the many we could possibly have and that it is worth actively choosing and creating the incentive systems and institutions we want. In particular those that best incentivise innovation. It is worth pointing out for example that our democracies and the compromises they are built on, as Peter Thiel points out, work much better when there the ‘proverbial pie is getting bigger.’ The moment we don’t have any innovation and by extension economic growth suddenly our societies and our politics becomes very polarised as it becomes a simple zero-sum fight for resources. Therefore I would even go as far to argue that innovation is a tax on the preservation of our democracies and the human rights that they bring. As Charlie Munger of Berkshire Hathaway says

‘It’s a very interesting problem, that our founders coped with which is just how democratic you wanted the system… I don’t think it is a pure democracy… I took political science… and everybody teaches the more people that vote the better systems will work and having a contrarian streak I am not so damn sure! That the civilization doesn’t work better when a lot of people don’t vote!’

The first type of incentive systems that we shall consider are financial ones which can be divided into big companies, small companies (i.e. start-ups) and prizes. Companies, whether big or small, are almost always driven by financial incentives. Big companies, especially those listed on a stock market, are focused on optimizing short-term profits, where optimization by quarter is probably unfair but certainly over 3-5 year time horizons. You would certainly expect it very hard for any CEO, outside perhaps Jezz Bezos of Amazon or Jack Ma of Alibaba, to post year on year losses without losing his job. This tends, to lead to cumulative marginal improvements in the products and services with much of the effort focused on expanding market share. The institutional structure is one of diversified power where boards and hierarchical management are, as far as I can tell, organised so that no one person can become too powerful and therefore do something stupid. It is easy of course to be cynical but from a company with a dominant oligopoly or even monopoly in a mature market where true innovation is likely to be into very new product groups with huge capital expenditures and very high chance of failure this is a good strategy. However, it does mean that from a societal stand-point big companies who often, in terms of balance sheet at least, are best placed to innovate and build our futures are the least likely to do so. In fact, in an interview with Charlie Rose Bob Lutz, then CEO of General Motors, said it was embarassing that General Motors with the billion dollars it spends on R&D every year was unable to do what Elon Musk with a shoestring budget in California was able to do namely build a compelling electric car.

‘So I said okay, this is outrageous this is a small, start-up car company on the West Coast, obviously very confident about lithium ion batteries and is going to go into production with this car and we (General Motors) many people would say technologically the most competent car company in the world, we say it can’t be done. So then we got into the well maybe let’s take a look phase which was the beginning of the Volt development… whether Tesla is ever hugely successful or not I’ll always owe him a debt of gratitude for having kinda broken the ice.’

I think one potential incentive structure that could work within firms is rather than keeping the standard hierarchical corporate structure for innovative projects you have the board act like a VC fund where employees (or even external entrepreneurs) can pitch for investment in projects. It is obviously risky to put your career on the line especially if the project doesn’t work out but I suspect that will be outweighed, for a lot of young talented people facing decades of office politics to climb up the management ranks, by the chance to quickly make a big impact. In fact, according to my Dad who works in a big corporate there are two behaviours which often occur which hurt the chances of a project being successful. The first is, just in case the project goes wrong, an executive will try and get as many people as possible (including their boss) signed off on in it so that there can be no blame-seeking afterwards. The second is that those who really put everything behind a project that doesn’t work out often find themselves frozen out of the company. The result of course is lots of ideas and projects but with no one who is really committed to them and to quote Michael Lewis’ book ‘The New New Thing’ on billionaire entrepreneur Jim Clark

‘Clark liked to say that human beings, when they took risks, fell into one of two types, pigs or chickens. ‘The difference between these two kinds of people,’ he’s say, ‘is the difference between the pig and the chicken in the ham-and-eggs breakfast. The chicken is interested, the pig is committed. If you are going to do anything worth doing, you need a lot of pigs.’

The standard narrative then is that innovation will come from start-ups and in particular the Silicon Valley-based, VC backed start-up. But I would argue that there is little differentiation in VC investment strategies which explains the limited returns because there are only a limited number of companies that will work in the current investment environment (software-based platforms) which through simple selection bias tend to gravitate to the best 20 firms. It is easy to feel like there is a great variety in the investment approaches because look, according to Mattermark, in 2014 there was VC money in healthcare, education, finance, hardware, enterprise software, marketing, social networking, cloud computing, entertainment etc. How could you get more diverse than that?

What is hidden is the nature of the start-up that is invested in particularly there is a high likelihood that is it software based where, initially at least, there is limited labour or capital investment. According to Mattermark, in 2014 $50bn (admittedly twice the PwC data) was invested in 5900 companies. This sounds like a lot of money but on a per company basis this averages out to just $8m per company. Compare this to Qin dynasty China where 3.8% of the population worked on the Terracotta warriors for almost forty years. A useful metric to think about this could be what percentage of resources a society can direct towards a big, expensive long-term project. Imperial China by our previous measurement is about 5% (consider working-age population) but it would be unimaginable for the US to embark on a similarly grand project such as colonizing space or curing Cancer. And this despite the fact that these projects would have offer some utility to the general populace! Whereas the Terracotta warriors did not (they were secret remember)! We think much of the problem has to do with the assumed and universal investment structure of seed-angel-venture-IPO which is so part of the accepted status quo in business practices that it is not even questioned. But as we shall see when we explore the hidden assumption of the current VC approach I believe, that it is possible, whilst still working within the restrictions and requirements of a capitalist system to make big bets on big risky projects not only work but also have that investment in innovation be profitable.

The final category is prizes. A clear working example of this is Peter Diamandis’ XPRIZE. What is most exciting about this form of incentive systems is that it is clearly not a purely financial one. In fact, taking the ‘Ansari XPRIZE for Suborbital Spaceflight’ as an example the winning team received $10m but more than $100m was invested in new technologies in pursuit of the prize. As Diamandis’ himself said what is coolest about the incentive structure is that it is a very efficient, you only reward the winners and yet at the same time you incentivise an (almost systematic) attempt of all the major ways a problem could be approached. This is in comparison to companies where often times multiple attempts at a problem, are just that, multiple attempts, rather than really different approaches. Diamandis’ HeroX is an attempt to broaden the impact and democratize prizes as a platform. I suspect the big problem with prizes is the prizes are funded almost entirely by wealthy benefactors. Similarly a lot of the attempts to win the prize are similarly funded (e.g. Microsoft’s Paul Allen funding the winning attempt for the Suborbital prize) so it is questionable whether this really would provide a very different incentive system to the VC world and non-profit world that already exists. Especially as the societal status of ‘winning an XPRIZE’ would inevitably lessen the more prizes there are. Although to be fair to Diamandis he specifically chooses the XPRIZES such that he thinks they will lead to a commercial product afterwards.

Fortunately, we have multiple incentive systems for innovation outside the purely profit-seeking. The university system has for more than a century been a source of progress in science and technology, especially when the efforts are still far from a product that can be directly commercialized. Academia has powerful incentive mechanisms particularly those around working up the graduate, post-graduate, professor, tenured professor career ladder where the social prestige of, in the extreme, being a Nobel Laureate from Harvard University can compete with much greater financial packages from the corporate world. In the context of societal-level innovation though academia seems to have three primary problems.

The first is that academic research, particularly in science and technology, tends to be under-funded and uncoordinated. In particular because the rewards, (degree, pHD, professorship, Nobel Prize etc.) are all to the individual, not to the collective (unlike say a company) I think this prevents coordinated action on big societal problems like space travel or curing cancer. Admittedly, there is a strong culture of colloboration within academia but perhaps the idea of a ‘group pHD’ where 20 scientists work on big intractable problems for periods longer than the standard 4 year pHD would make sense. Maybe it could be a 2nd pHD of sorts.

The second problem is that academics have teaching and lecturing commitments which distract from the research work. Of course, it is exactly this source of funding which allows for the pursuit of research which may not have immediate commercial benefits but clearly solving some of the big problems in technology are hard enough let alone if you’re doing it as a part-time job.

Finally, there is the problem that because academic research is not always of ‘value.’ Of course, this is one of the strengths of academia because after all there is more to life than making money. And actually oftentimes innovation is difficult to predict so one can think of academia as a series of (relatively) small bets on the advancement of lots of subjects, some of which are more useful than others. Nonetheless there is evidence to suggest that as much as 90% of business school academia literature is not only not widely read but furthermore no one is willing to pay for it. Companies, on the other hand have seeking profits as a forcing function to make sure they produce something of value or otherwise face bankruptcy.

Governments on the other hand can call on vast economic resources and can co-ordinate huge numbers of talented people to great and important projects. Classic examples of this are the Space programme and the Manhattan project. However, particularly in the West, there is little appetite, outside of war time, for government spending on big risky, innovative projects. This is perhaps one of welfare democracies major weaknesses something which less democratic governments like China and Singapore. Although, of course I would not argue that the United States should abandon its democracy I still think it is worth having the debate that perhaps it is worth restructuring our democracy for great innovation. It is worth remembering how awesome collective action can be. As the XPRIZE’s Peter Diamandis has said

‘And Gene (Eugene Cernan, Apollo astronaut and the last man to walk on the moon) said during a lecture You know we went from never having flown anyone to the moon in 1961 to landing on it in 8 and a half years. You tell me what’s impossible!’ Nothing. Nothing is impossible! I never forget that. The notion that in 1961 JFK said we’re going to the moon before the end of this decade and 400,000 Americans left their jobs, left school left work. And they descended on places like Titusville, Florida and Huntsville, Alabama and they invented everything that was needed to go to the Moon. We had never put a person in orbit before. We had no right to make this claim that we could go to the Moon. No right. And we invented the propulsion, the navigation and guidance, the rendez-vous and docking, the structures. Everything. The average age of the engineers who built those structures. Any guesses? Mid to late twenties. They didn’t know what couldn’t be done.’

I have two ideas on how to restructure government towards this aim. The first is that every term the President would be able to direct say $50 billion dollars to one big, risky innovative project to try and work towards solving one of the great societal problems. The President would be able to choose the project as he sees fit without needing Congress’ approval but with publically available information and the implications for the President’s legacy I suspect that there is unlikely to be a lot of corruption of Government inefficiency. The other suggestion I have would be for the four year political cycle to be changed to an eight-year cycle or longer. Sometimes, it is easy to forget that America is not a pure democracy, for example a political process where there are Presidential elections every year rather than every four years would be technically more democratic but no one would argue for this because of the clear instability and wasted time and resources spent on getting elected this would require. The natural fear is that a Hitler-type figure would appear but it is not clear to me, from a first principles stand-point, that 4 years is the optimal period and that six years would necessarily lead to a massively higher risk of dictatorship or corruption and thus it may be worth it, if it allows our political leaders more long term thinking. This is particularly important if future innovations, as I suspect they do, require increasingly large capital expenditure because all the ‘easy to reach apples’ have already been picked.

There are other incentive systems that I would include under the ‘cause’ banner and these include charities and non-profits. Although I clearly think they are valuable to society as they can help equalise some of the inequaliites in the world, particularly sharing basic healthcare and food products that companies don’t provide because they can’t make a profit, I would argue that non-profits do not have the resources to work on the big intractible societal innovation problems. Additionally, non-profits have a tendency to suffer from inefficiencies including a bias towards the emotionally payoff of helping someone in person (e.g. visiting an African village and building a well oneself) rather than more scalable and impactful solutions.

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Founder-led companies are an interesting new class of companies because they are those that are not driven by immediate short-term profit optimizations. Classic examples include Larry Page at Google and Elon Musk at Tesla and SpaceX. The only problem with these companies is that they are not scalable and from a societal stand-point they require an unreasonably high barrier to entry, namely they require talented people to not only make hundreds of millions of dollars but also for those people to be willing to risk all that money on ventures that will, in all likelihood fail. If a society we require once in a generation Elon Musk type characters to drive our societies forward I think our chances of innovating a lot in the future are not very high. Having said that though, at a cultural level I think the examples of CEOs like Elon Musk and Steve Jobs are potentially very influential where, especially if coupled with a more top-heavy power structure in standard corporations could lead to more innovation in our societies coming from big companies. Especially if CEOs start thinking about their legacies at the company rather than purely making investors happy on a quarterly basis.

The final category that I think is worth including is the attraction of fame to attract talented people. Just consider the number of people who are drawn to LA every year in an attempt to try and become actors. The big obvious problem with fame it is, almost be definition, unscalable where even for a corporation of the size and success of Apple few names outside Steve Jobs and maybe Steve Wozniak and Jonathan Ive are well known. Having said, that I think culturally there has been a shift where examples of entrepreneurs like Mark Zuckerberg (and the Facebook Movie) as well as entrepreneurs like Elon Musk have made the idea of founding a company not only more mainstream but also more aspirational. No doubt the idea of extreme wealth in your twenties is incredibly appealing to a lot of people and hopefully this is attracting more talented people to try and build companies.

ALTERNATIVES TO THE CURRENT VC APPROACH

To maximise future technological progress I think Venture Capital should evolve to include approaches that are 1) more long-term 2) invest more resources 3) more deterministic. Of course, that will only happen if these approaches can be made to be profitable. I think there are two main ways that this can be done 1) tranching risks, 2) layering incentives – in particular rewarding partial success.

CURRENT VC APPROACH

The current VC approach is guided almost entirely by the assumption that most startups will fail but the few that survive will succeed in extreme ways. This is best summed up by a slide from Ben Horowitz of legendary Silicon Valley VC firm Andreessen Horowitz where he shows that historically only a limited number of the 200 or so VC-backed companies every year (themselves just a few of the thousands of companies with seed funding etc) will ever make any returns for their investors.

As talked about extensively in a great article by Founder Equity’s Joe Dwyer the common rule of thumb for venture outcomes is that 30-40% completely fail, another 30-40% return the original investment and 10-20% produce substantial returns. However, according to research into more than 2,000 VC backed companies by the Harvard Business School’s Shikhar Ghosh the actual numbers are closer to 30-40% return nothing as assumed, but as many as 75% don’t return investor capital. More starkly if failure is defined as failing to meet the projected return on investment than more than 95% of start-ups fail. This it should be pointed out are for companies that received at least $1m in VC funding so with failure this common in the later rounds, the picture is even worse for the seed rounds.

Doing some venture math it becomes clear why VC investments are so unvaried, everyone is looking for ‘unicorns.’ It is worth pointing out that the number of businesses that have a chance in five years to reach valuations of $500m+ with just $5m investment are incredibly limited. Suddenly it becomes obvious why, what initially looks like a varied approach to investing by VCs is actually very unvaried with the focus on small teams working on software based platforms. This lack of variation is even acknowledged by those who are directly working in the big VCs, Marc Andreessen saying that

‘And I would say there is this very interesting kind of process where there’s the hard thing is deciding which ones we’re going to invest in, because we can invest in so few. The somewhat easier thing, it turns out, this has been a surprise, it’s actually after you have been in it for a while, the thing that is fairly easy to tell is will this team and company be fundable by a top VC. Will it get funded by a top VC? It may be Sequoia, or Excel or Greylock or who knows who it is but you know does this company kind of clear the bar? And I think the way the math works basically is there’s about 200 a year that are fundable by top VCs. That get funded. By the way within the top 200 about 15 of those will generate, you know, 95% plus of all the economic return.’

There are two ways to interpret this I think. One is that VCs have learnt from the lessons in the past and have just gotten extremely good at picking the winners, which has lead to the convergence in investing strategies in the top firms. The alternative interpretation, is that they have overlearnt the lessons of the past. A good test for this I think is are there any big successes they almost missed the boat on? I would argue there are, Airb’n’b being the classic example. In fact, it is exactly this idea of over-adaption and complacency in the allocaiton of capital that economist Carlota Perez talks about in her book ‘Technological Revolutions & Financial Capital.’ The question then is what will the new paradigm, the financial allocation systems of the next wave of technological revolutions look like?

INVEST IN LABOUR APPROACHES – TRANCHING RISKS

The best way to change venture capital is to fundamentally change the percentage of companies that succeed. I think VC is probalby suffering from a case of over-adaption, over-learning the lessons of the past. Quite obviously is you believe that most companies will fail than your investment strategy will be to invest small amounts in lots of companies. This is despite the fact that there may be a whole class of companies out there, particularly those that are capital intensive, which may require more money up front but actually have a much better chance of return. What you probably need is more examples of Elon Musk type companies where a $100m investment has given returns on a $10bn order of magnitude.

The key idea behind tranching risks is breaking a very risky bet into separate less risky bets. The reason for this is that investing in start-ups becomes about making a series of very risky bets all at the same time. You are betting that you’ve not only picked the right industry at the right time but also that you’ve backed the right time. A good comparison is the movie industry where the well known rule of theory is that studios will take a risk on one but not more than one of the director, the script and the lead actors.’ Venture capital on the other hand is about trying to pick a winner when you have an unproven team, an unproven idea and an unproven market. This, unsurprisingly, is really difficult to do, in fact Warren Buffett of Berkshire Hathaway actively avoids technological stocks exactly because he has no idea how to pick the winners. I should put out that if the greatest investor of your society doesn’t like to invest in innovation or innovative technologies but actually the complete opposite that is a clear warning sign that there is something fundamentally wrong with the incentive systems in your society.

‘It will change the world, the technological revolution, in dramatic ways. And quickly. And ironically our (Berkshire Hathaway) approach to that is just the opposite of Bill’s (Microsoft’s Bill Gates) but his is the right one for the world, ours is the right one for me. I look for businesses where I think I can see what they are going to look like in 10 or 15 or 20 years. We don’t own any but if you take Wrigley’s chewing gum, I don’t think the internet is going to affect how people chew gum… But I don’t think it’s going to affect that Coke will be the drink of preference or gain in per capita consumption around the world. I don’t think it will change whether people shave, or how they shave or whether they like a better shaving system… So we are looking for the very predictable. But you won’t find the very predictable in what Bill does. You’ll find the exciting things, the things that are going to change society more. As a member of society I applaud what he’s doing, and as an investor I keep a wary eye on it. It’s a different approach to investing. Most people think of it as opportunity and it has been an opportunity the internet stock has gone crazy in recent months but I don’t know how to look at one of the other and tell the winners.’

As Andreessen has himself said you can think of the different rounds of Venture Capital from seed on through as basically the start-up slowly eliminating investment risks. In the early rounds death can come in many forms and so investment is understandably small and only for a potentially high return. Through each round, team risk, technology risk, business risk, timing risk are slowly validated and the lower risk attracts more financial capital. The problem as we have discussed is that this greatly limits the type of companies that get invested in. So one approach is, right from the outside to try and separate out, or ‘tranche’ the different risks. If you can isolate say just the technology risk or just the team risk then you can attract more capital to invest because you are going to fit more investors risk-reward appetites.

1) Invest in experts

A lot of investors that have views on an industry being successful, maybe it’s biotech, maybe it’s drones, find that there is no way to make that bet without making something which becomes the very dangerous VC bet which is not only a bet on an industry but also the specific timing and team that will succeed. Like Buffett many investors don’t how to pick, and to be fair the data would suggest VCs don’t know that well either. Reading Michael Lewis’ ‘The New New Thing’ which is about serial billionaire entrepreneur Jim Clark gave me an idea of how it might be possible to make this bet. Jim Clark is exceptional in many ways but perhaps most significantly he is the first person to found three separate billion dollar companies. The first of this was Silicon Graphics in the 80s that invented 3D imaging in computers. Although largely forgotten now, it was the Google of its day attracting all the best and brightest people. That company ended up eventually failing but what is interesting is much of the talent that worked with Clark at Silicon Graphics eventually just transferred over to his second billion dollar business: Netscape. Which of course took Marc Andreessen’s Mosaic browser and helped open up the Internet. However, caught in a war with Microsoft Netscape lost and it too died (technically it was bought out). Interestingly, much of the talent that had transferred from Silicon Graphics to Netscape transferred again and now work at places like Google and Facebook today. Incredibly Google’s offices in Mountain View are built on the same site that Silicon Graphics occupied 20 years earlier. The lesson then is clear: companies come and go but the talent sticks around. And as we just mentioned in some cases literally to the same location!

The obvious idea then is that you tranche out the start-up risk by investing in top talent. To take an example, suppose an investor you believe that AI driven drones are going to be the next big industry but are not sure specifically which company will come out on top. What you can do then is invest, not in a specific company, but rather top experts in AI drone robotics. As an example a drone pHD from MIT would trade a percentage of his 25 year income stream (including equity) in return for a guaranteed income stream over that time. It’s financial equivalent would be trading a variable rate of interest for a fixed rate of interest. The thinking behind it is from the pHD’s perspective this guaranteed income stream means that, even for a more risk averse person (whose opportunity costs might include becoming an academic or working in a big corporate), you are going to get a guaranteed income stream no matter what. In return, the investor gets to make the long-term bet that even if the first few drone companies our imaginary MIT pHD join go bust eventually, because he’s a top drone expert, he will find his way to a top tier company and earn the appropriates awards to go with that. You can almost think of it as indirect diversification where traditional diversification is over multiple companies, where if you assume just a few winners for each given industry you are almost guaranteed some losers, investing in experts allows diversification over different companies over time where as top experts are likely to have at least some sort of income there will never be zero returns.

Of course, it would be worthwhile checking all these numbers, and having proof of concept but these seem to me to be a fairly reasonable starting point. The returns are not stellar but this is the assumed worst case scenario where essentially the first two companies that the pHD picks are bad ones where they don’t work out. Of course there would be a certain percentage of pHDs who would perhaps give up on working for a startup, maybe rejoining academia but putting tracking costs aside it is highly unlikely that more than 10% of all investments would return zero. It is also worth noting that underlying all this is the assumption that the industry works out, this is exactly what the investor is making a bet on of course but the fact that that bet is not guaranteed would of course affect the risk returns. Having 5% compound interest for 25 years with a fairly low risk profile seems like it could be an attractive offer from the investors point of view.

From the perspective of the pHD clearly $20,000 is not enough to live off of but it is worth pointing out that for incentive reasons they would also be drawing a wage from the start-up that they are working at. What is not clear is the indifference curve of the pHD so research would clearly need to be done in terms of what kind of numbers would incentivise pHDs who other wise might choose safer options to join a start-up. In particular, research would need to be done into what percentage of equity and income would make sense to both the investor and the pHD.

I think the most interesting aspect of this is the impact on start-ups. In particular, with the 30-40% of zero returns that investors usually are faced with when investing in start-ups reduced to less than 10% I think this fundamentally changes the amount of funding that early-stage companies can attract and therefore opens up much more capital-intensive industries. This is particularly true because even the most capital intensive industries are actually expensive not because of the capital but because of the high-skilled expensive labour! As Peter Diamandis of the XPRIZE points out

What percentage of a launch vehicle (rocket to space) do you think is the cost of labour? It’s about 80-90%! It’s labour!

So if with this structure you can essentially convert future equity (across potentially multiple companies) and future income streams and halve the wage bill and therefore the start-up costs of more capital-intensive industries I think you fundamentally change the numbers on what types of investments and how much money initially other investors in start-ups can make. So along the lines of the tranche idea you can have two funds, one which is less risky investing in experts in an industry and the other which invests in the specific companies covering the capital costs.

The net result is not just good I think for all parties involved but also society as well because projects that in the current capital environment that don’t get funded can now get funded.

2) Invest in automation

One lesson that I think has been overlearnt is ‘do not be deterministic in your thinking.’ As Ycombinator’s Paul Graham has said:

The last counter-intuitive is the way to get start-up ideas is not to try and think of start-up ideas… if you make a conscious effort to try and think of start-up ideas you will think of ideas that are not only bad but bad and plausible sounding meaning that you and everybody else will be fooled by them and you’ll waste a lot of time before realising they are no good. The way to come up with good start-up ideas is to take a step back. Instead of trying to make a conscious effort to make start-up ideas turn your brain into the type that has start-up ideas unconsciously in fact so unconsciously that you don’t even realise at first they’re start-up ideas. This is not only possible, Yahoo, Google, Facebook and Apple all got started this way. None of these companies were even supposed to be companies at first they were all just side-projects. The very best ideas almost always have to start as side projects because they are always such outliers that your conscious mind would reject them as ideas for companies.

However, I think Graham’s thinking has been skewed by the fact that he has overwhelming been exposed to and therefore learning from companies that were internet based and very dependent on product-market fit. This is difficult to predict up front and therefore the lesson is don’t try and be deterministic about choosing your start-up idea. However, in other arenas determinism is likely to work very well, in particular if you consider the trend of automation you could have a fund that invests in robotics or machine learning pHDs that are looking at building companies around automating labour.

Even in America, the most developed of nations, most people do very menial jobs. As the education reformist John Taylor Gatto in his ‘The Underground History of American Education’ points out the top ten most common jobs in 2020 is predicted to be, according to the US Bureau of Labour Statistics:

  1. Retail salesperson
  2. Registered nurse
  3. Cashier
  4. General office clerk
  5. Truck driver
  6. Managers
  7. Janitors, cleaners, domestic servants
  8. Nurse aids, orderlies, general attendants
  9. Food counter workers
  10. Waiters

Google is obviously already working on number five truck drivers and number three is being attacked with self-checkouts. Number seven is being attacked by Homejoy. My basic idea is to simply try and automate or at least greatly increase the productivity of workers in these industries. You could take this further than simply informing an investment strategy but actually have the robotics or machine learning engineer spend three months working a few of these menial jobs to help inspire them to come up with ways to automate them.

‘Ironically, the bestowing of some measure of respectability on physical work led eventually to its replacement. As White put it, ‘The goal of labour is to end labour.’ Only after intelligent and educated people roll up their sleeves and engage in physical labour will they get inspirations concerning how human muscles and sweat can be replaced by machines.’ Joel Mokyr – The Lever of Riches: Technological Creativity and Economic Progress in a chapter about how monks in the Middle Ages brought together the classical separation of educated intelligence from manual work.’

3) Invest in university students

The idea that most excites me is investing in university students. The investing in experts is compelling and would have a more direct impact on the VC world but the numbers aren’t that good and I worry that there are a lot of moving parts. Plus it is so counter to traditional venture capital’s approach that it would probably be difficult to get off the ground particularly as industry bets would only really work at scale.

A variation of this is to allow poor but talented students, particularly those in Asia, Africa and Latin America to go to university but in return the students would pay back the investors a percentage of lifetime annual income.

The key numbers in this would be 20% of lifetime income which is assumed to be $30,000 which is arguably quite a conservative bet. Meanwhile the initial investment paying for university fees and living expenses is assumed to be $20,000 for four years. This seems like a very low number but education is much cheaper in the developing world! Additionally, for incentive reasons you may want to have the students partially pay for themselves with part-time jobs etc.

Considering all sides. First from the student’s perspective the choice is either go to university and earn $24,000 (after removing the 20% that goes to investors) or do not go to university and earn $15,000. Of course it is going to be very imporant to be careful how the investment is publicised because taxes on lifetime income might be misinterpreted as some sort of slave contract. However, from the student’s perspective there is a clear benefit in going to university.

What is unclear is what type of student would benefit most. Is it the student that is so credit-constrained that the choice is university or no university? Or is perhaps the students whose choice is living at home and attending the local university versus going to the best university in their country? Or finally perhaps the numbers work best for the student whose choice is best national university or university abroad in America or Europe? It is possible that university students, even those in America may feel that converting the university debt into a variable percentage of income is attractive because it acts as an insurance against bad outcomes or allows the student to take more risks in choosing their careers. The big advantage of an income tax as opposed to fixed debt is that in bad times you pay less! Of course in good times you would have to pay more but for risk averse students this may be a bet worth making.

From an investors perspective I think the idea is potentially extremely lucrative. Unlike the invest in expert idea which requires constant negative cashflow investing in university students requires only a very small short-term bet with potentially huge long-term returns. Numbers even approach 7.6% annualised returns over 30 years are nothing short of outstanding. And if you conservatively half that number to take account of management fees as well as the inevitable % of students who are untrackable it still has the potential to be a very attractive investment. Taken to an extreme you could imagine putting tens, even hundreds of milllions of people through university this way. There are several aspects of this which are incredibly exciting. One is that if you had say three hundred thousand people on your books spread over thirty years. That means every year you are putting four year groups of 10,000 students (300,000 students divided by 30 years) for $5,000 a year of university. That adds up to $5,000 x 4 years x 10,000 students = $200m a year. Which seems like a huge amount but consider how much you would be taking in. You would have 26 years worth of students earning the assumed $30,000 a year where you are getting 20% or $6,000. Therefore the investors annual return would be an astonishing 260,000 students x $6,000 = $1.56bn.

What I think is potentially most interesting about it is what could be done with that type of money. For example for a VC firm making $1.4bn every year thirty years at a time in profit the type of VC investments that would be possible are truly astonishing. Suddenly investing $100m over a ten year span does not seem as risky. And in fact, in addition to choosing the best young university students (initially you could actually pass the selection costs onto the universities where you choose who you invest in purely based on what university they have gotten into) you could even choose students by subject. So again you could look to make a bet on a particular industry that you think might do well. For example you might believe that Indian software companies are the future therefore rather than investing $30m in a few software companies why not put 1,500 Indian software engineers through university?

You could even think about founding universities in developing countries where for students who get in the university fees are free but in return the university gets a percentage of lifetime income. What is interesting about this is it provides a potentially very lucrative funding model for universities and it may lead to a greater alignment of university and student incentives.

Finally from a societal stand-point I think this would be hugely beneficial. In economics there is much talk about the unpriced environmental externalities or the market failures from monopolies or problems with government failure and corruption. But I would argue the single biggest waste of resources is the brain power in the developing world that because of the lack of high quality education systems is being wasted. What force for bringing about world equality through economic development in developing countries could be more powerful than say putting a billion people through university this way. The reality is families, governments and charities are unable to fill the gap in funding but perhaps this type of investment could.

One critique has been the world doesn’t need another ten million software engineers! I.e. there is a zero-sum/signalling rather than human capital nature to university education. I think the obvious response is that if our economies can’t evolve to employ billions of people in high skilled jobs in the future then we have much larger problems on our hands. In the short-run and for less than a million students it seems likely that the effects on the world economy will be purely positive because even if it just a zero-sum game the world output would still be increasing because you are basically replacing the bottom million students from Europe and America with the brightest million from Asia, Africa and Latin America.

Finally one big advantage of this idea is that it could work even at a small scale. Thus you could, for limited risk (50 students perhaps?) test the idea and see what kind of returns you get.

LAYERED INCENTIVE APPROACHES

The idea behind layered incentives is rather than just having a single positive incentive force you have multiple. In practice this means making sure there are additional incentives like fame, competition and respect on top of the usual financial incentives. Part of this involves restructuring the returns such that even partial success has some positive returns.

1) Invest in pHD ideas

The assumed nature of innovation in the world of atoms is that you require a lot of investment which, after some time may or may not yield an economic return.

Understandably very few venture capitalists are willing to make investments of this type because they are very long-term, capital intensive and (unless Elon Musk is in charge) have a very high probability of failure.

This picture of technological progress, though is probably inaccurate. In fact, technological progress is often the accumulation of small breakthroughs which collectively lead to a commercially viable product. The crucial constraint is that the the breakthroughs need to be made either simultaneously or in a very specific order and individually do not have a commercial return. Only when integrated together into a final product to have they have any market value. A good example of this might be how advanced mathematics slowly builds on itself but often has extended periods of breakthroughs where there is very limited commercial value to the ideas.

In fact, Elon Musk argues that integration of not only the technological progress but even the manufacturing is absolutely vital: at least when it comes to make rockets and electric cars!

‘I think if you think of manufacturing not as some boring process of making rote copies but rather the manufacturing system that creates the car is itself a very complex machine and just as innovation applies to the design and engineering of the car you can apply and should apply innovation to the machine that makes the machine and you can come up with some really cool new ways to manufacture a vehicle. And I also believe in having a tight feedback loop between engineering and production and so if production is far away from engineering you lose that feedback loop. And so someone who designed the car in a particular way doesn’t realize it is very difficult to manufacture the particular way that it’s designed but if the factory floor is 50 feet away from their desk then they can go out and they can just see it and it’s obvious and they can have a dialogue with the people on the floor. And likewise a lot of people on the manufacturing team have great ideas about how to improve the car but if they’re far away they can’t communicate that to the engineers that designed it. So I think that it’s something that is often neglected but having that strong bi-directional feedback loop between engineering and manufacturing is really helpful in making the car better, finding efficiencies and lowering the cost.

The problem with all these narratives of innovation is that they are not very investment friendly. One working model for this type of innovation is university research where expectation of economic return is removed and technological progress is pursued purely for its own sake. I suspect though that this, at least for capital intensive projects, can lead to chronic under-funding. Another model that has worked is government, especially government at war, but western welfare democracies are increasingly incapable of making such investments. Perhaps for this reason it will be countries like China (in a rivalry against India?) that will be the future source of technological progress. A third model that has worked is one driven (and very wealthy) individual. Classic examples would be Elon Musk with Tesla and SpaceX or Larry Page with self-driving cars at Google. Neither could have realistically expected any economic return but they persisted because they felt it was important enough.

Despite all these models to achieve innovation, from a societal stand point, what you really what is to figure out a way to align the for-profit motive with technological progress. That way innovation in the world of atoms should really take off.

Up until now I feel that investors have been very unimaginative. They’ve forced technology to fit the financial needs and desires of investors, who have limited time horizons and risk appetites. This has lead to lots of investment in the world of bits but very little in the world of atoms. Of course, these preferences are unlikely to change so the question is can we more creatively mold the requirements of the investment world to match the true nature of innovation. Or perhaps, more accurately, mold technological change to match the investment appetites of venture capital.

My key assumption is that there are a significant number of technological breakthroughs that can be not only pursued individually but are crucially not very capital intensive.

This however is not enough because if you need all the breakthroughs to be made to have an economic return then in all but definition you have the same situation as before where you need a lot of technological breakthrough and investment before you can get a return.

The key then would be not only breaking up technological progress into independent components but also having each of those breakthroughs be individually lucrative. Perhaps after each of the breakthroughs you would still need an investor to come along and integrate all the breakthroughs which in turn would be its own investment risk but at least by breaking it up you can reduce the risk somewhat.

If the above is an accurate model of at least some types of technological progress then suddenly you could imagine a successful venture capital firm in the world of atoms.

The key question, of course, is what possible economic return could exist for small breakthroughs that, in themselves, do not have a commercial value? The obvious answer is patents. And in fact, rather than waiting for entrepreneurs to pitch projects as is done in typical venture capital you would proactively pursue projects with the patents as your exit strategy. You might want to specifically target breakthroughs that would be valuable in multiple industries or perhaps you might want to target a cluster of related breakthroughs. Even if of the six targeted breakthroughs only three were successful you might still be able to sell to a more established company the three successful patents because with half of the work done, the calculus might be sufficiently changed for the established company to risk investment in a expensive, long-run project.

My current bias would be to invest in pHD students right after they’ve finished their pHD and about to entire the workforce. I spent the last year studying at Tsinghua University in China and extrapolating from the admittedly limited number of conversations I had it seems to be a common experience that pHDs would after four years only make partial progress on a project. Certainly enough to warrant a pHD but not enough to qualify as a working prototype. For example one pHD I met had invented a filter that could be attached to any petrol car and dramatically reduce the amount of greenhouse gases emitted. The problem was he couldn’t get it to work in extreme cold and extreme hot conditions which meant that when he had negotiations with big car companies to purchase and work on his idea they weren’t interested because they didn’t want to make standardized adjustments to their entire fleet if they couldn’t then sell those adjusted cars in cold Russia or hot Africa. It is worth mentioning that this particular pHD had won numerous awards for being one of the top ten pHDs in Asia and yet even he couldn’t get funding. He is now trying to find a job in a big petrochemical company but frequently complained to me that there are much fewer roles available at pHD level compared to Masters and that even though he would be willing to start lower down the organisation companies are not interested. He even confided that he regretted doing his pHD and felt it was four years wasted. Another pHD, this time from Cambridge University in the UK, complained that his research was underfunded and he was often working by himself or with just one assistant to help him. He is now going to work for a consultancy firm.

Therefore my idea is there may be a great untapped resource of potential technological breakthroughs that pHDs at university almost but don’t quite make. Crucially given just a bit more time and a bit of investment a good proportion of these projects might yield an economic return. You could cherry pick the best pHD projects and you would have something tangible to judge each potential investment against. One of the problems with investing in online start-ups is that it is very difficult to judge an idea by itself, often times things that seem trivial will take off and seemingly great ideas will find it very difficult to get traction. Furthermore, because they require relatively little expertise, just a few average programmers with a great idea can build a successful business it is difficult to make judgments about which teams will be successful and which won’t. This has led to a bit of a spray and pray approach to investing. By investing in pHD projects you would have four years work to evaluate, as well as academic references which would significantly improve any investment’s chance of success. Furthermore, unlike say a drop-out founder, pHD students have a huge opportunity cost. This is crucial because venture capital inherently has a big information asymmetry. Having worked on the project intimately for four years, pHDs with the high value they would command on the job market would be unlikely to be willing to continue working on a project they do not believe will be successful, even if its on someone’s else budget.

This investment thesis turns on a number of key assumptions which need to be investigated.

  1. Technological progress in a significant number of industries follows the model of small, independent breakthroughs.
  2. Patents offer a legitimate exit strategy for these small breakthroughs.
  3. pHDs are under-funded and would benefit from more time, money and support to work on their projects.

2) Invest in pHD ideas – prizes

A variation of this might be to have to use prizes to incentivise teams and as Diamandis has done with XPRIZE leverage more investment and variation in approaches. Potentially if a cluster ideas where considered to be important for a big breakthrough you could have a prize for each and once each of those prizes was won and achieved then you could look to bring all those winning teams together to work on commercializing the collective product.

A particularly interesting variation of this would be one where different teams share infrastructure. The idea for this came from how Elon Musk is building a hyperloop track for teams to test their pods on.

3) Business pHDs

Another interesting variation is to try and incentivise risk-taking but making sure that even in the case of failure there are still some positive returns. For example, pHDs after doing a normal pHD could apply to do a business pHD where they look to take their pHD and commercialize it. If it was done with co-operation with their university you could imagine a situation where even if the product fails the business pHD could write up about what they have learnt and go into the job market with the very valuable business skills he has learnt a long the way.

4) Catalyst CEOs

When you are looking to invest in a start-up one of the key concerns is the founding team and in particular the CEO. Traditionally start-ups have had full time CEOs running the companies but I think there is potentially merit in dividing up the CEO role into two parts. The reasoning being the CEO plays two crucial but distinct roles. The first is more obviously running the company the second is less obvious, namely attracting both money and talent to the team. I would argue therefore, that one way to reduce the founder risk is to have an experienced founder as what I like to call a ‘Catalyst CEO.’ My inspiration for this is Elon Musk who is famously CEO of both SpaceX and Tesla. He is also Chairman of Solar City and is also building a track to help hyperloop pod designs be tested. The Catalyst CEO would, at most, only be able to work for 2/3 companies at a time but I think even having an Elon Musk type figure invovled, even if it’s just one day a week, I think would radically change the chances of success as well as the quality of talent and the amount of money that a project attracts. Of course to balance the Catalyst CEO who would play more of a strategic role you would need what I am calling a CEOO (a CEO variation of COO) who would be more involved in the day to day running of the company.

It is worth pointing out that venture capital firms, it could be argued already play a (less hands on version of) the Catalyst CEO where being Andreessen Horowitz backed attracts both high quality employees as well as directly attracts capital. What I’m arguing for essentially is a more hands on VC.

5) Societal problems VC firm

The aim would be to raise capital of the order of $100m plus to work on major problems in the world. Only invest in 1 company a year perhaps, with 10+ time scales. You’d probably make sure the person running the company has business experience etc.

Rather than Paul Graham approach to investing which is work on side projects, you write a list of the biggest problems that humanity is trying to solve and then raise the capital directly trying to solve those problems. Although business incentives are important for the running of the firm and keeping it accountable, investors would be made clear that should consider the effort partly charity because unlikely to get a return due to the high risks involved (although tranching some of these away e.g. by investing in experienced entrepreneurs might help mitigate these). You could offset this by making sure there was lots of positive incentives and status around giving to motivate the ‘big companies’ and ‘wealthy investors’ to invest. Companies for example could argue they are trying to promote big value-add risky projects which may help them add to their brand name. A few hundred million dollars say from a big oil company to tackle climate change or a multinational like McDonalds to tackle hunger could be very positive for their brand image, e.g. ‘this year we are spending our entire marketing budget on trying to feed poor Africans. Eat McDonalds. Save a Life.’

6) Property investment VC firm

Another variation might be to remove the labour cost part of a start-up by offering free food and accomodation to founders. In return the founders give up equity much like in a traditional VC firm. One advantage of this is that the VC firm could simultaneoulsy invest in property as well as the start-ups themselves.

7) Re-structuring VC returns. Securization of investments.

Finally you could imagine a system where each tier of investing sold out to the next tier. The thinking being that you can fundamentally change the risk profiles of early-stage investing where you’re exit is not the 1 in million chance that the start-up you’ve invested in will IPO but rather simply selling out all your equity to a Series A investor who in turn sells out all their equity to a series B investor and so in. Of course this is dangerously similar to securization where assets are bundled and then sold onwards but assuming that each tier did the proper due diligence knowing that, unlike in housing, they couldn’t immediatley sell on the equity they have bought and therefore the next tier VC knowing the same thing would do their due diligence I think you could help reverse the recent trend of later and less risky investments being the more popular ones.

CONCLUSION

A lot of contrarian ideas where put forth in this essay. Many of them may not even be contrarian, they may just be plain wrong. However, I reminded of the words of YCombinator’s great Paul Graham

The very best ideas usually seem like bad ideas at first. Google seemed like a bad idea. There were already several other search engines, some of which were operated by public companies. Who needed another? And Facebook? When I first heard about Facebook, it was for college students, who don’t have any money. And what do they do there? Waste time looking at one another’s profiles. That seemed like the stupidest company ever. I’m glad no one gave me an opportunity to turn it down.”

China's Democratic Future

The most pressing question about China in the 21st century is what is China’s long-run political system?  There seem to be two distinct futures.

  1. China moves to some version of Western democracy
  2. It does not.

This is a significant question because if it is the former the question then becomes will the transition to a Western democratic political system be through gradual change or through disruptive revolution? Which of these paths is taken hangs over China with great uncertainty and China’s central role in the global economy makes it impactful to all investors whether invested in China directly or not. Of course against the backdrop of short-term market movements such long-term concerns may seem unimportant and faraway but as the sub-prime mortgage crisis and the Grexit situation have shown these long-term build ups of systematic risks are worth keeping half an eye on.

‘It might seem unfair to concentrate on the ability to anticipate recessions, given that they are (thankfully) unusual events. But it is only sharp discontinuities in economic conditions that we want to know about in advance. To be informed that trade will grow roughly as it did last year is valueless as professional advice, given that our own intuition will tend to tell us that anyway.’ Dominic Lawson

This essay aims to give a tangible example of a framework (my Frodo risk idea for predicting recessions) for tracking potential long-term systemic risks against the stream of new data and events.

The widely held view on China, of course, is that it will gradually progress to a democracy with ‘Chinese characteristics’ where China’s sheer size, culture, and historical preference for ‘stability’ account for some non-democratic components of its future political structure. This view, although it may prove right, suffers from a) being very simplified and b) offers no systematic way to track this narrative against what is actually happening in China and whether events are aligning with the hypothesised future and c) perhaps most dangerously a sufficiently compelling explanation of what is going to happen that a market participant may not make the effort to dig deeper until it’s too late.

Regarding a transition to a Western-style democracy there are four scenarios that we are going to consider:

  1. Scenario 1 – Government led smooth transition
  2. Scenario 2 – Chinese middle class revolution
    • 5 worsening, 2 stable, 2 improving
  3. Scenario 3 – Chinese rural working class revolution
    • 2 worsening, 2 stable
  4. Scenario 4 – Chinese urban working class revolution
    • 1 worsening, 3 stable, 3 improving

TRANSITION TO WESTERN-STYLE DEMOCRACY

Scenario 1 – Government led smooth transition

If the Chinese government is to provide a smooth path to Western style democracy you would expect to see

1. A gradual introduction of the democratic process and a reduction in government autocracy.

2. Chinese government rhetoric around political reform.

TRANSITION TO WESTERN-STYLE DEMOCRACY

Scenario 2 – Chinese middle class revolution

There are a number of early warning signs that you might expect to see in the event of a Chinese middle class led change in government.

1. Increasing numbers of protests for democracy. ~ worsening

The 2014 student-led Hong Kong Protests, or so called Umbrella Revolution, were a reaction to the NPCSC’s proposed reforms to the Hong Kong electoral system which are widely perceived to be very restrictive in terms of the candidates that present themselves to the Hong Kong electorate. Police tactics (including the use of tear gas) triggered yet more protests. Despite this, the protests were ended without any political concessions from the government. Going forward, Hong Kong’s Western ties (with its British history) and non-Mandarin languages (English & Cantonese) may mean that it is a likely seed of future political dissension that could spread to the rest of the Mainland.

2. High government corruption ~ worsening

One of General Secretary Xi Jin Ping’s signature policies since taking power in 2012 is his anti-corruption campaign and his so-called crackdown on ‘tigers and flies’ (high level officials and civil servants respectively). There was initially much skepticism from the Chinese public however the extent and reach of the campaign has beaten expectations where there have been more than 70 provincial (or above) officials including four National Leaders including most notably Zhou Yongkang. Having said that, critics argue that the crackdown is politically rather than corruption prevention motivated. Alarmingly the 2014 Corruption Perceptions Index has found that, despite the crackdown, China’s corruption has worsened (40 in 2013 to 36 in 2014) not improved. The primary issue seems to be a lack of transparency and the ease with which officials can launder bribes offshore. If this downward trend continues this could be a potential seed for future unrest.

3. Difficult business conditions ~ stable

Chinese business sentiment has remained relatively stable over the past decade.

Future business conditions however look very uncertain with potential continuation of the recent decline. 

4. Lower economic growth rate ~ worsening

Of course a 7% growth rate is still extremely high but China’s history of decades worth of unprecedented growth rates has led to the narrative that the Chinese people will only compromise on democracy and human rights if the Chinese government continues to deliver continued economic development. For a long time growth rates of less than 8% was considered the crucial threshold but so far the Chinese government countered that perception with a ‘new normal’ rhetoric of sustainable growth.

5. Urban city problems ~  improving

There are many severe urban city problems in China and according to numbeo China has one of the lowest quality of life indexes in the world (where factors includng purchasing powers, safety, health care, consumer price, property price to income, traffic commute and pollution at just 15.99 in 2015 compared to  192.49 for the United States and 78.60 for India. However, this still makes for a significant improvement on China’s 2012 score of just -49.55.

On the pollution front in particular, in 2014 Xi Jinping has ‘declared war’ on pollution in China where new environmental laws past in April enabled environmental enforcement agencies with significant punitive powers. This law is coupled with a massive US$277 billion package with the goal to reduced air emissions by 25% by 2017(compared to 2012 levels).

Even more remarkably this March China Academy of Space Technology’s vice president Li Ming ‘China will build a space station in around 2020 which will open an opportunity to develop space solar power technology’ with the long-term view of having a commercially viable space power station by 2050. The plan would involve building a station with five to six square kilometres of solar panels (twice the size of the New York’s Central Park) and then beaming the energy back to Earth by microwaves or lasers.

Coming back down to Earth (literally), there is a worrying trend in China’s urban cities with the rise of the so called Diaosi or so called self-proclaimed ‘losers’ which a sociologist at the Chinese Academy of Social Sciences has attributed to a ‘feeling of relative deprivation is a troubling consequence of China’s growing wealth gap.’

It is also worth emphasising China’s cities suffer from a number of very significant challenges including the migrant underclass who toil in factories and menail jobs but are denied public services because of internal migration restrictions (from the household registration or hukou system). It is also concerning that farmers in China have no property rights which has left them open to exploitation by urban officials who are looking to make money through land grabs and selling the land onto developers.

6. Middle class emigrating abroad ~ worsening

According to a 2014 Barclays report which surveyed more than 2,000 wealthy individuals (>$1.5m) around the world showed that the Chinese want to emigrate more than any other any other region. The survey found 47% of rich Chinese planned to move abroad in the next half-decade compared with just 23% in Singapore, 16% in Hong Kong and only 6% of Americans and 5% of Indians.

The primary reasons cited were better children’s education and future job prospects were named as the main reason to emigrate by 78% of respondents. A better economic situation by 73%. The US and Europe were the favoured destinations.

7. Academia/public intellectuals criticizing government ~ stable

In 2008, 60 years after the UN’s original ‘Declaration of Human Rights’ 350 intellectuals and human rights activists signed ‘Charter 08’ (so named because of Czechoslovakia’s famous Charter 77). It has now has more than 10,000 signatures.

At least 70 of the original signatories have been summoned by the police and in December 2008 Charter 08’s leader Liu Xiaobo was sentenced to 11 years in prison for ‘inciting subversion of state power.’ In October 2010 he would be awarded the Nobel Peace Prize

8. Government finances are weak ~ improving

After the Financial Crisis most countries suffered a massive increase in public debt and China with it’s gargantuan stimulus package in 2009 was no different. The government since then has shown an impressive ability to bring that debt back under control.

Recent policies include this year a debt relief programme for local governments where they will be able to swap $160 billion of their high interest debts for lower costs bonds, according to the Economic Observer, a local newspaper, this may just be the first tranche with total refinancing being three times that amount.

9. Increase in Human rights/freedom of speech restrictions/violations ~ worsening

According to the Dui Hua Foundation China executed 2,400 peoplelast year this is compared to just 778 in the rest of the world combined. Nonetheless compared to in 2002 the 12,000 executions. According to Dui Hua China ‘has executed far fewer people since the power of final review of death sentences was returned to the Supreme People’s Court in 2007’, Dui Hua’s executive Director John Kamm told the Telegraph the decline was ‘the single most positive development in the field of human rights in China in decades.’

However general human rights abuses are worsening. According to a Hubei based Civil Rights and Livelihood Watch China is ‘worsening and regressive human rights situation… the stability maintenance regime is getting stricter and stricter, you could say it’s getting more and more brutal and more inhuman….[Last year – 2014] was the cruelest we have [seen] since 1989 which is cause for extreme concern.’

TRANSITION TO WESTERN-STYLE DEMOCRACY

Scenario 3 – Chinese rural working class revolution

1. Increasing urban-rural inequality ~ stable

Urban rural inequality has been fairly stable over the past decade although the residency permit or hukou system which prevents rural Chinese moving to the more prosperous cities may be an eventual seed of future dissension.

2. Cost of living/food/fuel rising ~ stable

According to Brien Chua a Singaporean job recruiter rural people in modern China told the Strait Times ‘With lodging expensive and food costing more than double the price than back home, no one wants to move to the big cities anymore.’

3. Rural unemployment ~ worsening

According to a 2014 report from the Chinese Academy of Social Sciences estimated that the rural area unemployment rate among college graduates could be as high as 30.5% which when coupled with the strict rural-urban migration laws would suggest a worsening situation. Unemployment statistics in China however are notoriously bad especially as the 274m migrant workers are completely ignored in the datasets.

Thus the implausibly stable official unemployment figures (in a range from 4.0% to 4.3% over thirteen years) despite drastic fluctuations in GDP/growth rate over that same period are given very little credence by most economic observers.

4. Increased rural-urban migration ~ worsening

China has a strict household registration system which is meant to limit internal migration (particularly rural to urban) within the country. Nonetheless there is a huge migration population of workers, estimates number at more than 250 million with that number expected to double by 2025.

TRANSITION TO WESTERN-STYLE DEMOCRACY

Scenario 4 – Chinese urban working class revolution

1. Increasing inequality ~ stable

Inequality in China increased significantly throughout the 90’s although it has stabilized since at a high Gini coefficient of ~0.47. In urban areas specifically Gini has risen significantly from 0.15 (1981) to 0.36 (2011).

2. Increased cost of living/food/fuel ~ stable

Against a back drop of a decade worth of 7%+ growth cost of living has remained relatively stable where according to numbeo data China’s cost of living index was 43.47 in 2009 and just 48.89 in 2015 still well below advanced developed Europe countries who are in the 90s and the United States which is in the mid-70s.

3. Increased housing costs ~ improving

In 2014 the Chinese government has cooled its effort to tighten lending and cool the housing market through a rate reduction on loans longer than five years by 40 basis points in November and 25 basis points in the following February. This explains the spike in real house prices in 2014 you can see below. However, since then the market has remained bearish where the latest tally by the Survey and Research Centre for China Household Finance of 28,000 households in 29 provinces indicated 22% of urban homes were vacant in 2013.

Generally, the interpretation is of the oversupply of housing and weak demand has been negative but for our purposes this is unequivocally good thing as it only makes housing more affordable for the urban working class.

4. Urban unemployment ~ stable

China’s economy job creation numbers remain strong such that rather incredibly there are more job offers than seekers.

Whilst unemployment number have stayed relatively stable.

There is much concern about the reliability of the data however.

5. Increased urban protests/meetings ~ improving

As you can see this is a definite uptick in strikes since 2011 although recently they have fallen dramatically in line with Xi Jin Ping’s recent crackdowns.

Of course it is worth noting that the crackdowns may have exactly the opposite effect, denying people a channel to express their views and led to a bubbling of dissent that may burst. This issue is worth watching very closely.

6. Worsening social mobility ~ worsening

Social mobility in China is very low where if one father earns 2x that of another than how much more on average will his children earn relative to the other father’s children (this is a calculation of the elasticity of inter-generational income) is 60%. This is in comparison to 47% in the US and just 15% in Denmark.

However, research from Yi Chen of Nanjing Audit University and Frank Cowell of the London School of Economics have found that since 2000 people at the bottom of society are more likely to stay there then in the 1990s ‘China has become more rigid.’

7. Culture ~ improving

Despite all the problems, China’s return to an elite place in global politics and the rise of Chinese both culturally with an increasing number of Chinese actors in Hollywood (e.g. Fan Bingbing) and the prominence of its business leaders, most notably Jack Ma of Alibaba. Jack Ma, the second richest man in China, has inspired an almost cult like following where he tours the country giving inspirational speeches saying that ‘if Jack Ma can be successful 90% of Chinese people can be successful.’ This could be dismissed as mere rhetoric but even the most cynical would have to admit that Jack Ma has succeeded against all the odds. For the young Chinese the most important thing is passing the gaokao (or university entrance exams) – Jack Ma failed three times. In China the ideal man is ‘Gao Fu Shuai’ (literally tall, rich and handsome) and the overwhelming profile of most romantic tv drama leads, is a cold, efficient Fu Er Dai (literally second-generation rich) or Guan Er Dai (son of a senior government official) who treats the poor, plain looking but positive female lead badly before slowly warming to her. The importance of a powerful father is especially true because of the importance of guanxi (or connections) in China. Jack Ma has succeeded (sensationally) despite none of these advantages which has only endeared him more to the Chinese people.

OR SOMETHING ELSE

Many of the indicators point to the fact that China requires drastic change. Interestingly though, according to a fascinating study by the Chinese Academy of Social Sciences found that there is a startling trend that young people in China are much less in favour of democracy than older generations.

Anecdotally, one of my teachers at Tsinghua University explained her indifference to the Government, ‘politics is like celebrities relationships, it’s got no impact on how I live my life.’ The best analogy I can come up with for a Western person to understand the Chinese mindsight is how for a European who the President of the United States is and which political party he belongs to actually has a significant impact on their lives but of course despite this no one in Europe is protesting for the right to vote in American elections. It is worth remembering of course that the combined populations of Europe and China is still several hundred million short of China’s which speaks to just how massive China really is and maybe explains the feeling that most Chinese people have which is of great distance and disconnect to government that most Europeans in states of just tens of millions can find it hard to relate to.

Finally, I think it is worth pointing out that post the Financial Crisis there has been more soul-searching in Western politics about the form and structure of government than in China.

And he [Lee Kuan Yew] was not only just thoughtful, just the very idea that he would take parts of the Western system and say ‘Oh this part is good, this part may not apply everywhere,’ this part he disagreed with. It was kind of bold because of course the Western system was succeeding, you know, basically all the rich countries in the world had followed the Western system, and so the idea that he thought he would do it slightly differently was a huge contribution. And so Singapore’s a city state, so you do things in terms of paying government salaries and excellence there that may not scale up but what he did was very incredible. What we really want is this mix of democracy and expertise and no country has that balance right. If you err on the side of democracy there are certain extreme thing about the wrong person getting in power and if you kick them out then how do you get new people? So a democracy they have some huge advantages that helped the US quite a bit. It is a little scary now when we have complex problems like how do you run a healthcare system efficiently. Why is the US paying so much? And there really isn’t at this time any elected representative who can have a good discussion about the dynamics of the system and why it’s different from other countries and how we might change that. So government has to deal with very complex issues and the Chinese government although I’d say the trend is a tiny bit away from it has had engineers and scientists in a lot of key positions and a willingness to look at what other countries do and also this notion that if you’re going to have a new policy you can often try it out in part of the country see if it works and tune the policy before you try to scale it up in a really broad way. So the Chinese government is a student of policy more than just a, say, the UK Parliament or the US Congress where people are kind of yelling at each other like ‘I’m right’, ‘No you’re right’ It’s not like ‘oh we’re going to do an experiment.’ I’ve never sat in the US Congress and had them say ‘Oh let’s try yours out in one state and we’ll try out mine and we’ll come together and let’s combine the best features.’ That’s not a typical electoral dialogue that we’re having right now. So it’s a work in progress. There are things like how you run your universities where the US model – you know other people should just adopt it. Then there’s things like health systems and governance where they should take some aspects that try out variation. So we get the benefit of 192 countries slightly different experience including at the sub-national level.’ Bill Gates

‘We have a very strong government and they have very strong execution capabilities when it comes to infrastructure, like high-speed rail or highways. We have massive constructions and now is probably the largest infrastructure in terms of transportation in the world. But when you have a strong government are you concerned about innovation?’ Robin Li, Baidu

‘…in terms of things like, how do you make energy the state policy’s not holding back somebody figuring out some big invention. In fact, I have a nuclear power company called Terra Power. That really, China’s the most natural partner for us with the breakthrough generation of nuclear. Because China’s a lot like the United States was in the 1960s, where the idea you want to go forward and do new things, it’s very clear. The idea that the status quo isn’t where you want to be. The US today is very careful that they were pretty happy with the current conditions, so if somebody wants to build a new building or take some new approach, there’s a lot of ‘Hmm, maybe no.’ There’s like five levels you go through, maybe no, maybe no. Whereas the bias towards moving and doing new things which has a small downside but a huge upside as well. I’d say in terms of breakthroughs in some areas, like nuclear, it’s more likely to come out of China than almost any other place because of this bias towards doing big projects. And 1950s, 1960s that was the US and the 70s it started to be Japan. Korea took on that role, that big engineering bias is great for the world.’ Bill Gates

‘It’s a very interesting problem, that our founders coped with which is just how democratic you wanted the system… I don’t think it is a pure democracy… I took political science… and everybody teaches the more people that vote the better systems will work and having a contrarian streak I am not so damn sure! That the civilization doesn’t work better when a lot of people don’t vote.’ Charlie Munger

‘I think the biggest blow to our relationships is the Chinese interpretation of the financial crisis. That we did not look at political matters in an identical way that was apparent after years of the relationship. And they sort of understood that this was the case but they did think that we had a magic formula for economic progress from which they could learn. (And they had in fact with Deng Xiao Ping). That’s right and basically when Deng Xiao Ping said reform which was his basic slogan he really meant learn from the Americans. And he sent tens of thousands of students abroad. And it suddenly turns out that the American financial model that they had really tried to copy in some respects started disintegrating in some of its assumptions and that has not only made them lose confidence that we knew what we were doing but those people inside the Chinese system who leaned towards the United States had a lot of explaining to do. And we still suffer from it, I saw some Chinese commentary on the Chinese 5 year plan, and I don’t pretend to be an economist but that commentary said ‘Don’t let the Americans seeming recovery fool you because the West is trying to solve the current Economic crisis by exactly the same methods that got them into the crisis. Although I’m not saying that they’re right there is some merit in it.’ Henry Kissinger

CONCLUSION

This piece represents my first serious attempt to flesh out my ‘Frodo Risk’ approach to systematic risks with respect to a specific problem. It is truly unbelievable the extent to which financial market participants are overwhelmed with information and having now seen it up front I’m starting to understand why even the smartest people can fold under the sheer weight of information overload. What I hope this approach represents is a structured filter for monitoring information about long-term threats/risks. Any one of the news stories above e.g. a 5% decrease in business confidence taken in isolation would justifiably be considered insignificant but if that 5% decrease coincided with all the other potential revolutionary factors moving in the same negative direction it might speak to a much larger truth.

Most market participants have a short-term focus of, at most, just a couple of years but having reports like this around specific systemic threats might be a useful and time efficient way to keep an eye on these long-build ups so that situations like in Greece and the housing market in the United States don’t come as such a surprise.

Feedback on the report and structure is, as always, most welcome especially as in writing this I have wrestled with how much information should be included. Should it be primarily links to other peoples research and datasets with just one sentence summaries or should the report (if it’s only going to be read once a quarter/half year say) be more comprehensive?