Is the US a post-innovation economy?

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[amazon_link id=”0262019922″ target=”_blank” ]Production in the Innovation Economy[/amazon_link] edited by Richard Locke and Rachel Wellhausen is a useful short summary of an interesting inter-disciplinary MIT research project, and is the companion to an earlier volume, [amazon_link id=”0262019914″ target=”_blank” ]Making in America: From Innovation to Market[/amazon_link]. Although entirely US-centric, the research into what is needed for firms to be able to innovate then commercialize and grow is fascinating. All the more so in the light of the mild furore yesterday about Facebook’s purchase of Oculus, which some commentators saw as a signal of the inability of start-ups to grow organically.

[amazon_image id=”0262019922″ link=”true” target=”_blank” size=”medium” ]Production in the Innovation Economy[/amazon_image]

The work, based on existing data sources and a new survey of US manufacturing companies – including high-tech spinouts from MIT, explores a number of potential barriers to turning research into successful US manufacturing entities. The papers in the book cover skills, what it rather coyly calls ‘complementary assets’ but is actually finance to grow past the start-up stage, and a thick, geographically specific ‘ecosystem’ of suppliers. Taking these in turn:

– most manufacturers have no specific high skill needs and have little sustained trouble filling jobs, but a significant minority (15-20%) need people with higher mathematics and computer skills, and team working capability, and find it hard to recruit. Small, innovative start-ups do not have the capacity to train up their own people – they need to hunt for them in the labour market.

– surprisingly, finance to get to the point of being able to manufacture at scale, including a stage of iterative incremental innovation and prototyping, is a major barrier even in the US (and surely all the more so elsewhere). Part of the problem is that VCs have become specialists in specific stages and are usually looking for an out from their stage – in other words, venture capital has become very short term finance, shorter term than needed to grow a successful innovative manufacturing business. Many US businesses now turn to strategic overseas investors in Asia, either their customers or even state-funded entities.

– a third requirement is the presence locally – for the exchange of tacit knowledge – of a thick enough market for components. The geographic co-location of members of a supply chain is becoming a constant theme of the literature on innovation. In the economy of the 1960s and 70s, large vertically integrated companies with their own R&D arms did not have this challenge of finding partners in the supply chain; but all new innovative manufacturers have to source components from suppliers who are prepared to work on the new products.

The specific policy conclusions are written for the US, and do not carry over elsewhere in the same form. But the general principles are very obvious. They all point to the need for government to play a co-ordination role. Whether it is ensuring the system of education, training and apprenticeships serves the needs of new start-ups, co-ordinating finance or tax breaks on the funding gap at the transitional stage of growth, or liaising between firms and with local authorities and educational establishments to achieve the geographic clustering, there is a strategic role for government.

Asian governments are superb at this, including focusing support on some specific sectors (such as energy innovation in China), and I think some European governments do a decent job too. The book is pretty clear, though, that the US government has not adapted its industrial strategy for an economy consisting of smaller innovators, one where it can’t rely on big defence contracts and the likes of Bell Labs to look after the business of growing new ideas into large-scale commercial success. In the US and UK there is still the official delusion that industrial policy is equivalent to pouring a bath of taxpayers’ money in which lame ducks can splash around in unproductive luxury. This book provides a really solid evidence base concerning the barriers to growing small, innovative manufacturers, and is very persuasive about the need for policy action to lower those barriers – otherwise, it implies, the US economy can have superb universities and early stage research but will be nevertheless a post-innovation economy.

Secret statistics

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Is there a word for ‘serendipity’ that doesn’t have the overtone of being a positive thing? In a horrible coincidence, over the past day or two I’ve been reading the sections of [amazon_link id=”0300188226″ target=”_blank” ]The Theory That Would Not Die[/amazon_link] by Sharon Bertsch McGrayne concerning using Bayes’ Theorem to help trace missing objects at sea, albeit submarines and nuclear missiles gone astray rather than airplane debris. The theme of the book is the way Bayesian statistics survived two centuries of dismissive treatment by the academic statistics establishment because the techniques are just so useful in an acute situation. The description in today’s newspapers of the way Inmarsat and other investigators combined different sources of information to piece together the path of the vanished plane could slot in to the book very neatly.

[amazon_image id=”0300188226″ link=”true” target=”_blank” size=”medium” ]The Theory That Would Not Die: How Bayes’ Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant from[/amazon_image]

Although an econometrician by training, and trained in the early 1980s so almost wholly in frequentist techniques, I’ve never really understood the ‘either-or’ aspect of the debate about Bayesian techniques. Why would you expect the same technique to be useful in every situation? Why would you throw away information? I was a bit disappointed in [amazon_link id=”0300188226″ target=”_blank” ]The Theory That Would Not Die[/amazon_link] for two reasons. One is that it actually doesn’t do a very good job of explaining what the alternative approaches actually are – the explanation of Bayes Rule and how to apply it is left until Appendix B. Maybe an editor said it would be too scary for readers to have any actual statistics in the main text.

The second reason is that I ended up not really clear why Bayesian methods were infra dig for so long – the book describes one thing after another conspiring against public acclaim for ‘Bayesianism’ without giving a synthesis. The closest it comes to a theory is that the code-breakers from Bletchley Park on and the military were such heavy users of Bayesian methods that statisticians actually did use them but never talked about them.

Having said that, the book’s a very enjoyable read. There are lots of episodes and characters new to me, and it’s very well written. The problem-solving sections – finding those nuclear warheads gone astray – are gripping. Bayesian statistics seem particularly appropriate to economics, which has relatively little scope for repeated experiments and much historical, context -specific, non-repeatable evidence. This book made me think I ought to find something far more practical to read – recommendations welcome.

The unsustainable is never sustained

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There’s a flurry of interest about a new NASA-funded study on the prospect that our complex industrial societies are poised for collapse. It indeed sounds like a very interesting piece of work. But is this new? Jared Diamond’s Collapse was surely a forerunner. Paul Seabright’s The Company of Strangers majored on the potential fragility of the complex, intertwined global economy in which we all depend on the activities and goodwill of strangers – so far, so good but clearly vulnerable.

But the grandfather of this complexity and collapse of civilisations theme was Joseph Tainter‘s 1988 The Collapse of Complex Societies. He talks about it here on You Tube.

For me (as discussed in The Economics of Enough) the moral is that the unsustainable is never sustained, and the only question is how society moves to the sustainable trajectory – sustainability including the social and financial as well as the ecological dimensions. Collapse is the least desirable option.

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A fresh Perspective on identity

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The next title in our Perspectives series will be published soon: Identity is the New Money by Dave Birch. It is packed full of incredibly persuasive ideas for enabling digital technology to enhance both security and privacy – I think Dave is completely convincing on the scope to eliminate what we think of as a trade-off by shaping the technology infrastructure in an alternative, non-centralised manner.

Meanwhile, there have been some new reviews of earlier titles in the series. Julia Unwin’s Why Fight Poverty? has just been reviewed in the LSE Politics and Policy blog and Bridget Rosewell’s Reinventing London was reviewed in the LSE Review of Books recently. The series was featured in 3am Magazine too, with an interesting take from a non-economist, the philosopher Richard Marshall.

[amazon_image id=”1907994165″ link=”true” target=”_blank” size=”medium” ]Why Fight Poverty? (Perspectives)[/amazon_image]   [amazon_image id=”1907994149″ link=”true” target=”_blank” size=”medium” ]Reinventing London (Perspectives)[/amazon_image]

[amazon_image id=”1907994157″ link=”true” target=”_blank” size=”medium” ]Rediscovering Growth: After the Crisis (Perspectives)[/amazon_image]   [amazon_image id=”1907994130″ link=”true” target=”_blank” size=”medium” ]The BRIC Road to Growth (Perspectives)[/amazon_image]

Thinking, long and hard

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Behavioural economics is very much in the news. Tim Harford takes a slightly skeptical look at it in the Financial Times today. In a recent post Chris House noted that few young researchers were taking up the baton, and anyway there are relatively few significant papers in the field. These have followed (in my order of reading) a rather intriguing OECD blog post by Vela Velupillai distinguishing between classical and modern behavioural economics (CBE and MBE). The classical version, from Herbert Simon’s 1953 paper, A Behavioural Model of Rational Choice, onward, is presented as more radical because it assumes no preference ordering and where there is no optimising but rather satisficing. Prof Velupillai writes:

“MBE concerns the behaviour of agents and institutions in or near equilibrium; CBE investigates disequilibrium or non-equilibrium phenomena…. MBE accepts mathematical analysis of (uncountably) infinite events or iterations, infinite horizon optimisation problems and probabilities defined over s-algebras and arbitrary measure spaces; CBE only exemplifies cases which contain finitely large search spaces and constrained by finite-time horizons.”

I have no idea what an s-algebra is, but the point is clear – as the post continues:

“Though the behavioural models do consider more realistic psychological or social effects, economic agents are still assumed to be optimising agents, whatever the objective functions may be. In other words, MBE is still within the ambit of the neoclassical theories, or is in some sense only an extension of traditional theory, replacing and repairing the aspects which proved to be contradictory.”

This is interesting in the light of having read recently Gerd Gigerenzer’s [amazon_link id=”0141015918″ target=”_blank” ]Gut Feelings[/amazon_link], which one could sum up as being about the rationality (in the sense of using mental resources efficiently) of non-rational (in the sense of calculating) decision-making via the use of rules of thumb. Gigerenzer takes issues with precisely the frequent behavioural econ assumption that decision-making is done as in the rational choice framework except with biases. Not always – Kahneman’s [amazon_link id=”0141033576″ target=”_blank” ]Fast Thinking[/amazon_link] clearly overlaps with the use of rules of thumb.

[amazon_image id=”0141015918″ link=”true” target=”_blank” size=”medium” ]Gut Feelings: Short Cuts to Better Decision Making[/amazon_image]

[amazon_image id=”0141033576″ link=”true” target=”_blank” size=”medium” ]Thinking, Fast and Slow[/amazon_image]

Fascinating territory. I need to think, not fast or slow, but long and hard about this.