Basic research and economic growth

With some reflections on the impact of astronomy, its large facilities and educational efforts

Paper at http://hdl.handle.net/1887/18636

Overview

• Astronomy’s four economic roles

• Research and growth literature

• Modeling technology and applied R&D

• Modeling basic research.

• Return on applied R and D and basic research

• Science policy implications

• Astronomy’s four economic roles revisited

Astronomy’s four economic roles

• Economic impact of astronomical results

• Knowledge spin-offs of instrument building

• People spin-offs

• Civilizing influence of astronomical knowledge

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Economic impact of astronomical results

• Copernicus observed, Newton explained and communication satellites make money

• Mercury anomaly, Eddington’s starlight bending, Einstein, and car navigation systems

• Today: dark energy, high energy accelerators in the sky, astronomical work like that of George Miley; tomorrow new basic physics, next century new industries?

• Insurance: Near Earth Objects, communication disruption warnings

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Knowledge and people spin-offs

• Old paper by Ben Martin and John Irvine (1981)

• Cutting edge instrumentation leads to spin-off results in other industries (cf. examples on the internet; George Miley’s LOFAR ground based sensor network).

• Both instrument building and pure astronomy lead to excellently trained young people, popular elsewhere in research and in industry.

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Civilizing influence• Steven Pinker (2011): why has violence declined? Both

violent crime and war. Refers to Elias: the civilizing process

• Knowledge is a civilizing force (p. 174): “[…] people […] had more to read about. The Scientific Revolution had revealed that everyday experience is a narrow slice of a vast continuum of scales from the microscopic to the astronomical, and that our abode is a rock orbiting a

star rather than the centre of creation.”

• This broadening of horizons adds a dose of humanitarianism to peoples minds, increasing empathy and reducing violence.

• George Miley’s Universe Awareness perfectly fits the bill of the civilizing process

• Economic impact of lower rates of violent crime and of war needs no arguing

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Quantifying economic impact of research

• Empirical macro case studies: discoveries (‘One half of our GDP is based on quantum mechanics’)

• Empirical regional studies: spin-offs from facilities, calculations of regional science based employment and business (Silicon valley, bio-science parks)

• Empirical innovation studies based on innovation surveys (what are the sources of knowledge and ideas in innovative businesses), patent data bases and cooperation patterns in scientific publications by people from business

• Empirical econometric studies: higher growth in countries that spend more on R& D ?

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Doubts• Dutch CPB (Government institute for economic policy

analysis) said last week that they neglect impact of science funding on economy because they don´t know how to put it in a model.

• Empirical literature inconclusive because the attributions of economic effects to R&D are debatable: what would have happened if the money would have been spend directly on productive engineering projects or on applied R&D?

• Therefore let’s start at the other side: how does economic growth occur and what are the relative roles of capital, applied R&D and basic research.

• This means turning to the macro-economic theory of economic

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Research and growth literature

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Growth literature: production function

Y GDP

f Linear homogeneous, K Capital stock, L Labor force

A Constant

• A is called ‘Total factor productivity’• If A grows exponentially: constant rate growth of

factor productivity• Also called ‘technological progress’

Neoclassical growth: Robert Solow (1960)

• Production function plus capital accumulation (based

on saving) sustained per capita growth

• Technological progress needed

• This result earned Solow the 1987 Nobel and caused more attention for innovation

• But: until 1990 technological progress was exogenous (“Manna from

heaven”).

• Then endogenous growth theory

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Endogenous growth: basic model• A seen as stock of knowledge, produced by R&D: research

labor using the existing stock of knowledge.

• Then technological progress proportional to amount spent on R & D

• Constant level of R&D = constant rate of economic growth.

• More R&D: higher rate of economic growth.

• But there is a ‘scale problem’: large country spends more on R&D and must have higher growth rate.

• And: GDP growth means more money for R&D, thus more technological progress, yet more money for R&D, ……….

• Economy explodes

Taming the scale problem

• Knowledge accumulation progressively more difficult

• Then growth of A no longer proportional to amount of R&D

• Explosion vanishes, but growth too. Only some very slow growth if population grows

• Most used mechanism: product diversity increases with scale, equal knowledge needed for all products

• Growing number of products plus fixed amounts of R&D per product total R&D grows

• Growth rate depends on level of R&D again

Diminishing returns

Need for knowledge scale dependent

BUT

• Extreme fine-tuning of unknown parameters needed

• Measurement tough and inconclusive (Donselaar)

• Relation between country size and product diversity?

• Disaggregation of R&D extremely difficult.

• Therefore almost no policy conclusions

• Policy (such as top-sectors) more hype and fashion than solid theory

Modeling technology and applied R&D

Trial and error• Technological progress is not accumulation but trial and error

discovery (Kortum, 1997)

• Solutions, ideas, possibilities tried out all the time, the best are retained and replace current practice:

• Repeated sampling and selection from random distribution of technological possibilities, the technology function.

• The number of technological possibilities falls off with increasing productivity: distribution is skewed, with a long tail

• I use inverse power law (= Pareto distribution), common in scientometrics but also in income distribution

Pure trial and error (pre-industrial)• Before scientific revolution: a-select trial and error until

something better than current practice is found

• The higher the level achieved, the more additional experimentation needed for further progress

• Thus no growth; or very slow growth if means for experimentation grow by population growth.

• As diminishing returns case, but with historical interpretation; agrees with very long term growth data.

Experimental learning• Suppose there is enough basic knowledge so that by

experimentation, you not just find better technologies, but also learn to experiment with greater chance of success.

• Technology function the same, but lower boundary shifts upward as a consequence of the experiments

China and other transitionals

• This is the situation of transitional countries such as China, India, Brasil, Japan until 1980.

• Applications of experiments (including “ experiments” with licensed and copied technologies) generate ever more means for further experiments

• and render those successful too, as the lower boundary shifts upwards.

• Easy to prove that this generates double digit, explosive growth

• Until catching up with the advanced economies and running into lack of basic knowledge. Cf. Japan from 1980 on.

• Newton spent half his life on alchemist experiments that we now know, from basic knowledge, to be fruitless

Modeling basic research

The hypothesis function• Basic research is: developing hypotheses, testing whether they explain

the experimental results, retain the best, and continue.

• At any level of technology basic research is needed until experimental results are explained and further experiments with lesser results can be avoided. Thus:

• The hypothesis function: the distribution of basic research hypotheses

• Trial and error of hypotheses until their explanatory power exceeds the minimum level of the technology function.

• Then the technology level increases and the process repeats at a higher level.

• Again let the shape of the hypothesis function be Pareto with the same shape as the technology function

• How fast can hypotheses be developed and tested? I assume:

• proportionality with amount of basic R and D labor/spending; • fixed rate of learning. In every field the first hypothesis requires

the same amount of work as in earlier fields, the next one a bit less, and so on.

Modern economic growth

• This yields a nice and globally stable rate of long term growth.

• Its value depends on two opposing forces: the downward slope of the technology/ hypothesis function (‘how difficult is further innovation?’) and the rate of learning in basic research.

• If further innovation is more difficult, growth is lower.

• If the rate of learning is high, growth is higher.

• Growth depends a bit on labor growth, but if that is zero, per capita income still grows, due to the learning in basic research.

• Thus this is a theory of basic and applied research that explains actual historical developments

Return on applied R&D and basic research spending

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Level of income and spending proportions• The growth rate does not depend on the proportions of income that are

spent on physical capital, applied R&D and basic research

• Similar to a long standing result (Solow, 1956) in growth theory: the rate of saving does not influence the growth rate.

• But it does influence the level of income and consumption. This is the same for research.

• Surprisingly easy to derive the rates of return once you have the model.

• They depend on the two parameters (slope of technology/hypothesis distributions and rate of learning in basic research) we do not yet know.

• But they are not very sensitive to these unknown parameters.

• Rather, they are very sensitive to the current levels of spending: the rate saving, the applied R& D intensity and the basic research intensity.

• These we know from OECD statistics

Rates of return

• For the rate of saving we may use 15 %, for applied R and 1.5 % and for basic research at most 0.5%.

• Then the return on applied R&D spending is about ten times that on physical capital investment and that on basic research about three times higher still

• One euro extra applied R&D generates about 15 euro national income.

• One euro extra basic research generates about 50 euro extra national income.

Incubation time

• Great rates of return, but how long does it take?

• Time between start of new fields and application in actual economy increases monotonously

• But average time between basic research and applications constant

• Large and constant proportion of all spending on basic research leads to applications within short period

• Therefore if a country has insufficient basic research it will be too late in picking up the useful results

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Policy implications

Rate of learning: facilities

• Rate of learning in basic research fundamentally determines economic growth. Main aim of science policy should be to raise this rate of learning. Thus:

• Open access to publications and data

• Excellent high speed research networks

• Ample research facilities (small and big), and easy access to them

Rate of learning: human resources

• Excellent training for young researchers (PhD’s)

• Reduction of learning losses by massive outflow of PhD’s and Post Docs: much earlier up or out decisions (tenure track selection)

• Stimulation of independence of young talent (they learn faster and are quicker to choose the new approaches)

• Defragmentation within universities, much easier multidisciplinary collaboration; reduction of within university invoice culture

Astronomy again

• Spending on large facilities such as George Miley’s LOFAR is at least as profitable as spending on the physical infrastructure

• Training young people in an interdisciplinary field such as astronomy and spinning the out to other fields increases the rate of learning in basic research in general.

• Opening children’s minds and hence societies’ minds as in Universe awareness creates the culture needed for basic research

• And thus lays the groundwork for sustained economic growth and prosperity

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Thank you for your attention!