Economics 124/PP 190-5/290-5 Innovation and Technical Change

Science, invention, and innovationProf. Bronwyn H. HallUC Berkeley

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Today

Science, invention and innovation The linear model Deviations from the linear model

Technology driving science Learning by using

Chance and unexpected innovations Lessons for policy

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The linear model of innovation

A useful conceptualization, but not the whole story. The idea:science base → basic research → applied

research → invention → prototype → development → commercialization → diffusion → technical progress → economic growth

Sometimes the entire process in red is referred to as innovation

Which stages need funding, and how?

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Example – new drug Basic research – microbiology, etc. Applied research – screening compounds in

test tubes; testing on animals Invention – successful in laboratory Development – Phase I and II clinical trials Commercialization – packaging; marketing;

dosage info Diffusion – spread throughout the

patient/doctor population

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Example – new software Basic research – mathematics, queuing theory Applied research – cryptography, sorting

algorithms, data storage systems Invention – idea of program, design, basic

features Development – programming, detailed

specifications, alpha testing Commercialization – beta testing, marketing,

sale Diffusion – adoption by consumers; large

market share

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Modifying the linear model Importance of backward links (reverse the

arrows)Commercialization and diffusion → new innovation

& developmentInvention/innovation → science base/basic research

Rosenberg emphasizes this point in a series of papers “How exogenous is science?” (1981) “Learning by using” (1978) Both published in Inside the Black Box (CUP

1982)

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Backward links in the linear model

"How Exogenous is Science?" how applied research and innovation have yielded

new scientific knowledge and created new scientific fields, both accidentally and purposefully

=> feedback from applied research, innovation, and development to the science base.

"Learning by Using" a term modeled on learning by doing describes how products are improved and developed

in both embodied and disembodied ways, based on experience of the product in use

=> feedback from diffusion to development

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How exogenous is science? exogenous means determined outside the

system in this case, the innovation system

Technological knowledge often precedes scientific knowledge Scientific progress can be an accidental byproduct of

searching for an answer to a technological puzzle A technological discovery can dictate the direction in

which subsequent scientific research should go Improvements in instrumentation (technology)

A major determinant of subsequent scientific progress nanotechnology (1910) and the electron microscope

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Science from technology (1)Inventor/scientist

Technological source

Years Scientific field developed

Toricelli improved pump – explored the weight of the atmosphere

1600s Atmospheric/pressure science/barometer

Watt/Carnot steam engines 1830s Thermodynamics

Pasteur wine industry/ fermentation

1850s Bacteriology/germ theories

Perkin /Hoffman

Synthesis of mauve, first aniline dye

1870s Organic chemistry

Wilm Bessemer process; age-hardening of duraluminum

1850-1900s

Metallurgy/materials science

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Science from technology (2)Inventor/scientist

Technological source Years Scientific field developed

Davisson vacuum tubes – patterns of emission from nickel crystal due to electrons

1920s Wave nature of matter/ Nobel prize 1937

Jansky/ Bell labs

radio noise 1932 Radio astronomy (star noise)

Townes/Bell labs

Laser technology for optic fiber cables

1950s Optics resurgence

Shockley Transistor/ semiconductor

1948 Solid state physics

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Innovation and learning During the R&D process

Knowledge concerning laws of nature (basic R) Knowledge with useful applications (applied R) Knowledge directed towards optimal design

characteristics and satisfying consumer wants (development)

After the R&D process During manufacturing – learning by doing During the use of the product – learning by using

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Learning by doing Widespread phenomenon in the repeated

manufacture of a good (airframes, chemicals, semiconductors) First measured for aircraft in the 1930s-1940s

labor = N-1/3 where N=N airplanes produced Observed in a number of industries by the

Boston Consulting Group – plotted learning curves with downward slope ~ 0.3

A major feature of semiconductor manufacturing as the number of rejected chips falls over time

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Learning in chemicals

.15 .2 .25 .3 .35 .4 .45 .5 .55

5000

10000

15000

20000

25000

Cumulative Production

Unit C

ost

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Learning by using Technological change does not end after

the technology is diffused Technologies continue to improve due to

feedback from use and users software; skateboards

Some improvements embodied learning how to stretch a Boeing 747

Some disembodied learning the maintenance frequency necessary

for aircraft learning that a drug good for one condition

actually works for another

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Learning by using

Performance of complex capital goods not fully understood until they are used Technological knowledge required highly

specialized, includes user knowledge Product differentiation valuable to users,

can be achieved by them e.g., skateboard innovations from users

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Aircraft example

Embodied learning Uncertainty in aircraft design, plus

caution in first use As time goes by, experience leads to

stretched aircraft, larger payloads Disembodied learning

Extensive maintenance and overhaul requirements of jet engines

Over time, service intervals lengthened

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Boeing 737-100,737-600/900

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Boeing 747-100,747-300,747-500

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Uncertainty and chance

History of innovation replete with examples of discoveries that Were a side effect of a completely

different investigation, as in technology->science examples

were unappreciated at the time they were made (consequences or usefulness unforeseen) Examples: laser, radio, computer

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Examples of forecasting failure Laser (Light Amplification by Stimulated

Emission of Radiation) invented by Townes at Bell Labs around 1960 now used in navigation, precision measurement,

chemical research, surgery, compact discs and printing

most important and widespread use is probably fiber-optic cable for telecommunications

But…..lawyers at Bell labs did not apply for a patent, thinking it not relevant for their industry, which was the telephone industry!

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Examples of forecasting failure Radio

invented by Marconi to be useful when wire communication impossible, such as ship-to-ship, that is, narrowcasting, not broadcasting

Computers Watson, Sr. (head of IBM) saw a need for only

one computer to solve all the world’s scientific problems

In 1949, IBM forecast world demand at 10-15 computers

(ENIAC contained 18,000 vacuum tubes and was 100 feet long, so this is understandable)

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Some lessons for policy Macro-inventions (scarce ideas?; radical innovation)

Unexpected sources and consequences May require a broad science base

Micro-inventions (well-known needs?; incremental innovation) More predictable Often a result of natural evolution of a technology Easier to pay for

Distinction does not necessarily correspond to the increment in economic welfare (e.g. malaria vaccine)