NAEC Workshop - Complexity of the Economy: Research and Policy Implications (26-27 October 2015, Paris)

1. An evolutionary approach to predicting
technological progress and economic growth
(work funded by GROWTHCOM (FP7) and DOE)
Complexity of the Economy:
Research and Policy Implications
OECD, Paris, October 26, 2015
J.
Doyne
Farmer
Ins$tute
for
New
Economic
Thinking
at
the
Oxford
Mar$n
School
Mathema$cal
Ins$tute,
University
of
Oxford
External
professor,
Santa
Fe
Ins$tute

2. from: A Short History of Technology, by T.K. Derry and Trevor I. Williams (1960). p.191.
Technology co-evolved with early humans

3. Tool use predates genus homo
3
Choppers, scrapers and
pounders, 3.3 M years ago
homo habilis
Australopithecus garhi
Oldewan tools
2.6 M years ago

4. Without technology,
none of us would exist
4

5. Complexity economics and
economic growth
• Traditional growth economics focuses on
production function description of aggregate
economies.
• Technology is main driver of growth (Solow)
• Complexity economics views technology as a
interacting and evolving ecology.
• To understand economic growth we must
“get inside the black box” and understand
what drives technological progress.

6. Two examples of how complexity
economics can help us
understand economic growth
(1) Prediction of progress of individual
technologies based on empirical regularities
(2) Using an ecological network approach
to understanding economic growth

9. Wright’s law
(1936)
Theodore Paul Wright
Cost vs. cumulative production = power law y = x

10. Moore’s law
(1965)
Originally a statement about density of transistors
We will use to refer to the hypothesis that technological
performance improves exponentially with time
Gordon Moore

11. 1970 1980 1990 2000
1e-081e-051e-021e+01
Moore
Time (years)
Averageunitprice(real$)
Transistor
Photovoltaics
HardDiskDrive
Ethanol

12. 1e+01 1e+03 1e+05 1e+07 1e+09
1e-081e-051e-021e+01
Wright
Total cumulative production (# of units)
Averageunitprice(real$)
Transistor
Photovoltaics
HardDiskDrive
Ethanol

13. Laws can be used to make
forecasts with reliable error bars
• Nagy, Farmer, Bui,Trancik (2013)
• Farmer and Lafond, (2015)
• Methods are based on experience from
hindcasting 50 different technologies
• Clever mathematical tricks make it possible
to use experience with all technologies to
get errors in forecasting a given technology

14. Forecasts without error bars
are useless
Need reliable distributional forecasts, i.e.
“how likely is a given outcome”
14

15. Distributional forecast of solar PV
assuming business as usual
15
PVmodulepricein2013$/Wp
1980 1986 1992 1998 2004 2010 2016 2022 2028
0.020.050.120.330.92.466.69
± 1σ
± 1.5σ
± 2σ

16. World energy usage by source
} (BP statistical review of world energy)
} IEA projection: PV will generate 16% of electricity by 2050
16
1980 2000 2020 2040 2060
1e−031e−011e+011e+03
milliontonsoilequivalent
Primary Energy
Oil
Gas
Coal
Nuclear
Hydro
Solar
Wind
Geothermal, Biomass,
and other renewables
2027
20% primary energy

17. Policy implications
• Can do something similar for Wright’s law
• Important policy implication: Supporting a
technology accelerates its progress
• Some technologies are better than others!

19. Part II: Ecological approach to
understand growth

20. Generalized ecosystems
Ecosystems exist in many different contexts
• Biology
• Production networks in economy
• Financial markets
- Market force, ecology and evolution, Farmer, 2002
• Internet, e.g. Page Rank
• …

21. leontief: input-output
model of an economy
Nodes are industries, each producing one good.
Weighted directed links are inputs to each industry.
Can be based on physical flows or on monetary flows.
Precise analogy to equilibrium chemical kinetics
(allowing non-integer stoichiometric parameters)
Conservation laws lead to linear system of equations
Used in national accounting, central planning.
21

22. Production Network
F
S2
F
S2S1
S1
S2
F
Flat economy Chain economy
¯L = 1 ¯L > 1
22
Chain economy amplifies improvements multiplicatively

23. Evolutionary theory of ecological
effects of technological growth
(McNerney, Savoie, Caravelli, Farmer, 2015?)
• Assume a simple local process for
technological improvement
• How is this improvement amplified as it
propagates through the economy?
• Predicts that, all else equal, rate of
economic growth is proportional to
aggregate output multiplier of economy
• In ecology called “trophic depth”
- measures number of layers in food chain

24. The U.S. and China
24
China U.S.

25. Growth rate and trophic depth
R2
= 0.452
P-Value = 2.0 10-6
¯L
2 2.5 3 3.5 4 4.5 5
GrowthrateofGDPpercapita
0
0.02
0.04
0.06
0.08
0.1
0.12
CHN
LUX TUR
KOR
TWN
ROM
IND
LVAPOL
CZE
BGR
IRL
EST
LTU
RUS SVK
HUN
MLT
CYP SVN
FINAUT
DNK
IDNCAN
GRC ESP
NLD AUS
BELMEX
DEU
JPN
ITA
BRA
USA
PRT
SWE
FRA
GBR
25

26. Comparison to standard growth
models
• Tested against several standard growth
models with varying numbers of factors
• Explains as much variance as seven best
standard growth factors combined
- (log GDP, savings, labor share, depreciation,
R&D, population growth,TFP, …)
• When included with standard factors is
always by far most statistically significant

27. Developmental paths
• Undeveloped countries: Low trophic depth,
low growth rate
• Developing countries: High trophic depth,
high growth rate
• Developed countries: Low trophic depth,
low growth

28. Trophic depth vs. GDP
¯L
2.5 3 3.5 4 4.5
AverageGDPpercapitain$
×104
0
1
2
3
4
5
6
7
Lower-middle-income economies
Upper-middle-income economies
Lower-high-income economies
Upper-high-income economies
SVK
TUR
CZE
KOR
EST
MEX
LVA
ROM
LUX
HUN
TWN
MLT
POL
BEL
IRL
BRA
AUS
IND
FIN
ESP
PRT
LTU
IDN
RUS
ITA
SVN
SWE
JPN
NLD
AUT
FRA
GRC
CYP
DEU
GBR
USA
DNK
CAN
BGR
CHN
28

29. Path of GDP and trophic depth
for USA
¯L
2.5 3 3.5 4 4.5
AverageGDPpercapitain$
×104
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
1970
1950
1900
18501790
2014
29

30. Key points
• To explain economic growth we need to
understand technologies and their
networked interactions with each other
• Technological progress is predictable
- empirical regularities (Moore,Wright)
- investment must take this into account
- More fundamental understanding?
• Growth is an evolutionary process
- biological analogies are very useful!

31. Aggregation
NAICS digits
0 1 2 3 4 5 6
L
1
1.5
2
2.5
3
3.5
4
4.5
5 Agricultural, Forestry, Fishing and Hunting
Mining, Utilities, Construction
Manufacturing
Wholesale, Trade, Transportation and Warehousing
Information, Finance, Insurance, Real-Estate, Leasing,
Professional and Technical Services, Management of
Companies,
Admisnistrative and Waste Services
Educational Services, Health Care
and Social Assistance
Arts, Entertainment, Recreation,
Accomodation and Food Services
Other Services
Government Industries
31

32. Comparison to standard growth models
gc,t hc,t = T
Xc,t + b1
log(yc,t 1) + ✏c,t
32

33. Determinants of trophic depth
• Labor share
• The country’s industry GDP distribution
¯L
2 2.5 3 3.5 4 4.5 5
Averagefractionofexpenditures
paidtoemployeecompensation
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
TUR
CZE
BGR
EST
MEX
LVA
ROM
HUN
KOR
LUX
TWN
MLT
POL
BEL
IRL
AUS
IDN
ESP
FIN
SVN
IND
LTU
GRC
AUT
DEU
GBR
DNK
USA
SWE
NLD
ITA
PRT
FRA
BRA
JPN
RUS
CHN
SVK
CYP
R2
= 0.80
Cross-country
growth rate
R2
= 0.22
¯LU
= ✓c · LU
R2
= 0.22
33

34. Time series
1996 1998 2000 2002 2004 2006 2008
¯L
2
2.5
3
3.5
4
4.5
5
5.5
China
Slovakia
Turkey
Korea
Brazil
India
USA
UK
34

35. Regression with country-specific
Predicted Average Growth Rate of GDP per capita (1995-2011)
0 0.02 0.04 0.06 0.08 0.1
AverageGrowthRateofGDPpercapita(1995-2011)
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
0.1
ROM
LTU
CHN
EST
IND
TUR
IRLCZE
RUS
LVA
IDN
SVK
GRC
LUX
KOR
SVN
TWN
AUS
MLTCYP
HUN
CAN
BRA
GBR
ESP
DNK
BGR
FIN
POL
FRA
USA
MEX
NLD
SWE
AUT
PRT
BEL
ITA
DEU
JPN
y=x
¯
R2
= 0.33
35