Varieties of
the NIS (National Innovation Systems)
and RIS (regional innov. Systems)
around the World:
Measurement and Analysis using patent citation data
Keun Lee (www.keunlee.com)
Distinguished Professor, Economics, Seoul National University
Editor, Research PolicyNelson/Lundvall (1992):
a key concept in Schumpeterian economics
NIS (national Innovation system) =Elements and relationships
which interact in the production, diffusion and use of knowledge-> Differences in NIS determines competitiveness ofnations, sectors and firms.
=> System failure cf) market failureLee (2013) Innovation systems at 3 Levels:
country; Sector; firm=> 2014 Schumpeter Prize

Cambridge Univ. Press, 2019

Several Levels of Innovation systems:
1) Macro: National Innovation Systems = NIS
2) Meso: Sectoral = Sectoral SI (SSI)
Regional = Regional IS (RIS)
3) Micro: Corporate innovation systems (CIS)
Q: How to measure Innovations systems:
1) many variables from diverse sources
vs. 2) several key variables from the same sources (patents)Eg) 1) Knowledge localization, 2) tech diversifications 3) originality
4) Concentrations, 5) cycle time of technologies (CTT)A talk drawing upon 3/4 papers•Lee, K., & Lee, J. (2019). National innovation systems, economic complexity, & economic growth. J of Evolutionary
Economics, 1-32.
•Lee, Keun, et al. (2021). "Variety of National Innovation Systems (NIS) and Alternative Pathways to Growth beyond the
Middle-Income Stage," World Development•Kim, J., and Keun Lee (2022), "Local–Global Interface as a Key Factor in the Catching Up of Regional Innovation Systems:
Fast versus Slow Catching Up among Taipei, Shenzhen, and Penang in Asia," Technological Forecasting and Social Change,* RIS Analysis: 30 cities/regions around world;-> working paper (Utrecht Univ, Evolutionary Economic Geography Group, Ron Boschma)https://peeg.wordpress.com/2022/10/06/22-19-varieties-of-regional-innovation-systems-around-the-world-and-catch-upby-latecomers/

Key question: identification of different varieties of NIS/RIS; cf) VoC (capitalism)
to link them to different performance•eg) mature/advanced RIS/NIS; catching up or trapped NIS/RIS in EEs

Varieties of the NIS (National Innovation Systems)
around the World:
Alternative Pathways for Growth beyond the Mid. income trap;

catching up vs. trapped NIS(Lee et al 2021, WD)

Korean Catch-up beyond the MIT (Middle income trap)
MIT = 20- 40% % of US GDP per capita (ppp $) for several decades:
Thailand, Indonesia, Philippines, Vietnam, Myanmar% (of United States' GDP per capita in current PPP $)80
100

United States

70=Japan

Korea

60
50

Malaysia
40
30

Thailand20

Indonesia
Philippines

100Vietnam
Myanmar

1980

1983

1986

1989

1992

1995

1998

2001

2004

2007

2010

2013

2014

2015

United States

100

100

100

100

100

100

100

100

100

100

100

100

100

100

Korea

17.4

21.3

24.2

29.1

35.8

42.0

40.3

46.9

50.8

55.1

61.7

64.2

65.0

65.3

Malaysia

26.2

28.7

25.3

27.3

32.1

36.8

34.1

34.4

36.1

38.7

42.1

44.8

46.0

46.7

Thailand

12.8

13.8

13.4

16.4

20.4

23.8

20.0

20.7

23.1

24.8

27.3

28.9

28.6

28.8

Indonesia

9.9

10.5

10.5

11.4

13.4

15.2

13.1

13.0

13.6

14.7

17.5

19.2

19.6

19.9

Philippines

15.0

14.9

10.9

11.0

10.4

10.2

9.4

9.4

9.7

10.2

11.5

12.4

12.7

13.0

Vietnam

3.5

3.9

3.9

3.9

4.4

5.1

5.5

6.0

6.7

7.7

9.1

10.1

10.4

10.8

Myanmar

0.0

0.0

0.0

0.0

0.0

0.0

2.8

3.6

4.9

6.6

7.6

8.8

9.4

9.8

slide 7

Middleincometrap

Mexico, Brazil, Chile, Argentina, South Africa, and Mauritius:
Middle income Trap: Per capita Income less than 40% of the US=> All the same trapped NIS:

Q: Who escaped the Middle income Trap: only 13, and How?
1) Periphery Europe +Israel: Greece, Portugal, Spain, Ireland, Israel2) Japan + 4 E Asian Tigers: Korea; Taiwan, Hong Kong, Singapore
3) Others: an oil exporter (Equatorial Guinea), Puerto Rico, Mauritous5 patent-citation variables to measure the NIS (Lee 2013):
Basis for a composite index of NIS (Lee&Lee 2019, JEE)
Localization of knowledge ( Intra-national creation and diffusion)(vs. reliance on foreign sources)
Dispersed vs. Concentration = 1-HHI
of knowledge creation (by assignees)
Short vs. long cycle technologies Specialization
Originality (high if citing and combining widely)(= Technological Convergence /combination)
Technological Diversification(Wide vs. Deep in patent portfolio)=> Somewhat Narrow but close to the definition by Lundvall (about knowledge)
also better in terms of homogenous dataset (US patents) over longer term
10

Data from USPTO;

Basically, inventor’s information, ownership -> assignee’s information▪ Patent related data: from USPTO (Unites States Patent and Trademark Office)▪ Information regarding patent: patent number, inventors’ address (city,
country), citation information, sector classification, etc.

Download patent bulk data from
USPTO (1976-2017)
Data mining process using SAS
program (Porter and Hatton,
2013)

Data cleaning

NBER DB(1963-1993, 1976-2006),
Harvard Dataverse,
United States Patent Dataset (1926-2010)
U.S. Patent and citation Data
Based on USPTO patents

NIS Index of 45 economies, 2011~2015:
Lee & Lee (2019, JEE), NIS robust than Econ. Complexity to predict Econ Growth
Country

Localization

Japan
United States
Germany
FranceUnited Kingdom
Italy
Australia
Switzerland
CanadaTaiwanNetherlands
IsraelSouth Korea
Denmark
NorwayAustria
SwedenBelgium
China
New Zealand
Spain

0.407
0.246
0.140
0.111
0.070
0.090
0.134
0.042
0.065
0.129
0.075
0.066
0.137
0.081
0.080
0.076
0.098
0.065
0.048
0.043
0.044

Tech
Diversif’n
0.866
0.937
0.844
0.735
0.687
0.611
0.469
0.657
0.671
0.674
0.582
0.431
0.705
0.374
0.268
0.405
0.568
0.378
0.643
0.172
0.324

Originality
0.354
0.503
0.455
0.402
0.450
0.408
0.466
0.434
0.486
0.331
0.434
0.498
0.339
0.429
0.482
0.422
0.390
0.418
0.332
0.481
0.400

Relative cycle Decentral’n
time
1-HHI
0.942
0.980
1.005
0.994
1.106
0.984
1.083
0.975
1.157
0.993
1.163
0.981
1.176
0.923
1.159
0.984
1.014
0.935
0.828
0.971
1.041
0.903
1.044
0.990
0.846
0.854
1.169
0.971
1.200
0.985
1.133
0.967
0.992
0.824
1.130
0.955
0.854
0.944
1.251
0.976
1.107
0.986

Index of
NIS-5
3.566
3.495
3.147
2.873
2.855
2.763
2.742
2.730
2.709
2.575
2.564
2.551
2.533
2.516
2.503
2.496
2.435
2.421
2.343
2.341
2.308

Rank of NIS5
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21

Country

Localization

Diversif’n

Originality

Finland
South Africa
BrazilMexicoHong Kong
IrelandSingapore
India
Luxembourg
PolandSaudi Arabia
Malaysia
Chile
Portugal
Hungary
Argentina
RussiaCzech Republic
ThailandSlovenia
GreeceIcelandIndonesia
Philippines

0.095
0.072
0.022
0.014
0.037
0.023
0.037
0.028
0.007
0.069
0.020
0.035
0.014
0.032
0.033
0.041
0.039
0.018
0.009
0.014
0.016
0.039
0.000
0.002

0.418
0.116
0.158
0.096
0.289
0.241
0.323
0.243
0.221
0.074
0.191
0.084
0.042
0.045
0.049
0.028
0.102
0.056
0.031
0.038
0.031
0.038
0.006
0.011

0.426
0.424
0.390
0.485
0.388
0.465
0.437
0.371
0.472
0.369
0.467
0.399
0.426
0.418
0.384
0.392
0.423
0.332
0.467
0.335
0.327
0.420
0.445
0.465

Relativecycle time
0.976
1.231
1.237
1.216
0.978
0.993
0.889
1.057
1.032
1.156
1.130
1.129
1.175
1.106
1.116
1.135
0.934
1.110
1.107
1.272
1.179
1.300
1.361
1.121

1-HHI

NIS5

0.770
0.959
0.957
0.933
0.965
0.929
0.915
0.969
0.928
0.952
0.774
0.917
0.939
0.932
0.939
0.909
0.889
0.945
0.824
0.831
0.870
0.563
0.442
0.547

2.249
2.249
2.134
2.129
2.126
2.109
2.106
2.097
2.088
2.072
1.999
1.982
1.976
1.956
1.934
1.926
1.871
1.845
1.837
1.822
1.781
1.735
1.562
1.528

Rank of
NIS5
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45

Sectoral Specialization in CTT Matter:

Eg) Brazil: long CTT = Mid Inc Trap; Korea = short to long CTT detour

Measure of CTT (cycle time of technology)= time lag between the application years of the citing and cited patent.
= speed of depreciation of technologies→ Korea & and Taiwan specialized in short CTT sectors during their catching-up period.

The Cluster Analysis using the 32 Economies to identify NIS types (cf VoC)

1) Balanced and Mature NIS (6): Canada, Germany, France, Italy, Switzerland, and the United Kingdom.
2) Balanced, med cycle catching-up NIS (8): Ireland, Spain, Hong Kong, Singapore, India, Russia
3) Imbalanced, short-cycle, Catching-up NIS (3): China, South Korea, and Taiwan.
4) Imbalanced, long cycle, Trapped (9): Argentina, Brazil, Chile, Malaysia, Mexico, S. Africa, Thailand, Greece, Portugal

Part A: Two Balanced vs. Two Imbalanced NIS

Balanced, med cycle, catching < Balanced, Mature

Imbalanced, long cycle, trapped vs. Imbalanced, short cycle, & catching up

1) Balanced and Mature NIS (6): Canada, Germany, France, Italy, Switzerland, and the United Kingdom.
2) Balanced, med cycle (catchup) NIS (8): Singapore. Ireland, Spain, Hong Kong, India, Russia
3) Imbalanced, short cycle, Catching-up NIS (3): China, South Korea, and Taiwan.
4) Imbalanced, long cycle, Trapped NIS (7): Argentina, Brazil, Chile, Malaysia, Mexico, South Africa, and Thailand.

Relative Cycle Time of Technologies: short cycle, catching up NIS
Getting into short cycles (less entry barrier) sectors-> higher/quicker localization (less need to rely on incumbents)-> more tech. diversification by keep entering newly emerging classes➔ A Detour from short to long cycles in Korea, Taiwan, China

Dynamic Change of the NIS Variables, Knowledge Localization

Dynamic Change of the NIS Variables: Technological Diversification

Dynamic Evolution of the NIS over time (Cluster Analysis) :Korea = Used to be in mixed group but created catching up NIS; joined by China
Grou
p
1984
-91

Group 1

Group 2Gr.
3Group 4

Group 5

Group 6

Argentina, Brazil, China, Denmark,
Chile,
Tha
Finland, Hong Kong, India, Ireland,
Greece, ilan
Israel, Malaysia, Mexico, Norway,
Portugal dSingapore, S. Africa,
South Korea, Spain, Taiwan

Argentina, Brazil, Chile, China, Denmark, Finland, Hong Kong, Greece,
1992
Israel, Ireland, India, Mexico,-99
Malaysia, Norway, Portugal, Russia,
S. Africa, Spain, Thailand
2000
-07

Argentina, Brazil, Chile, Denmark,Hong Kong, Greece, Mexico,
Norway, Portugal,
Russia, South Africa,
Spain, Thailand

2008
-15

Argentina, Brazil, Chile,
Malaysia, Mexico,South Africa, Thailand,
Greece, Portugal

Chile,
Portu
gal

Group 7

Group
Gr. 9
8

Canada, France,
Germany, Italy, UK,
Swiss, Sweden,
Netherlands

Japan

USA

South
Korea

Canada, France,
Germany, Italy, UK,
Swiss, Sweden,
Netherlands, Taiwan

Japan

USA

Singapore,
Ireland, India
Malaysia,
China

South
Korea,
TaiwanJapan

USA

Singapore,
Ireland, India,
Spain, H.Kong,
Denmark,
Norway, Russia

Canada, France,
Germany, Italy, UK,
Swiss, Sweden,
Netherlands,
Finland, Israel,

South
Korea,
Taiwan,China

Canada, France,
Germany, Italy,
UK, Swiss

Japan

USA

Singapore

Finland,Sweden,Israel,Netherlands

From NIS Types to Economic Growth:
two catching up vs trapped NIS

Dependent var.: average GDP per
capita growth by periodsln(Initial GDP)
Population Growth Rate
Fixed Investment Rate
Enrollment Rate Secondary
Balanced and Mature NIS
Balanced Catching-up Group
Other Balanced Group
Imbalanced Catching-up NIS
Imbalanced and Trapped NIS
Constant
Observations
Adjusted R-squared
Wald chi-squared

Model (4)

GLS: Pooled
estimation with
dummiesModel (6)Periods: 1992-2015

Periods: 1992-2015

GLS: Pooled estimation with dummies
Model (3)Periods:
1983-2015
Coefficientt-value

Coefficientt-value

Coefficientt-value-0.0086***-0.31**
0.13***-0.0015(-4.20)(-2.01)(4.94)(-0.15)-0.0081***-0.097
0.10***-0.000029(-4.00)(-0.51)(3.52)(-0.003)-0.016***-0.11
0.12***
0.0069(-5.88)(-0.58)(4.29)(0.66)

0.018***
0.0046*0.030***-0.00560.080***
230(3.33)(1.72)(4.14)(-1.59)(4.48)

0.014**
0.00490.037***-0.00480.076***
177(2.39)(1.38)(4.54)(-1.41)(4.30)

0.020***
0.00300.023***-0.0062*0.15***
177(3.23)(0.88)(5.61)(-1.87)(5.64)

423.66***213.51***

242.66***

Summary: NIS to Economic Growth beyond MIT
1) Variety of NIS <= similar to Variety of Capitalism:-- Confirms correspondence between diverse NIS types
and catching-up/falling behind performance.
-- Balanced NIS (mature or catching-up)
vs. Imbalanced (catching-up vs. Trapped)
2) Imbalanced, Short cycle, Catching-up NIS in E Asia =specialization into short cycle Tech.; increased localization & diversification;-- Getting into short cycles -> higher localization (less need to rely on
incumbents) -> tech. diversification by keep entering newly emerging classes-- These catching up economies all used to be the same type as other MICs
but they created their own catching up NIS since the 1990s

3) Balanced, med cycle, Catching-up NIS = alternative to E Asian Path;
Spain, Ireland -> India/Russia in the NIS : promising future: Turkey ?
common pattern of diversification = manuf + services

Balanced vs. Imbalanced Development: Nurkse (1953) vs Hirschman (1958):

balance between agriculture and manuf => between manuf and services & balances in NIS

Low coeff
Of variation
Ireland, Spain,
Hong Kong,
Singapore;
India, Russia

Korea, Taiwan,
ChinaHigh coeff
Of variation

Smaller NIS-5

Bigger NIS-5

Varieties of Regional Innovation Systems (RIS)and Catch-up by Latecomers
1) RIS of Taipei, Shenzhen, and Penang in Asia
(Kim & Lee, TFSC 2022)
2) Cluster Analysis RIS of 30 cities/regions around world)

Introduction: Why from NIS to RIS and How* National innovation system (NIS)- a key concepts in Schumpeterian economics (Freeman, 1987; Lundvall, 1992; Nelson, 1993).
- differences in NIS ➔ differences in innovation performance ➔ economic growth.
▪ Question? Uneven distribution of innovations even in the same nation? (Asheim et al., 2019: 1)▪ To see what elements (dimension) of innovation is binding at regional level development beyond
the middle-income stage of development where innovation becomes a more binding factors thanprices or costs at the earlier stages (Mazzoleni and Nelson, 2007; Lee 2013).

▪ Patent-citations based measurement and analysis of RIS:

Literature: Regional Innovation Systems:
Less quantitative analysis (typology/regressions)▪ Various RIS studies explaining a typology and dynamic change of RIS and showing a varietyof criteria and perspectives on the RIS. (Asheim, 1998; Asheim et al., 2019; Asheim andGertler, 2006; Cooke, 2005, 2001, 1998): Qualitative measurement▪ Some quantitative approaches have been used to study the efficiency of different kinds ofregional innovation systems (Fritsch and Slavtchev, 2011; Zabala-Iturriagagoitia et al., 2007).
▪ Lack of researches of RIS typology for emerging economies as well as generalized typology

covering various regions over the world.
▪ Goal Suggest new RIS typology with RIS variables referring to the NIS variables in Lee(2013), Lee and Lee (2019), and Lee et al. (2021).
- Especially in terms of catching-up

A key aspect of Catch-up RIS = Localization of knowledge & ownership▪ Peripheral /immature RIS = heavy reliance on external knowledge given its lack of indigenous knowledge base, andlow level of regional embeddedness (Rodriguez et al. 2014; Asheim et al. 2019: Park & Markusen 1995; Hassink 2001).

▪ Latecomers’ reliance on foreign knowledge: latecomer economies tend to achieve economic growth by relying on FDI
and learning from MNCs (Bernardes and Albuquerque, 2003; Lebdioui et al 2021; Amsden and Chu 2003).
=> consistent with the NIS of emerging or catching-up economies (Lee 2013; Lee et al. 2021a).

- From low level of knowledge localization at the early stage to an increasing trend.
▪ The importance of acquiring indigenous technological capabilities or knowledge ownership is emphasized when it
comes to catching up or at the later stage of development (Mazzoleni and Nelson 2007; Lebdioui et al. 2020)=> introduce a variable representing ownership of knowledgeA) ownership matter: MNC dominance-> keep relying on their home R&D-> no local creation and diffusion of knowledge
Eg) Different speed of Catching up in same short cycle cluster. Q) why?

Thee RIS: fast vs. slow catching up: despite a common initial conditions
TaipeiShenzhen

Penang▪ share common take-off by promoting foreign direct investment (FDI) by MNCs; industrial parks,- Export Processing Zones (EPZs) in Taipei in 1960s,- the Special Economic Zones in Shenzhen in 1980,- the Free Industrial Zone in Penang in1972 (Hsu, 2005; UNDP, 2006).
▪ economic performance and catching up show some variance: esp. Shenzhen and Penang.

Different speed of Catching up in same short cycle cluster
per capita GDP (PPP, US$)The number of Patents
5000

60,0004500

50,000

4000
3500

40,000

3000
2500

30,0002000

20,000

1500

1000

10,000500

00ShenzhenTaipei

PenangShenzhenper capita GDP relative to US

PenangTaipeiThe number of Patents per capita

120

0.0007100

0.000680

0.000560

0.000440

0.0003200.000200.00010200020012002200320042005200620072008200920102011201220132014201520162017
ShenzhenTaipei

PenangShenzhen

PenangTaipeiHypotheses 1: decreasing role of foreign knowledge;=> Intra-region, Inter-region, Inter-national knowledge diffusion▪ Intra-regionalization: how many patents invented in region 𝑥 cite patents invented in its own region.

▪ Inter-regionalization: how many patents invented in a region cite patents invented in other regions in thesame nation▪ Internationalization: how many patents invented in region 𝑥 cite patents invented in other countries.

▪ The more advanced economies, the less dependent on foreign knowledge(⇔ high intra-regionalization, low internationalization).

Hypo: Taipei would show a high and increasing level of intra-regional knowledge localization and, at the same time,a low and decreasing level of internationalization (less relying on foreign knowledge).
- Shenzhen:= to be similar to Taipei. Cf) Penang : no such trend- Inter-regionalization: a more advanced or catching up region would show a high or increasing level of interregionalization (a high or increasing citations to patents by other regions).

Three measures: (Jaffe et al., 1993; Lee and Yoon, 2010; Lee, 2013)▪ Intra-regionalization.
𝑛• 𝐼𝑛𝑡𝑟𝑎 − 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑥𝑡 = 𝑥𝑥𝑡 ,𝑛𝑥𝑡: the ratio of region 𝑥 citing its own region invented patents.
▪ Inter-regionalization𝑛• 𝐼𝑛𝑡𝑒𝑟 − 𝑟𝑒𝑔𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑥𝑡 = 𝑥𝑥′𝑡 , where 𝑥+𝑥’= 𝑐, and 𝑥 means a region located in country 𝑐.
𝑛𝑥𝑡: the ratio of citation made from patents invented in region 𝑥 to patents invented in other regions
than region 𝑥 (but in the same country).
▪ Internationalization𝑛• 𝐼𝑛𝑡𝑒𝑟𝑛𝑎𝑡𝑖𝑜𝑛𝑎𝑙𝑖𝑧𝑎𝑡𝑖𝑜𝑛𝑥𝑡 = 𝑥𝑑𝑡 , where 𝒙 means a region located in country 𝒄 and d means𝑛𝑥𝑡other countries than country c.
: the ratio of citations made by region 𝑥 to other countries (𝑑)𝑛𝑥𝑥𝑡 : the number of citations made to region 𝑥’s patents by region 𝑥’s granted in year t .
𝑛𝑥𝑡 : the number of all citations made by region 𝑥’s patents granted in year t
𝑛𝑥𝑥′𝑡 : the number of citations made from patents invented in region 𝑥 granted in year 𝑡 to patents
invented in region 𝑥′ granted in year 𝑡, where region 𝑥′ means other regions than region 𝑥 andis located into the same country with region 𝑥.
𝑛𝑥𝑑𝑡 : the number of citations made to country 𝑑 by region 𝑥’s patents granted in year 𝑡, wherecountry 𝑑 is different from the country that region 𝑥 belongs to.

Order of Internationalization => Opposite to level of Per capita Income
internationalization
1
0.95
0.9
0.85
0.8
0.75
0.7
0.65
0.6
1982-1985

1986-1989

1990-1993

Taipei, TW▪

1994-1997

1998-2001

Shenzhen, CN

2002-2005

2006-20092010-20132014-2017

Penang, MY

Taipei shows a clear trend of reducing the internationalization or the reliance on foreign patents, whichreflects the enhancement of its own indigenous technological capabilities and of the RIS.
- 95% in the early 1980s to less than 82% in the early 2000s despite slight increase in 2010s▪

Shenzhen or Penang with level of internationalization still higher than 90%.

▪

Shenzhen’s level is lower than that of Penang,Intra-regionalization
intra-regionalization
0.15

0.13

0.11

0.09

0.07

0.05

0.03

0.01

1982-1985

1986-1989

1990-1993

1994-1997

1998-2001

2002-2005

2006-20092010-2013-0.01

Taipei, TW

Shenzhen, CN

Penang, MY

2014-2017

Inter-regionalizationinter-regionalization
0.1

0.08

0.06

0.04

0.02

0
1982-1985

1986-1989

1990-1993

Taipei, TW

1994-1997

1998-2001

2002-2005

Shenzhen, CN

2006-20092010-2013

Penang, MY

2014-2017

Hypotheses 2: role of local/foreign ownership of patents- Relying on foreign-owned knowledge (patents) is not enough to sustain the catch-up up to
the later stage because foreign firms would become increasingly reluctant to transfer or sell
their technologies to latecomers gettting close to the frontier (Lebdioui et al 2021: Lee 2005).
- Amsden and Chu (2003) : Taiwan = increasing locally-owned firms.
- Mazzoleni and Nelson (2007) : South Korea and Taiwan, successfully catching-up
countries, as acquiring indigenous technological capability.
 Hypo: Taipei has a high level of local ownership of patents (high share of patents filed by
locally-owned firms);
 Shenzhen : a trend of increasing share of locally-owned patents, compared to Penang.

Hypotheses 3: high originality from foreign ownership▪ Originality (Hall et al. (2001; Trajtenberg et al. (1997) = degree that an innovation (patent) combines
knowledge from diverse fields.
- = a degree of knowledge convergence and combination

NIS: Originality tend to be high in advanced economies but not that high in catching up economies;
no robust relationship between high originality and economic growth of countries (Lee 2013: ch.3).
- If a region’s economy and innovation is dominated by foreign MNCs from advanced economies, its level oforiginality would be higher▪ Hypo: Penang with continuing dominance by MNCs would show a high level of originality than Taipei or
Shenzhen as these latter two regions are now increasingly dominated by indigenous firms..

Hypo 2.3: Local firm ownership/Originality
3. Local firm ownership on knowledge : Indigenous knowledge
𝐿𝑜𝑐𝑎𝑙 𝑜𝑤𝑛𝑒𝑟𝑠ℎ𝑖𝑝 =

𝑁𝑐𝑥𝑡
,
𝑁𝑥𝑡

𝑁𝑐𝑥𝑡 : the number of patents invented in a region 𝑥 and assigned to a firm with its nationality inthe host country 𝑐.
𝑁𝑥𝑡 : the total number of patents assigned to any firms invented in a region with the first inventor

address located in region 𝑥, granted in time 𝑡.
4. Originality (Hall et al., 2001; Trajtenber et al., 1997): how many various fields(classes) of knowledge are used, and thus cited to invent a patent.
𝑁𝐶𝐼𝑇𝐸𝐷𝑖𝑘
𝑁𝑖
𝑂𝑟𝑖𝑔𝑖𝑛𝑎𝑙𝑖𝑡𝑦𝑖 = 1 − σ𝑘=1
𝑁𝐶𝐼𝑇𝐸𝐷𝑖2,

where 𝑘 is patent class, 𝑁𝐶𝐼𝑇𝐸𝐷𝑖𝑘 is the number of citations made by the patent 𝑖 topatents belonging to patent class 𝑘, and 𝑁𝐶𝐼𝑇𝐸𝐷𝑖 is the total number of citations madeby patent 𝑖.
▪ To transform this variable into a regional level variable, after calculating the originality of eachpatent, we average the values of originality over all the invented patents in a region.

Local firm ownership of knowledge: catching up Shenzhen
Local ownership
1.2

1

0.8

0.6

0.4

0.2

0
1982-1985

1986-1989

1990-1993

1994-1997

Taipei

1998-2001

Shenzhen

2002-2005

2006-2009

Penang2010-20132014-2017

Originality; highest in Penang with foreign ownership
Originality
1
0.8
0.6

0.4
0.2
0

Shenzhen

PenangTaipei▪ Dominance of foreign MNCs in Penang seems to be related to the fact that the degree oforiginality is highest in Penang, compared to Shenzhen or Taipei.

The ratio of firms’ nationality in top 10 assignee firms:
increasing share by locals in Taipei
Taipei

1.0
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0.0
77-

80-

83-

86-

89-

92-

95-

98-

01-

04-

07-

10-

79

82
85
88
United States

91

94
97
Taiwan

00

03
06
Japan

09

12
15
China

▪ Taipei: the dominance by the Taiwanese firms since the mid 1990s. .

13-The ratio of firms’ nationality in top 10 assignee firms:
increasing share by locals in Shenzhen

Shenzhen
1.0
0.9
0.8
0.7

0.6
0.5
0.4
0.3
0.2
0.1
0.0
`95-`97`98-`00

United States`01-`03

Taiwan`04-`06`07-`09

Hong Kong`10-`12China`13-`15

Germany▪ :kept increasing since the late 1990s; reached almost 100% during the 2013-2015.

The ratio of firms’ nationality in top 10 assignee firms:
no increase of local ownership in Penang
Penang

1.0
0.8
0.6

0.4
0.2
0.0
`95-`97 `98-`00 `01-`03 `04-`06
United StatesJapan
Malaysia`07-`09 `10-`12 `13-`15
Germany
Singapore▪ Penang: has remained dominated by the US firms with 50 to 70% shares since the 1990s to present- decrease of the shares by the Malaysian firms from 20% to zero in the mid 2010s.

Summary: three regions as alternative models of catching up RIS
in terms of the local-global interface▪Taipei and Shenzhen: a mode of eventually creating indigenous firms,-in contrast to the Penang model of continuing reliance on MNCs.
-The former not easy to realize, but leading to a faster catching-up than Penang.
▪To promote locally-owned firms eventually, Taipei and Shenzhen have been more aggressive in terms of the degree of public
intervention than Penang = a reasons for differences▪If a latecomer region wishes a fast catching up,▪To promote localization of innovation and its ownership,
after the initial stage of learning from foreign knowledge sources.
▪Various policy initiatives in Taipei and Shenzhen to promote indigenous innovation-a) promote technology transfer from foreign to domestic firms,-b) private and public joint R&Ds, and in-house R&D center by the local firms,-C) attract branches of universities; encourage academic spinoffs / venture financing for them.

Varieties of RIS around the world
and Catch up by Latecomers,
30 cities around the world- 7 European :Berlin, Munich, London, Cambridge, Stockholm, Paris, Milan, Moscow- 4 USA :Silicon Valley (CA), Boston Area (MA), Austin , Houston- 13 Asian :Shenzhen, Penang, Taipei, Tokyo, Beijing, Osaka, Seoul, Daejeon, Gyeonggi-do,
Bangalore, New Delhi , Tel Aviv, Hong Kong, Shanghai, Singapore- 3 Latin America: Santiago (Chile), Sao Paulo , Mexico City
- 1 Africa:

Johannesburg

Variables in NIS and RIS▪ Transform the NIS variables introduced in Lee (2013) and Lee et al. (2021) into regional level.
▪ Five variables in NIS analysis, and seven variables in RIS analysis

NIS
Localization

RIS

Intra-regionalization (New)Inter-regionalization (New)Internationalization(= 1- localization)

Internationalization(= 1- intra - inter)

Technological Diversification

Technological Diversification

Knowledge Decentralization

Knowledge Decentralization

Tech cycle tech. (sectoral specialization)

Technology cycle time

Originality

X
Local ownership of knowledge (New)Table 1 Average values of RIS Variables by region (annual average for 2013-2017)
Paris
Silicon Valley
Boston Area
AustinHoustonTokyo
LondonMilan
Cambridge
TaipeiStockholm
MunichHong Kong
Tel Aviv
Moscow
BerlinSeoul
Mexico City
Osaka
ShenzhenGyeonggi-do
PenangBeijing
DaejeonShanghai
Sao Paulo
Santiago
New Delhi
Bangalore
Johannesburg
Average

Localization
0.05180.24320.09050.07110.186
0.14880.00090.03410.02420.10270.02530.02530.04360.02890.05190.03750.06560.01310.10450.04080.11540.0341
0.04480.04670.02580.01310.02420.01460.01360.02150.0581

Nationalization
0.07240.51830.62680.68020.56680.31540.04110.03580.03810.07
0.06910.08940.00960.07280.005
0.07340.08810.008
0.38630.02080.06420.00960.009
0.05860.01540.00580.00190.02090.01150.02400.1336

Internationalization
0.87580.23850.28270.24870.24720.53580.958
0.93010.93770.82730.90560.88530.94680.89830.94320.88910.84630.97890.50920.93850.82050.95630.94620.89470.95880.9812
0.97390.9646
0.97490.95450.8083

Local ownership
0.75240.894
0.94050.91330.95240.95280.57210.62350.465
0.85220.76030.73290.255
0.49770.37210.7151
0.96780.60770.96280.92470.97940.08380.55040.9876
0.52770.36850.693
0.19620.10820.66500.6625

Relative cycle time
1.075
0.8736
0.99210.90791.12150.96941.003
1.144910.85110.87911.03050.99960.893
0.95181.07980.85291.21130.98810.81650.822
0.90310.8076
0.94260.897
1.1164
1.23770.82790.787
1.18230.9722

Diversification
0.277
0.58690.47190.35770.41230.62830.23040.21990.1962
0.49890.15430.31330.337
0.17210.16790.27780.482
0.05920.51290.34550.52430.08540.36240.35520.3573
0.09680.06130.10530.2152
0.05370.2973

Decentralization
0.89120.82510.87380.80530.89440.93840.88330.924
0.88870.83250.78590.91480.87790.83640.84090.91480.81730.81520.83050.673
0.78880.83290.82480.70330.80770.8444
0.79150.86090.91710.72120.8385

Cluster Analysis using the 7 RIS Variables: 4 Major Groups

Table 2. Results of the Cluster Analysis: Using 7 RIS variables (level of dissimilarity : 0.449)
2000-20082009-2017
Group 1
Silicon Valley, Boston Area, Austin, Houston,
Silicon Valley, Boston Area, Austin, Houston,Tokyo, Osaka
Group 2 A: Paris, Berlin, Milan, Stockholm,
Tel Aviv, London, Cambridge,

Tokyo, Osaka
A: Paris, Berlin, Milan, Stockholm,
Tel Aviv, London, Cambridge, Munich

B: Mexico City, Santiago, Sao Paulo, Johannesburg B: Mexico City, Santiago, Sao Paulo, Johannesburg,
Hong Kong, Moscow, Beijing, Shanghai
Group 3

Taipei, Seoul, Gyeonggi-do, Daejeon,

Taipei, Seoul, Gyeonggi-do, Daejeon,Group 4

MunichPenang, New Delhi, Bangalore,

Shenzhen
Penang, New Delhi, BangaloreHong Kong, Moscow, Shanghai, Beijing,
ShenzhenMature RIS(group 1)
Long cycle &high ownership

Mixed RIS
(group 2):
Long cycle &mid ownership

Strong Catch-up(group 3):
Short cycle & highlocal ownership

Weak Catch-up(group 4):
Short cycle & low
local ownership

Internationalsourcing

Low(0.32)

High(0.93)

Mid & decreasing(0.86)

High(0.96)

Local ownershipof knowledge

High(0.94)

Mid(0.60)

high(0.94)

Low(0.20)

Knowledge
decentralization

High(0.86)

High(0.86)

Lowest(=high concentration)(0.76)

Low(0.87)

TechnologicaldiversificationHigh(0.49)

Low(0.23)

High/increasing(0.44)

Low/increasing(0.17)

Relative cycle
time of techLong
(0.98)

Long(1.01)

Short(0.86)

Relatively Short(0.93)=> Two types of catching-up RIS at different stage- Weak catching-up : low indigenous knowledge relying on FDI- Strong catching-up : increasing local ownership by big businessesAverage values of RIS variables by group: average for 2013 to 201720132017

CitiesMature Silic. Valley, Boston, Austin,
RIS
Houston, Tokyo, Osaka

Total 18 in this group
Mixed [A] Paris, London, Milan,
RIS
Cambridge, Stockholm,
Munich, Hong Kong, TelAviv, Moscow, Berlin[B] Mexico City, Beijing, Shanghai, Sao Paulo, Santiago,
Johannesburg

Strong Taipei, Seoul, Shenzhen,
Catch Gyeonggi-do, Daejeon
weak
Catch
AveragePenang, New Delhi,
Bangalore

Local
ownership

decen
tral

Diversi
fic’n

cycle
time

p. c. GDP

Growth
p. c.
GDP %

0.34

0.94

0.86

0.49

0.98

84592.7

2.27

0.04

0.94

0.57

0.85

0.21

1.03

51658.4

4.44

0.03

0.05

0.92

0.57

0.88

0.23

1.01

65571.8

3.33

0.02

0.01

0.97

0.57

0.80

0.17

1.08

28469.4

6.30

0.07

0.06

0.87

0.94

0.76

0.44

0.86

43748.2

5.33

0.02

0.01

0.97

0.13

0.87

0.14

0.84

20173.9

10.36

0.06

0.13

0.81

0.66

0.84

0.30

0.97

53778.5

4.75

Inter-

Intraregion

Interregion

national

0.14

0.52

0.03

Figure 2A Trends of Relative cycle time by group
1.1

1

0.9

0.8

0.7

0.6

2001-2004

2004-2007

2007-20102010-2013group 1 (mature RIS)

group 2 (mixed RIS)

group 3 (strong catch-up RIS)

group 4 (weak catch-up RIS)

2013-2017

Dynamic change: _International sourcing (=inverse of localization): lowest in mature = more local creation and diffusion of nowledge;
Internationalization
1.2

Internationalization
1
0.98
0.96
0.94
0.92
0.9
0.88
0.86
0.84
0.82
0.8
0.78

1
0.8
0.6
0.4
0.2
0

Mature RISCatching-up 1 RIS

Mixed RIS

Catching-up 2 RIS

Catching-up 1 RISCatching-up 2 RIS

Mixed RIS

Dynamic change: _International sourcing (=inverse of localization): lowest in mature = more local creation and diffusion of knowledge;
Internationalization

1.2

Figure 2C Trends of International Sourcing
1

0.98

1

0.96

0.8

0.94

0.6

0.9

0.92

0.4

0.88

0.2

0.84

0.86
0.82

0

0.8
0.78

2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017group 2 (mixed RIS)

Mature RIS

group 3 (strong catch-up RIS)

Catching-up 1 RIS

group 4 (weak catch-up RIS)

Mixed RIS
Catching-up 2 RIS

Values for Group 1 (mature) ; below 40%; not shown here.

Dynamic change in Local ownership of knowledge
Figure 2B Trends of Local ownership of knowledge
1.2

1

0.8

0.6

0.4

0.2

0

2000-2002

2003-2005

2006-20082009-20112012-2014group1 (mature RIS)

group 2 (mixed RIS)

group 3 (strong catch-up RIS)

group 4 (weak catch-up RIS)

Source: Author’s calculation

2015-2017

Figure 2G Trends of Knowledge decentralization
1

0.95

0.9

0.85

0.8

0.75

0.7

0.65

0.6

2000-2002

2003-2005

2006-20082009-20112012-2014group 1 (mature RIS)

group 2 (mixed RIS)

group 3 (strong catch-up RIS)

group 4 (weak catch-up RIS)

2015-2017

Strong catch up led by big businesses (centralization than decentralization into SMEs)

Clustering over time : dynamic upgrading (dissimilarity = 0.449)
2001-2004
Mature,Silicon Valley
Boston Area
Large cities
AustinHoustonTransitorygroup 1
Mixed
group

Tokyo, Osaka

2004-2007
Silicon Valley
Boston Area
AustinHoustonTaipei, Munich
Seoul
Gyeonggi-do
DaejeonWeak catch Hong Kong. Moscow,
Hong Kong, Moscow
Shenzhen, Penang, Be
Shenzhen, Penang,upijing, Shanghai, New Beijing, Shanghai, New
Delhi, Bangalore
Delhi, Bangalore
Transitory
2

Mexico City,
Sao Paulo Santiago,
Johannesburg

2010-2013
Silicon Valley
Boston Area
AustinHoustonTokyo, Osaka

2013-2017
Silicon Valley
Boston Area
AustinHoustonTokyo, Osaka

Tokyo, Osaka

Paris, London, Milan,
Paris, London, Milan,
Cambridge Taipei
Cambridge, Stockholm,
Stockholm, Munich Tel Aviv. Berlin, Mexico C
Tel Aviv, Berlin, Seoul ity. Sao Paulo, Santiago,
Gyeonggi-do Daejeon
Johannesburg

Strongcatch up2007-2010
Silicon Valley
Boston Area
AustinHoustonTokyo, Osaka

Paris, London, Milan
Cambridge, Stockholm
Tel Aviv, Berlin
Mexico City
Santiago, Johannesburg

Paris, Milan,
Paris, London, Milan
Stockholm, Munich, Ber
Cambridge, Stockholm
lin, Mexico City
Munich, Hong Kong, Tel Aviv,
Sao Paulo, Santiago,
Moscow, Berlin, Mexico City,
Johannesburg,
Beijing, Shanghai, Sao Paulo,
Santiago, Johannesburg
Taipei, Munich
TaipeiTaipeiSeoul, Shenzhen
Seoul, Shenzhen
Seoul, Shenzhen
Gyeonggi-do
Gyeonggi-do
Gyeonggi-do
DaejeonDaejeonDaejeonHong Kong, Moscow
Bangalore, New Delhi,
PenangPenang, Beijing, Shanghai, Penang, Beijing, Shang
New Delhi
Sao Paulo, New Delhi, Ban hai, Cambri, Tel Aviv,
Bangalore
galoreLondon, Hong Kong,
Moscow

Regressions of per cap GDP Growth: faster growth/catch-up than large mature RISLSDV (4 groups)

GMM (4 groups)

LSDV (6 groups)

GMM (6 groups)

log(initial per capita
GRDP)-0.00259***(0.000901)-0.00600(-0.26)-0.00242**(0.00111)-0.0296**(-2.02)

No. of patents/1000

0.00754*(0.00386)

0.0188***(2.96)

0.00741*(0.00425)

0.0185***(4.76)

population growth0.952***(0.279)0.346(0.19)

0.936***(0.290)1.024**(2.38)

mixed RIS

0.0446***(0.00623)

0.0704**(2.50)

0.0443***(0.00758)

0.0422**(1.97)

strong catch-up RIS

0.0432***(0.00719)

0.0529*(1.85)

0.0434***(0.00812)

0.0331**(1.97)

weak catch-up RIS

0.0937***(0.0139)0.101**(2.30)

0.0952***(0.0157)-0.000967(0.00816)
0.0801***(0.0113)0.0575**(2.55)
0(.)0(.)
0.296*(1.87)

Transitory group 1
Transitory group 2Constant
adj. R2

0.734

0.0307(0.12)

0.731

Importance of local ownership for Catchup; by localization & diversification
Figure 3A Local ownership of knowledge▪ In NIS analysis,, catching-up NIS specialize
in short cycle technologies, highlocalization and diversification .

1
0.9
0.8
0.7

▪ in RIS analysis, both (weak & strong)

0.6

catching-up RIS groups specialize in

0.5

short cycle technologies;-but, weak group still low localization,
diversification.
▪ Why?

Answer) Difference in“local ownership of knowledge”;=. Not only cycle time but ownershipmatter

0.4
0.3
0.2
0.1
0

2001-2004

2004-2007

2007-2010

Group 4 Core 3

BeijingShanghai

Shenzhen

2010-2013

Core 3 include Bangalore, New Delhi, and Penang

2013-2017

Moscow

Key findings▪ Regions in 4 groups;• Mature RIS, Mixed RIS, strong Catching-up RIS, Weak Catch-up 2 RIS.
▪ Two types of catching-up: catching-up 1 RIS, catching-up 2 RIS
1) Weak Catching-up: faster than mature, but lowest level of per capita GDP(lack of indigenous knowledge -> relying on external knowledge)
2) Strong Catching-up: faster than mature, & higher level of per capita GDP than catching-up 1(increasing indigenous knowledge -> decreasing dependency on foreign knowledge)▪ For latecomers, economic growth by relying on FDIs/MNCs easy but limited in long run.
(advanced countries or firms are reluctant to transfer knowledge : Lebdioui et al., 2021; Lee, 2005).
▪ Indigenous knowledge is a base for increasing localization of knowledge,

which is the basis for innovation.
▪ How? Promote local big businesses and have them to create own knowledge• Strong Catching-up RIS: show a higher concentration of knowledge creation

Thank you!
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References (www.keunlee.com)• Lee, Keun, 2013 , Schumpeterian Analysis of Economic Catch-up: Knowledge, Path-creation and middle income trap, Cambridge
Univ. Press• Lee, K., and F. Malerba. (2017). "Catch-up Cycles and Changes in Industrial Leadership: Windows of Opportunity and Responsesof Firms and Countries in the Evolution of Sectoral Systems." Research Policy, 46(2): 338-351.
• Lee, K., B.-Y. Kim, Y.-Y. Park, and E. Sanidas. (2013). "Big Businesses and Economic Growth: Identifying a Binding Constraint
for Growth with Country Panel Analysis." Journal of Comparative Economics, 41(2): 561-582.

• Lee, Keun 2014. “Capability Failure and Industrial Policy to Move beyond the Middle-Income Trap: From Trade-based toTechnology-based Specialization,” in J. Lin and J. Stiglitz (eds.), Industrial Policy Revolution I. Palgrave• Lee, Keun, & BY Kim,”Both Institutions & Policies matter but differently at differnent income levels: long run economic growth,:
World Development (2009)• Lee, Keun, & C. Lim (2001), “Technological Regimes, Catching-up & Leapfrogging: the Findings from the Korean Industries”,
Research Policy, 459-483.
• Lee, Keun, W. Shin and H. Shin, (2015), “How Large or Small is the Policy Space? WTO Regime and Industrial Policy.' In Jose
Alonso, and José Ocampo (eds.) Global Governance and Rules for the Post-2015 Era (UN Series on Development), Bloomsbury.

• Kim, Y. K., K. Lee, W. G. Park, and K. Choo. (2012). "Appropriate Intellectual Property Protection and Economic Growthin Countries at Different Levels of Development." Research Policy, 41(2): 358-375.
• Shin, W., K. Lee, and W. G. Park. (2015). "When an Importer's Protection of IPR Interacts with an Exporter’s Level of Technology.”
World Economy.
• Lee, K., M. Szapiro, and Z. Mao. (2017). "From Global Value Chains (GVC) to Innovation Systems for Local Value Chains andKnowledge Creation." The European Journal of Development Research: 1-18 (published online).
• Lebdioui, K. Lee, & C. Pietrobelli (2021), Local-Foreign Technology Interface, Resource-based Development, andIndustrial Policy: How Chile and Malaysia are escaping the Middle-Income Trap, Journal of Technology Transfer
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