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Technology upgrading of middle-income economies:
a new framework and evidence
Slavo Radosevic and Esin Yoruk
UCL CCSEE Conference
20-21 June 2016
Outline
• Motivations
• Conceptual framework
• Towards metrics of technology upgrading
• Analysis and results
2
Motivation I
• Aggregate theories of growth are not useful
• Search for universal factors of growth is futile
• Technology is not reducible to single variable (TFP?R&D?)
• Current metrices are atheoretical or not relevant for low/middle income
economies
• Global Innovation index and Innovation Union Scoreboard are
pragmatic but atheoretical
• CDM model (RD>innovation>productivity) is theoretically grounded
but irrelevant for middle income economies (see EBRD 2014
Innovation in Transition report)
• WEF GCI recognizes differences in drivers of growth (theoretically
grounded) but is mixing technological and institutional variables
3
Motivation II
• Motivation 3: wrong metrics leads to irrelevant policies
• The contradiction in the current EU approach between its dominant metrics
(cf. IUS) which assumes identical technological paths and drivers of growth
and the wish to push countries along divergent ‘smart specialization’ paths.
• The EU is pushing countries and regions to embark on process of
formulating their SS strategies to avoid so called ‘adding up’ problem
(Spence 2011: 94-96) or situation that too many regions are aiming for
similar technologies and markets and thus competing each other out.
• Its dominant metrics, IUS which countries and regions are using as policy
targets is actually reinforcing imitative policies towards R&D based growth.
4
Outcome: metrics which determines policy instead of policy determining
metrics
Source: Innovation Union Scoreboard, 2014
Our aims
• to explore the issues related to measurement of technology upgrading of the
economies moving from middle to high income status. We examine a sample of 42
economies ranging from lower middle income to upper high income level.
• to generate theoretically relevant but empirically grounded middle level conceptual
and statistical framework which could illuminate the type of challenges relevant for
low to middle income economies in their path out of potential middle-income trap.
• to construct composite indicators for components of technology upgrading, which
can complement, not replace, IUS and which reflect better different drivers and
patterns of technology upgrading in the economies.
• cf. ‘measurement without theory’
6
Technology upgrading and growth
Macroeconomic
context
Technology
upgrading
Institutional
context
Growth
7
Multi-level perspectives on technology upgrading
Types / Levels
Conceptual framework
Intra firm level
- Production vs. technology capability (Bell and
Pavit, 1993)(Lall, Dahlman and Westphal, et al)
-Reverse product life cycle: A combination of the
product life cycle model in advanced firms by
Utterback and Abernathy (1975) and Kim’s (1980)
three stage catch-up model of Acquisition –
Assimilation - Implementation
- Importance of minor improvements during
reverse learning trajectory (Hobday 1995, 1998,
2004)
- Different entry points for latecomer in post-catch
up stage (Choung et al, 2014)
Intra-industry and interindustry level
-Industry life cycle and dominant design (Klepper)
-Upgrading towards high value-added industries
(value chain upgrading)
Country level
Sequential upgrading of countries based on
‘leading-sector’ (Ozawa 2009)
WEF rankings based on differing drivers of growth
IUS innovation capacity of countries based on
composite indicators of innovation activities
8
TOWARDS THEORY OF TECHNOLOGY UPGRADING
• A key to economic growth is in improved technology capability, which cannot be
reduced to a single variable (Lee, 2012) > a number of drivers.
• A multidimensional process = technology, structural change, interaction with
global economy
• Based on broader understanding of innovation, which goes well beyond R&D.
• A multi-level process = micro, mezzo and macro grounded
• At its core is structural change in various dimensions: technological, industrial,
organisational.
• It is also an outcome of interaction between global forces (embodied in
international trade and investment flows) and local strategies (pursued by host
country firms and governments)
9
Limitations (assumptions) of our conceptual
(statistical) framework
• Functional approach to technology upgrading ie. not institutional
setups
• We abstract of demand upgrading (hierarchy of consumption)
• We are skeptical about the notion of industry upgrading and idea
of hierarchy of industries (from low VA to high VA industries)
• Technology and production are strictly interconnected
(embodied knowledge)
10
Technology upgrading is three-dimensional
process proxied by a variety of indicators:
• Intensity of technology upgrading through various types of innovation
and technology activities (the depth)
• Widening or broadening of technology upgrading through different
forms of technology and knowledge diversification (the breadth)
• Interaction with the global economy
11
Dimensions of technology upgrading
DIMENSION1
Intensity of technology
upgrading by types
DIMENSION 3
Interaction with global economy
DIMENSION 2
Width of technology upgrading
(structural features)
12
Dimensions of technology upgrading
Intensity of
technology
upgrading by types
Breadth of
technology
upgrading
Interaction with
global economy
13
• Production capability
• Technology capability
• R&D and knowledge intensity
• Infrastructure (human, physical, organizational)
• Structural change
• Firms’ structure
• Technology imports
• Knowledge imports
• Knowledge cooperation
Intensity of technology upgrading
Production capability
• ISO9001 certificates
• Trademark applications, resident
• On the job training
Technology capability
•
•
•
•
R&D capability
14
Patents resident applications
US patents
EPO patents
Resident industrial designs
• BES R&D as % GDP; GERD as % of GDP
• Researchers; Technicians in R&D
• S&T journal articles; Science citations
• Company spending on R&D
• Quality of scientific research institutes
• University - industry collaborations
Table 1. Components and indicators of Technology Upgrading Intensity Index (Index A).
INTENSITY OF TECHNOLOGY UPGRADING BY TYPES
(DEPTH)
Index
15
Component
1. Production
capability
(Index 1)
2. Technology
capability
(Index 2)
3. R&D and
knowledge
intensity
(Index 3)
Quantitative Indicators
Source
Year
1.ISO9001 certificates pmi
2.Trademark applications, resident pmi
3.On the job training Q.5.C
ISO
WB
WEFGCR
2007-11 avg
2012-13
4.Patents resident applications to national office pmi
5.Patent applications to USPTO pmi
6.Patent applications to EPO pmi
7.Resident's industrial design count pmi
WB
WIPO
WIPO
WIPO
2007-11 avg
8.Business Enterprise Sector R&D expenditures (% of GDP)
9.Research and development expenditure (% of GDP)
10.Researchers in R&D pmi
11.Technicians in R&D pmi
12.Scientific and technical journal articles pmi
13.Science citations pmi
14.Quality of scientific research institutions Q.12.02
15.University - industry collaboration in R&D Q.12.04
UNESCO
2011
WB
2010
Comp
weight
1/3
1/3
0.946
1/3
ThomsonNSI
WEFGCR
Cronbach’s
alpha
2007-11 avg
2012-13
Breadth of technology upgrading
Infrastructure:
human capital and
physical
Structural changes
Firm capabilities
16
• Average years of schooling
• Quality of math and science education
• Availability of research and training services
• Availability of scientists and engineers
• Fixed broadband Internet subscriptions
• Gross fixed investments in GDP
• Technology diversification: changes in patenting structure
(WIPO, EPO, USPTO)
• Buyer sophistication: levels and changes
• Availability of the latest technologies: levels and changes
• Number of firms in Forbes2000
• Firm level technology absorption
Table 2. Components and indicators of Technology Upgrading Breadth Index (Index B).
BREADTH OF TECHNOLOGY UPGRADING:
STRUCTURAL FEATURES (SCOPE)
Index
Component
4. Infrastructure:
human capital
and physical and
organisational
(Index 4)
5. Structural
change
(Index 5)
6. Firm level
capabilities
(Index 6)
Quantitative Indicators
Source
Year
1.Average years of schooling 25+
2.Quality of maths and science education Q.5.04
3.Availability of research and training services Q.5.07
4.Availability of scientists and engineers Q.12.06
5.Fixed broadband Internet subscribers (per 100 people)
6.Gross Fixed Investment as % of GDP
7.Herfindahl-Hirschman Index for total national patent applications
8.Herfindahl-Hirschman Index for patent applications to EPO
9.Herfindahl-Hirschman Index for patent applications to USPTO
10.Buyer sophistication Q.6.16
11.Change in buyer sophistication( % change in Q. 6.16 from 2006-07
to 2012-13)
12.Availability of state-of-the-art technologies Q.9.01
13.Change in availability of latest technologies( % change in 9.01
from 2006-07 to 2012-13)
Barro-Lee
WEFGCR
2010
2012-13
14.Number of firms in Forbes 2000 pmi
15.Firm level technology absorption Q.9.02
Forbes
WEFGCR
Comp
weight
Cronbach’s
alpha
1/3
WB
2012
WIPO
2007-12 avg
2012-13
WEFGCR
0.893
1/3
2013
2012-13
1/3
Table 3. Indicators of technology and knowledge exchange index (Index C)
Index
INTERACTION WITH
GLOCBAL ECONOMY
(TECHNOLOGY AND
KNOWLEDGE EXCHANGE)
Quantitative Indicators
Source
Year
1.Licensing receipts as % of GDP
2.Licensing payments as % of GDP
3.Share of exports in complex industries in total exports (SITCRev3 5 71-79
87 88)
4.Foreign direct investment, net outflows (% of GDP)
5.Foreign direct investment, net inflows (% of GDP)
WB
2012
UNComtrade
2008-12 avg
WB
2007-12 avg
Cronbach’s
alpha
0.827
A typical composite indicator will take the form (Freudenberg, 2003: 7):
(1)
=

=1  X 
where
I: Composite index,
Xi: Normalised variable,

=1 
wi: Weight of the Xi,
i: 1,…, n.
= 1 and 0 ≤ w ≤ 1
Equation (2) shows explicitly the normalisation method (Min-Max) used:

(2)  =
 =1
19

=1
min
max
min
 (X − X
) (X 
− X
)
B: BREADTH OF
TECHNOLOGY
UPGRADING:
STRUCTURAL
FEATURES
(SCOPE)
A. INTENSITY
AND TYPES OF
TECHNOLOGY
UPGRADING
(SCALE)
Lower Middle Income
(GNI pc atlas method 2012
$1046-4125)
Upper Middle Income
(GNI pc atlas method 2012
$4126-12175)
Lower High Income
(GNI pc atlas method 2012
$12176-30000 )
Upper High Income
(GNI pc atlas method 2012
$30001-)
Income
Group
Range of
Index
Country Name
Sweden
Germany
Japan
Belgium
Austria
UHI AVERAGE
United States
United Kingdom
Ireland
Italy
Korea, Rep.
Slovenia
Spain
Czech Republic
Portugal
Estonia
LHI AVERAGE
Chile
Poland
Greece
Russian Federation
Malaysia
China
Hungary
Turkey
Brazil
South Africa
Bulgaria
UMI AVERAGE
Mexico
Jordan
Peru
Romania
Thailand
Kazakhstan
Belarus
Albania
Indonesia
India
Ukraine
Philippines
Morocco
LMI AVERAGE
Vietnam
Ghana
Moldova
5.1 to 63.4
INDEX A
63.4
58.7
57.5
52.0
51.1
49.7
48.8
44.1
36.1
35.3
66.6
35.5
34.5
33.9
31.4
30.1
30.0
21.8
17.7
15.2
13.9
23.1
21.8
21.3
18.3
17.9
15.4
14.7
14.2
12.9
12.1
11.6
11.4
11.4
8.4
6.5
6.4
10.9
10.7
10.1
10.1
8.9
8.8
8.5
6.0
5.1
Sweden
Japan
Ireland
United States
Belgium
UHI AVERAGE
United Kingdom
Austria
Germany
Italy
Korea, Rep.
Portugal
Spain
Estonia
Chile
LHI AVERAGE
Czech Republic
Greece
Slovenia
Poland
Russian Federation
Malaysia
China
Jordan
South Africa
Mexico
Turkey
Brazil
Hungary
Thailand
UMI AVERAGE
Kazakhstan
Romania
Bulgaria
Peru
Albania
Belarus
Ukraine
Morocco
India
Indonesia
Philippines
LMI AVERAGE
Moldova
Vietnam
Ghana
20.7 to 77.1
INDEX B
77.1
73.2
69.8
68.2
65.9
65.8
65.6
64.6
62.8
44.6
67.5
53.6
51.6
50.0
49.3
48.4
47.7
46.5
42.9
40.0
34.4
55.8
47.9
45.1
42.8
42.2
42.2
42.1
42.0
41.9
38.7
35.1
33.3
32.7
30.8
29.2
17.7
44.1
40.7
39.9
37.4
36.6
33.3
25.3
21.5
20.7
C: INDEX OF
INTERACTION
WITH GLOBAL
ECONOMY
Ireland
Belgium
UHI AVERAGE
Sweden
United Kingdom
Austria
United States
Japan
Germany
Italy
Estonia
Korea, Rep.
Spain
Slovenia
Czech Republic
Chile
LHI AVERAGE
Portugal
Poland
Russian Federation
Greece
Hungary
Jordan
Bulgaria
Malaysia
China
UMI AVERAGE
Thailand
Mexico
Turkey
Kazakhstan
Romania
Albania
Belarus
Brazil
South Africa
Peru
Vietnam
Ukraine
LMI AVERAGE
Moldova
Philippines
India
Morocco
Ghana
Indonesia
6.4 to 100
INDEX C
100.0
57.1
37.7
36.0
29.1
27.5
24.7
24.4
23.5
17.1
25.0
22.3
21.7
20.4
19.6
18.5
18.0
17.3
17.1
11.1
7.1
62.6
25.0
21.2
20.4
19.0
18.7
17.9
17.7
15.8
15.0
13.9
13.1
12.6
9.8
9.5
6.9
18.2
12.5
10.9
10.7
10.3
9.9
9.8
9.1
6.4
Table 4. OLS regressions for Index A, Index B and Index C.
Index A
Model 1
1.1
1.2
662
800
(0.000) (0.000)
1.3
805
(0.000)
1.4
846
(0.000)
1.5
493
(0.000)
Index B
Model 2
2.1
2.2
2.3
2.4
2.5
638
(0.000)
877
(0.000)
944
(0.000)
471
(0.000)
884
(0.000)
Index C
constant
5611
(0.157)
Dummy MI
-7716
(0.021)
Dummy LMI
Dummy UMI
Dummy LHI
-1378
(0.549)
-1383
(0.582)
-2264
(0.162)
1423
(0.294)
-4443
(0.483)
-21858
(0.000)
-20553
(0.000)
-25408
(0.000)
23037
(0.000)
-12999
(0.000)
-3181
(0.304)
Model 3
3.1
3.2
3.3
3.4
3.5
247
(0.01)
381
(0.006)
437
(0.001)
491
(0.001)
41
(0.611)
24901
(0.000)
12249
(0.003)
13199
(0.001)
6215
(0.118)
9449
(0.000)
-22796
(0.000)
-5100
(0.180)
-2092
(0.412)
-13917
(0.016)
-5431
(0.071)
-3629
(0.162)
Dummy UHI
Number of observations 42
42
42
42
F-test sig.
0.000
0.000
0.000
0.000
R2
0.84
0.82
0.82
0.83
2
Adjusted R
0.83
0.81
0.81
0.82
Note: Values in parentheses are corresponding p values for t-test.
-13399
(0.003)
-724
(0.823)
19259
(0.000)
42
0.000
0.93
0.92
4475
(0.397)
-8848
(0.020)
42
0.000
0.81
0.80
42
0.000
0.73
0.72
42
0.000
0.74
0.73
42
0.000
0.72
0.70
42
0.000
0.88
0.88
34133
(0.000)
42
0.000
0.69
0.67
42
0.000
0.35
0.32
42
0.000
0.40
0.37
42
0.000
0.26
0.22
42
0.000
0.80
0.79
Table 5. OLS regressions for Index A sub-categories.
Index1: Production Capability
Model 4
4.2
4.1
2289
1467
(0.000) (0.000)
4.3
2181
(0.000)
4.4
2493
(0.000)
4.5
1189
(0.000)
Model 5
5.2
5.1
1291
(0.000)
Index2: Technology capability
1905
(0.000)
5.3
5.4
5.5
1917
(0.000)
2096
(0.000)
859
(0.000)
Index3: R&D capability
constant
10521
(0.071)
Dummy MI
-13093
(0.003)
Dummy LMI
Dummy UMI
-4278
(0.271)
-1583
(0.662)
-5144
(0.082)
782
(0.665)
21790
(0.000)
12436
(0.000)
12405
(0.000)
9218
(0.000)
8664
(0.000)
Model 6
6.2
6.1
1534
(0.000)
5152
(0.224)
42
42
42
Number of observations
0.000
0.000
0.000
F-test sig.
0.69
0.67
0.74
R2
2
0.68
0.65
0.72
Adjusted R
Note: Values in parentheses are corresponding p values for t-test.
1990
(0.000)
-3228
(0.111)
1136
(0.000)
926
(0.514)
-4108
(0.125)
1684
(0.659)
-6322
(0.077)
42
0.000
0.69
0.68
1894
(0.000)
-2512
(0.355)
-1775
(0.504)
-5887
(0.103)
-5672
(0.085)
Dummy UHI
6.5
-3740
(0238)
-10947
(0.008)
Dummy LHI
6.4
-7948
(0.022)
-16604
(0.000)
-2007
(0.651)
1861
(0.000)
-2077
(0.394)
6.3
25600
(0.000)
42
0.000
0.90
0.89
42
0.000
0.78
0.77
42
0.000
0.67
0.65
42
0.000
0.63
0.61
42
0.000
0.61
0.59
26547
(0.000)
42
0.000
0.85
0.85
42
0.000
0.83
0.82
42
0.000
0.81
0.80
42
0.000
0.81
0.80
42
0.000
0.82
0.81
19824
(0.000)
42
0.000
0.92
0.92
Table 6. OLS regressions for Index B sub-categories.
Index4:
Infrastructure:
Human and
physical
Index5: Structural
change indicators
Index6: Firm level
organisational
capabilities
constant
Dummy MI
Dummy LMI
Dummy UMI
Dummy LHI
Model 7
7.1
1644
(0.000)
-370
(0.963)
7.2
2427
(0.000)
-18412
(0.001)
7.3
2487
(0.000)
-19400
(0.002)
7.4
2725
(0.000)
-23060
(0.000)
7.5
1285
(0.000)
-7355
(0.032)
-13237
(0.002)
Model 8
8.1
8.2
8.3
8.4
8.5
934
(0.021)
1819
(0.001)
1936
(0.000)
2212
(0.000)
842
(0.003)
11020
(0.221)
-15898
(0.125)
-16112
(0.081)
-25223
(0.009)
-5361
(0.289)
-21192
(0.000)
-6503
(0.109)
Model 9
9.1
9.2
9.3
9.4
9.5
1164
(0.000)
1639
(0.000)
1647
(0.000)
1824
(0.000)
694
(0.002)
14176
(0.000)
1546
(0.630)
2535
(0.450)
-3277
(0.294)
4367
(0.042)
-16861
(0.000)
-10249
(0.069)
-3272
(0.345)
-9363
(0.025)
-11239
(0.008)
-4904
(0.167)
Dummy UHI
Number of
42
42
42
42
observations
F-test sig.
0.000
0.000
0.000
0.000
2
R
0.74
0.69
0.68
0.69
2
Adjusted R
0.73
0.68
0.66
0.67
Note: Values in parentheses are corresponding p values for t-test.
-8017
(0.018)
-1211
(0.805)
24535
(0.000)
4276
(0.253)
30065
(0.000)
26594
(0.000)
42
42
42
42
42
42
42
42
42
42
42
0.000
0.88
0.88
0.000
0.68
0.66
0.000
0.42
0.40
0.000
0.47
0.45
0.000
0.37
0.34
0.000
0.84
0.83
0.000
0.81
0.80
0.000
0.66
0.65
0.000
0.67
0.65
0.000
0.63
0.61
0.000
0.84
0.84
Different patterns of technology
upgrading at different income levels
High income
Middle income
Low income
24
Technology
frontier activities
Technology
diversification
Imitative
technology effort
Is there middle income trap in technology upgrading?
• Middle income trap is present in all dimensions of technology
upgrading but its degree vary across different dimensions.
• The biggest coefficient is in regression with index C
technology and knowledge exchange followed by index 5 of
structural change.
Comparison of Middle Income dummies across
OLS regression models
Dummy MI Coef. Std. Err.
t
P>|t| [95% Conf. Interval]
Index C -22795.7 3076.517 -7.41
0.000 -29018.54 -16572.86
Index 5 -20455.11 3400.798 -6.01 0.000 -27333.87 -13576.34
• A trap seems to be higher for dimensions of breadth of
technology Index B than for index of intensity of technology
upgrading Index A.
Index 6 -17819.95 2624.515 -6.79 0.000 -23128.53 -12511.36
• Index A of intensity of technology upgrading reflects
cumulative technology capability while index B of breadth of
technology upgrading refers to structural, infrastructural and
organisational features of economies.
Index 4 -15424.98 3769.616 -4.09 0.000 -23049.74 -7800.209
• These latter are subject to various market and system failures
and are outcomes of variety of non-technological factors the
most important of which seem to be political economy of a
specific economy.
25
Index 2 -16621.22 2980.479 -5.58 0.000 -22649.81 -10592.63
Index 1 -14563.4 3965.638 -3.67
0.001 -22584.66 -6542.135
Index 3 - 8210.954 3286.42 -2.50 0.017 -14858.36 -1563.543
Index B -12999
Index A -7717
3106.8 -4.18 0.000 -19282.8 -6714.48
3204.5 -2.41 0.021 -14198.2 -1234.6
Technology upgrading indexes: methodological lessons
• We have given up of composing one index of technology upgrading for two reasons
• Ambiguous relationship between openness and autonomy
• An intensive interaction with global economy is not by itself contributing to technology upgrading: not necessarily complements but
substitutes
• Dual nature of structural change:
• Structural change is non-linearly related to technology upgrading: from MIC to HIC technology diversification is desirable but from LHI to
UHI both diversification and specialization are viable options
• Technology upgrading is a multidimensional construct but aiming for aggregate
composite indicator may actually mask the key issues which arise from different stages
of technology upgrading in which countries find themselves and from their specific paths
of technology upgrading.
26
Conclusions I:
• In regressions with dummy for upper high income group the regression fit is
significantly positive (except for structural change index) suggesting upper
high income group can fully exploit benefits from favourable technological,
structural and interaction conditions to generate growth.
• In regressions with dummy for aggregate middle income group the regression
fit is significantly negative suggesting the existence of technology middle
income trap.
• A broadly defined middle income trap is present in all dimensions of
technology upgrading but its importance varies across different dimensions.
27
Conclusions II (Indexes 1,2,3):
• Generation of technology is domain of only the highest income group. (DUHI=25600,
DMI=-13000)
• Index of production capability is positively related to all income levels (as would be
expected), but in regressions with dummy for aggregate middle income group the
regression fit is significantly negative suggesting the existence of production
capability middle income trap (as would not be expected). (DMI=-16600)
• R&D capabilities are much stronger in middle income economies than would be
expected based on their income levels, even though there is still and R&D capability
middle income trap (albeit less than that of technology generation and production
capability). This is largely due to dual face of R&D which operates not only as factor of
technology frontier activities but also as factor of absorptive capacity. This is the only
area where encouraging R&D investment by policy must have worked! However,
transferring R&D capability to technology generation and even production capability
for
28 the middle income group is not evidenced. (DMI=-7950)
Conclusions III (Indexes 4,5,6):
• The levels of infrastructures of middle income economies are very often equal to
those of lower high income group. So it seems that the MITU is high in the case of
infrastructure, but infrastructure is not reflected in income levels. Educated young
population, developed R&D services and physical infrastructures do not
necessarily convert into proportionate levels of income.  Infrastructural middle
income trap (DMI, infrastructure=-13200 and DMI, structural change= -21200)
• One of major constraining structural feature of technology upgrading are firms’
organisational capabilities which are located much higher in terms of income than
for infrastructure, R&D or production capabilities. (DMI=-16860)
29
Conclusions III (Indexes A, B,C):
• Intensity of technology upgrading is the less trapped area for middle income
countries (DMI=-7700) driven by achievements in R&D-driven policies.
• Interaction with global economy which is based on proxies of knowledge and
technology exchanges is not robust due to several ‘outliers’ (Ireland, Belgium,
Hungary, Greece) but this is the most trapped area for middle income economies
(DMI=-22800) whereby it is the most exploited area by upper high income group
(DUHI= 34100). This suggests that middle income economies are not benefiting
from being engaged in global technology and knowledge exchange as much as
they should compared to high income groups.
• Breadth of technology upgrading index, structural dimension, shows negative
dummies for both middle income (DMI=-13000) and upper high income
economies (DUHI=-8850) suggesting infrastructural issues are global and not only
confined to emerging countries.
30
Conclusions IV:
• The highest ranked countries in terms of index of technology
upgrading are Sweden, Korea, Japan and Germany.
• Poland has been fast growing CEE economy in the last 20 years
but its potential for technology based growth is moderate.
• China’s ranking in terms of index of technology upgrading is well
above its income per capita which suggests a room for further
growth based on technology.
31
Rank correlation of selected indexes
Index a
Index a (2007-2013)
1
Index b (2007-2013)
0.889
Index c (2007-2013)
0.7293
WEF technological Readiness index (2015-16)
0.8683
WEF technological Readiness index (2010-11)
0.9066
WEF Technological Innovation Index (2015-16)
0.8174
WEF Technological Innovation Index (2010-11)
0.8538
INSEAD Global innovation Index (2014)
0.9030
UNIDO Competitive Industrial Performance Index (2012) 0.8224
World Bank Knowledge Economy Index (2012)
0.8741
Technological Capabilities Index ArCo (2000)
0.8355
Index b Index c
1
0.7009
0.7915
0.8075
0.8608
0.8383
0.8202
0.7800
0.7851
0.7366
1
0.7017
0.8082
0.6669
0.6585
0.7943
0.6898
0.7509
0.6672
Note: Number of countries for all indexes is 41 out of 42 with the exception of Belarus. All correlations are statistically significant at the level of 0.0000.
Thank you.
Conclusions
• The existing indicators do not reflect specificities of technology upgrading
of middle-income economies. Dominant metrices such as Innovation Union
Scoreboard (IUS) are suitable largely for high-income economies.
• We build-up of theoretically relevant and empirically grounded middle
level conceptual and statistical framework which could illuminate type of
challenges relevant for middle-income economies. It conceptualizes
technology upgrading as a three-dimensional process composed of
intensity and different types of technology upgrading through various types
of innovation and technology activities; broadening of technology
upgrading through various forms of technology and knowledge
diversification, and interaction with global economy through knowledge
import, adoption, and exchange.
• We show that technology upgrading of middle-income economies is
distinctively different from low and high-income economies.