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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) 𝐼= 𝑛 𝑖=1 𝑤𝑖 X 𝑖 where I: Composite index, Xi: Normalised variable, 𝑛 𝑖=1 𝑤𝑖 wi: Weight of the Xi, i: 1,…, n. = 1 and 0 ≤ w ≤ 1 Equation (2) shows explicitly the normalisation method (Min-Max) used: 𝐽 (2) 𝐼𝑐 = 𝑗 =1 19 𝑀 𝑚=1 min max min 𝑤𝑗𝑚 (X𝑗𝑚𝑐 − X𝑗𝑚 ) (X 𝑗𝑚 − X𝑗𝑚 ) 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.