## wpiea2025020-print-pdf — INTRODUCTION ## Source details **Canonical URL:** [wpiea2025020-print-pdf — INTRODUCTION](https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025020-print-pdf.pdf) ## Other formats - [Markdown version](/-/media/files/publications/wp/2025/english/wpiea2025020-print-pdf.pdf.md) - [Structured JSON version](/-/media/files/publications/wp/2025/english/wpiea2025020-print-pdf.pdf.json) --- ### Research question and scope - Objective: explain realized market demand for technology (expenditures on external knowledge) by examining industry-level drivers. - Key industry-level drivers examined: - Internal R&D spending (industry R&D intensity). - Realized demand for machinery and software. - Sectoral patterns of technical change (Pavitt taxonomy: supplier-dominated, scale-intensive, specialized supplier, science-based). - Industry’s average firm size. - Industry’s distance to the global technological frontier (relative R&D measures). - Data coverage and sample: - Industry-level Community Innovation Survey (CIS) across 20 manufacturing industries in 24 European countries. - Four survey waves: 2008, 2010, 2012, 2014. - Final regression sample: 1,584 country-industry-year observations. - Estimation approach: - Correlations estimated (not causal inference). - Zeros in dependent variable (~10 percent of sample) addressed using iterated OLS (iOLS); comparisons with log-specification dropping zeros reported. - Robust standard errors; country, industry, and year fixed effects included. ### Theoretical framework - Market functioning depends on market thickness, lack of congestion, and market safety; codification and appropriability regimes determine tradability of knowledge. - Realized demand determined by: - Technologies offered by suppliers. - Buyers’ expectations about absorptability and contribution of acquired knowledge relative to internal R&D. - Complementarity expected between internal R&D and external knowledge via absorptive capacity. - Pavitt taxonomy implications: - Science-based: internal R&D intensive; patents common. - Specialized suppliers: advanced machinery producers; rely on engineering/design and user knowledge. - Scale-intensive: linked to specialized suppliers; operative efficiency–rooted knowledge. - Supplier-dominated: source disembodied tech from suppliers. ### Measurement and key variables - Dependent variable: expenditures for “other external knowledge” from CIS (purchase or licensing of patents and non-patented inventions, know-how, and other types of knowledge from other enterprises or organisations). Excludes external R&D in general and external knowledge embodied in machinery, equipment, or software. - Technological frontier (distance): relative internal R&D expenditure normalized by GDP and differenced from the average of other countries’ R&D-to-GDP ratios; alternative: difference between industry R&D intensity and the sample maximum R&D intensity. - Industry R&D intensity = internal R&D expenditures over sales; squared term included to capture nonlinearity. - Controls: spending on machinery/equipment/software (share of sales), average firm size (log employees per firm), share of firms with any external collaborative activities, country GERD as share of GDP, PCT patent applications per GDP (billion euros), survey-based IP protection index (1–7 scale). ### Data caveats and limitations - CIS coverage incomplete due to country-Eurostat agreements; seller’s country not identifiable. - Measure captures only realized demand; cannot isolate knowledge acquisitions via hiring R&D personnel. - Cross-country pooled data capture equilibrium outcomes; cannot disentangle supply vs demand drivers or establish causality. ### Appendix mapping (Pavitt’s taxonomy by NACE codes) - Supplier-dominated examples: D13, D14, D15, D16, D17, D18, D31, D32. - Scale-intensive examples: D10, D11, D12, D19, D20, D22, D23, D24, D25, D29, D30. - Specialized suppliers examples: D27, D28, D33. - Science-based examples: D21, D26. ### Primary datasets and figures (listed in source) - Figures include total expenditures by industry group, share relative to R&D, and by country. - Tables include descriptive statistics (sample of 1,584 observations) and regression results (demand for technology; different types of collaboration). ### Final empirical objective - Explain heterogeneity in realized demand for external knowledge across industries and countries, and identify complementarities and policy-relevant patterns. --- ### Data and methods - Data: - CIS industry-level responses for 20 manufacturing industries × 24 European countries × waves 2008–2014. - Final regression sample: 1,584 observations (tables display N = 1617, 1383, 1462, 1563, 1402, 1530 in different specifications). - Variables: - Dependent: expenditures on other external knowledge (share of sales; includes purchases/licensing of patents, know-how, other knowledge). - Key regressors: R&D as share of sales (and squared), expenditure on machinery/software as share of sales, log average firm size, distance to technological frontier, GERD as percentage of GDP, patent activity per GDP, IP protection index, collaboration shares by partner type. - Empirical approach: - iOLS to include zeros (about 10 percent zeros in dependent variable); log specification dropping zeros used for comparison. - Fixed effects: country, industry, year. - Robust standard errors reported. --- ### Descriptive findings and key statistics - Aggregate expenditures in the market for technology (total external knowledge expenditures across sample): - 5.0 billion euros in 2008 - 2.1 billion euros in 2010 - 2.6 billion euros in 2012 - 2.6 billion euros in 2014 - Industry-group patterns (Pavitt taxonomy; data composition): - Scale-intensive industries: 43 percent of sample. - Supplier-dominated industries: 34 percent. - Specialized suppliers: 15 percent. - Science-based: 8 percent. - Time trend: except for specialized suppliers, expenditures for external knowledge decreased substantially from 2008 to 2014 (roughly cut in one-half for many groups). - Country patterns: - German firms most active, followed by France, Spain, and Italy. - Large decline in demand between 2008 and 2010 in Germany, France, Spain, Italy; France rebounds strongly 2012–2014. - Smaller member states show episodic high demand relative to GDP (Czech Republic, Hungary, Slovak Republic, Ireland). - Selected summary statistics (sample of 1,584 observations): - Expenditures on external knowledge as a share of sales (%) — mean: 0.06; Std. dev.: 0.31; Min: 0; Max: 10.5 - R&D as a share of sales (%) — mean: 1.11; Std. dev.: 2.09; Min: 0; Max: 26.3 - Average firm size (number of employees) — mean: 108; Std. dev.: 182; Min: 13; Max: 3,676 - Share of collaborating firms — mean: 0.18; Std. dev.: 0.15; Min: 0; Max: 1 - GERD as percentage of GDP (%) — mean: 1.57; Std. dev.: 0.83; Min: 0.4; Max: 3.7 - Expenditure on machinery/software as a share of sales (%) — mean: 0.99; Std. dev.: 1.53; Min: 0; Max: 28.5 - Supplier-dominated industry share — mean: 0.34 - Scale-intensive industry share — mean: 0.43 - Specialized suppliers industry share — mean: 0.15 - Science-based industry share — mean: 0.08 - Distance to technological frontier — mean: 0.02; Std. dev.: 0.55; Min: -0.9; Max: 8.7 --- ### Main empirical findings - Zeros and estimation: - About 10 percent of observations are zeros in the dependent variable; iOLS used to include zeros. Log specification excluding zeros yields similar core results. - Complementarity between internal R&D and external knowledge: - An inverse U-shaped relationship between industry internal R&D intensity and realized demand for external knowledge. - Interpretation: - At low-to-moderate internal R&D intensity, increases in internal R&D are associated with higher expenditures on external knowledge (complementarity via absorptive capacity). - Beyond a threshold, further increases in internal R&D are associated with lower realized demand for external knowledge. - Reported thresholds and magnitudes: - Turning point corresponds to internal R&D intensity above 20 percent in one specification (about 95 percent of observations are below this threshold). - In a comprehensive specification (model 4), all observations are below the turning point (threshold about 30 percent). - A 0.23 percentage points increase in the demand for technology is estimated when evaluated at the average internal R&D intensity of about 1 percent. - Marginal impacts at higher R&D intensities: - At internal R&D intensity of 10 percent, the effect is 0.16 percentage points. - At internal R&D intensity of 20 percent, the effect is 0.08 percentage points. - Selected coefficient examples (standard errors in parentheses; significance markers retained from source): - R&D as a share of sales: coefficient estimates around 0.252***, 0.260***, 0.211***, 0.238***, 0.178***, 0.230***. - R&D as a share of sales squared: negative coefficients (e.g., -0.008***, -0.009***, -0.007**, -0.008***, -0.005*, -0.007**). - Complementarity with machinery and software: - Expenditure on machinery/software as a share of sales has positive elasticity of about 0.1 (examples: 0.110**, 0.136**, 0.097*, 0.081*, 0.097*, 0.121**). - Firm size and GERD: - Average firm size (log) positive and significant (examples: 0.435***, 0.357***, 0.416***, 0.355***, 0.351**, 0.388***). - GERD as percentage of GDP positive and significant (examples: 0.916***, 0.995***, 0.956***, 0.904***, 1.022***, 0.880***). - Distance to technological frontier (aggregated effect): - Pooled models report negative coefficients on distance to tech frontier (examples: -0.255***, -0.234***, -0.228***, -0.227***, -0.226***, -0.224***), suggesting being closer to the frontier tends to reduce realized demand for external knowledge in aggregate. --- ### Heterogeneity by industry type and technological distance - Aggregate vs disaggregated patterns: - Aggregate: being closer to the technological frontier tends to reduce realized demand. - Disaggregated by Pavitt industry groups (model 6): - Supplier-dominated industries: closer to the frontier → increased realized demand; effect magnitude: a 2.8 percentage point increase for each percentage point increase of industry R&D toward the frontier (reported as "almost three times as large as the effect of general R&D in the country"). - Science-based industries: effect essentially zero. - Scale-intensive industries and specialized suppliers: effect negative but small (about -0.5). - Industry group comparisons and implications: - Science-based industries: most likely to show high realized demand; largest consumers of external knowledge on average; market for technology integral to innovation. - Specialized suppliers: high realized demand when further away from the frontier; on average rank after science-based industries. - Scale-intensive industries: create significantly less realized demand than science-based and specialized suppliers; distance-from-frontier effect suggestive of catching up but relatively small. - Supplier-dominated industries: generally lower realized demand, except those closer to the frontier which increase demand substantially. - Alternative specification (model 5) omitting industry effects: - Internal R&D elasticity increases from 0.24 to about 0.37. - Specialized suppliers and science-based industries on average have higher demand for technology; supplier-dominated industries demand the least on average. --- ### Collaboration effects on realized demand for technology - Aggregate findings: - Share of firms collaborating on innovation: not statistically significant in baseline models. - Any type of collaboration or collaboration on innovation nationally: statistically insignificant in aggregate. - Partner-specific effects: - Collaboration with public research institutes: negatively associated with realized demand (example coefficient: -1.244*** (0.465)). - Collaboration with universities: statistically insignificant (example: 0.384 (0.650)). - Collaboration with suppliers: positive and significant (example: 0.958* (0.558)). - Collaboration with clients: positive and significant (example: 1.331** (0.588)). - Interpretation: collaboration with suppliers and clients increases realized demand for market-based external knowledge; collaboration with public research institutes may substitute for market-based acquisitions. --- ### Cyclicality and market size - Market size (24 countries, 2008–2014): - 5.0 billion Euro in 2008 - 2.1 billion Euro in 2010 - 2.6 billion Euro in 2012 - 2.6 billion Euro in 2014 - Realized demand for external knowledge is procyclical and affected by the 2008 financial crisis in Europe. - Economic downturns increase uncertainty and reduce realized demand, shifting firms toward internal exploitation of knowledge rather than external exploration; markets for technology appear volatile and account for a small, variable share of overall innovation expenditures relative to internal R&D. --- ### Policy implications and recommendations - Innovation policy should be sector-specific and account for: - Industry-related factors such as patterns of innovation and technological sophistication. - Country-level factors such as GERD as percentage of GDP. - Policies should encourage R&D and investments complementary to external knowledge acquisition (e.g., machinery/software) because complementarities matter for realized demand. - Tailored policies to expand market size and utilization should acknowledge heterogeneity across sectors and leadership status. - Sector-specific policies require weighing social benefits against fiscal costs and assessing implementation capacity to minimize resource misallocation. --- ### Conclusion - Markets for technology facilitate vertical specialization, division of labor in innovation, and allocative efficiency. - Realized demand in the market for technology is heterogeneous across industries and depends on sectoral innovation patterns and technological leadership. - Markets for technology can help lagging industries catch up, but benefits are uneven across industry groups. - Internal R&D, firm size, and complementary investments (machinery/software) are important drivers of realized demand for external knowledge. *Source: IMF Working Paper — INTRODUCTION (wpiea2025020-print-pdf)* ### INTRODUCTION ........................................................................................................... ### wpiea2025020-print-pdf - INTRODUCTION ........................................................................................................... ### INTRODUCTION - Page 3 ### Theory Review: Market Size and the Nature of Demand - Page 5 - A. The Size of the Market for Technology - B. The Nature of Realized Demand in the Market for Technology - C. Drivers of the Demand for Technology ### Data and Methods - Page 8 - A. Data - B. Variables - C. Empirical Approach ### Results - Page 11 - A. Descriptive Statistics - B. Estimation Results ### Discussion - Page 17 ### Conclusion - Page 18 ### References - Page 20 ### Appendix - Page 25 ### FIGURES - 1. Total Expenditures in the Market for Technology by Industry Group - 2. Total Expenditures in the Market for Technology by Industry Group (as a share of R&D) - 3. Total Expenditures in the Market for Technology by Country ### TABLES - 1. Descriptive Statistics (A Sample of 1584 Observations) - 2. Demand for Technology: Main Results - 3. Demand for Technology: Different Types of Collaboration *Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025020-print-pdf.pdf* ### INTRODUCTION ### INTRODUCTION ### Market for technology: role and gaps in knowledge - The market for technology (also called the market for ideas) facilitates exchange of external knowledge through mechanisms such as licenses and IP transfers and is a crucial instrument for firms acquiring external knowledge for innovation. - Prior literature emphasizes supply-side issues and market inefficiencies (e.g., underdeveloped markets, imperfections in market design, unwillingness of suppliers to offer technologies), while demand-side heterogeneity across industries and countries is less understood. - This study focuses on realized market demand for technology—measured as expenditures on external knowledge—at the industry level to uncover heterogeneity in demand across sectors and countries. ### Research question and scope - Objective: explain realized market demand for technology (expenditures on external knowledge) by examining industry-level drivers. - Key industry-level drivers examined: - Internal R&D spending (industry R&D intensity) — potential direct interaction with knowledge production / absorptive capacity. - Realized demand for machinery and software — potential non-R&D, procurement-related linkages. - Sectoral patterns of technical change (Pavitt taxonomy: supplier-dominated, scale-intensive, specialized supplier, science-based). - Industry’s average firm size. - Industry’s distance to the global technological frontier (relative R&D measures). - Data: industry-level Community Innovation Survey (CIS) across 20 manufacturing industries in 24 European countries over four survey waves (2008, 2010, 2012, 2014) covering an eight-year period; final regression sample: 1,584 country-industry-year observations. - Estimation approach: correlations estimated (not causal inference). Address zeros in dependent variable (~10 percent of the sample) using iterated OLS (iOLS); comparison with log-specification dropping zeros also reported. Robust standard errors used. ### Theoretical framework: market size and nature of demand - Market thickness and institutional rules (especially IP protection and codification costs) are central to market functioning. Three features associated with efficient market operations: market thickness, lack of congestion, and market safety. - Codification and appropriability regimes determine whether knowledge is tradable; high codification costs or weak appropriability reduce supply offered on the market. - Realized demand depends on: - The set of technologies made available by suppliers (universities, research institutes, firms). - Buyers’ expectations about (a) which market technologies can be effectively acquired and absorbed and (b) the contribution of acquired knowledge to innovation relative to internal R&D. - Complementarity between internal R&D and external knowledge acquisition is expected via absorptive capacity: internal R&D builds capability to evaluate, assimilate, and transform external knowledge. - Sectoral differences (Pavitt taxonomy) imply a division of innovative labor: - Science-based: technologically most advanced; internal R&D intensive; patents common (examples: pharmaceuticals, electronics). - Specialized suppliers: smaller firms producing advanced machinery; rely on engineering/design and user knowledge (examples: machinery, electrical equipment). - Scale-intensive: linked to specialized suppliers; complex knowledge rooted in operative efficiency (examples: chemicals, automobile). - Supplier-dominated: least technologically advanced; source disembodied tech from suppliers (examples: textiles, paper). ### Measurement and key variables - Dependent variable: expenditures for “other external knowledge” from CIS (purchase or licensing of patents and non-patented inventions, know-how, and other types of knowledge from other enterprises or organisations). Excludes external R&D in general and external knowledge embodied in machinery, equipment, or software. Captures realized demand (not unrealized deals). - Technological frontier (distance) measured primarily by relative internal R&D expenditure normalized by GDP and differenced from the average of other countries’ R&D-to-GDP ratios (increasing values indicate relative technological leadership, i.e., decreasing distance). An alternative measure uses the difference between industry R&D intensity and the sample maximum R&D intensity. - Industry R&D intensity = internal R&D expenditures over sales (proxy for absorptive capacity). Squared term included to capture nonlinearity. - Controls include spending on machinery/equipment/software (as share of sales), average firm size (log of employees per firm), share of firms with any external collaborative activities, country GERD as share of GDP, PCT patent applications per GDP (billion euros), and a survey-based IP protection index (1–7 scale). Country, industry, and year fixed effects included. ### Data caveats and limitations (as stated) - CIS coverage incomplete due to country-Eurostat agreements; questionnaire does not allow identification of the seller’s country. - Measure captures only realized demand (not deals not done) and cannot isolate knowledge acquisitions via hiring R&D personnel. - Cross-country pooled data capture equilibrium outcomes and cannot disentangle supply vs demand drivers or establish causality. ### Descriptive findings and key statistics - Aggregate expenditures in the market for technology (total external knowledge expenditures across sample): - 5.0 billion euros in 2008 - 2.1 billion euros in 2010 - 2.6 billion euros in 2012 - 2.6 billion euros in 2014 - Industry-group patterns (Pavitt taxonomy): scale-intensive industries exhibit the highest overall expenditures, followed by science-based industries, specialized suppliers, and supplier-dominated industries. - Time trend: except for specialized suppliers, expenditures for external knowledge decreased substantially from 2008 to 2014 (roughly cut in one-half for many groups). - Relative to internal R&D, markets for technology are more important in technologically less sophisticated groups (supplier-dominated and scale-intensive industries); technologically more sophisticated industries rely more on internal R&D. - Country patterns: among largest economies in sample, German firms appear most active in the market for technology, followed by France, Spain, and Italy; large decline in demand between 2008 and 2010 in Germany, France, Spain, and Italy (France rebounds strongly from 2012 to 2014). Smaller member states show episodic high demand relative to GDP (Czech Republic, Hungary, Slovak Republic, Ireland). - Summary statistics (sample of 1,584 observations): - Expenditures on external knowledge as a share of sales (%) — mean: 0.06; Std. dev.: 0.31; Min: 0; Max: 10.5 - R&D as a share of sales (%) — mean: 1.11; Std. dev.: 2.09; Min: 0; Max: 26.3 - Average firm size (number of employees) — mean: 108; Std. dev.: 182; Min: 13; Max: 3,676 - Share of collaborating firms — mean: 0.18; Std. dev.: 0.15; Min: 0; Max: 1 - GERD as percentage of GDP (%) — mean: 1.57; Std. dev.: 0.83; Min: 0.4; Max: 3.7 - Expenditure on machinery/software as a share of sales (%) — mean: 0.99; Std. dev.: 1.53; Min: 0; Max: 28.5 - Supplier-dominated industry share — mean: 0.34 - Scale-intensive industry share — mean: 0.43 - Specialized suppliers industry share — mean: 0.15 - Science-based industry share — mean: 0.08 - Distance to technological frontier — mean: 0.02; Std. dev.: 0.55; Min: -0.9; Max: 8.7 - Data composition: most industries classified as scale-intensive (43 percent), followed by supplier-dominated (34 percent), specialized suppliers (15 percent), and science-based (8 percent). ### Estimation results — main empirical findings - Zero observations: about 10 percent of the sample are zeros in the dependent variable; iOLS used to account for zeros. Log specification dropping zeros produces similar results for core variables. - Complementarity between internal R&D and external knowledge: - Across specifications, an inverse U-shaped relationship is found between industry internal R&D intensity and realized demand for external knowledge. - Interpretation: at low-to-moderate internal R&D intensity, increases in internal R&D are associated with higher expenditures on external knowledge (complementarity via absorptive capacity); beyond a threshold, further increases in internal R&D are associated with lower realized demand for external knowledge (markets may not offer sufficiently novel/useful knowledge for highly R&D intensive industries). - Reported thresholds and coefficients: - Turning point corresponds to internal R&D intensity above 20 percent in one specification (about 95 percent of observations are below this threshold). - In a more comprehensive specification (model 4), all observations are below the turning point (threshold about 30 percent). - Coefficient estimates cited (model 4): about 0.24 on internal R&D intensity and -0.008 on its square (interpreted in the text: "suggest that a one percentage point increase in internal R&D intensity is associated with about" — sentence in source truncated). - Complementarity with machinery and software: - Industry expenditures on machinery and software are found to be complementary to expenditures on external knowledge in the market for technology at the industry level, suggesting positive spillovers from these investments on innovation activities. - Technological distance heterogeneous effects: - The effect of technological distance to the global frontier varies by industry type: - Scale-intensive and specialized supplier industries that are comparatively more distant from the global technological frontier increase realized demand in the market for technology. - Supplier-dominated industries increase realized demand when they are closer to the frontier. - Global technological distance does not seem to matter for science-based industries. - Firm size and demand: - Larger firms in an industry are associated with more demand in the market for technology. - Overall interpretation: - Buying knowledge in the market for technology is an integral part of sectoral innovation patterns in some industries, depending on technological distance; internal R&D and firm size are important drivers of realized demand for technology. - Markets for technology appear volatile over the 2008–2014 period and account for a small, variable share of overall innovation expenditures relative to internal R&D. *Source: IMF Working Paper — INTRODUCTION (wpiea2025020-print-pdf)* ### 0.23 percentage points increase in the demand for technology (evaluated at the average internal R&D intensity ### wpiea2025020-print-pdf - 0.23 percentage points increase in the demand for technology (evaluated at the average internal R&D intensity ### Main empirical findings - A 0.23 percentage points increase in the demand for technology is estimated when evaluated at the average internal R&D intensity of about 1 percent. - The marginal impact of increasing internal R&D intensity declines at higher internal R&D levels: - At internal R&D intensity of 10 percent, the effect is 0.16 percentage points. - At internal R&D intensity of 20 percent, the effect is 0.08 percentage points. - Average firm size and a country’s internal R&D intensity (GERD as percentage of GDP) are positively associated with the realized demand for technology. - The share of firms collaborating on innovation is not statistically significant for the realized demand for technology in the baseline models. ### Key regression results (selected coefficients and signs) - R&D as a share of sales: coefficient estimates around 0.252***, 0.260***, 0.211***, 0.238***, 0.178***, 0.230*** (standard errors in parentheses, significance: *** p<0.01, ** p<0.05, * p<0.1). - R&D as a share of sales squared: negative coefficients (e.g., -0.008***, -0.009***, -0.007**, -0.008***, -0.005*, -0.007**) indicating diminishing returns. - Average firm size (log): positive and significant (e.g., 0.435***, 0.357***, 0.416***, 0.355***, 0.351**, 0.388***). - GERD as percentage of GDP: positive and significant (e.g., 0.916***, 0.995***, 0.956***, 0.904***, 1.022***, 0.880***). - Expenditure on machinery/software as a share of sales: positive elasticity of about 0.1 (examples: 0.110**, 0.136**, 0.097*, 0.081*, 0.097*, 0.121**). - Distance to tech frontier (based on R&D as a share of GDP): negative coefficients in pooled models (e.g., -0.255***, -0.234***, -0.228***, -0.227***, -0.226***, -0.224***). - Collaboration variables (examples): - Share of collaborating firms with government and public research institutes: -1.244*** (0.465). - Share of collaborating firms with universities: 0.384 (0.650) — not statistically significant. - Share of collaborating firms with suppliers: 0.958* (0.558). - Share of collaborating firms with clients: 1.331** (0.588). - Share of collaborating firms on innovation nationally: 0.297 (0.520) — not statistically significant. - Model controls and sample: - Year dummies: Included. - Country dummies: Included. - Industry dummies: Included. - Sample sizes (N) in shown specifications: 1617, 1383, 1462, 1563, 1402, 1530. - Standard errors shown in parentheses. ### Complementarities and channels of knowledge acquisition - Realized demand for external knowledge is strongly complementary with: - Internal R&D spending (elasticities reported above). - Expenditures on machinery and software (elasticity ≈ 0.1). - Interpretation: spending on knowledge via multiple channels is complementary and reinforcing, enabling firms and industries to obtain different kinds of knowledge and build on them. ### Heterogeneity by technological distance and industry type - Aggregate effect: being closer to the technological frontier tends to reduce realized demand for external knowledge (model 4). - When disaggregated by industry type (model 6): - Supplier-dominated industries closer to the frontier are associated with increased realized demand for external knowledge; effect magnitude: a 2.8 percentage point increase for each percentage point increase of industry R&D toward the frontier (reported as "almost three times as large as the effect of general R&D in the country"). - Effect is essentially zero for science-based industries. - Effect is negative, but small (about -0.5), for scale-intensive industries and specialized suppliers. - Alternative specification (model 5) omitting industry effects: - Internal R&D elasticity increases from 0.24 to about 0.37. - Specialized suppliers and science-based industries on average have higher demand for technology; supplier-dominated industries demand the least on average. ### Industry group comparisons and implications - Science-based industries: - Most likely to show high realized demand for external knowledge. - On average the largest consumers of external knowledge. - Market for technology is integral to innovation in these industries; higher propensity to patent supports thicker markets for technology. - Specialized supplier industries: - High realized demand when further away from the frontier; on average rank after science-based industries in demand. - Scale-intensive industries: - Create significantly less realized demand in the market for technology than science-based and specialized supplier industries. - Distance-from-frontier effect suggestive of catching up but relatively small. - Supplier-dominated industries: - Generally lower realized demand, except those closer to the technological frontier, which increase realized demand substantially. ### Collaboration effects on realized demand for technology - Any type of collaboration or collaboration on innovation nationally: statistically insignificant in aggregate (not material to realized demand). - Collaboration with universities: statistically insignificant. - Collaboration with public research institutes: negatively associated with realized demand for technology (firms collaborating with public research institutes probably require less external market-based knowledge). - Collaboration with suppliers and clients: positive and significant association with realized demand (collaboration with suppliers and clients increases realized demand for technology; may reflect informational improvements or problem-solving in firm relations). ### Cyclicality and market size - Market size in the 24 European countries (2008–2014): - 5.0 billion Euro in 2008. - 2.1 billion Euro in 2010. - 2.6 billion Euro in 2012. - 2.6 billion Euro in 2014. - Realized demand in markets for technology is procyclical and was affected by the financial crisis in Europe after 2008. - Economic downturns: increased uncertainty and declining realized demand reduces R&D investment; firms rely more on internal knowledge exploitation rather than exploration. ### Policy implications and recommendations - Innovation policy should be sector-specific and account for: - Industry-related factors such as the pattern of innovation and technological sophistication. - Country-level factors such as GERD as percentage of GDP. - Policies should encourage R&D and other investments that are complementary to expenditures on external knowledge acquisition (e.g., machinery/software), since complementarities matter for realized demand. - Tailoring innovation policies to expand the overall size and utilization of markets for technology requires acknowledging heterogeneity across sectors and industry leadership status. - Note: sector-specific policies may need to assess social benefits against fiscal costs and consider implementation capacity to minimize resource misallocation. ### Conclusion summary - Markets for technology facilitate vertical specialization, division of labor in innovation, and allocative efficiency. - Realized demand in the market for technology is heterogeneous across industries and depends on sectoral innovation patterns and technological leadership. - Markets for technology help lagging industries to catch up to some extent, but benefits are uneven across industry groups. - Policy design should reflect heterogeneity and complementarities across knowledge acquisition channels to effectively foster markets for technology. *IMF WORKING PAPERS Mapping the Market for Technology Across European Industries — INTERNATIONAL MONETARY FUND* ### Chapter 2, April. ### Chapter 2, April. ### Key references cited - Jaffe A, Trajtenberg M, Henderson R. 1993. 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OECD STI Working Paper (2009/9): 1–52. ### Appendix — NACE Codes and Pavitt’s Taxonomy - D13: Textiles — 13 — Supplier-dominated - D14: Wearing apparel — 14 — Supplier-dominated - D15: Leather and related products, footwear — 15 — Supplier-dominated - D16: Wood and of products of wood and cork, except furniture; articles of straw and plaiting materials — 16 — Supplier-dominated - D17: Paper and paper products — 17 — Supplier-dominated - D18: Printing and reproduction of recorded media — 18 — Supplier-dominated - D31: Furniture — 31 — Supplier-dominated - D32: Other manufacturing — 32 — Supplier-dominated - D10: Food products — 10 — Scale-intensive - D11: Beverages — 11 — Scale-intensive - D12: Tobacco products — 12 — Scale-intensive - D19: Coke and refined petroleum products — 19 — Scale-intensive - D20: Chemicals and chemical products — 20 — Scale-intensive - D22: Rubber and plastic products — 22 — Scale-intensive - D23: Other non-metallic mineral products — 23 — Scale-intensive - D24: Basic metals — 24 — Scale-intensive - D25: Fabricated metal products, except machinery and equipment — 25 — Scale-intensive - D29: Motor vehicles, trailers and semi-trailers — 29 — Scale-intensive - D30: Other transport equipment — 30 — Scale-intensive - D27: Electrical equipment — 27 — Specialized suppliers - D28: Machinery and equipment n.e.c. — 28 — Specialized suppliers - D33: Repair and installation of machinery and equipment — 33 — Specialized suppliers - D21: Pharmaceuticals, medicinal chemical and botanical products — 21 — Science-based - D26: Computer, electronic and optical products — 26 — Science-based *External Knowledge, R&D, and Innovation: Mapping the Market for Technology Across European Industries — Working Paper No. WP/2025/020* --- _Source: https://www.imf.org/-/media/files/publications/wp/2025/english/wpiea2025020-print-pdf.pdf_