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The knowledge-intensive direction of technological change

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Abstract

The paper articulates and tests the hypothesis that the current direction of technological change is knowledge- rather than capital intensive. The new accounting procedures that identify and quantify intangible assets allow us to test the role of capitalized knowledge as an input in the technology production function. The micro-level evidence from US listed companies included in Compustat, over the period 1977–2016, confirms that the direction of technological change has been increasingly knowledge intensive and tangible-capital saving. It also shows that this trend has increased in its strength over time and across all US sectors. The most dramatic increase in the output elasticity of knowledge occurred in the high-tech and manufacturing sectors. Furthermore, the output elasticity of tangible capital has constantly reduced in the consumer and high-tech sectors over time.

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The data and the analysis that support the findings of this article are available from the authors upon request.

Notes

  1. As explained in Sect. 3, from 2008 the System of National Accounts (SNA) has included five new standard accounting items: 1) ICT equipment included as a new category under machinery and equipment; 2) intellectual property practices (in place of ‘Intangible fixed assets’), which include R&D outcomes; 3) other intellectual property products (replacing ‘Other intangible fixed assets’), which include R&D, mineral exploration and evaluation, computer software and databases, literary or artistic originals; 4) mineral exploration and evaluation (replacing ‘Mineral exploration’ to conform with international accounting standards; 5) computer software modified to include databases.

  2. https://asc.fasb.org.

  3. We exclude firms in these SIC Codes, following a standard practice in related studies (e.g., Chen et al., 2015), because regulated utility and financial firms have different Compustat Balancing Models and reporting standards.

  4. We follow a standard practice in the literature when winsorizing the main variables at the 1% level to minimize the influence of possible spurious outliers (e.g., Borisova and Brown, 2013; Green et al., 2022). However, we check the robustness of our preferred winsorizing threshold by either removing it or setting it at 0.5% and 2%, respectively. The results of these robustness checks, available from the authors upon request, confirm the validity of our preferred estimates.

  5. This approach presents some drawbacks to the extent to which firms are heterogeneous based on the workforce composition regarding skills and educational attainment. Indeed, skilled wages usually earn a wage premium on lower-skilled workers; therefore, wage heterogeneity among firms, due to different skill compositions, might be artificially flattened. We thank an anonymous referee for this remark. To limit the concerns raised by this caveat, we also conduct estimations using average wages calculated at finer levels of industry disaggregation (i.e., 3-digit SIC and 4-digit SIC). Results are robust to these checks and are available from the authors upon request.

  6. Tables A1 and A2 in the Appendix report, respectively, the description and the summary statistics for manufacturing, consumer and high-tech, separately.

  7. Figure 1 (which is adapted from Ewens et al. (2020), Fig. 6), provides time series trends for intangible capital intensities, on average and by industry, as discussed by the same authors.

  8. The higher output elasticity of intangible capital estimated for consumer than high-tech firms may also be explained by the fact that the consumer sector is populated by big corporations that generate and exploit intangibles extensively to increase productivity. On the other hand, firms in the high-tech sector benefit from intangibles in terms of increasing mark-ups and barriers to entry, reflected in higher levels of sales and market shares but less in productivity compared to consumer firms. As a result, the output elasticity of knowledge for high-tech firms may be lower than for consumer firms. Our interpretation is in line with Crouzet and Eberly (2019) and Orhangazi (2019).

  9. The \({R}^{2}\) is not reported but all the estimations display high levels of goodness of fit with an \({R}^{2}\) above 0.9 across all the specifications. We also implement a Wald test for the joint significance of macro-industry and year dummies in columns (1) and (2) reporting that the dummies are jointly statistically significant at the highest confidence level.

References

  • Acemoglu, D. K. (2002). Directed technological change. Review of Economic Studies, 69(4), 781–810.

    Article  Google Scholar 

  • Acemoglu, D. K. (2003). Labor- and capital-augmenting technical change. Journal of European Economic Association, 1(1), 1–37.

    Article  Google Scholar 

  • Acemoglu, D. K. (2015). Localised and biased technologies: Atkinson and Stiglitz’s new view: Induced innovations and directed technological change. Economic Journal, 125(583), 443–463.

    Article  Google Scholar 

  • Ackerberg, D., Caves, K., & Frazer, G. (2006). Structural identification of production functions. MPRA Paper 38349, University Library of Munich.

  • Añón Higón, D., Gómez, J., & Vargas, P. (2017). Complementarities in innovation strategy: Do intangibles play a role in enhancing firm performance? Industrial and Corporate Change, 26(5), 865–886.

    Article  Google Scholar 

  • Antonelli, C. (2017). Digital knowledge generation and the appropriability trade-off. Telecommunications Policy, 41(10), 991–1002.

    Article  Google Scholar 

  • Antonelli, C. (2018). Knowledge exhaustibility and Schumpeterian growth. The Journal of Technology Transfer, 43(3), 779–791.

    Article  Google Scholar 

  • Antonelli, C. (2019a). Knowledge as an economic good: Exhaustibility versus appropriability? The Journal of Technology Transfer, 44(3), 647–658.

    Article  Google Scholar 

  • Antonelli, C. (2019b). The Knowledge Growth Regime A Schumpeterian Approach. London: Palgrave MacMillan.

    Book  Google Scholar 

  • Antonelli, C., & Feder, C. (2020). The new direction of technological change in the global economy. Structural Change and Economic Dynamics, 52, 1–12.

    Article  Google Scholar 

  • Arrighetti, A., Landini, F., & Lasagni, A. (2014). Intangible assets and firm heterogeneity: Evidence from Italy. Research Policy, 43(1), 202–213.

    Article  Google Scholar 

  • Arrighetti, A., Landini, F., & Lasagni, A. (2015). Intangible asset dynamics and firm behaviour. Industry and Innovation, 22(5), 402–422.

    Article  Google Scholar 

  • Arrow, K. J. (1962). Economic welfare and the allocation of resources for invention. In R. R. Nelson (Ed.), The Rate and Direction of Inventive Activity: Economic and Social Factors (pp. 609–625). Princeton University Press for NBER.

    Chapter  Google Scholar 

  • Autor, D. H., Levy, F., & Murnane, R. J. (2003). The skill content of recent technological change: An empirical exploration. The Quarterly Journal of Economics, 118(4), 1279–1333.

    Article  Google Scholar 

  • Baldwin, R. (2016). The Great Convergence Information Technology and the New Globalization. Harvard University Press.

    Book  Google Scholar 

  • Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120.

    Article  Google Scholar 

  • Bartelsman, E. J., Falk, M., Hagsten, E., & Polder, M. (2019). Productivity, technological innovations and broadband connectivity: Firm-level evidence for ten European countries. Eurasian Business Review, 9(1), 25–48.

    Article  Google Scholar 

  • Bassanini, A., & Manfredi, T. (2014). Capital’s grabbing hand? A cross-industry analysis of the decline of the labor share in OECD countries. Eurasian Business Review, 4(1), 3–30.

    Article  Google Scholar 

  • Berman, E., Bound, J., & Griliches, Z. (1994). Changes in the demand for skilled labor within US manufacturing: Evidence from the annual survey of manufactures. The Quarterly Journal of Economics, 109(2), 367–397.

    Article  Google Scholar 

  • Bessen, J. (2020). Industry concentration and information technology. The Journal of Law and Economics, 63(3), 531–555.

    Article  Google Scholar 

  • Bloom, N., Sadun, R., & Van Reenen, J. (2012). The organization of firms across countries. The Quarterly Journal of Economics, 127(4), 1663–1705.

    Article  Google Scholar 

  • Bontempi, M. E., & Mairesse, J. (2015). Intangible capital and productivity at the firm level: A panel data assessment. Economics of Innovation and New Technology, 24(1–2), 22–51.

    Article  Google Scholar 

  • Borgo, M. D., Goodridge, P., Haskel, J., & Pesole, A. (2013). Productivity and growth in UK industries: An intangible investment approach. Oxford Bulletin of Economics and Statistics, 75(6), 806–834.

    Article  Google Scholar 

  • Borisova, G., & Brown, J. R. (2013). R&D sensitivity to asset sale proceeds: New evidence on financing constraints and intangible investment. Journal of Banking & Finance, 37(1), 159–173.

    Article  Google Scholar 

  • Bresnahan, T. F., Brynjolfsson, E., & Hitt, L. M. (2002). Information technology, workplace organization, and the demand for skilled labor: Firm-level evidence. The Quarterly Journal of Economics, 117(1), 339–376.

    Article  Google Scholar 

  • Brynjolfsson, E., & Hitt, L. M. (2003). Computing productivity: Firm-level evidence. Review of Economics and Statistics, 85(4), 793–808.

    Article  Google Scholar 

  • Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. US: WW Norton & Company.

    Google Scholar 

  • Cefis, E., & Orsenigo, L. (2001). The persistence of innovative activities: A cross-countries and cross-sectors comparative analysis. Research Policy, 30(7), 1139–1158.

    Article  Google Scholar 

  • Chen, S., Miao, B., & Shevlin, T. (2015). A new measure of disclosure quality: The level of disaggregation of accounting data in annual reports. Journal of Accounting Research, 53(5), 1017–1054.

    Article  Google Scholar 

  • Cohen, W. M., & Levinthal, D. A. (1990). Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 35(1), 128–152.

    Article  Google Scholar 

  • Corrado, C., Haskel, J., Jona-Lasinio, C., & Iommi, M. (2013). Innovation and intangible investment in Europe, Japan, and the United States. Oxford Review of Economic Policy, 29(2), 261–286.

    Article  Google Scholar 

  • Corrado, C., Hulten, C., & Sichel, D. (2005). Measuring capital and technology: An expanded framework. In C. Corrado, J. Haltiwanger, & D. Sichel (Eds.), Measuring Capital in the New Economy (pp. 11–46). Chicago: University of Chicago Press.

    Chapter  Google Scholar 

  • Corrado, C., Hulten, C., & Sichel, D. (2009). Intangible capital and US economic growth. Review of Income and Wealth, 55(3), 661–685.

    Article  Google Scholar 

  • Crépon, B., Duguet, E., & Mairesse, J. (1998). Research, innovation and productivity: An econometric analysis at the firm level. Economics of Innovation and New Technology, 7(2), 115–158.

    Article  Google Scholar 

  • Dierickx, I., & Cool, K. (1989). Asset stock accumulation and sustainability of competitive advantage. Management Science, 35(12), 1504–1511.

    Article  Google Scholar 

  • Ebenstein, A., Harrison, A., McMillan, M., & Phillips, S. (2014). Estimating the impact of trade and offshoring on American workers using the current population surveys. Review of Economics and Statistics, 96(4), 581–595.

    Article  Google Scholar 

  • Ewens, M., Peters, R. H. Wang, S. (2020). Measuring intangible capital with market prices. NBER Working Paper No. 25960.

  • Falk, M., & Hagsten, E. (2021). Innovation intensity and skills in firms across five European countries. Eurasian Business Review, 11, 371–394.

    Article  Google Scholar 

  • Forman, C., Goldfarb, A. (2020). Concentration and agglomeration of IT innovation and entrepreneurship: Evidence from patenting. NBER Working Paper No. 27338.

  • Garicano, L., & Rossi-Hansberg, E. (2006). Organization and inequality in a knowledge economy. The Quarterly Journal of Economics, 121(4), 1383–1435.

    Google Scholar 

  • Goldin, C., & Katz, L. F. (2010). The Race Between Education and Technology. Harvard University Press.

    Book  Google Scholar 

  • Goos, M., Manning, A., & Salomons, A. (2014). Explaining job polarization: Routine-biased technological change and offshoring. American Economic Review, 104(8), 2509–2526.

    Article  Google Scholar 

  • Gould, E. D. (2019). Explaining the unexplained: Residual wage inequality, manufacturing decline and low-skilled immigration. The Economic Journal, 129(619), 1281–1326.

    Article  Google Scholar 

  • Green, J., Louis, H., & Sani, J. (2022). Intangible investments, scaling, and the trend in the accrual–cash flow association. Journal of Accounting Research, 60(4), 1551–1582.

    Article  Google Scholar 

  • Griliches, Z. (1969). Capital-skill complementarity. The Review of Economics and Statistics, 51(4), 465–468.

    Article  Google Scholar 

  • Griliches, Z. (1979). Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10(1), 92–116.

    Article  Google Scholar 

  • Griliches, Z. (Ed.). (1984). R&D Patents and Productivity. US: University of Chicago Press for NBER.

    Google Scholar 

  • Griliches, Z. (1986). Productivity, R and D, and basic research at the firm level in the 1970’s. The American Economic Review, 76(1), 141–154.

    Google Scholar 

  • Hagsten, E. (2016). Broadband connected employees and labor productivity: A comparative analysis of 14 European countries based on distributed microdata access. Economics of Innovation and New Technology, 25(6), 613–629.

    Article  Google Scholar 

  • Hall, B. H. (1990). The Manufacturing sector master file: 1959–1987. NBER Working Paper No. 3366.

  • Hall, B. H. (2007). Measuring the returns to R&D: The depreciation problem. NBER Working Paper No. 13473.

  • Haskel, J., & Westlake, S. (2017). Capitalism without Capital: The Rise of Intangible Economy. UK: Princeton University Press.

    Book  Google Scholar 

  • Hendarman, A. F., & Cantner, U. (2018). Soft skills, hard skills, and individual innovativeness. Eurasian Business Review, 8, 139–169.

    Article  Google Scholar 

  • Karabarbounis, L., & Neiman, B. (2014). The global decline of the labor share. The Quarterly Journal of Economics, 129(1), 61–103.

    Article  Google Scholar 

  • Kogut, B., & Zander, U. (1992). Knowledge of the firm, combinative capabilities, and the replication of technology. Organization Science, 3(3), 383–397.

    Article  Google Scholar 

  • Koh, D., Santaeulàlia-Llopis, R., & Zheng, Y. (2020). Labor share decline and intellectual property products capital. Econometrica, 88(6), 2609–2628.

    Article  Google Scholar 

  • Leiponen, A. (2006). Skills and innovation. International Journal of Industrial Organization, 23(5–6), 303–323.

    Google Scholar 

  • Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. The Review of Economic Studies, 70(2), 317–341.

    Article  Google Scholar 

  • Lippman, S. A., & Rumelt, R. P. (1982). Uncertain imitability: An analysis of interfirm differences in efficiency under competition. The Bell Journal of Economics, 13(2), 418–438.

    Article  Google Scholar 

  • Marrocu, E., Paci, R., & Pontis, M. (2012). Intangible capital and firms’ productivity. Industrial and Corporate Change, 21(2), 377–402.

    Article  Google Scholar 

  • Montresor, S., & Vezzani, A. (2016). Intangible investments and innovation propensity: Evidence from the Innobarometer 2013. Industry and Innovation, 23(4), 331–352.

    Article  Google Scholar 

  • Montresor, S., & Vezzani, A. (2022). Financial constraints to investing in intangibles: Do innovative and non-innovative firms differ? Journal of Technology Transfer, 47, 1–32.

    Article  Google Scholar 

  • Nadiri, M. I., & Prucha, I. R. (1996). Estimation of the depreciation rate of physical and R&D capital in the US total manufacturing sector. Economic Inquiry, 34(1), 43–56.

    Article  Google Scholar 

  • O’Mahony, M., Vecchi, M., & Venturini, F. (2021). Capital heterogeneity and the decline of the labor share. Economica, 88(350), 271–296.

    Article  Google Scholar 

  • Olley, G. S., & Pakes, A. (1996). The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64(6), 1263–1297.

    Article  Google Scholar 

  • Orhangazi, Ö. (2019). The role of intangible assets in explaining the investment–profit puzzle. Cambridge Journal of Economics, 43(5), 1251–1286.

    Article  Google Scholar 

  • Ortega-Argilés, R., Piva, M., & Vivarelli, M. (2014). The transatlantic productivity gap: Is R&D the main culprit? Canadian Journal of Economics/revue Canadienne D’économique, 47(4), 1342–1371.

    Article  Google Scholar 

  • Ortega-Argilés, R., Piva, M., & Vivarelli, M. (2015). The productivity impact of R&D investment: Are high-tech sectors still ahead? Economics of Innovation and New Technology, 24(3), 204–222.

    Article  Google Scholar 

  • Penrose, E. T. (1959). The Theory of the Growth of the Firm. John Wiley.

    Google Scholar 

  • Peters, R. H., & Taylor, L. A. (2017). Intangible capital and the investment-q relation. Journal of Financial Economics, 123(2), 251–272.

    Article  Google Scholar 

  • Piekkola, H. (2018). Broad-based intangibles as generators of growth in Europe. Economics of Innovation and New Technology, 27(4), 377–400.

    Article  Google Scholar 

  • Piva, M., & Vivarelli, M. (2009). The role of skills as a major driver of corporate R&D. International Journal of Manpower, 30(8), 835–852.

    Article  Google Scholar 

  • Shapiro, C., & Varian, H. R. (1998). Information Rules: A Strategic Guide to the Network Economy. Harvard Business Press.

    Google Scholar 

  • Tambe, P., Hitt, L., Rock, D., & Brynjolfsson, E. (2020). Digital capital and superstar firms. NBER Working Paper No. 28285.

  • Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Article  Google Scholar 

  • Van Roy, V., Vértesy, D., & Vivarelli, M. (2018). Technology and employment: Mass unemployment or job creation? Empirical evidence from European patenting firms. Research Policy, 47(9), 1762–1776.

    Article  Google Scholar 

  • Weitzman, M. L. (1996). Hybridizing growth theory. The American Economic Review, 86(2), 207–212.

    Google Scholar 

  • Weitzman, M. L. (1998). Recombinant growth. The Quarterly Journal of Economics, 113(2), 331–360.

    Article  Google Scholar 

  • Zeira, J. (1998). Workers, machines, and economic growth. The Quarterly Journal of Economics, 113(4), 1091–1117.

    Article  Google Scholar 

  • Zhang, M., & Mohnen, P. (2022). R&D, innovation and firm survival in Chinese manufacturing, 2000–2006. Eurasian Business Review, 12(1), 59–95.

    Article  Google Scholar 

  • Ziesemer, T. (2021). Labor-augmenting technical change data for alternative elasticities of substitution: growth, slowdown, and distribution dynamics. Economics of Innovation and New Technology, forthcoming.

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Authors and Affiliations

  1. University of Turin and Collegio Carlo Alberto, Torino, Italy

    Cristiano Antonelli

  2. University of Turin, Torino, Italy

    Gianluca Orsatti & Guido Pialli

  3. University of Maastricht, Maastricht, The Netherlands

    Guido Pialli

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  1. Cristiano Antonelli
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  2. Gianluca Orsatti
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Correspondence to Cristiano Antonelli.

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The authors gratefully acknowledge the comments of two anonymous referees and the Editor of this Journal to previous versions of this paper, as well as the funding from the Italian Ministry of Education as part of the PRIN research project 20177J2LS9, and for support from University of Turin and the Collegio Carlo Alberto local research funds.

Appendix

Appendix

Table A1: Macro-sectors composition
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Table A2: Summary statistics by macro-sector
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Antonelli, C., Orsatti, G. & Pialli, G. The knowledge-intensive direction of technological change. Eurasian Bus Rev 13, 1–27 (2023). https://doi.org/10.1007/s40821-022-00234-z

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