Skip to main content

Advertisement

SpringerLink
Log in

Beyond R&D: the role of embodied technological change in affecting employment

  • Regular Article
  • Published:
Journal of Evolutionary Economics Aims and scope Submit manuscript

Abstract

In this work, we test the employment impact of distinct types of innovative investments using a representative sample of Spanish manufacturing firms over the period 2002–2013. Our GMM-SYS estimates generate various results, which are partially in contrast with the extant literature. Indeed, estimations carried out on the entire sample do not provide statistically significant evidence of the expected labor-friendly nature of innovation. More in detail, neither R&D nor investment in innovative machineries and equipment (the so-called embodied technological change, ETC) turn out to have any significant employment effect. However, the job-creation impact of R&D expenditures becomes highly significant when the focus is limited to the high-tech firms. On the other hand - and interestingly - ETC exhibits its labor-saving nature when SMEs are singled out.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Notes

  1. See The Guardian, January 5th, 2017.

  2. A different perspective is opened by Aldieri and Vinci (2018), providing empirical evidence that financial shocks and economic crises may actually amplify the possible labor-saving impact of innovation.

  3. However, this result can be due to the discrete nature of the adopted measure of process and product innovation (dummy variables). Interestingly enough, once the authors restrict their attention to (important) product innovation which went along with patent applications, they found out a highly positive and significant employment effect.

  4. While Section 2.1 has focused on studies based on firm-level data (as the one used in this paper), analyses conducted at the sectoral level provide consistent results, with the labor-friendly impact of innovation - in most cases proxied by R&D expenditures - limited to the high-tech sectors (see Bogliacino and Pianta 2010; Bogliacino and Vivarelli 2012; Piva and Vivarelli 2018).

  5. The only notable exception is the already mentioned recent article by Barbieri et al. (2019), where the Authors use four Italian CIS innovation surveys to assess the impact of R&D expenditures and ETC (defined as the innovative expenditures devoted to the acquisition of machinery, equipment and software, excluding expenditures on equipment for R&D) on employment. Differently from this study, data limitations (both in terms of lack of a proper longitudinal dimension and of paucity of observations, namely 892) have prevented the Authors to perform a proper panel estimation of a dynamic and augmented demand for labor as the one proposed in eq.(2) in the present paper.

  6. Averaging the Community Innovation Surveys (CIS) all across European countries and sectors, the mean outcome is that about 50% of innovation inputs can be accounted for R&D while the rest for ETC; obviously enough, these proportions change across countries and sectors.

  7. ETC is generally very difficult to measure because of the complexity in singling out the two different components of capital formation (that one characterized by standard technologies and that one characterized by ETC incorporating new technologies). In this context, disentangling ETC is one of the novel contributions of this study (see Section 3.2).

  8. This broader perspective is also endorsed in methodological advice as to the collection of data regarding innovation; in particular, this is well represented by the shift from the R&D-focused Frascati Manual (“Guidelines for the collection of R&D data”, first published in 2015) to the Oslo Manual in the 1990s (OECD, 1st edn. 2018).

  9. Obviously enough, the distinction between product and process innovation is often ambiguous from an empirical point of view (see, for instance, the diffusion of computers and telecommunication devices) and in many cases the two forms of innovation are strictly interrelated. However, from a theoretical point of view, we can conclude that two are the innovative inputs and two are the innovative outputs, with R&D mainly (but not only) related to product innovation and ETC mainly (but not only) related to process innovation.

  10. GMM-SYS requires that T is, at least, equals to 3.

  11. NACE is the usual industrial classification of economic activities within the European Union while CLIO is the nomenclature used by the Spanish input–output tables. The Spanish Accounting Economic System (Spanish National Statistics Institute: http://www.ine.es/) officially recognizes both classifications.

  12. As a consequence, our final sample does include zero values both with regard R&D and ETC; this means that discontinuous and occasional innovators are considered in our analysis and participate to generate the obtained results.

  13. Specifically, information provided in current prices in the ESEE database were converted into constant prices by using sectoral GDP deflators (source: INE-Spanish National Statistics Institute) centered on the year 2010.

  14. The average R&D intensities (computed as R&D/VA ratios) are respectively 5.68% for the high-tech, 2.00% for the low-tech, 3.26% for the large and 1.42% for the small firms. The average ETC intensities (computed as ETC/VA ratios) are respectively 0.6% for the high-tech, 1.97% for the low-tech, 1.6% for the large and 0.93% for the small firms.

  15. Notice that in the following tables the GMM-SYS estimated coefficients for the lagged dependent variable always turn out within the upper bound given by the corresponding POLS estimated coefficients and the lower bound given by the FE estimated coefficients; these outcomes strongly support the chosen methodology (see Bond 2002).

  16. Moreover, a battery of differenced Hansen tests has been run to test the alternative ways to instrument the various variables (available upon request). In the preferred specification, the lagged dependent variable and the investment variable have been considered endogenous. In addition, in the Appendix, a summary table (Table 8) with a lower number of instruments to test for robustness for severely reducing the instrument count (instruments including lags from two to four when AR(2) is not rejected and from three to four when AR(2) is rejected) is reported, as suggested by Roodman (2009). As can be seen, results from Tables 5, 6, and 7 are confirmed.

  17. For sake of space, single results for the time dummies are not reported in the Tables, but are available upon request.

  18. The correlation coefficient between Value added and Investment in physical capital turned out to be as high as 0.78. To mitigate possible multicollinearity issues, we decided to lag the Investment variable, as well (the correlation coefficient dropping to 0.35).

  19. As a robustness check, we tested specification (2) using stocks (instead of flows) for the investment, ETC and R&D variables. Stocks were computed through the perpetual inventory method, assuming an obsolescence rate of 6% for investment and ETC and 15% for R&D, as common in the literature. Results are consistent with those reported in Table 5 and are available upon request.

  20. Control variables have the expected signs both in high-tech and low-tech firms. No relevant differences emerge in the magnitude of coefficients, with the exception of the cost of labor which affects employment more in high-tech companies than in low-tech ones (−0.284 vs. -0.236). This may suggest that more qualified and expensive workers are employed in the high-tech sectors.

  21. The chosen size threshold is 200 employees, very close to the size median of our sample (199) and allowing a good balance between the two estimates (1,233 observations vs 1,171).

  22. As in the previous estimates, control variables have the expected signs both in large and small firms. However, employment in small firms reveals to be positively and significantly affected by capital formation and more sensitive to the cost of labor.

References

  • Abowd, J.M., Haltiwanger, J., Lane, J., McKinney, K.L., Sandusky, K., 2007. Technology and the demand for skill: an analysis of within and between firm differences. NBER working papers, 13043

  • Acs, Z. J., Audretsch, D. B., 1990. Innovation and Small Firms. Cambridge (Mass.): MIT press

  • Aldieri L, Vinci CP (2018) Innovation effects on employment in high-tech and low-tech industries: evidence from large international firms within the triad. Eur Bus Rev 2:229–243

    Article  Google Scholar 

  • Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–297

    Article  Google Scholar 

  • Audretsch DB, Vivarelli M (1996) Firms size and R&D spillovers: evidence from Italy. Small Bus Econ 8:249–258

    Article  Google Scholar 

  • Autor D, Katz L, Krueger A (1998) Computing inequality: have computers changed the labor market? Q J Econ 113:1169–1214

    Article  Google Scholar 

  • Autor DH, Katz L, Kearney M (2006) The polarization of U.S. labor market. Am Econ Rev 96:184–194

    Article  Google Scholar 

  • Barbieri L, Piva M, Vivarelli M (2019) R&D, embodied technological change and employment: evidence from Italian microdata. Ind Corp Chang 28:203–218

    Article  Google Scholar 

  • Berman E, Bound J, Griliches Z (1994) Changes in the demand for skilled labor within US manufacturing industries: evidence from the annual survey of manufacturing. Q J Econ 109:367–397

    Article  Google Scholar 

  • Blanchflower D, Burgess SM (1998) New technology and jobs: comparative evidence from a two-country study. Econ Innov New Technol 5:109–138

    Article  Google Scholar 

  • Blundell R, Bond S (1998) Initial conditions and moment restrictions in dynamic panel data models. J Econ 87:115–143

    Article  Google Scholar 

  • Bogliacino F, Pianta M (2010) Innovation and employment. A reinvestigation using revised Pavitt classes. Res Policy 39:799–809

    Article  Google Scholar 

  • Bogliacino F, Vivarelli M (2012) The job creation effect of R&D expenditures. Aust Econ Pap 51:96–113

    Article  Google Scholar 

  • Bogliacino F, Piva M, Vivarelli M (2012) R&D and employment: an application of the LSDVC estimator using European data. Econ Lett 116:56–59

    Article  Google Scholar 

  • Bond, S., 2002. Dynamic panel data models: a guide to micro data methods and practice. Cemmap Working Paper CWP09/02, London

  • Brouwer E, Kleinknecht A (1996) Firm size, small business presence and sales of innovative products: a micro-econometric analysis. Small Bus Econ 8:189–201

    Article  Google Scholar 

  • Brynjolfsson E, McAfee A (2011) Race against the machine: how the digital revolution is accelerating innovation, driving productivity, and irreversibly transforming employment and the economy. Digital Frontier Press, Lexington, MA

    Google Scholar 

  • Brynjolfsson E, McAfee A (2014) The second machine age: work, Progress, and prosperity in a time of brilliant technologies. W.W. Norton, New York

    Google Scholar 

  • Capone G, Malerba F, Nelson RR, Orsenigo L, Winter SG (2019) History friendly models: retrospective and future perspectives. Eur Bus Rev 9:1–23

    Article  Google Scholar 

  • Clark J (1987) A vintage-capital simulation model. In: Freeman C, Soete L (eds) Technical change and full employment. Basil Blackwell, Oxford, pp 86–98

    Google Scholar 

  • Coad A, Rao R (2011) The firm-level employment effects of innovations in high-tech US manufacturing industries. J Evol Econ 21:255–283

    Article  Google Scholar 

  • Conte A, Vivarelli M (2014) Succeeding in innovation: key insights on the role of R&D and technological acquisition drawn from company data. Empir Econ 47:1317–1340

    Article  Google Scholar 

  • Crépon B, Duguet E, Mairesse J (1998) Research, innovation and productivity: an econometric analysis at the firm level. Econ Innov New Technol 7:115–158

    Article  Google Scholar 

  • Edquist C, Hommen L, McKelvey M (2001) Innovation and employment: process versus product innovation. Edward Elgar Publishing, Cheltenham

    Book  Google Scholar 

  • Evangelista R, Vezzani A (2012) The impact of technological and organizational innovations on employment in European firms. Ind Corp Chang 21:871–899

    Article  Google Scholar 

  • Falk M, Hagsten E (2018) Employment impacts of market novelty sales: evidence for nine European countries. Eur Bus Rev 8:119–137

    Article  Google Scholar 

  • Freeman C (1982) The economics of industrial innovation. Pinter, London

    Google Scholar 

  • Freeman C, Soete L (eds) (1987) Technical change and full employment. Basil Blackwell, Oxford

    Google Scholar 

  • Freeman C, Soete L (1994) Work for all or mass unemployment? Computerised technical change into the twenty-first century. Pinter, London-New York

    Google Scholar 

  • Freeman C, Clark J, Soete L (1982) Unemployment and technical innovation. Pinter, London

    Google Scholar 

  • Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Chang 114:254–280

    Article  Google Scholar 

  • Garcia-Quevedo J, Pellegrino G, Vivarelli M (2014) R&D drivers and age: are young firms different? Res Policy 43:1544–1556

    Article  Google Scholar 

  • González X, Jaumandreu J, Pazo C (2005) Barriers to innovation and subsidy effectiveness. RAND J Econ 36:930–949

    Google Scholar 

  • Goos M, Manning A, Salomons A (2014) Explaining job polarization: routine-biased technological change and offshoring. Am Econ Rev 104:2509–2526

    Article  Google Scholar 

  • Greenan N, Guellec D (2000) Technological innovation and employment reallocation. Labour 14:547–590

    Article  Google Scholar 

  • Greenwood J, Hercowitz Z, Krusell P (1997) Long-run implications of investment-specific technological change. Am Econ Rev 87:342–362

    Google Scholar 

  • Griliches Z (1979) Issues in assessing the contribution of research and development to productivity growth. Bell J Econ 10:92–116

    Article  Google Scholar 

  • Griliches Z (1995) R&D and productivity: econometric results and measurement issues. In: Stoneman P (ed) Handbook of the economics of innovation and technological change. Blackwell, Oxford, pp 52–89

    Google Scholar 

  • Griliches Z (2000) R&D, education, and productivity. Harvard University Press, Cambridge (Mass)

    Google Scholar 

  • Hall BH, Lotti F, Mairesse J (2008) Employment, innovation, and productivity: evidence from Italian microdata. Ind Corp Chang 17:813–839

    Article  Google Scholar 

  • Hall BH, Lotti F, Mairesse J (2009) Innovation and productivity in SMEs: empirical evidence for Italy. Small Bus Econ 33:13–33

    Article  Google Scholar 

  • Harrison, R., Jaumandreu, J., Mairesse, J., Peters, B., 2005. Does innovation stimulate employment? A firm-level analysis using comparable micro-data from four European countries. MPRA Paper n. 1245, Munich

  • Harrison R, Jaumandreu J, Mairesse J, Peters B (2014) Does innovation stimulate employment? A firm-level analysis using comparable micro-data from four European countries. Int J Ind Organ 35:29–43

    Article  Google Scholar 

  • Hatzichronoglou, T., 1997. Revision of the high-technology sector and product classification. OECD Science, Technology and Industry Working Papers No. 1997/02, Paris

  • Hercowitz Z (1998) The ‘embodiment’ controversy: a review essay. J Monet Econ 41:217–224

    Article  Google Scholar 

  • Hulten CR (1992) Growth accounting when technical change is embodied in capital. Am Econ Rev 82:964–980

    Google Scholar 

  • Jorgenson DW (1966) The embodiment hypothesis. J Polit Econ 74:1–17

    Article  Google Scholar 

  • Katsoulacos YS (1986) The employment effect of technical change. Wheatsheaf, Brighton

    Google Scholar 

  • Lachenmaier S, Rottmann H (2011) Effects of innovation on employment: a dynamic panel analysis. Int J Ind Organ 29:210–220

    Article  Google Scholar 

  • Machin S, Van Reenen J (1998) Technology and changes in skill structure: evidence from seven OECD countries. Q J Econ 113:1215–1244

    Article  Google Scholar 

  • Mairesse, J., Mohnen, P., 2001. To be or not to be innovative: an exercise in measurement. NBER working papers, 8644

  • Mairesse, J., Sassenou, M., 1991. R&D productivity: a survey of econometric studies at the firm level. NBER working papers, 3666

  • Morrison Paul CJ, Siegel DS (2001) The impacts of technology, trade and outsourcing on employment and labor composition. Scand J Econ 103:241–264

    Article  Google Scholar 

  • Mukoyama T (2006) Rosenberg’s “learning by using” and technology diffusion. J Econ Behav Organ 61:123–144

    Article  Google Scholar 

  • Nomura Research Institute (2015) 49% of the workforce in Japan is substitutable by artificial intelligence and robots. NRI, Tokyo. https://www.nri.com/jp/news/2015/151202_1.aspx. Accessed 2 December 2015.

  • OECD, 2015. Frascati Manual - Guidelines for Collecting and Reporting data on Research and Experimental development. Paris: OECD (first edn. 1963)

  • OECD, 2018. Oslo Manual - Guidelines for Collecting, Reporting and Using Data on Innovation. Paris: OECD (first edn. 1997)

  • Parisi ML, Schiantarelli F, Sembenelli A (2006) Productivity, innovation and R&D: micro evidence for Italy. Eur Econ Rev 50:2037–2061

    Article  Google Scholar 

  • Pavitt K (1984) Patterns of technical change: towards a taxonomy and a theory. Res Policy 13:343–374

    Article  Google Scholar 

  • Pianta M (2005) Innovation and employment. In: Fagerberg J, Mowery D, Nelson RR (eds) Handbook of innovation. Oxford University Press, Oxford, pp 568–598

    Google Scholar 

  • Piva M, Vivarelli M (2004) Technological change and employment: some micro evidence from Italy. Appl Econ Lett 11:373–376

    Article  Google Scholar 

  • Piva M, Vivarelli M (2005) Innovation and employment: evidence from Italian microdata. J Econ 86:65–83

    Article  Google Scholar 

  • Piva M, Vivarelli M (2018) Technological change and employment: is Europe ready for the challenge? Eur Bus Rev 8:13–32

    Article  Google Scholar 

  • Ricardo, D., (1951). Principles of political economy. In P. Sraffa (ed), The Works and Correspondence of David Ricardo. Cambridge: Cambridge University press, vol. 1, third edn 1821

  • Rifkin J (1995) The end of work: the decline of the global labor force and the Dawn of the post-market era. Putnam Publishing Group, New York

    Google Scholar 

  • Roodman D (2009) A note on the theme of too many instruments. Oxf Bull Econ Stat 71:135–158

    Article  Google Scholar 

  • Salter WEG (1960) Productivity and technical change. Cambridge University Press, Cambridge

    Google Scholar 

  • Solow RM (1960) Investment and technical progress. In: Arrow KJ, Karlin S, Suppes P (eds) Mathematical methods in the social sciences. Stanford University Press, Stanford, pp 89–104

    Google Scholar 

  • Triguero Á, Córcoles D (2013) Understanding innovation: an analysis of persistence for Spanish manufacturing firms. Res Policy 42:340–352

    Article  Google Scholar 

  • Van Reenen J (1997) Employment and technological innovation: evidence from U.K. manufacturing firms. J Labor Econ 15:255–284

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Vivarelli M (1995) The economics of technology and employment: theory and empirical evidence. Elgar, Aldershot

    Google Scholar 

  • Vivarelli M (2014) Innovation, employment and skills in advanced and developing countries: a survey of economic literature. J Economic Issues 48:123–154

    Article  Google Scholar 

  • Wilson DJ (2009) IT and beyond: the contribution of heterogeneous capital to productivity. J Bus Econ Stat 27:52–70

    Article  Google Scholar 

  • Yang C-H, Lin C-HA (2008) Developing employment effects of innovations: microeconometric evidence from Taiwan. Dev Econ 46:109–134

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. EPFL, Lausanne, Switzerland

    Gabriele Pellegrino

  2. Department of Economic Policy, Università Cattolica del Sacro Cuore, Piacenza, Italy

    Mariacristina Piva

  3. Department of Economic Policy, Università Cattolica del Sacro Cuore, Largo Gemelli 1, 20123, Milan, Italy

    Marco Vivarelli

  4. UNU-MERIT, Maastricht, The Netherlands

    Marco Vivarelli

  5. IZA, Bonn, Germany

    Marco Vivarelli

Authors
  1. Gabriele Pellegrino
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mariacristina Piva
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Marco Vivarelli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marco Vivarelli.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

Table 8 Dependent variable: ln(Employment)
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pellegrino, G., Piva, M. & Vivarelli, M. Beyond R&D: the role of embodied technological change in affecting employment. J Evol Econ 29, 1151–1171 (2019). https://doi.org/10.1007/s00191-019-00635-w

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00191-019-00635-w

Keywords

JEL classification

Search

Navigation