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Handbook of Labor, Human Resources and Population Economics pp 1–23Cite as

Innovation, technology adoption and employment: Evidence synthesis

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Abstract

Researchers, policymakers, trades unions, and employees are all interested in the employment effects of innovation and technological change. This interest has generated a long-standing debate and a rich set of theoretical and empirical findings. Because the innovation-employment relationship is conditional on innovation and skill types and a wide range of compensation and displacement mechanisms, reported findings have remained varied. This chapter draws on meta-analysis and mixed-method systematic review evidence to establish where the balance of the evidence lies and what explains the heterogeneity therein. After controlling for publication selection bias, the “average” effect of innovation on employment is positive but small and conceals a high degree of heterogeneity. Other findings indicate that: (i) the effect on unskilled labor employment is negative and the adverse effect is more pronounced in developing countries; (ii) the job-creation effect is relatively smaller in innovation-intensive industries apart from the ICT industry; (iii) patented innovations are associated with a relatively smaller employment effect; (iv) the difference between the employment effects of product and process innovation is more evident in less developed countries; (v) job-creating effects tend to diminish over time; (vi) the structure of North-South and South-South trade is likely to accentuate the skill bias of technological change; and (vii) the effect of innovation on employment follows a U-pattern when plotted against employment protection legislation in OECD countries. The chapter concludes with a brief discussion on what these findings imply for future research and public policy debate.

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Notes

  1. 1.

    See Ugur et al. (2018) for further information on calculation of PCC and its standard error.

  2. 2.

    The majority of the studies are published journal articles (71%), followed by working papers (26%). While 74% of the studies utilized firm-level data, 14% utilized industry-level data, with the remainder using sector-level data.

  3. 3.

    This meta-regression model has been applied and evaluated widely (see Stanley 2005, 2008; Stanley and Doucouliagos 2012). The underpinning theoretical framework is that of Egger et al. (1997), who postulate that researchers search across model specifications, econometric techniques, and data measures to find sufficiently large (hence statistically significant) effect-size estimates.

  4. 4.

    Testing for selection bias is justified given the evidence about its prevalence in both social-scientific and medical research (Card and Krueger 1995; Dickersin and Min 1993; Ioannidis 2005; and Simmons et al. 2011).

  5. 5.

    Discussion on other issues and the way in which they are addressed is in Ugur et al. (2018). It must also be noted that the choice between ordinary least squares (OLS) and hierarchical model (HM) estimations is made on the basis of likelihood ratio (LR) tests. The HM estimator is applied if the LR test rejects the null hypothesis that the OLS estimates is nested within (i.e., is consistent with) the HM estimate.

  6. 6.

    The high levels of publication selection bias are observed despite the inclusion journal articles and non-journal articles such as working papers and reports. The evidence on whether selection bias is larger (or smaller) in journal articles is mixed. Costa-Font et al. (2013) put forward the “winner’s curse” hypothesis and report that journals tend to exploit the quality reputation of the review process and publish more selected evidence. On the other hand, Ugur et al. (2016b) report that R&D productivity effect-size estimates published in journals are not larger than estimates published in working papers and reports.

  7. 7.

    Empirical estimates from LMIC studies are also derived from a DLDM. Hence, they are comparable with the meta-analysis evidence discussed above. The difference between the two is twofold. On the one hand, the agriculture feature more often in LMIC studies. On the other, the link between the empirical and theoretical DLDM in LMIC studies is less systematic.

  8. 8.

    These conclusions are based on evidence from: Chopra (1974) on farmers in 13 Punjabi villages in India; Bhatia and Gangwar (1981) on 965 small farms in Karhal district of India; Agarwal (1981) on 240 farms in India; De Klerk (1984) on 61 maize farms in South Africa; Inukai (1970) on rice farmers in Thailand; and Lalwani (1992) on dairy farming in India.

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Authors and Affiliations

  1. Department of International Business and Economics, University of Greenwich Business School, London, UK

    Mehmet Ugur

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  1. Mehmet Ugur
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Correspondence to Mehmet Ugur .

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Editors and Affiliations

  1. UNU-MERIT & Princeton University, Maastricht, The Netherlands

    Klaus F. Zimmermann

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  1. Department of Economic Policy, Università Cattolica del Sacro Cuore, Milano, Italy

    Marco Vivarelli

Additional information

Due to space limitations, primary studies included in the meta-analysis and the systematic review cannot be listed here. The authors acknowledge their debt to all authors of the primary studies and reiterate that full references are available in Ugur and Mitra (2017) and Ugur et al. (2018).

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Ugur, M. (2020). Innovation, technology adoption and employment: Evidence synthesis. In: Zimmermann, K. (eds) Handbook of Labor, Human Resources and Population Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-57365-6_2-1

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  • DOI: https://doi.org/10.1007/978-3-319-57365-6_2-1

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