Technological Innovation and Employment in Derived Labour Demand Models: A Hierarchical Meta‐regression Analysis

Abstract

Abstract. The effect of technological innovation on employment is of major concern for workers and their unions, policy makers and academic researchers. We meta-analyse 570 estimates from 35 primary studies that estimate a derived labour demand model. We contribute to existing attempts at evidence synthesis by addressing the risks of selection bias and that of data dependence in observational studies. Our findings indicate that: (i) hierarchical meta-regression models are sufficiently versatile for addressing both selection bias and data dependence in observational data; (ii) innovation's effect on employment is positive but small and highly heterogeneous; (iii) only a small part of residual heterogeneity is explained by moderating factors; (iv) selection bias tends to reflect preference for upholding prevalent hypotheses on the employment effects of process and product innovations; (v) country-specific effect-size estimates are related to labour market and product market regulation in six OECD countries in a U-shaped fashion; and (vi) OLS estimates reflect upward bias whereas those based on time-differenced or within estimators reflect a downward bias. Our findings point out to a range of data quality and modelling issues that should be addressed in future research.

Technology Adoption and Employment in Less Developed Countries: A Mixed-Method Systematic Review

“…This is reflected in relatively smaller effect-size estimates obtained from IV estimators that take account of endogeneity. This is in line with Ugur et al, (2016), who report relatively smaller estimates from IV estimators in the context of both developed and developing countries.…”

“…The third issue is about potential data dependence, which arises when a primary study that draws on a particular dataset reports multiple estimates; or when different studies use overlapping datasets (Doucouliagos and Laroche, 2009;Ugur et al, 2016). We address this issue by estimating (3b) and (4b) with different estimators: (i) pooled OLS that overlooks the study-specific fixed effects; (ii) fixed effect estimator in which we model the within-study data dependence as part of the study-specific fixed effects; and (iii) hierarchical model estimators that take into account between-and within-study dependence.…”

“…The latter issue, however, can be addressed by augmenting the meta-regression model of (3b) with covariates that capture the sources of heterogeneity. This is accomplished by estimating a multivariate meta-regression model (MVMRM) discussed in Stanley (2008), Stanley and Doucouliagos (2012), and Ugur et al (2016) among others. The MVMRM can be stated as follows:…”

“…A recent meta-analysis (Ugur et al, 2016) synthesizes the empirical evidence from a subset of the literature that estimates a derived labour demand model (DLDM) of technological change and employment. This study reports that the extent of betweenstudy heterogeneity is high (over 75%).…”

How Does Technology Startups Increase Innovative Performance? The Study of Technology Startups on Innovation Focusing on Employment Change in Korea

“…Thus, this study suggests that the concerns of unemployment and insecure labor forces by environment changes could be resolved by enhancing technology startup companies. This result is similar to the discussion of Aldieri and Vinci, arguing that economic crisis could be an opportunity for all firms to invest more in innovation to improve their competitiveness, develop new external knowledge, and eventually benefit all workers [47].…”