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.
M. Ugur, Sefa Awaworyi Churchill, and Solomon, E., “Technological Innovation and Employment in Derived Labour Demand Models: A Hierarchical Meta-Regression Analysis”, Journal of Economic Surveys, vol. 32, pp. 50–82, 2018.