This study analyses the impact of the use of digital technology on economic growth for 39 African countries from 2012 to 2016. This analysis applies a system GMM estimator to understand the extent to which the usage of digital technology facilitates growth using a measure of digitalisation from the Networked Readiness Index. Unlike previous research, we distinguish between the impact of individual, business, and government ICT usage on growth and show that only individual usage has a positive impact. Furthermore, a disaggregated analysis of the types of usage reveals that two indicators, social media and the importance of ICTs to government vision, are significant for growth.

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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.

Business research and development (R&D) expenditures in the UK is low by international standards. To encourage investment, the UK government has been providing both direct and indirect support. The aim of this paper is to address four inter-related and policy-relevant questions: (i) what do we know about the UK regime for direct grant schemes? (ii) is the UK subsidy complementary or substitute for privately-funded R&D? (iii) does the UK funding regime differ from the EU regime in terms of selection and effect? (iv) does the scope for complementarity/additionality differ between different rates of funding? We address these questions using a rich dataset for more than 44 thousand UK firms from 1998-2012; and a range of treatment-effect estimators with and without control for selection. We report that the EU selection regime is more likely to support firms with long-term R&D plans. In addition, the UK subsidy is not associated with additionality, but the EU subsidy is. Finally, leverage estimations indicate that targeting a particular rate of subsidy is not likely to make a difference to private R&D effort in the UK subsidy case; but an increase in EU subsidy intensity does create leverage among firms that fall between the median and 75 th percentile of the subsidy intensity distribution.