References

4. The following regression was estimated: ln(V AP W )f,c = αc + βs + γ ∗ Tf,c + ρ ∗ Xf,c + vf,c, where αc and βs are country and sector fixed effects, Tf,c is a vector of firm-level technology measures, and Xf,c is a vector of controls that includes the observable variables discussed plus 12 dummies for the sectors for which the sample includes data on sector-specific technologies and other services. 5. Caselli (2005) uses purchasing power parity (PPP) adjustments to compute sectoral productivity.

This may induce additional discrepancies in cross-country productivity gaps across sectors if the

PPP price index differs more across countries in agriculture than in nonagricultural sectors. 6. This does not necessarily mean that these technologies are biased toward unskilled workers, given that the results could be driven by the growth effect. Yet, evidence in the literature suggests that technologies such as online platforms used for export sales can lead to reduction in the wage skill premium (Cruz, Milet, and Olarreaga 2020). 7. There is, however, significant heterogeneity across countries. In Senegal, for example, there is a positive and strong correlation between technology sophistication and changes in the share of low-skilled workers. 8. The results do not claim any causal relationship, given that the analysis is unable to control for unobservable characteristics for workers and firms and the fact that more productive or higherability workers self-select into firms that use more advanced technologies.

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