Digital transformation and wage distortion in R&D and innovation activities - Causal inference based on double machine learning

In the context of digital economy driven by innovation, examining the impact of digital transformation (DT) on wage distortion (WD) in R&D and innovation activities, termed WD-RDIA, alongside its underlying mechanisms, is critical for building a fair and efficient innovation ecosystem and achieving sustainable socio-economic development. Drawing on data from China's listed manufacturing enterprises from 2018 to 2023, this study applies a double machine learning model to rigorously assess how DT influences WD-RDIA and to investigate the mechanisms involved. The findings reveal that DT significantly reduces WD-RDIA, a conclusion that remains robust after controlling for endogeneity and conducting various robustness checks. Additionally, improved innovation conversion efficiency and enhanced governance capacity are two key channels through which DT alleviates WD-RDIA. Further, the effect of DT on mitigating WD-RDIA varies significantly across different types of enterprises. Specifically, high-tech enterprises exhibit a stronger effect than non-high-tech enterprises. By ownership type, private enterprises experience the greatest improvement, followed by state-owned enterprises, whereas foreign-funded enterprises show the weakest effect. Regarding industry characteristics, labor-intensive enterprises benefit the most, followed by technology-intensive enterprises, while capital-intensive enterprises show the least improvement. This study provides a new perspective on the role of DT in R&D and innovation and offers valuable policy insights for advancing digital development and addressing WD.