Digital technology is the representation of information in bits. This technology has reduced the cost of storage, computation, and transmission of data. Research on digital economics examines whether and how digital technology changes economic activity. In this review, we emphasize the reduction in five distinct economic costs associated with digital economic activity: search costs, replication costs, transportation costs, tracking costs, and verification costs.

在数字经济蓬勃发展的同时，中国劳动收入份额出现止降转升的新变化。本文基于沪深A股上市公司微观数据，考察了数字化对劳动收入份额的影响及其作用机制。研究发现：劳动收入份额随数字化程度的提升而呈向下的“倒U型”关系；机制检验表明，数字化程度对技能结构的“倒U型”影响是数字化影响劳动收入份额的重要路径；进一步研究发现，现阶段上市公司数字化程度处于“倒U型”曲线的左侧，在未来一段时间内数字化程度提升仍将有助于改善劳资分配关系；不同要素密集度、所有制的上市公司数字化程度对劳动收入份额的“倒U型”影响具有显著差异。为持续提升劳动收入份额，政府应继续加速推进与数字化相关的基础设施建设，加大对企业数字化转型的扶持力度；企业应定期组织职工参与数字化培训，推动劳动者技能结构升级。

In recent years, with the rapid development of new technologies such as big data, artificial intelligence and cloud computing, the digital economy has deeply integrated with the real economy. The digital economy has become a new wave, a new driving force and a new engine leading economic development. With the vigorous development of digital economy, China’s income distribution pattern has also seen new changes. In recent years, the labor income share has shown a new trend of turning from declining to rising, and the labor-capital distribution relationship has improved. Therefore, this paper puts forward two important questions: Is there a significant relationship between the new change in China’s labor income share in recent years and the improvement of the degree of digitalization? What channels does digitalization promote the increase of labor income share?This paper uses the micro level data of A-share listed companies in Shanghai and Shenzhen in China, adopts the method of combining text analysis and manual judgment, and constructs indicators to measure the degree of enterprise digitalization from the two dimensions of digital intangible assets and digital keywords. The research finds that the labor income share presents an “inverted U-shaped” downward relationship with the increase of digitization degree. The mechanism test shows that the “inverted U-shaped” effect of digitalization on skill structure is an important path for digitalization to affect labor income share. Further research shows that the digitalization degree of listed companies at the present stage is on the left side of the “inverted U-shaped” curve, and the improvement of digitalization degree will still help to improve labor distribution relations in the future. The “inverted U-shaped” effect of different factor intensities and ownership of listed companies on labor income share is significant. The innovation of this paper mainly includes the following two aspects: First, this paper finds that booming digitalization is promoting the increase of labor income share, which provides important evidence to explain the new change of the increase of labor income share in recent years. Moreover, it is expected that in the future period of time, the improvement of digitalization degree will continue to drive the rise of labor income share. Second, from the perspective of skill structure, this paper discusses the important mechanism of the influence of digitalization on the “inverted U-shaped” labor income share. Accelerating the upgrading of labor skill structure will help to continuously increase the labor income share, and provide feasible solutions for the continuous improvement of labor distribution relations. This paper recommends that the government should continue to accelerate the construction of digital-related infrastructure and increase the support for enterprises’ digital transformation. Enterprises should regularly organize employees to participate in digital training and promote the upgrading of workers’ skill structure.

We provide a systematic empirical study of the diffusion and adoption patterns of data-driven decision making (DDD) in the U.S. Using data collected by the Census Bureau for a large representative sample of manufacturing plants, we find that DDD rates nearly tripled (11%-30%) between 2005 and 2010. This rapid diffusion, along with results from a companion paper, are consistent with case-based evidence that DDD tends to be productivity-enhancing. Yet certain plants are significantly more likely to adopt than others. Key correlates of adoption are size, presence of potential complements such as information technology and educated workers, and firm learning.