The microeconomics of data – a survey

Abstract

In recent years, academia, institutions, and policymakers have been focusing their attention on the impact of data in digital markets. The economic literature that explicitly models data and their collection as strategic variables is growing, but most studies focus on distinct settings with specific data uses. This survey aims to organise this literature to extract general insights that hold across different models and assumptions. To do so, I identify three classes of models according to the way they model data collection. I find that each class is characterised by a specific impact of data on the market outcomes, regardless of the specific data use. First, when firms obtain data without strategic interactions, their use has a pro-competitive effect on the market. However, firms fail to fully internalise the data externalities, leading to data overuse and, in turn, privacy concerns. Second, when firms collect data from their interaction with consumers, data can facilitate market tipping, especially if firms are asymmetric in their starting positions. Third, when firms acquire data from data intermediaries, data are strategically sold to temper competition in the downstream market, allowing intermediaries to extract most of the surplus at the expense of firms and consumers. These general insights can facilitate future research and help policymakers to have a more general understanding of the competitive effects of data, depending on the situation at hand.

How Does Digital Economy Affect Rural Revitalization? The Mediating Effect of Industrial Upgrading

“…The existence of mediation effect of industrial upgrading requires that α 1 is significant in Equation (1). The following situations are considered: (1) If β 1 and γ 2 are significant, but γ 1 is not significant, the industrial upgrading is a complete mediating effect.…”

“…At the same time, the constructed instrumental variable will closely relate to the digital economy, becausethe lag term of the digital economy relates to the digital economy, considering that the digital economy develops continuously. Following Yi and Zhou (2018), this paper constructed the instrumental variable and used the TSLS method to estimate the results, shown in Column (1). The coefficient of the digital economy is 0.68 and is significant at the 5% significance level, implying the promotion effect of the digital economy on rural revitalization.…”

“…In recent years, the digital economy has attracted increasing attention from academia, institutions, and policymakers [1,2]. Information processing technologies are becoming increasingly mature, strengthening human data analysis capabilities.…”