Journal of Economics & Management Strategy最新文献

I study how heterogeneity in competition can affect the demand for skilled labor in a way that generates a dispersion in productivity across otherwise similar firms. This is explored in the setting of professional basketball where heterogeneity in competition is easily measured. I develop and estimate a matching model with externalities, where the value of a match between a firm and worker depends on the entire allocation of matches. I find that competition has a significant effect on hiring decisions in the National Basketball Association, resulting in a clustering of talent among rivals. A counterfactual shows that competition in the league's playoff format explains 17.59% of the widely observed gap in talent between the league's two conferences.

This paper contributes to the literature that postulates a relationship between income inequality and the relative importance of “market” and “organization” in the direction of resources. The paper emphasizes that both are endogenous; therefore, the empirical associations observed in the empirical data between inequality measures and production organization variables cannot be interpreted as indicative of causal relationships. The paper solves for the composition and size of occupational groups, the distribution of firm size, and the distribution of income as market equilibrium outcomes of an occupational choice economy, and performs a comparative static analysis. We find that the interaction between cross-economy differences in the distribution of general skills in the labor force and the loss of control in the supervision of workers by managers can explain the empirical regularities observed in the relationship between the organization of production (distribution of firm size) and income inequality (distribution of labor income). This explanation of the empirical regularities differs from others proposed in the literature, such as those based on institutional constraints or the market power of firms.

We study an agency model with vertical hierarchy—the principal, the prime-agent and the subagent. The principal faces a project that needs both agents' services. Due to costly communication, the principal receives a report only from the prime-agent, who receives a report from the subagent. The principal can directly incentivize each agent by setting individual transfers (insourcing), or sets only one overall transfer to an independent organization in which the prime-agent hires the subagent (outsourcing). We show that insourcing is always optimal when the principal can perfectly process the prime-agent's report. When the principal's information process is limited, however, outsourcing can be the prevailing mode of operation. In addition, insourcing under limited information process is prone to collusion between the agents, whereas no possibility of collusion arises with outsourcing.

This study characterizes role assignments in maximizing a group's winning probability under the influence of the complementarity of group members' efforts in a group contest, in contrast to prize and multiple resource allocations. We use a constant elasticity of substitution effort aggregator function to parameterize the complementarity. While the prize and resource allocation rules depend on the complementarity, the assignment rule does not when multiple roles are assignable to a single group member: All roles are assigned only to the most productive group member. However, when only a single role per group member is assignable, the assignment rule depends on the complementarity: Roles from greater to less importance are assigned to group members in descending order of their productivity under strong complementarity; only the most important role is assigned to the most productive group member and the others have no effect under weak complementarity.

In this essay, we identify several themes of the digital business transformation, with a particular focus on the economy-wide impacts of artificial intelligence and digital platforms. In doing so, we highlight specific industries, beyond just the high-profile “Big Tech” firms, where the digital business revolution is having, or promises to have, significant impact. The papers in this special issue (flagged with bold font below) provide a deeper analysis of the themes and applications we touch on here.

We study the early adoption and diffusion of five artificial intelligence (AI)-related technologies (automated-guided vehicles, machine learning, machine vision, natural language processing, and voice recognition) as documented in the 2018 Annual Business Survey of 850,000 firms across the United States. We find that fewer than 6% of firms used any of the AI-related technologies we measure, though most very large firms reported at least some AI use. Weighted by employment, average adoption was just over 18%. AI use in production, while varying considerably by industry, was found in every sector of the economy and clustered with emerging technologies, such as cloud computing and robotics. Among dynamic young firms, AI use was highest alongside more-educated, more-experienced, and younger owners, including owners motivated by bringing new ideas to market or helping the community. AI adoption was also more common in startups displaying indicators of high-growth entrepreneurship, including venture capital funding, recent product and process innovation, and growth-oriented business strategies. Early AI adoption was far from evenly distributed: a handful of “superstar” cities and emerging hubs led startups' adoption of AI. These patterns of early AI use foreshadow economic and social impacts far beyond this limited initial diffusion, with the possibility of a growing “AI divide” if early patterns persist.