Optimal compensation scheme in networked organizations

I study the optimal compensation scheme in organizations in Shi (2020), in which agents connected in a network are endowed with the same ability, and work collectively to produce a team output. Connected agents can help neighbors to reduce the disutility of working. For any given network structure, the optimal compensation scheme exists and is unique. There are two effects determining the optimal compensation: (a) ``incentivizing the peripheral'' effect: the nodes with a smaller centrality have to enjoy a relatively higher pay to be incentivized to work harder, since they receive relatively less help; and (b) ``incentivizing the central'' effect: the nodes with a larger centrality have to enjoy a relatively higher pay to be incentivized to help others more, due to the externality of help. When helping efforts are relatively unimportant for the team output, same pay goes to agents in different network positions, because the the first and the second effect neutralizes each other. When helping efforts are relatively important, the pay varies with the position, because the second effect dominates. Due to these two conflicting effects, the relationship between the centrality and the optimal pay may not be monotonic