Reputation in Endogenous Production Teams

Team production analysis are usually carried in static frameworks where employees choose neither their teammates nor between working in a team or by themselves. This hypothesis does not reflect certain work environments. For example, academics are seldom forced to work as a team. They usually choose with whom they want to work, and once a project is completed they may part. In the industry, a manager at Jet Propulsion Laboratory states: "There is some say by team members on whom they want to work with. Regarding rewards, a good job by the team bestow the reputation for further jobs'' (Sherstyuk, 1998). This paper computes optimal work strategies when agents either work in a team or by themselves. Agents of different age and reputation are randomly matched. Matched agents, but not employers, observe each other's abilities. Agents' abilities and production are stochastic, and wages equal the conditional probability of being of high-ability (i.e an agent's reputation). Given that a teammate's decision to work or not in a team (control variable), affects the other agent's current and future utility and reputation (the state variable), this problem is a dynamic game. We focus on Markov strategies which are subgame perfect. The nature of the game does not allow for closed form solutions and we resort to numerical methods. Results show that a worker opts in team provided her teammate's reputation does not penalize him. If working in a team damages an agent's reputation she opts out unless her teammate wishes and can compensate him. For instance, a high-ability young worker chooses to work with a high-ability adult when the latter's reputation is sufficiently high compared with the unconditional probability that she be of the high ability. Interestingly, a young agent who {chooses} to work by himself enjoys a higher utility than when she is compelled to do so. This result arises as agents value the option of forming a team when adult. In other words workers derive non-negative utility from the team option which affect the conditional probability that they be identified of high ability.Dynamic games, Learning models