We formulate a model of social interactions and misinferences by agents who neglect assortativity in their society, mistakenly believing that they interact with a representative sample of the population. A key component of our approach is the interplay between this bias and agents’ strategic incentives. We highlight a mechanism through which assortativity neglect, combined with strategic complementarities in agents’ behavior, drives up action dispersion in society (e.g., socioeconomic disparities in education investment). We also show how the combination of assortativity neglect and strategic incentives may help to explain empirically documented misperceptions of income inequality and political attitude polarization.
We formulate a model of social interactions and misinferences by agents who neglect assortativity in their society, mistakenly believing that they interact with a representative sample of the population. A key component of our approach is the interplay between this bias and agents’ strategic incentives. We highlight a mechanism through which assortativity neglect, combined with strategic complementarities in agents’ behavior, drives up action dispersion in society (e.g., socioeconomic disparities in education investment). We also suggest that the combination of assortativity neglect and strategic incentives may be relevant in understanding empirically documented misperceptions of income inequality and political attitude polarization.
In many labor markets, e.g., for lawyers, consultants, MBA students, and professional sport players, workers get offered and sign long-term contracts even though waiting could reveal significant information about their capabilities. This phenomenon is called unraveling. We examine the link between wage bargaining and unraveling. Two firms, an incumbent and an entrant, compete to hire a worker of unknown talent. Informational frictions prevent the incumbent from always observing the entrant’s arrival, inducing unraveling in all equilibria. We analyze the extent of unraveling, surplus shares, the average talent of employed workers, and the distribution of wages within and across firms.
We study a canonical model of reputation between a long-run player and a sequence of short-run opponents, in which the long-run player is privately informed about an uncertain state that determines the monitoring structure in the reputation game. The long-run player plays a stage-game repeatedly against a sequence of short-run opponents. We present necessary and sufficient conditions (on the monitoring structure and the type space) to obtain reputation building in this setting. Specifically, in contrast to the previous literature, with only stationary commitment types, reputation building is generally not possible and highly sensitive to the inclusion of other commitment types. However, with the inclusion of appropriate dynamic commitment types, reputation building can again be sustained while maintaining robustness to the inclusion of other arbitrary types.
Motivated by the rise of social media, we build a model studying the effect of an economy’s potential for social learning on the adoption of innovations of uncertain quality. Provided consumers are forward-looking (i.e., recognize the value of waiting for information), equilibrium dynamics depend non-trivially on qualitative and quantitative features of the informational environment. We identify informational environments that are subject to a saturation effect, whereby increased opportunities for social learning can slow down adoption and learning and do not increase consumer welfare. We also suggest a novel, purely informational explanation for different commonly observed adoption curves (S-shaped vs. concave).
We build a model studying the effect of an economy’s potential for social learning on the adoption of innovations of uncertain quality. Provided consumers are forward-looking (i.e., recognize the value of waiting for information), we show how quantitative and qualitative features of the learning environment affect observed adoption dynamics, welfare, and the speed of learning. Our analysis has two main implications. First, we identify environments that are subject to a “saturation effect,” whereby increased opportunities for social learning can slow down adoption and learning and do not increase consumer welfare, possibly even being harmful. Second, we show how differences in the learning environment translate into observable differences in adoption dynamics, suggesting a purely informational channel for two commonly documented adoption patterns—S-shaped and concave curves.