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To Rest on One´s Laurels: Effectiveness and Sustainability of Status Orders

Mario Molina
Laboratory of Digital and Computational Demography, November 16, 2022

Mario Molina from the New York University Abu Dhabi compared the cooperative behavior of high-status individuals with similar others who are not high status.

Abstract

Individuals recognized and celebrated by their peers are generally more motivated to be productive and cooperative, turning social status into a critical organizational feature to incentivize individual behavior and build successful collective enterprises. A significant limitation of the studies on this topic is that they fail to show whether status attainment can produce sustainable performance and cooperation that can persist while status increases. As individuals climb the status ladder, a high social standing may create opportunities for distraction and complacency, rendering status hierarchies ineffective in producing long-term cooperation.

To tackle this puzzle, Mario Molina compares the cooperative behavior of high-status individuals with similar others who are not high status (i.e., their counterfactuals). He collected the data from a large Q&A online community comprising 130 million contributions and 235 million status changes from 16 million users. The findings uncover the benefits of status for collective efforts by showing that high status motivates cooperation among the most celebrated. Nonetheless, this positive effect declines over time, revealing that status orders cannot secure collective outcomes effectively in the long run. More generally, this investigation helps us understand the broader structural consequences of social rewards for cooperative behavior.

About

Mario D. Molina is a postdoctoral associate in the Social Science Division at New York University Abu Dhabi. Before joining NYUAD, he received his Ph.D. in Sociology from Cornell University (2020). His research lies at the interface of organizations, social networks, and computational social science. He broadly considers how organizational structures and network dynamics impact beliefs about unequal rewards and prosocial behaviors and the mechanisms that feedback to sustain or disrupt these social systems. His work builds on different computational and methodological skills, including experimental design, multilevel modeling, network models, machine learning, and natural language processing.