The role of luck in the success of social media influencers.

Motivation: Social media platforms centered around content creators (CCs) faced rapid growth in the past decade. Currently, millions of CCs make livable incomes through platforms such as YouTube, TikTok, and Instagram. As such, similarly to the job market, it is important to ensure the success and income (usually related to the follower counts) of CCs reflect the quality of their work. Since quality cannot be observed directly, two other factors govern the network-formation process: (a) the visibility of CCs (resulted from, e.g., recommender systems and moderation processes) and (b) the decision-making process of seekers (i.e., of users focused on finding CCs). Prior virtual experiments and empirical work seem contradictory regarding fairness: While the first suggests the expected number of followers of CCs reflects their quality, the second says that quality does not perfectly predict success. Results: Our paper extends prior models in order to bridge this gap between theoretical and empirical work. We (a) define a parameterized recommendation process which allocates visibility based on popularity biases, (b) define two metrics of individual fairness (ex-ante and ex-post), and (c) define a metric for seeker satisfaction. Through an analytical approach we show our process is an absorbing Markov Chain where exploring only the most popular CCs leads to lower expected times to absorption but higher chances of unfairness for CCs. While increasing the exploration helps, doing so only guarantees fair outcomes for the highest (and lowest) quality CC. Simulations revealed that CCs and seekers prefer different algorithmic designs: CCs generally have higher chances of fairness with anti-popularity biased recommendation processes, while seekers are more satisfied with popularity-biased recommendations. Altogether, our results suggest that while the exploration of low-popularity CCs is needed to improve fairness, platforms might not have the incentive to do so and such interventions do not entirely prevent unfair outcomes.

Field-specific ability beliefs as an explanation for gender differences in academics' career trajectories: Evidence from public profiles on ORCID.Org.

Academic fields exhibit substantial levels of gender segregation. Here, we investigated differences in field-specific ability beliefs (FABs) as an explanation for this phenomenon. FABs may contribute to gender segregation to the extent that they portray success as depending on "brilliance" (i.e., exceptional intellectual ability), which is a trait culturally associated with men more than women. Although prior work has documented a relation between academic fields' FABs and their gender composition, it is still unclear what the underlying dynamics are that give rise to gender imbalances across academia as a function of FABs. To provide insight into this issue, we custom-built a new data set by combining information from the author-tracking service Open Researcher and Contributor ID (ORCID) with information from a survey of U.S. academics across 30 fields. Using this expansive longitudinal data set (Ns = 86,879-364,355), we found that women were underrepresented among those who enter fields with brilliance-oriented FABs and overrepresented among those who exit these fields. We also found that FABs' association with women's transitions across academic fields was substantially stronger than their association with men's transitions. With respect to mechanisms, FABs' association with gender segregation was partially explained by the fact that women encounter more prejudice in fields with brilliance-oriented FABs. With its focus on the dynamic patterns shaping segregation and its broad scope in terms of geography, career stage, and historical time, this research makes an important contribution toward understanding the factors driving gender segregation in academia. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Understanding Inequalities in Ride-Hailing Services Through Simulations.

Despite the potential of ride-hailing services to democratize the labor market, they are often accused of fostering unfair working conditions and low wages. This paper investigates the effect of algorithm design decisions on wage inequality in ride-hailing platforms. We create a simplified city environment where taxis serve passengers to emulate a working week in a worker's life. Our simulation approach overcomes the difficulties stemming from both the complexity of transportation systems and the lack of data and algorithmic transparency. We calibrate the model based on empirical data, including conditions about locations of drivers and passengers, traffic, the layout of the city, and the algorithm that matches requests with drivers. Our results show that small changes in the system parameters can cause large deviations in the income distributions of drivers, leading to an unpredictable system that often distributes vastly different incomes to identically performing drivers. As suggested by recent studies about feedback loops in algorithmic systems, these short-term income differences may result in enforced and long-term wage gaps.