Based on the stylized facts, I develop a tractable dynamic model with heterogeneous financially constrained entrepreneurs and an imperfectly competitive banking sector. bank concentration, together with the bank capital, have been rising over the last thirty years. “Bank Concentration, Bank Capital, and Misallocation.” SSRN VersionĪbstract: U.S. Present at LSE CFM WiP seminar 2022, MMF 2022, PhD Macro Workshop Xiamen University 2022, China Economics Annual Conference 2022 (Scheduled), SWFA Conference 2023, CES North American Conference (Rising Star Session) 2023, AMES 2023 (Scheduled). I discuss how efficiency and stability can be enhanced simultaneously. support the model predictions: first, the relationship between bank concentration and loan rate is non-monotonic second, the effect of bank concentration on the loan rate is positive when the bank capital ratio is low. Two pieces of micro-level evidence in the U.S. The two mechanisms also jointly establish a non-monotonic relationship between bank concentration and allocative efficiency. Considering the risk shifting mechanism and the non-binding capital constraint, the model suggests that there is non-monotonic relationship between bank concentration and the loan rate. To explain the equilibrium characterization, I propose two mechanisms, a net margin mechanism and a risk shifting mechanism, whose direction depends on banks’ optimal decisions regarding loan quantity and the accumulation of excess bank capital. When the bank capital ratio exceeds the minimum requirement, reducing bank concentration leads to more entrepreneurs’ risk taking otherwise, the concentration-risk relationship is ambiguous. Taken together, the five papers offer diversified perspectives for both understanding and critically assessing emergent forms of datafied living.“Clarifying the Relationship Between Bank Concentration and Risks: Role of Bank Capital.” ( Job Market Paper) SSRN VersionĪbstract: How does bank capital affect the relationship between bank concentration and risk taking? I develop a tractable dynamic model with heterogeneous financially constrained entrepreneurs and an imperfectly competitive banking sector. Finally, Nick Couldry, Andreas Hepp and Jun Yu (LSE, UK, and University of Bremen, Germany) reflect the different imaginations of datafied living: on the one hand, the active imagination of pioneer communities (the Maker and Quantifed Self movements) and on the other hand, the repressed imagination of the facts of data collection in public ‘big data’ discourse. Andrew Iliadis (Temple University, USA) investigates ‘data forging’ to provide a critical assessment of ‘datasmith’ ontologies, ontologists, and ontology-making practices. Examining the Chinese Sesame Credit – one of the most prominent prototypes of its sort – Alison Hearn (University of Western Ontario, Canada) discusses the potential effects of living with credit scoring. The second paper presented by Göran Bolin (Södertörn University, Sweden) reflects on how the deeper penetration of algorithmically generated metrics into our life-worlds produces a new environment in which we live. In the first paper, Joseph Turow (University of Pennsylvania, USA) analyzes how the multifaceted retailing activities are reshaping the ways companies construct shoppers, and creating a new environment of discrimination through which shoppers will be purchasing products. More specifically, we will discuss five different dimensions of datafied living: shopping, the metricated mindset, credit scoring, data forging and imaginations of datafied living in times of deep mediatization. Referring to such examples, the panel will reflect on datafied living from multiple perspectives that each take a critical point of view, so as to get a sense of this transformation’s complexity. There are already many examples for this in everyday life. Therefore, datafied living means that everyday practices are related to data in a constitutive way. In times of deep mediatization (Couldry / Hepp 2017), ‘living’ is deeply entangled with digital media and their infrastructures, which continuously produce, assess and communicate data back and forth. Investigating ‘living’ entails not focusing on a single practice of media use but rather researching the range of everyday practices overall. This becomes possible as more and more media and media business models rely on algorithmic processing of data extracted from everyday life Besides ‘tools’ of communication, digital devices and platforms also become generators of data. Datafication means the representation of social life through computerized data (Schäfer & van Es, 2017 van Dijck, 2014). Remove from Personal Schedule Datafied Living: The Everyday of Datafication Sun, May 27, 15:30 to 16:45, Hilton Old Town, Floor: M, Mozart Iĭatafied living is an emerging new ‘way of life’ (Williams, 1971) that is based on datafication.
0 Comments
Leave a Reply. |