Algorithms and their Affordances: How Crowdworkers Manage Algorithmic Scores in Online Labour Markets

On online labour platforms, algorithmic scores are used as indicators of freelancers' work quality and future performance. Recent studies underscore that, to achieve good scores and secure their presence on platforms, freelancers respond to algorithmic control in different ways. However, we argue, to fully understand how freelancers deal with algorithmic scores, we first need to investigate how they interpret scores and, more specifically, what scores can do for them, i.e., perceived algorithmic affordances and constraints. Our interviews and other qualitative data collected with knowledge intensive gig workers on a major platform allow us to explain how the perceived affordances of algorithms (i.e., barrier, individual visibility, self-extension, rule of the game) act as mechanisms that explain different behavioural and emotional responses over time. Our work contributes to the current debate on the positive and negative consequences of algorithmic work by portraying the fundamental role paid by the individual interpretation of algorithmic scores and by integrating the affordance perspective into our understanding of algorithmic work.     

Are covered bonds different from securitization bonds? A comparative analysis of credit spreads

This study compares credit spreads and pricing determinants of securitization vis-à-vis covered bonds. Our analysis reveals that although ratings are the most important pricing determinant for asset-backed securities (ABS) and mortgage-backed securities (MBS) investors place relatively more importance on contractual, macroeconomic and banks' characteristics rather than ratings in pricing covered bonds. We find evidence of a mispricing effect in structured finance markets: ABS and MBS have higher credit spreads than similarly rated public-covered bonds and mortgage-covered bonds and security prices reflect information beyond credit ratings. We find no evidence of borrowing costs affecting banks' choice between securitization and covered bonds.     

A story to sell: The influence of storytelling on consumers' purchasing behavior

Storytelling can arouse consumers' emotions and affect purchasing behavior through desires and attitudes. While the marketing literature discusses storytelling, there is a lack of consensus because of the diverse conceptual and operational definitions used. To untangle the complexities and consolidate the fragmented knowledge about storytelling in marketing, this research examines how the marketing literature has addressed the influence of storytelling on consumers' purchasing behavior. The findings aid in understanding how the topic has been discussed from a marketing perspective in consumer behavior studies. Through a systematic literature review using a bibliometric analysis, we demonstrate that the marketing literature features four strands about the uses of storytelling to influence consumers' purchasing behavior. First, storytelling stimulates the consumer's identification with the brand. Second, storytelling allows consumers to experience emotional value. Third, storytelling supports engagement behaviors. Finally, storytelling has a downside in that it also propagates harmful speech. This study concludes with a roadmap for future research about how storytelling impacts consumers' purchasing behavior.     

Rules versus discretion in Central Bank communication

We propose a theoretical framework to study Central Bank communication and to assess the benefits of commitment to a disclosure rule. Our model features a coordination environment, where agents have dispersed private information and their interests are not aligned with those of the Central Bank. Public information can lead to undesirable coordination among agents. We show that anything goes when the Central Bank cannot commit; both its most and least preferred outcomes can be supported in equilibrium, and so can anything in between. We find that commitment does not necessarily implement the Central Bank's most preferred outcome. However, the Central Bank can avoid the least desirable outcomes by choosing an information structure with only two messages that act as action recommendations for agents. Furthermore, our results suggest a higher correlation between fundamentals and outcomes under commitment.     

Profitability and size of newly established firms

International Entrepreneurship and Management Journal - Does size matter for new firms and do they need to be large to be profitable? From small to large firms, a wide range of arguments have...     

The entanglement of time and cost deviations in public projects

There is growing concern regarding cost and time overruns in public projects. However, despite the extensive literature on the subject, there is relatively little research on the impact of time overruns on cost overruns. This study assesses whether larger time deviations can lead to larger cost deviations. Our hypothesis is that a project with a time overrun is more likely to also have a cost overrun. The authors used a sample of 208 projects in Portugal, with data collected from the Portuguese Court of Auditors. Using the data, a number of econometric models were developed: Ordinary Least Squares, Generalized Linear Model, Tobit, and Probit. The instrumented variables and structural equation modelling techniques were applied to address potential endogeneity in the data. The analysis was controlled for factors such as political, governance, economic, and project variables. The results suggest that larger time deviations are associated with larger cost deviations. The inference is that projects that take longer to complete also tend to suffer from cost overruns.     

The state of Portuguese research in economics: 20 years after

Portuguese Economic Journal - In this paper we look at the state of Portuguese research in economics, with an emphasis on the 2000–2019 period. For this purpose we use three distinct data...     

Density forecasting with Bayesian Vector Autoregressive models under macroeconomic data uncertainty

Macroeconomic data are subject to data revisions. Yet, the usual way of generating real-time density forecasts from Bayesian Vector Autoregressive (BVAR) models makes no allowance for data uncertainty from future data revisions. We develop methods of allowing for data uncertainty when forecasting with BVAR models with stochastic volatility. First, the BVAR forecasting model is estimated on real-time vintages. Second, the BVAR model is jointly estimated with a model of data revisions such that forecasts are conditioned on estimates of the ‘true’ values. We find that this second method generally improves upon conventional practice for density forecasting, especially for the United States.