18,511 research outputs found
Disentangling the Demand-enhancing Effect and Trade-cost Effect of Technical Measures in Agricultural Trade among OECD countries
Domestic technical measures such as SPS and TBTs can enhance import demand via information disclosure and quality improvement, or hamper foreign export supply via imposing sizeable compliance costs, or both. The traditional gravity equation model estimates the net effect of these measures on international trade with a loss of useful inference on separate effects. We stipulate a generalized gravity equation model to disentangle the two effects. We apply the augmented approach to agricultural trade among OECD countries in 2004. We find that technical measures in agriculture often jointly enhance import demand and hinder export supply with the net effect of promoting the propensity to trade. Further disaggregated data analysis reveals heterogeneity across sectors in terms of net effects of technical measures, despite common demand-enhancing and supply-hindering effects. These measures in the net decrease the probability of intra-OECD trade in dairy products, whereas they increase that of intra-OECD trade in cereal preparations.sanitary and phytosanitary, SPS, technical measures, NTM, TBT, standards, gravity equation, protectionism, OECD, International Relations/Trade,
Auditing Search Engines for Differential Satisfaction Across Demographics
Many online services, such as search engines, social media platforms, and
digital marketplaces, are advertised as being available to any user, regardless
of their age, gender, or other demographic factors. However, there are growing
concerns that these services may systematically underserve some groups of
users. In this paper, we present a framework for internally auditing such
services for differences in user satisfaction across demographic groups, using
search engines as a case study. We first explain the pitfalls of na\"ively
comparing the behavioral metrics that are commonly used to evaluate search
engines. We then propose three methods for measuring latent differences in user
satisfaction from observed differences in evaluation metrics. To develop these
methods, we drew on ideas from the causal inference literature and the
multilevel modeling literature. Our framework is broadly applicable to other
online services, and provides general insight into interpreting their
evaluation metrics.Comment: 8 pages Accepted at WWW 201
How algorithmic popularity bias hinders or promotes quality
Algorithms that favor popular items are used to help us select among many
choices, from engaging articles on a social media news feed to songs and books
that others have purchased, and from top-raked search engine results to
highly-cited scientific papers. The goal of these algorithms is to identify
high-quality items such as reliable news, beautiful movies, prestigious
information sources, and important discoveries --- in short, high-quality
content should rank at the top. Prior work has shown that choosing what is
popular may amplify random fluctuations and ultimately lead to sub-optimal
rankings. Nonetheless, it is often assumed that recommending what is popular
will help high-quality content "bubble up" in practice. Here we identify the
conditions in which popularity may be a viable proxy for quality content by
studying a simple model of cultural market endowed with an intrinsic notion of
quality. A parameter representing the cognitive cost of exploration controls
the critical trade-off between quality and popularity. We find a regime of
intermediate exploration cost where an optimal balance exists, such that
choosing what is popular actually promotes high-quality items to the top.
Outside of these limits, however, popularity bias is more likely to hinder
quality. These findings clarify the effects of algorithmic popularity bias on
quality outcomes, and may inform the design of more principled mechanisms for
techno-social cultural markets
Information is not a Virus, and Other Consequences of Human Cognitive Limits
The many decisions people make about what to pay attention to online shape
the spread of information in online social networks. Due to the constraints of
available time and cognitive resources, the ease of discovery strongly impacts
how people allocate their attention to social media content. As a consequence,
the position of information in an individual's social feed, as well as explicit
social signals about its popularity, determine whether it will be seen, and the
likelihood that it will be shared with followers. Accounting for these
cognitive limits simplifies mechanics of information diffusion in online social
networks and explains puzzling empirical observations: (i) information
generally fails to spread in social media and (ii) highly connected people are
less likely to re-share information. Studies of information diffusion on
different social media platforms reviewed here suggest that the interplay
between human cognitive limits and network structure differentiates the spread
of information from other social contagions, such as the spread of a virus
through a population.Comment: accepted for publication in Future Interne
Disentangling agglomeration and network externalities : a conceptual typology
Agglomeration and network externalities are fuzzy concepts. When different meanings are (un)intentionally juxtaposed in analyses of the agglomeration/network externalities-menagerie, researchers may reach inaccurate conclusions about how they interlock. Both externality types can be analytically combined, but only when one adopts a coherent approach to their conceptualization and operationalization, to which end we provide a combinatorial typology. We illustrate the typology by applying a state-of-the-art bipartite network projection detailing the presence of globalized producer services firms in cities in 2012. This leads to two one-mode graphs that can be validly interpreted as topological renderings of agglomeration and network externalities
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