472 papers
The "Econ — Gn" category on Gist.Science explores the intersection of economics and general science, covering how empirical data and scientific methods shape our understanding of markets, policy, and human behavior. These studies often bridge gaps between abstract theory and real-world application, offering fresh perspectives on everything from climate change economics to the mechanics of digital currency.
We pull every new preprint in this field directly from arXiv, ensuring you see the latest research as soon as it is published. Our team processes each paper to provide both accessible plain-language overviews for general readers and detailed technical summaries for specialists, making complex findings easier to digest. Below are the latest papers in this category, curated and summarized for your convenience.
This paper analyzes a novel dataset on internet display advertising to show that switching from second-price to first-price auctions causes an immediate, substantial increase in revenue due to insufficient bid shading, which gradually diminishes over time as bidders learn to adjust their strategies.
This paper employs a game-theoretical Cournot model calibrated to Denmark's future 100% renewable market to demonstrate that while large-scale energy storage enhances system stability and welfare, concentrated ownership can induce strategic withholding of flexibility, leading to higher prices and reduced efficiency.
This paper utilizes a dual labor market DSGE model to demonstrate that declining AI prices can either expand formal employment or drive workers into informality, depending on whether AI acts as a complement to or a substitute for formal labor.
This paper argues that the international transmission of U.S. monetary policy to emerging markets is destabilized not by algorithmic intermediation itself, but by the similarity of models across funds which causes correlated errors and herding, suggesting that policy should prioritize preserving model diversity over limiting non-bank intermediation.
Using Patreon earnings data, this paper demonstrates that platform attention algorithms drive a "rich-get-richer" dynamic where earnings follow a highly concentrated Pareto distribution (), with algorithmic shifts disproportionately harming the creator middle class and causing inequality across different social media platforms to converge toward increasingly heavy-tailed distributions.
This study demonstrates that while large language models can be equally effective at convincing people to believe or disbelieve conspiracy theories depending on their instructions, implementing accurate information guardrails and leveraging specific model capabilities can significantly mitigate the risk of AI-driven misinformation.
This paper argues that in South Korea's passenger vehicle market, subsidizing intermediate hybrid electric vehicles (HEVs) is currently more effective at reducing total greenhouse gas emissions than subsidizing the cleanest battery electric vehicles (BEVs), because HEVs induce greater substitution away from high-emission conventional cars until electricity generation becomes sufficiently decarbonized.
Based on a conjoint survey experiment across seven countries, this paper reveals that citizens globally strongly prefer regulating AI with a focus on safety, public oversight, and international cooperation, highlighting a significant misalignment between these public preferences and current dominant regulatory approaches.
Based on a June 2026 survey of nearly 2,000 US AI users, this paper reveals that while the market is dominated by ChatGPT and Gemini, task allocation is highly specialized by platform, trust is primarily built through direct usage rather than reputation, and users prioritize human-free data handling over model privacy, with willingness to pay increasing for sensitive tasks.
This study empirically demonstrates that Google's transition from Manifest V2 to V3 has not significantly reduced the effectiveness of popular ad blockers in blocking ads and trackers, with some instances even showing slight improvements in privacy protection.