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Navigating Generative AI: Bangladeshi Journalists’ Insights and Challenges
This study examines the adoption of Generative Artificial Intelligence among Bangladeshi journalists, revealing their awareness, acceptance, and usage patterns despite limited institutional support. Through interviews with 23 journalists, it highlights their reliance on AI tools like ChatGPT for efficiency and improved content. The research underscores similarities in concerns regarding AI’s implications for journalism, such as accuracy and job security, while suggesting modifications to the Unified Theory of Acceptance and Use of Technology specific to non-Western contexts.
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Exploring Market Competition’s Impact on Poverty Dynamics
This paper investigates the intricate relationship between market competition and poverty dynamics, utilizing data from 48 countries over 27 years. Employing a functional econometric framework, the study reveals that while stronger competition initially increased poverty, its negative effects weakened post-2010 as economies adapted. The paper highlights the significance of market development levels, showing pro-poor benefits in low and medium financial development scenarios, while significant adverse effects exist in high-development contexts. Results underscore the need for nuanced, dynamic analysis of competition and poverty interactions.
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Enhancing Web Crawling with Scalable Algorithms and Noisy Signals
A Scalable Crawling Algorithm Utilizing Noisy Change-Indicating Signals By Róbert Busa-Fekete, Julian Zimmert, András György, Linhai Qiu, Tzu-wei Sung, Hao Shen, Hyomin Choi, Sharmila Subramaniam, Li Xiao DOI https://doi.org/10.48550/arXiv.2502.02430 Abstract Web refresh crawling is the problem of keeping a cache of web pages fresh, that is, having the most recent copy available when a page is requested, given a limited bandwidth available to the crawler. Under the assumption that the change and request events, resp., to each web page follow independent Poisson processes, the optimal scheduling policy was derived by Azar et al In this paper, we study an extension…
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Understanding Climate Risk: Its Impact on Corporate Value and Asset Management
Climate Risk and Corporate Value: Evidence from Temperature Bins and Panel Regression By Bo Wu DOI https://doi.org/10.48550/arXiv.2503.14233 Abstract In empirical research, this article uses daily climate data provided by the National Oceanic and Atmospheric Administration (NOAA) of the United States to construct a temperature box with a range of 5°C, focusing on analyzing the impact of extreme high temperatures (>30°C) and extreme low temperatures (≤-10°C) on the asset value of enterprises. The results based on panel regression model show that extreme high and low temperatures can significantly reduce the asset value of enterprises. In the robustness test, this article used…
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Understanding Behavioral Biases in Machine Learning Predictions of Corporate Earnings
Behavioral Machine Learning? Computer Predictions of Corporate Earnings also Overreact By Murray Z. Frank, Jing Gao, Keer Yang DOI https://doi.org/10.48550/arXiv.2303.16158 Abstract Machine learning algorithms are known to outperform human analysts in predicting corporate earnings, leading to their rapid adoption. However, we show that leading methods (XGBoost, neural nets, ChatGPT) systematically overreact to news. The overreaction is primarily due to biases in the training data and we show that it cannot be eliminated without compromising accuracy. Analysts with machine learning training overreact much less than do traditional analysts. We provide a model showing that there is a trade off between predictive…
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Understanding Public Support for Environmental Regulations: Ideology vs. Knowledge
Public Support for Environmental Regulation: When Ideology Trumps Knowledge By Markus Dertwinkel-Kalt, Max R. P. Grossmann DOI https://doi.org/10.48550/arXiv.2503.10821 Abstract When environmental regulations are unpopular, policymakers often attribute resistance to information frictions and poor communication. We test this idea in the context of a major climate policy: Germany’s Heating Law of 2023, which mandates the phase-out of fossil fuel heating. Through a survey experiment with property owners, we examine whether providing comprehensive information about the regulation’s costs, requirements, and timeline affects adoption decisions and policy support. Despite successfully increasing factual knowledge, information provision has no significant effect on intended technology adoption,…
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Economic Impact of China’s Tariff on U.S. Soybean Farmers: An Analysis
Economic Impact of China’s Retaliatory Soybean Tariff on U.S. Soybean Farmers By Xinyu Li DOI https://doi.org/10.48550/arXiv.2503.10715 Abstract This paper analyzes the economic impact of China’s retaliatory soybean tariff on U.S. soybean farmers using advanced econometric methods and comprehensive datasets including USDA reports, trade data, and historical price movements. Theanalysis employs a Structural Vector Autoregression (SVAR), a Difference-in-Differences (DiD) estimation, and a Dynamic Stochastic General Equilibrium (DSGE) model, revealing the impacts of China’s retaliatory tariff on soybean prices, exports, farm incomes, and acreage decisions. U.S. policy responses, including direct subsidies and market diversification strategies, are also evaluated. Overview The paper studies…
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Exploring Consumer Advertising Response: Symmetries, Scaling Laws, and Phase Transitions
Understanding how consumers respond to business advertising efforts is essential for optimizing marketing investment. This research introduces a new modeling approach based on the concepts of symmetries and scaling laws in physics to describe consumer response to advertising dynamics. Drawing from mathematical frameworks used in physics and social sciences, we propose a model that accounts for a key aspect: the saturation effect. The model is validated against commonly used models, including the Michaelis-Menten and Hill equations, showing its ability to better capture non linearities in advertising effects. We introduce new key parameters like Marketing Sensitivity, Response Sensitivity, and Behavioral Sensitivity,…
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Understanding Inequality in Capitalist Economies: Insights from the Social Architecture Model
Imagine a classroom where every student begins with the same amount of candy. As the teacher allows trading based on simple rules (some students become “bosses” and start collecting more candy from others), soon a few students end up with large piles of candy while most have much less. This simulation, though simplified, reflects the real-world dynamics of wealth concentration and inequality that the SA model aims to capture.
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Enhancing Armed Conflict Fatality Forecasts: Bin-Conditional Conformal Prediction
Imagine you are forecasting the number of injuries in different regions after a natural disaster. Traditional models might predict “0 injuries” for most regions but miss the possibility of a few regions experiencing hundreds of injuries. With BCCP, you could divide the regions into groups (bins) based on factors like population density or previous injury counts. Then, for each group, you calculate a range that reliably includes the true injury count 90% of the time. This way, decision-makers can see not just an average prediction but a reliable range tailored to each specific group.