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We investigate whether artificial intelligence can address the peer review crisis in economics by analyzing 27,090 evaluations of 9,030 unique submissions using a large language model (LLM). The experiment systematically varies author characteristics (e.g., affiliation, reputation, gender) and publication quality (e.g., top-tier, mid-tier, low-tier, AI generated papers). The results indicate that LLMs effectively distinguish paper quality but exhibit biases favoring prominent institutions, male authors, and renowned economists. Additionally, LLMs struggle to differentiate high-quality AI-generated papers from genuine top-tier submissions. While LLMs offer efficiency gains, their susceptibility to bias necessitates cautious integration and hybrid peer review models to balance equity and accuracy., Comment: 72 pages

Surnames often convey implicit markers of social status, wealth, and lineage, shaping perceptions in ways that can perpetuate systemic biases and intergenerational inequality. This study is the first of its kind to investigate whether and how surnames influence AI-driven decision-making, focusing on their effects across key areas such as hiring recommendations, leadership appointments, and loan approvals. Using 72,000 evaluations of 600 surnames from the United States and Thailand, two countries with distinct sociohistorical contexts and surname conventions, we classify names into four categories: Rich, Legacy, Normal, and phonetically similar Variant groups. Our findings show that elite surnames consistently increase AI-generated perceptions of power, intelligence, and wealth, which in turn influence AI-driven decisions in high-stakes contexts. Mediation analysis reveals perceived intelligence as a key mechanism through which surname biases influence AI decision-making process. While providing objective qualifications alongside surnames mitigates most of these biases, it does not eliminate them entirely, especially in contexts where candidate credentials are low. These findings highlight the need for fairness-aware algorithms and robust policy measures to prevent AI systems from reinforcing systemic inequalities tied to surnames, an often-overlooked bias compared to more salient characteristics such as race and gender. Our work calls for a critical reassessment of algorithmic accountability and its broader societal impact, particularly in systems designed to uphold meritocratic principles while counteracting the perpetuation of intergenerational privilege., Comment: 33 pages, 5 figures, 1 table

This paper studies natural disasters and the psychological costs of climate change. It presents what we believe to be the first evidence that higher temperature variability and not a higher level of temperature is what predicts natural disasters. This conclusion holds whether or not we control for the (incorrectly signed) impact of temperature. The analysis draws upon long-differences regression equations using GDIS data from 1960-2018 for 176 countries and the contiguous states of the USA. Results are checked on FEMA data. Wellbeing impact losses are calculated. To our knowledge, the paper's results are unknown to natural and social scientists., Comment: 54 pages

To end the COVID-19 pandemic, policymakers have relied on various public health messages to boost vaccine take-up rates amongst people across wide political spectra, backgrounds, and worldviews. However, much less is understood about whether these messages affect different people in the same way. One source of heterogeneity is the belief in a just world (BJW), which is the belief that in general, good things happen to good people, and bad things happen to bad people. This study investigates the effectiveness of two common messages of the COVID-19 pandemic: vaccinate to protect yourself and vaccinate to protect others in your community. We then examine whether BJW moderates the effectiveness of these messages. We hypothesize that just-world believers react negatively to the prosocial pro-vaccine message, as it charges individuals with the responsibility to care for others around them. Using an unvaccinated sample of UK residents before vaccines were made widely available (N=526), we demonstrate that the individual-focused message significantly reduces overall vaccine skepticism, and that this effect is more robust for individuals with a low BJW, whereas the community-focused message does not. Our findings highlight the importance of individual differences in the reception of public health messages to reduce COVID-19 vaccine skepticism.

Emotional volatility is a human universal. Yet there has been no large-scale scientific study of predictors of that phenomenon. Building from previous works, which had been ad hoc and based on tiny samples, this paper reports the first large-scale estimation of volatility in human emotional experiences. Our study draws from a large sample of intrapersonal variation in moment-to-moment happiness from over three million observations by 41,023 UK individuals. Holding other things constant, we show that emotional volatility is highest among women with children, the separated, the poor, and the young. Women without children report substantially greater emotional volatility than men with and without children. For any given rate of volatility, women with children also experience more frequent extreme emotional lows than any other socio-demographic group. Our results, which are robust to different specification tests, enable researchers and policymakers to quantify and prioritise different determinants of intrapersonal variability in human emotions., Comment: 30 pages, 1 figure, 2 tables

We examine whether a company's corporate reputation gained from their CSR activities and a company leader's reputation, one that is unrelated to his or her business acumen, can impact economic action fairness appraisals. We provide experimental evidence that good corporate reputation causally buffers individuals' negative fairness judgment following the firm's decision to profiteer from an increase in the demand. Bad corporate reputation does not make the decision to profiteer as any less acceptable. However, there is evidence that individuals judge as more unfair an ill-reputed firm's decision to raise their product's price to protect against losses. Thus, our results highlight the importance of a good reputation in protecting a firm against severe negative judgments from making an economic decision that the public deems unfair., Comment: 42 pages, 5 figures

The COVID-19 pandemic has led many governments to implement lockdowns. While lockdowns may help to contain the spread of the virus, it is possible that substantial damage to population well-being will result. This study relies on Google Trends data and tests whether the lockdowns implemented in Europe and America led to changes in well-being related topic search terms. Using different methods to evaluate the causal effects of lockdown, we find a substantial increase in the search intensity for boredom in Europe and the US. We also found a significant increase in searches for loneliness, worry and sadness, while searches for stress, suicide and divorce on the contrary fell. Our results suggest that people's mental health may have been severely affected by the lockdown.

Ordered probit and logit models have been frequently used to estimate the mean ranking of happiness outcomes (and other ordinal data) across groups. However, it has been recently highlighted that such ranking may not be identified in most happiness applications. We suggest researchers focus on median comparison instead of the mean. This is because the median rank can be identified even if the mean rank is not. Furthermore, median ranks in probit and logit models can be readily estimated using standard statistical softwares. The median ranking, as well as ranking for other quantiles, can also be estimated semiparametrically and we provide a new constrained mixed integer optimization procedure for implementation. We apply it to estimate a happiness equation using General Social Survey data of the US.