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on Big Data |
| By: | Abdel Haq, Omar (Harvard Business School); Chandra, Amitabh (Harvard Business School and Harvard Kennedy School); Jagelka, Tomáš (University of Bonn); Luttmer, Erzo (Dartmouth College); Schwartzstein, Joshua (Harvard Business School) |
| Abstract: | Large Language Models (LLMs) are trained on a prodigious corpus of human writing and may reveal human preferences over characteristics of life courses, such as income, longevity, and working conditions. We present OpenAI's GPT-5.4 and a broadly representative sample of Americans with pairs of life stories and ask them to choose the life they would prefer for themselves. A person's choice is better predicted by the LLM's choice than by another person’s choice over the same stories, and LLM valuations of several life attributes are similar to those derived from human responses. Our results suggest that LLM responses offer a scalable and cost-effective complement to existing methods for studying human preferences. |
| Keywords: | generative AI, preference estimation methods, choice experiments, survey validation |
| JEL: | D0 H0 I0 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18634 |
| By: | Jiayu Yi; Minxuan Hu; Wenxi Sun; Ziheng Chen |
| Abstract: | This research establishes ESG as a state dependent insurance mechanism against equity crashes by addressing the decoupling of unconditional alpha from tail risk resilience. By validating market stress regimes as distinct economic states through a drawdown-based truncation rule, the study demonstrates that high ESG ratings materially reduce the incidence of discrete crash events during systemic drawdowns. To address the selection bias and high-dimensional confounding inherent in traditional linear frameworks, we implement Double Machine Learning as a structural deconfounding layer. Unlike simple predictive modeling, the Double Machine Learning framework utilizes machine learning to handle complex nuisance parameters, allowing us to isolate the asymmetric treatment effects of ESG across different market states. Distributional analysis reveals the underlying mechanism as ESG specifically attenuates the severity of realized tail losses at the most adverse quantiles instead of shifting the entire return distribution. Confirmed by structural estimates, this protection functions as priced insurance that incurs performance drags during stable periods while providing critical resilience when tail risks are most acute. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.04479 |
| By: | Datt, Gaurav; Nguyen, John; Salas Rojo, Pedro; Ferreira, Francisco H. G.; Brunori, Paolo; Peragine, Vito; Park, Albert; Martinez Jr., Arturo; Bulan, Joseph Albert Nino |
| Abstract: | This paper aims to contribute to an understanding of the extent, nature and persistence of unfair inequality in the Asia Pacific region, building on a rich literature on the measurement of inequality of opportunity (IOp). As part of a project to build a global database of IOp, the paper uses microdata from 39 nationally representative household surveys to present IOp estimates for 14 countries that account for about three-quarters of the region’s population. We use consistent data protocols to ensure a high degree of cross-country comparability of IOp estimates. A distinguishing feature of the exercise is the use of machine learning methods to construct IOp estimates, which efficiently balances the risks of potential under- or over-fitting. The resultsshow that, on average, nearly two-fifths of income or consumption inequality across the Asia-Pacific region represents inequality of opportunity attributable to inherited circumstances, though with wide variation across countries, ranging from about a quarter to over half. The cross-country variation in IOp is consistent with a Great Gatsby curve for the Asia-Pacific. A decomposition analysis assesses the relative contributions of different circumstances to IOp |
| Keywords: | inequality of opportunity; economic mobility; Asia-Pacific; machine learning |
| JEL: | D31 D63 O15 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:ehl:lserod:138045 |
| By: | Jonathan Benchimol; Sathya Mellina |
| Abstract: | We examine how inflation language in FOMC statements and Chair press conferences maps into the breakeven inflation (BEI) term structure from two to ten years. Five indices capture stance, broad and current inflation language, and the Delphic/Odyssean decomposition. Conditional on the high-frequency rate surprise, statement language is associated with lower BEI at short-to-intermediate maturities, consistent with markets reading committee-vetted text through the policy reaction function. Press conferences differ: Delphic language loads positively on long-horizon forwards; Odyssean language compresses the short-to-intermediate segment. Intraday BEI moves positively with every press-conference index; no analogous statement-window effect survives partialling out the rate surprise. |
| Keywords: | central bank communication, breakeven inflation, high-frequency identification, event-study methods, Delphic and Odyssean forward guidance, natural language processing |
| JEL: | C45 E43 E52 E58 G12 G14 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:een:camaaa:2026-29 |