Naim Bro

Marcelo Mendoza, Naim Bro

From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago.

Naim Bro, Juan Pablo Luna

The link between descriptive and substantive representation is well established in the literature but is hard to trace historically, where class records are thin. We introduce a replicable enduring-elite surname measure, pairing a contemporary socioeconomic criterion with historical elite registers, and apply it across the Chilean Congress, 1834-2020. Against a dynamic population reference built from 22.65 million birth registrations, the enduring-elite share of Congress falls from about half in the 1860s to about 12% in the 2010s, with a sharp drop of 11 to 13 points around the 1925 constitutional reform. In 1910-1950, composition co-moves with the legislative agenda, net of party: common-surname legislators emphasize labor foremost, elite legislators a statecraft agenda of defense, foreign affairs, and administration. Across this window, who sits in Congress moves together with what Congress attends to.

Andrés Abeliuk, Vanessa Gaete, Naim Bro

Large Language Models (LLMs) excel in text generation and understanding, especially in simulating socio-political and economic patterns, serving as an alternative to traditional surveys. However, their global applicability remains questionable due to unexplored biases across socio-demographic and geographic contexts. This study examines how LLMs perform across diverse populations by analyzing public surveys from Chile and the United States, focusing on predictive accuracy and fairness metrics. The results show performance disparities, with LLM consistently outperforming on U.S. datasets. This bias originates from the U.S.-centric training data, remaining evident after accounting for socio-demographic differences. In the U.S., political identity and race significantly influence prediction accuracy, while in Chile, gender, education, and religious affiliation play more pronounced roles. Our study presents a novel framework for measuring socio-demographic biases in LLMs, offering a path toward ensuring fairer and more equitable model performance across diverse socio-cultural contexts.