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on Social Norms and Social Capital |
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Issue of 2026–01–26
four papers chosen by Fabio Sabatini, Università degli Studi di Roma “La Sapienza” |
| By: | Fergusson Leopoldo (Universidad de los Andes); Natalia Garbiras-Díaz (Harvard University); Michael Weintraub (Universidad de los Andes) |
| Abstract: | Do accents—the way that language is pronounced—shape social and economic interactions? We answer this question using an experiment embedded in an online survey of 6, 000 Colombian adults. Respondents evaluated paired profiles in which audio introductions were randomly assigned to feature either a high- or low-class accent, while income, education, and other attributes were independently randomized. We find a sizable accent premium: speakers with high-class accents are 5–16 percentage points more likely to be chosen as friends, business partners, colleagues, or bosses. This premium is significantly larger among respondents with high socioeconomic status, consistent with an in-group favoritism capable of reproducing inequality. By varying the information we present to respondents, our experiment allows us to conclude that the premium cannot be attributed solely to inferences about income or education. We further show that the premium vanishes for high-class foreign accents, suggesting that class cues are culturally specific and difficult for outsiders to detect. Finally, we document that respondents systematically associate high-class accents with multiple proxies of social status and that they elicit more deferential treatment. Overall, our findings reveal that accents function as a form of capital: culturally specific linguistic signals that reproduce social hierarchies, with implications for labor markets and efforts to promote mobility and integration. |
| Keywords: | Accents; Class-based Discrimination; Social Capital; Cultural Capital; Inequality. |
| JEL: | C90 D63 J15 Z13 O15 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:col:000089:022044 |
| By: | Erickson, Jacob (Vassar College) |
| Abstract: | As conversational AI systems become increasingly integrated into everyday life, they raise pressing concerns about user autonomy, trust, and the commercial interests that influence their behavior. To address these concerns, this paper develops the Fake Friend Dilemma (FFD), a sociotechnical condition in which users place trust in AI agents that appear supportive while pursuing goals that are misaligned with the user’s own. The FFD provides a critical framework for examining how anthropomorphic AI systems facilitate subtle forms of manipulation and exploitation. Drawing on literature in trust, AI alignment, and surveillance capitalism, we construct a typology of harms, including covert advertising, political propaganda, behavioral nudging, and surveillance. We then assess possible mitigation strategies, including both structural and technical interventions. By focusing on trust as a vector of asymmetrical power, the FFD offers a lens for understanding how AI systems may undermine user autonomy while maintaining the appearance of helpfulness. |
| Date: | 2026–01–08 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:4ahj9_v1 |
| By: | Schewe, Marc; Bahník, Štěpán |
| Abstract: | Recommender systems play a crucial role in e-commerce by simplifying consumer searches, improving decision making, and increasing user satisfaction, ultimately boosting e-vendors’ revenues. Their effectiveness depends on the trust of the users and the availability of data to generate accurate recommendations. Using an online experiment, we examined the effect of privacy policies and the amount of requested information on trust in recommender systems and willingness to share data. The results showed that a long privacy policy reduced trust compared to a short or absent policy. The presentation of a privacy policy and the request for more data decreased participants’ willingness to share data with the system, but a long policy did not further decrease the sharing beyond the effect of a short policy. To maintain trust and encourage data sharing, e-vendors may benefit from offering privacy policies upon request, keeping them concise, and minimizing data requests. |
| Keywords: | recommender systems, privacy policies, trust, data sharing, e-commerce |
| JEL: | M31 L81 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esprep:335109 |
| By: | David Loschiavo; Olivier Armantier; Antonio Dalla Zuanna; Leonardo Gambacorta; Mirko Moscatelli; Ilaria Supino |
| Abstract: | This paper explores the household adoption of Generative Artificial Intelligence (GenAI) in the United States and Italy, leveraging survey data to compare usage patterns, demographic influences, and employment sectoral composition effects. Our findings reveal higher adoption rates in the US, driven by socio-demographic differences between the two countries. Despite their lower usage of GenAI, Italians are more confident in its potential to improve their well-being and financial situation. Both Italian and US users tend to trust GenAI tools less than human-operated services, but Italians report greater relative trust in government and institutions when handling personal data with GenAI tools. |
| Keywords: | generative artificial intelligence, technology adoption, cross-country comparison, socio-demographic factors, trust in technology, cultural attitudes |
| JEL: | O33 D10 J24 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:bis:biswps:1322 |