Conference Agenda

Access to credit is vital for economic well-being, yet millions of rural households in India remain financially vulnerable despite the expansion of formal institutions (Rajeev & Nagendran, 2024). This issue is particularly acute in the semi-arid tropics, where agrarian distress, farmer suicides, and caste-driven social stratification persist alongside weak financial infrastructure (Bhende, 1983). In this context, social networks serve as crucial conduits for informal credit (Wydick et al., 2011), enabling households to invest in agriculture, adopt new technologies, and smooth consumption during economic shocks. These networks, deeply embedded in kinship, caste, and trust, shape financial access. However, the formation and evolution of these networks—and their influence on credit access—remain insufficiently understood (Banerjee et al., 2024).

This study examines informal borrowing-lending networks in Kanzara village, addressing three key questions. First, it investigates how endogenous network structures shape borrowing relations. Second, it explores how household characteristics and tie-specific factors influence these ties. Third, it assesses the role of caste in credit access. Using longitudinal data from the ICRISAT VDSA survey (2007, 2013) and a Stochastic Actor-Oriented Model (SAOM) (Snijders, 2017), we find borrowing relations are highly reciprocal and caste-homophilous, with dominant castes lending within their groups while lower castes depend on them. Spatial proximity, patron-client ties, and external financial links further shape borrowing dynamics. These findings underscore the need for financial inclusion policies to address caste-based inequalities in credit access.

References

Banerjee, A., Breza, E., Chandrasekhar, A. G., Duflo, E., Jackson, M. O., & Kinnan, C. (2024). Changes in social network structure in response to exposure to formal credit markets. Review of Economic Studies, 91(3), 1331-1372.

Bhende, M. J. (1983). Credit markets in the semi-arid tropics of rural south India.

Rajeev, M., & Nagendran, P. (2024). Has Farmers' Access to Credit Improved Over Time? An Analysis of NSSO Data. In Institutions and Public Policy for India’s Sustainable Development (pp. 117-136). Routledge India.

Snijders, T. A. (2017). Stochastic actor-oriented models for network dynamics. Annual review of statistics and its application, 4(1), 343-363.

Wydick, B., Hayes, H. K., & Kempf, S. H. (2011). Social networks, neighborhood effects, and credit access: evidence from rural Guatemala. World Development, 39(6), 974-982.

Economic inequality remains a persistent issue, shaping societal structures and influencing public attitudes toward redistribution. While socioeconomic status and ideology are well-established predictors of redistributive preferences, the role of social networks has gained increasing attention. This study examines how homophily -the tendency of individuals to associate with others who share similar characteristics- affects beliefs about inequality and support for redistribution.

Using Chile as a case study, a country with high levels of inequality and pronounced social stratification, we analyze how educational and ideological homophily shape perceptions of meritocracy, tolerance for inequality, and redistributive policies. Based on data from the Longitudinal Social Survey of Chile (ELSOC, 2016) and the United Nations Development Programme (UNDP) survey for Chile (2016), we apply a case-control model to estimate homophily patterns, followed by multiple linear regressions to assess their impact on redistributive preferences.

Our findings indicate that educational homophily reinforces meritocratic beliefs and weakens support for systemic redistribution. In contrast, ideological homophily produces divergent effects: in left-leaning contexts, it tends to challenge inequality-justifying narratives, while in right-leaning contexts, it reinforces them. These results highlight the role of social networks in shaping economic attitudes, demonstrating the potential of a social network approach to understand better how beliefs, attitudes, and preferences about inequality are formed.

The principal eigenvector of an adjacency matrix is the basis of several important metrics in social network analysis. Bienenstock and Bonacich (2021) recently proposed using the Gini-coefficient of the principal eigenvector of the communication matrix to measure the extent to which the centralization of communication in an organization prevents that organization from incorporating outside information. This problem can be exacerbated when informational flows are one way. On directed graphs, nodes that are informed may not be influential, and influential nodes may not be informed. This subtle distinction is highlighted by the relationship between the forward and reverse versions of the DeGroot influence model with the corresponding reverse and forward versions of PageRank. We thus introduce two separate Gini-coefficient measures of influence inequality and information inequality for directed graphs and explore their implications for understanding influence and access in organizational networks.

Cryptocurrencies have recently been in the spotlight of public debate due to their embrace by the new US President, with crypto fans expecting a 'bull run'. The global cryptocurrency market capitalisation is more than $3 trillion, with 1 Bitcoin exchanging for more than $109,000 at the end of January 2025. Monitoring the evolution of these systems is key to understanding whether the popular perception of cryptocurrencies as a new, sustainable economic infrastructure is well-founded. In this paper, we have reconstructed the network structures and dynamics of Bitcoin from its launch in January 2009 to December 2023 and identified its key evolutionary phases. Our results show that network centralisation and wealth concentration increased from the very early years, following a richer-get-richer mechanism. This trend was endogenous to the system, beyond any subsequent institutional or exogenous influence. The evolution of Bitcoin is characterised by three periods, Exploration, Adaptation and Maturity, with substantial coherent network patterns. Our findings suggest that Bitcoin is a highly centralised structure, with high levels of wealth inequality and internally crystallised power dynamics, which may have negative implications for its long-term sustainability.

Through a survey, carried out for my thesis, based on the study of 464 YouTube channels and their videos, I am interested by the collective dynamics that structure the space of scientific popularization on the Internet. I study the nature of the social relations created by popularizers to structure their sphere and develop their activity. I observe how these relationships evolve and multiply over time and the individual's place in the network. I look at which actors are mobilized and the nature of these relationships. They collaborate not only with each other, but also with scientific institutions, with media and with commercial partners. Video collaborations are dated, so they can be studied over time. In addition, YouTube channels are studied in terms of their scientific discipline, seniority, audience and gender, enabling us to compare them in terms of the differences that can be observed about their trajectories and networks.