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on Network Economics |
| By: | Harris, Tom (Harvard University); Iyer, Shankar; Rutter, Tom; Chi, Guanghua; Johnston, Drew; Lam, Patrick; Makinson, Lucy; Silva, Antonio S.; Wessel, Martin; Liou, Mei-Chen |
| Abstract: | Social capital is widely believed to impact a wide range of outcomes including subjective well-being, social mobility, and community health. We aggregate data on over 20 million Facebook users in the United Kingdom to construct several measures of social capital including cross-type connectedness, social network clustering, and civic engagement and volunteering. We find that social networks in the UK bridge class divides, with people below the median of the socioeconomic status distribution (low-SES people) having about half (47%) of their friendships with people above the median (high-SES people). Despite the presence of these cross-cutting friendships, we find evidence of homophily by class: high-SES people have a 28% higher share of high-SES friends. In part, this gap is due to the fact that high-SES individuals live in neighbourhoods, attend schools, and participate in groups that are wealthier on average. However, up to two thirds of the gap is due to the fact that high-SES people are more likely to befriend other high-SES peers, even within a given setting. Cross-class connections vary by region but are positively associated with upward income mobility: low-SES children who grew up in the top 10% most economically connected local authorities in England earn 38% more per year on average (£5, 100) as adults relative to low-SES children in the bottom 10% local authorities. The relationship between upward mobility and connectedness is robust to controlling for other measures of social connection and neighbourhood measures of income, education, and health. We also connect measures of subjective well-being and related concepts with individual social capital measures. We find that individuals with more connections to high-SES people and more tightly-knit social networks report higher levels of happiness, trust, and lower feelings of isolation and social disconnection. We make our aggregated social capital metrics publicly available on the Humanitarian Data Exchange to support future research. |
| Date: | 2025–03–23 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:kb7dy_v1 |
| By: | Marcello Nieddu (University of Genova); Marco Raberto (University of Genova); Andrea Teglio (Ca' Foscari University of Venice) |
| Abstract: | This study develops a family of models to evaluate how agent heterogeneity and interactions shape macroeconomic dynamics, challenging the adequacy of representative agent frameworks. Building on dynamic multi-sector models populated by boundedly rational firms and households, we conduct both analytical and computational comparisons between aggregated and disaggregated representations across equilibrium and disequilibrium regimes. We identify precise conditions –individual and relational indistinguishability– under which representative constructs successfully replicate multi-agent dynamics, and we demonstrate their failure in constrained regimes where rationing-induced network shocks generate irreversible structural changes. The analysis reveals that aggregation errors escalate with heterogeneity, asymmetric interactions, and shock-driven reconfigurations of economic networks, critically undermining policy inferences. The proposed family of multi-agent models, grounded in minimal realistic principles, allows us to systematically quantify the errors derived by treating the response to exogenous shocks as a dynamic sequence of equilibria, rather than explicitly accounting for out-of-equilibrium dynamics. These insights bridge Keynesian coordination failures with modern complexity economics, offering methodological rigor to address Blanchard's critique on the relevance of interactions for macroeconomic modeling. |
| Keywords: | macroeconomics, aggregation, interaction, multi-agent, multi-sector |
| JEL: | E00 E12 C63 C67 D85 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2025:04 |
| By: | Aranya Chakraborty (Ahmedabad University); Digvijay Singh Negi (Ashoka University); Rahul Rao (Ahmedabad University) |
| Abstract: | Does information and communication technology (ICT) based provision of agricultural extension services help improve agricultural productivity in poor or developing countries? We answer this question in the case of rice production in rural Bangladesh. We exploit the spatiotemporal variation in the availability of village-level phone services and the temporal variation in the timing of an ICT-based intervention to identify the differential impact by input use, network centrality, and geographic proximity. We observe that, in the villages with access to phone service, there is a 50 percent reduction in plot-level inefficiency after the intervention, driven by plots that used rainfed water for cultivation. We provide evidence suggesting that these effects are due to increased input use by the farmers using rainfed farming. Our results also document that the intervention benefits geographically remote farmers differentially more, whose information needs are otherwise unfulfilled by traditional extension services. However, the diffusion of information via networks remains relevant as we document significant cross-community spill overs through geographic ties. |
| Keywords: | agriculture; Extension; ICT; Inefficiency; networks |
| Date: | 2024–09–14 |
| URL: | https://d.repec.org/n?u=RePEc:ash:wpaper:125 |
| By: | Boeing, Geoff (Northeastern University) |
| Abstract: | Street intersection counts and densities are ubiquitous measures in transport geography and planning. However, typical street network data and typical street network analysis tools can substantially overcount them. This article explains the three main reasons why this happens and presents solutions to each. It contributes algorithms to automatically simplify spatial graphs of urban street networks---via edge simplification and node consolidation---resulting in faster parsimonious models and more accurate network measures like intersection counts and densities, street segment lengths, and node degrees. These algorithms' information compression improves downstream graph analytics' memory and runtime efficiency, boosting analytical tractability without loss of model fidelity. Finally, this article validates these algorithms and empirically assesses intersection count biases worldwide to demonstrate the problem's widespread prevalence. Without consolidation, traditional methods would overestimate the median urban area intersection count by 14%. However, this bias varies drastically across regions, underscoring these algorithms' importance for consistent comparative empirical analyses. |
| Date: | 2025–03–26 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:xf7wm_v1 |
| By: | Siddharth, L. |
| Abstract: | Technological evolution depends not only on the invention of new artefacts but also on how their knowledge is structured, represented, and propagated. In this study, we examine how the modularity of artefact knowledge influences technological impact. We utilize a dataset of 33, 803 patents from the United States Patent & Trademark Office (USPTO) and their knowledge graphs constructed using the facts extracted from patent descriptions. Using a regression analysis controlling for several structural properties of the knowledge graphs, we establish a significant positive relationship between modularity of the graph structures—measured using the Louvain method and the technological impact, as quantified by normalized forward citations. To further examine this relationship, we develop a predictive framework integrating Graph Neural Networks (GNNs) and regression models to estimate normalized citation scores from patent knowledge graphs. We then apply this framework to conduct a counterfactual analysis, wherein, we tune the modularity of knowledge graphs and assess the enhancement in expected citations. The analysis reveals that patents with less or no citations could benefit the most from modularization of their knowledge, as a citation gain could help initiate their knowledge propagation. We also discuss with a few examples as to how re-representation of artefact knowledge is necessary in addition to re-designing artefacts for modularity. |
| Date: | 2025–03–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:osfxxx:fd36m_v1 |