|
on Network Economics |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper develops a dual-channel framework for analyzing technology diffusion that integrates spatial decay mechanisms from continuous functional analysis with network contagion dynamics from spectral graph theory. Building on our previous studies, which establish Navier-Stokes-based approaches to spatial treatment effects and financial network fragility, we demonstrate that technology adoption spreads simultaneously through both geographic proximity and supply chain connections. Using comprehensive data on six technologies adopted by 500 firms over 2010-2023, we document three key findings. First, technology adoption exhibits strong exponential geographic decay with spatial decay rate $\kappa \approx 0.043$ per kilometer, implying a spatial boundary of $d^* \approx 69$ kilometers beyond which spillovers are negligible (R-squared = 0.99). Second, supply chain connections create technology-specific networks whose algebraic connectivity ($\lambda_2$) increases 300-380 percent as adoption spreads, with correlation between $\lambda_2$ and adoption exceeding 0.95 across all technologies. Third, traditional difference-in-differences methods that ignore spatial and network structure exhibit 61 percent bias in estimated treatment effects. An event study around COVID-19 reveals that network fragility increased 24.5 percent post-shock, amplifying treatment effects through supply chain spillovers in a manner analogous to financial contagion documented in our recent study. Our framework provides micro-foundations for technology policy: interventions have spatial reach of 69 kilometers and network amplification factor of 10.8, requiring coordinated geographic and supply chain targeting for optimal effectiveness. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.24781 |
| By: | Vincent Starck |
| Abstract: | This paper develops a method to conduct causal inference in the presence of unobserved confounders by leveraging networks with homophily, a frequently observed tendency to form edges with similar nodes. I introduce a concept of asymptotic homophily, according to which individuals' selectivity scales with the size of the potential connection pool. It contributes to the network formation literature with a model that can accommodate common empirical features such as homophily, degree heterogeneity, sparsity, and clustering, and provides a framework to obtain consistent estimators of treatment effects that are robust to selection on unobservables. I also consider an alternative setting that accommodates dense networks and show how selecting linked individuals whose observed characteristics made such a connection less likely delivers an estimator with similar properties. In an application, I recover an estimate of the effect of parental involvement on students' test scores that is greater than that of OLS, arguably due to the estimator's ability to account for unobserved ability. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.00612 |
| By: | Tatsuru Kikuchi |
| Abstract: | This paper develops and empirically implements a continuous functional framework for analyzing systemic risk in financial networks, building on the dynamic spatial treatment effect methodology established in our previous studies. We extend the Navier-Stokes-based approach from our previous studies to characterize contagion dynamics in the European banking system through the spectral properties of network evolution operators. Using high-quality bilateral exposure data from the European Banking Authority Transparency Exercise (2014-2023), we estimate the causal impact of the COVID-19 pandemic on network fragility using spatial difference-in-differences methods adapted from our previous studies. Our empirical analysis reveals that COVID-19 elevated network fragility, measured by the algebraic connectivity $\lambda_2$ of the system Laplacian, by 26.9% above pre-pandemic levels (95% CI: [7.4%, 46.5%], p |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.24775 |
| By: | Federico Crippa |
| Abstract: | This paper studies a class of models for two-sided interactions, where outcomes depend on latent characteristics of two distinct agent types. Models in this class have two core elements: the matching network, which records which agent pairs interact, and the interaction function, which maps latent characteristics of these agents to outcomes and determines the role of complementarities. I introduce the Tukey model, which captures complementarities with a single interaction parameter, along with two extensions that allow richer complementarity patterns. First, I establish an identification trade-off between the flexibility of the interaction function and the density of the matching network: the Tukey model is identified under mild conditions, whereas the more flexible extensions require dense networks that are rarely observed in applications. Second, I propose a cycle-based estimator for the Tukey interaction parameter and show that it is consistent and asymptotically normal even when the network is sparse. Third, I use its asymptotic distribution to construct a formal test of no complementarities. Finally, an empirical illustration shows that the Tukey model recovers economically meaningful complementarities. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22884 |
| By: | Hideo Konishi (Boston College); Michel Le Breton (Toulouse School of Economics); Shlomo Weber (Southern Methodist University) |
| Abstract: | In this paper, we define additive dyadic social interactions games (ADG), in which each player cares not only about the selected action, but also about interactions with other players, especially those who choose the same action. This class of games includes alliance formation games, network games, and dis- crete choice problems with network externalities. While it is known that games in the ADG class admit a pure strategy Nash equilibrium that is a maximizer of the game's potential, the potential approach does not always apply if all coalitional deviations are allowed. We then introduce a novel notion of a strong landscape equilibrium, which relies on a limited scope of coalitional deviations. We show the existence of a strong landscape equilibrium for a class of basic additive dyadic social interactions games (BADG), even though a strong Nash equilibrium may fail to exist. Somewhat surprisingly, a potential-maximizing strong landscape equilibrium is not always a strong Nash equilibrium even if the set of the latter is nonempty. We also provide applications and extensions of our results. |
| Keywords: | social interactions games, coalition, landscape equilibrium |
| Date: | 2025–10–25 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1098 |
| By: | Vincent Starck |
| Abstract: | I study peer effects that arise from irreversible decisions in the absence of a standard social equilibrium. I model a latent sequence of decisions in continuous time and obtain a closed-form expression for the likelihood, which allows to estimate proposed causal estimands. The method avoids regression on conditional expectations or linear-in-means regression -- and thus reflection-type problems (Manski, 1993) or simultaneity issues -- by modeling the (unobserved) realized direction of causality, whose probability is identified. Under a parsimonious parametric specification, I introduce a peer effect parameter meant to capture the causal influence of first-movers on their peers. Various forms of peer effect heterogeneity can be accommodated. Parameters are shown to be consistently estimated by maximum likelihood methods and lend themselves to standard inference. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.02764 |
| By: | Avirup Chakraborty |
| Abstract: | The European Union Emissions Trading System (EU ETS), the worlds largest cap-and-trade carbon market, is central to EU climate policy. This study analyzes its efficiency, price behavior, and market structure from 2010 to 2020. Using an AR-GARCH framework, we find pronounced price clustering and short-term return predictability, with 60.05 percent directional accuracy and a 70.78 percent hit rate within forecast intervals. Network analysis of inter-country transactions shows a concentrated structure dominated by a few registries that control most high-value flows. Country-specific log-log regressions of price on traded quantity reveal heterogeneous and sometimes positive elasticities exceeding unity, implying that trading volumes often rise with prices. These results point to persistent inefficiencies in the EU ETS, including partial predictability, asymmetric market power, and unconventional price-volume relationships, suggesting that while the system contributes to decarbonization, its trading dynamics and price formation remain imperfect. |
| Date: | 2025–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2510.22341 |