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on Network Economics |
| By: | Guy Tchuente |
| Abstract: | This paper develops a unifying theory of peer effects that treats the peer aggregator (the social norm mapping peers' actions into a scalar exposure) as the central behavioral primitive. We formulate peer influence as a norm game in which payoffs depend on own action and an exposure index, and we provide equilibrium existence and uniqueness for a broad class of aggregators. Using economically interpretable axioms, we organize commonly used exposure maps into a small taxonomy that nests linear-in-means, CES (peer-preference) norms, and smooth ``attention-to-salient-peers'' aggregators; rank-based quantile norms are treated as a complementary class. Building on this unification, we show that each aggregator induces an operator that governs how exogenous variation propagates through the network. Linear-in-means corresponds to constant transport (adjacency matrix), recovering the classic (friends-of-friends) instrument families. For nonlinear norms, operator becomes state- and preference-dependent and is characterized by the Jacobian of the exposure map evaluated at an exogenous predictor. This perspective yields geometry-induced instrument that exploit heterogeneity in marginal influence and nonredundant paths, and can remain informative when one-step moments or adjacency-power instruments become weak. Monte Carlo evidence and an application to NetHealth illustrate the practical implications across alternative aggregators and outcomes. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.23594 |
| By: | Gianmarco Ottaviano |
| Abstract: | This paper studies how digital infrastructure is associated with the spatial structure of international trade in goods. We embed data availability into a structural gravity framework, conceptualizing it as an information friction that interacts with geographic distance and equilibrium market access. Using the topology of the global subsea cable network, we construct country-level measures of digital network position. We find that countries with greater digital network embeddedness, particularly on the exporter side, exhibit lower distance elasticities of trade. Other dimensions of digital connectivity are more closely associated with multilateral resistance, highlighting distinct channels through which digital infrastructure affects goods trade. |
| Keywords: | digital network centrality, spatial integration, international trade, subsea cable networks |
| Date: | 2026–03–11 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2158 |
| By: | Nuriye Melisa Bilgin; Ester Faia; Gianmarco Ottaviano |
| Abstract: | We examine how upstream firms' technology adoption affects the performance and adoption decisions of downstream partners. Using business-to-business data with administrative records on advanced technology adoption, we find gains in productivity, performance, adoption probabilities of firms connected to the adopter, relatively to those that are not. Identification combines staggered event studies, balanced panels of pre-existing relationships, and recentering methods to address expected exposure within the network. Gains vary along firm size, centrality, technology quality, but do not systematically increase with input exposure, suggesting that knowledge spillovers may induce organizational adjustments. Adoption by competitors is associated with short-run negative effects. |
| Keywords: | technology diffusion, adoption and propagation, firm networks, firm productivity, imported inputs |
| Date: | 2026–03–11 |
| URL: | https://d.repec.org/n?u=RePEc:cep:cepdps:dp2157 |
| By: | Nathan Canen; Shantanu Chadha |
| Abstract: | In the linear-in-means model, endogeneity arises naturally due to the reflection problem. A common solution is to use Instrumental Variables (IVs) based on higher-order network links, such as using friends-of-friends' characteristics. We first show that such instruments are unlikely to work well in many applied settings: in very sparse or very dense networks, friends-of-friends may be similar to the original links. This implies that the IVs may be weak or their first stage estimand may be undefined. For a class of random graphs, we use random graph theory and characterize regimes where such instruments perform well, and when they would not. We prove how weak-IV robust inference can be adapted to this environment, and how scaling the network can help. We provide extensive Monte Carlo simulations and revisit empirical applications, showing the prevalence of such issues in empirical practice, and how our results restore valid inference. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.24215 |
| By: | De Munck, Thomas (Université catholique de Louvain, LIDAM/CORE, Belgium); Tancrez, Jean-Sébastien (Université catholique de Louvain, LIDAM/CORE, Belgium); Chevalier, Philippe (Université catholique de Louvain, LIDAM/CORE, Belgium) |
| Abstract: | We consider the problem of a ride-hailing platform (e.g., Uber, Lyft) that connects supply with demand over a network of locations. To this aim, the platform makes pricing, driver repositioning, and customer admission decisions. Customers are impatient and have distinct willingness to pay. Drivers can be repositioned by the platform, or can choose to relocate to other locations by themselves. We formulate this problem as a discrete-time Markov decision process and propose a transfer learning approach to find an efficient policy. Our approach first derives a rolling-horizon strategy by repeatedly solving a deterministic optimization problem. Then, two neural networks are pretrained to replicate the strategy and learn the associated value function. Finally, the policy is further improved through deep reinforcement learning (DRL). Using data from New York City, we apply our approach to networks of up to 20 locations. The results show that our approach outperforms alternative DRL algorithms and rolling-horizon strategies while reducing computation time and stabilizing learning. We also explore the interplay between pricing, driver repositioning, and customer admission, providing insights into their respective roles. |
| Keywords: | Transportation ; Ride-hailing platforms ; Pricing and repositioning decisions ; Transfer learning ; Deep reinforcement learning |
| Date: | 2025–02–01 |
| URL: | https://d.repec.org/n?u=RePEc:cor:louvco:2025004 |
| By: | Hauck, Florian; Güth, Albrecht; Kliewer, Natalia; Rößler-von Saß, David |
| Abstract: | This paper examines the possibilities of creating synthetic train trip data with Generative Adversarial Networks (GANs). A real data set from Deutsche Bahn is enhanced with synthetic data created by using a Conditional Wasserstein Generative Adversarial Network (CWGAN). The synthetic data is analyzed and compared with the original data using statistical methods as well as machine learning models. The results show that the synthetic data is very similar to the original data in terms of data structure and dependencies, but at the same time contains enough noise to not just copy already existing instances. To analyze and measure the quality of the synthetic data, different supervised machine learning models are trained to predict the change of delay of trains at a specific station based on the arrival delays of other trains at that station. These models are then each trained once using the real data and once using the real data enhanced by synthetic data. All models are evaluated using a test set containing only real data that was not used to train the models. The results show that the R2 value of delay predictions increases significantly when using the enhanced data set. In particular, neural network-based models can benefit from the larger amount of input data. The proposed approach of generating synthetic train trip data with a CWGAN can also be applied to various other railway data analysis projects that require a large amount of input data. In addition, the presented approach is particularly interesting because, unlike most GAN approaches discussed in current literature, the data basis contains numerical data and not image data. |
| Keywords: | Generative Adversarial Networks, Train Delay Prediction, Railway Analysis |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:fubsbe:338080 |