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on Network Economics |
| By: | Patrick Allmis; Luca Paolo Merlino |
| Abstract: | In this paper, players contribute to two local public goods for which they have different tastes and sponsor costly links to enjoy the provision of others. In equilibrium, either there are several contributors specialized in public good provision or only two contributors who are not entirely specialized. Higher linking costs have a non-monotonic impact on welfare and polarization, as they affect who specializes in public good provision. When the available budget is small, subsidies should be given to players who already specialize in public good provision; otherwise, they should target only one player who specializes in public good provision. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.00457&r=net |
| By: | Carlevaro Emiliano A. |
| Abstract: | Capital regulation on banks aims to reduce the probability of failures. In theory, the effect of capital buffers in preventing failures could depend on the linkages among financial institutions. These linkages are nevertheless usually omitted in empirical models. I study the effectiveness of capital regulation in preventing failures using a spatial autoregressive probit model, which accommodates links among banks and feedback effects. I study the Argentinian banking crisis of 2001 for which I build the complete interbank network. By allowing linkages between banks, estimates from the spatial model show that capital regulation is 50% less effective than estimates of a model in which banks are not interconnected. |
| JEL: | E44 C21 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:aep:anales:4631&r=net |
| By: | Giang Nguyen (EM - emlyon business school); My Nguyen; Anh Viet Pham; Man Duy Marty Pham |
| Abstract: | "This study examines how the geographical structure of social networks shapes venture capital (VC) investment decisions. We find that VC firms invest more in portfolio companies in socially connected regions. The effect is more pronounced among independent, smaller, less reputable, early–stage–focused VC firms and those not from a VC hub. We further document that social connectedness lowers the likelihood of a successful exit since it induces VC firms to undertake suboptimal investment decisions. Overall, our findings highlight the role of social connectedness in constituting the geographical differences in VC firms' capital allocation and investment outcomes." |
| Keywords: | Social connectedness, Venture capital, Capital allocation, Investment performance |
| Date: | 2023–10–01 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04325756&r=net |
| By: | Svetlana Klessova; Sebastian Engell; Catherine Thomas (GREDEG - Groupe de Recherche en Droit, Economie et Gestion - UNS - Université Nice Sophia Antipolis (1965 - 2019) - CNRS - Centre National de la Recherche Scientifique - UCA - Université Côte d'Azur) |
| Abstract: | Publicly funded multi-actor research, development and innovation projects are a setting where a network of multiple organizational actors form a temporary consortium to jointly create new knowledge and market-upstream innovations. The couplings between the organizational actors and sub-groups of these actors represent joint work that leads to flows of knowledge and flows of activities. The dynamics of the couplings in this empirical context and their implications are not well understood yet. Using an inductive comparative multiple case study of projects funded in European Research and Innovation Programmes, we investigated 4 projects with 54 organizational actors, which produced 50 innovations. The evolutions of all couplings went through the same phases, although the temporality of the phases differed. We identified eight types of evolutions of couplings and their underlying generative mechanisms. These evolutions led to different, mostly negative implications on the planned collaborative innovations. Particularly, we observed a systematic degradation of the couplings that were planned to connect sub-groups of organizational actors. Over time, the projects became less collaborative than planned, and they have a tendency to fragment into isolated activities by subgroups of actors. Based on these findings, we propose an emerging process model which helps to better understand how and why the couplings evolve in multi-actor RDI projects. |
| Keywords: | multi-actor projects, collaborative innovation |
| Date: | 2022–07 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04314362&r=net |
| By: | Julian di Giovanni; Åžebnem Kalemli-Özcan; Alvaro Silva; Muhammed A Yildirim |
| Abstract: | We estimate a multi-country multi-sector New Keynesian model to quantify the drivers of domestic inflation during 2020–2023 in several countries, including the United States. The model matches observed inflation together with sector-level prices and wages. We further measure the relative importance of different types of shocks on inflation across countries over time. The key mechanism, the international transmission of demand, supply and energy shocks through global linkages helps us to match the behavior of the USD/Euro exchange rate. The quantification exercise yields four key findings. First, negative supply shocks to factors of production, labor and intermediate inputs, initially sparked inflation in 2020–2021. Global supply chains and complementarities in production played an amplification role in this initial phase. Second, positive aggregate demand shocks, due to stimulative policies, widened demand-supply imbalances, amplifying inflation further during 2021–2022. Third, the reallocation of consumption between goods and service sectors, a relative sector-level demand shock, played a role in transmitting these imbalances across countries through the global trade and production network. Fourth, global energy shocks have differential impacts on the US relative to other countries' inflation rates. Further, complementarities between energy and other inputs to production play a particularly important role in the quantitative impact of these shocks on inflation. |
| Keywords: | inflation; supply chains; trade economics; structural global network model; supply shocks |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:rba:rbaacp:acp2023-01&r=net |
| By: | Mikropoulou, Christina D.; Vouldis, Angelos T. |
| Abstract: | The analysis of contagion in financial networks has primarily focused on transmission channels operating through direct linkages. This paper develops a model of financial contagion in the interbank market featuring both direct and indirect transmission mechanisms. The model is used to analyse how shocks originating from outside sectors impact the functioning of the interbank market and investigates the emergence of instability in this setting. We conduct simulations on actual interbank bilateral exposures, constructed manually from a supervisory dataset reported by the largest euro area banks. We find that while the impact of direct contagion increases gradually with the shock intensity, the effect of indirect contagion is subject to threshold effects and can increase abruptly when the threshold is exceeded. In addition, the risk posed by indirect contagion has a higher upper bound compared to direct contagion. Finally, we find that in terms of overall impact, the shocks to the value of sovereign debt and non-bank financial institutions represent the most significant risk to the functioning of the interbank market. JEL Classification: G01, G21, G23, D85 |
| Keywords: | banking sector, contagion, funding concentration risk, network analysis |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20232883&r=net |
| By: | Neil Kichler; Sher Afghan; Uwe Naumann |
| Abstract: | The increasing use of stochastic models for describing complex phenomena warrants surrogate models that capture the reference model characteristics at a fraction of the computational cost, foregoing potentially expensive Monte Carlo simulation. The predominant approach of fitting a large neural network and then pruning it to a reduced size has commonly neglected shortcomings. The produced surrogate models often will not capture the sensitivities and uncertainties inherent in the original model. In particular, (higher-order) derivative information of such surrogates could differ drastically. Given a large enough network, we expect this derivative information to match. However, the pruned model will almost certainly not share this behavior. In this paper, we propose to find surrogate models by using sensitivity information throughout the learning and pruning process. We build on work using Interval Adjoint Significance Analysis for pruning and combine it with the recent advancements in Sobolev Training to accurately model the original sensitivity information in the pruned neural network based surrogate model. We experimentally underpin the method on an example of pricing a multidimensional Basket option modelled through a stochastic differential equation with Brownian motion. The proposed method is, however, not limited to the domain of quantitative finance, which was chosen as a case study for intuitive interpretations of the sensitivities. It serves as a foundation for building further surrogate modelling techniques considering sensitivity information. |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2312.03510&r=net |
| By: | Semeshenko Viktoriya; De Raco Sergio Andrés |
| Abstract: | In this paper we systematically explore high granularity economic activity data of labor flows using a network filtering reduction technique, focusing on a meso-structure analysis of relevant groups of economic activities. Using administrative data of interindustry labor flows for 1996-2020, we built the networks, extracted their representative backbones and applied a community detection algorithm. The results unmask inter-industry connectivity with persistent structures and well-identified communities organized in a core-periphery structure. |
| JEL: | J6 B4 |
| Date: | 2023–11 |
| URL: | http://d.repec.org/n?u=RePEc:aep:anales:4693&r=net |
| By: | Kramer, Berber; Porter, Maria; Wassie Bizuayehu, Solomon |
| Abstract: | Index insurance is considered an important strategy to reduce agricultural risk and increase smallholder farmers’ investments. However, insured farmers may develop mistrust of insurance if they experience crop losses and do not receive a payout, for instance because index insurance covers only a subset of covariate risks. At the same time, insurance for idiosyncratic risks would introduce differences in payouts within social networks, which might be considered unfair, introduce jealousy, and further depress demand for insurance. We conduct lab-in-the-field experiments with farmers in Ethiopia to examine the effects of a novel insurance approach that ensures insurance payouts for farmers with crop losses due to idiosyncratic events. We also examine the effects of informing farmers about their neighbors’ experiences alongside their own. We find that such social comparison increases perceived fairness of weather index insurance. In addition, providing complete insurance coverage for crop losses increases farmers’ perceived fairness of outcomes and willingness to pay, without introducing jealousy over neighbors receiving different payouts. Finally, we find that the increase in willingness to pay for complete insurance is concentrated among men and risk averse respondents. |
| Keywords: | Agricultural Finance, Risk and Uncertainty |
| Date: | 2023–12–18 |
| URL: | http://d.repec.org/n?u=RePEc:ags:assa24:339075&r=net |
| By: | Effrosyni Adamopoulou (ZEW); Jeremy Greenwood (University of Pennsylvania); Nezih Guner (Centro de Estudios Monetarios y Financieros (CEMFI)); Karen A. Kopecky (Federal Reserve Bank of Cleveland) |
| Abstract: | The role of friends in the US opioid epidemic is examined. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), adults aged 25-34 and their high school best friends are focused on. An instrumental variable technique is employed to estimate peer effects in opioid misuse. Severe injuries in the previous year are used as an instrument for opioid misuse in order to estimate the causal impact of someone misusing opioids on the probability that their best friends also misuse. The estimated peer effects are significant: Having a best friend with a reported serious injury in the previous year increases the probability of own opioid misuse by around 7 percentage points in a population where 17 percent ever misuses opioids. The effect is driven by individuals without a college degree and those who live in the same county as their best friends. |
| Keywords: | opioid, friends, instrumental variables, Add Health, severe injuries, peer-group effects |
| JEL: | C26 D10 I12 J11 |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:eag:rereps:38&r=net |