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on Network Economics |
| By: | Leonardo Costa Ribeiro (CEDEPLAR/UFMG); Jorge Nogueira de Paiva Britto (UFF); Eduardo da Motta e Albuquerque (CEDEPLAR/UFMG) |
| Abstract: | This paper investigates a structural change: the emergence of a Global Innovation System (GIS). Focusing on international knowledge flows (IKFs) we organize the network in three layers according to the type of IKF that connects the institutions: scientific collaboration, patent citation or article citation in patents. We investigate how those three layers overlap and entangle, figuring out a network of networks. We found that each layer follows a free-scale network structure associated with a self-organized system and creates an intrinsic hierarchy. The subnetwork that connects the three layers is also a free-scale network. The intertemporal analysis shows that those properties persist from 2009 to 2017.Therefore, we identified a complex network structure that is very unlike being created by a random process. This structure shows hierarchy, association with self-organized systems, robustness, and specialization, which are the fundamental aspects necessary to define a system. In the context of this analysis, that is the Global Innovation System. |
| Keywords: | International knowledge flows; Innovation systems; Networks of networks |
| JEL: | O32 O34 O39 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:cdp:texdis:td645&r= |
| By: | Alex Centeno; Leidy Garc\'ia |
| Abstract: | The objective of this paper is to identify and analyze the response actions of a set of partially rational players embedded in sub-networks in the context of social interaction and learning. We characterize strategic network formation as a static game of interactions with incomplete information, where players maximize their utility depending on the connections they establish and multiple interdependent actions that permit group-specific parameters of players. It is challenging to apply this type of model to real-life scenarios for two reasons: The computation of the Bayesian Nash Equilibrium is highly demanding and the identification of social influence requires the use of excluded variables that are oftentimes unavailable. Based on the theoretical proposal, we propose a set of simulant equations and discuss the identification of the social interaction effect employing multi-modal network autoregressive. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.08380&r= |
| By: | Xintong Han (Concordia University and CIREQ, Department of Economics, 1455 Boulevard de Maisonneuve Ouest, Concordia University, Montreal, H3G 1M8, Canada); Shaojia Wang (Concordia University, Department of Economics, 1455 Boulevard de Maisonneuve Ouest, Concordia University, Montreal, H3G 1M8, Canada); Tong Wang (University of Edinburgh, Business School, 29 Buccleuch Pl, Edinburgh EH8 9JS. United Kingdom) |
| Abstract: | Using unique data from a leading Chinese content platform with more than 300,000 users, we propose a structural approach to evaluate the effect of the structure of a referral network on users’ renewal decisions. Referral networks provide essential identification sources, which enable us to embed the expectation of network peers’ behavior into the utility function as an important component to capture the decision variations. We find that these networks play an essential role in users’ renewal decisions, which are significantly and positively associated with the renewal decisions of both referrers and referrals. Our counterfactual analysis has important implications for the referral policies of digital platforms. First, we find that the referral-targeted discount discrimination policy is more effective than the uniform discount policy. More optimistic expectations for referrals’ decisions due to the price discount generate a snowball effect on referral networks, which in turn increases renewal rates. Compared to a uniform discount policy, a more referral-targeted discount policy would significantly increase renewal rates while reducing overall revenue loss. Second, our results highlight the importance of the structure of a referral network. With the same beta index, a high-centrality network implies a reduction in the chain hierarchy, which is detrimental to customer retention. We suggest that an efficient referral network should be highly connected with a lower degree of closeness-based centrality. |
| Keywords: | network structural; renewal decision; referral programs; structural estimation |
| JEL: | C51 L53 L82 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:net:wpaper:2205&r= |
| By: | Ovielt Baltodano L\'opez; Roberto Casarin |
| Abstract: | We propose a flexible stochastic block model for multi-layer networks, where layer-specific hidden Markov-chain processes drive the changes in the formation of communities. The changes in block membership of a node in a given layer may be influenced by its own past membership in other layers. This allows for clustering overlap, clustering decoupling, or more complex relationships between layers including settings of unidirectional, or bidirectional, block causality. We cope with the overparameterization issue of a saturated specification by assuming a Multi-Laplacian prior distribution within a Bayesian framework. Data augmentation and Gibbs sampling are used to make the inference problem more tractable. Through simulations, we show that the standard linear models are not able to detect the block causality under the great majority of scenarios. As an application to trade networks, we show that our model provides a unified framework including community detection and Gravity equation. The model is used to study the causality between trade agreements and trade looking at the global topological properties of the networks as opposed to the main existent approaches which focus on local bilateral relationships. We are able to provide new evidence of unidirectional causality from the free trade agreements network to the non-observable trade barriers network structure for 159 countries in the period 1995-2017. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.09354&r= |
| By: | Michel Grabisch (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UP1 - Université Paris 1 Panthéon-Sorbonne); Antoine Mandel (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, UP1 - Université Paris 1 Panthéon-Sorbonne); Agnieszka Rusinowska (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris sciences et lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | We propose a model of the joint evolution of opinions and social relationships in a setting where social influence decays over time. The dynamics are based on bounded confidence: social connections between individuals with distant opinions are severed while new connections are formed between individuals with similar opinions. Our model naturally gives raise to strong diversity, i.e., the persistence of heterogeneous opinions in connected societies, a phenomenon that most existing models fail to capture. The intensity of social interactions is the key parameter that governs the dynamics. First, it determines the asymptotic distribution of opinions. In particular, increasing the intensity of social interactions brings society closer to consensus. Second, it determines the risk of polarization, which is shown to increase with the intensity of social interactions. Our results allow to frame the problem of the design of public debates in a formal setting. We hence characterize the optimal strategy for a social planner who controls the intensity of the public debate and thus faces a trade-off between the pursuit of social consensus and the risk of polarization. We also consider applications to political campaigning and show that both minority and majority candidates can have incentives to lead society towards polarization. |
| Keywords: | opinion dynamics,network formation,network fragility,polarization,institution design,political campaign |
| Date: | 2022–07–02 |
| URL: | http://d.repec.org/n?u=RePEc:hal:pseptp:hal-03770884&r= |
| By: | Michelle Gonz\'alez Amador; Robin Cowan; Eleonora Nillesen |
| Abstract: | Continuing education beyond the compulsory years of schooling is one of the most important choices an adolescent has to make. Higher education is associated with a host of social and economic benefits both for the person and its community. Today, there is ample evidence that educational aspirations are an important determinant of said choice. We implement a multilevel, networked experiment in 45 Mexican high schools and provide evidence of the malleability of educational aspirations. We also show there exists an interdependence of students' choices and the effect of our intervention with peer networks. We find that a video intervention, which combines role models and information about returns to education is successful in updating students' beliefs and consequently educational aspirations. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.08340&r= |
| By: | Zonghui Yao (Northeastern University); Dunia López-Pintado (Universidad Pablo de Olavide); Sara López-Pintado (Northeastern University) |
| Abstract: | The spread of a disease (idea or product) in a population is often hard to predict. In reality, we tend to observe only few specific realizations of the contagion process (e.g., the recent COVID-19 pandemic), therefore limited information can be obtained for predicting future similar events. In this work, we use large-scale simulations to study under different exogenous network properties the complete time course of the contagion process focusing on its unpredictability (or uncertainty). We exploit the functional nature of the data, i.e., the number of infected agents as a function of time, and propose a novel non-parametric measure of variance for functional data based on a weighted version of the depth-based central region area. This methodol-ogy is applied to the susceptible-infected-susceptible epidemiological model and the small-world networks. We find that the degree of uncer-tainty of a contagion process is a non-monotonic (increas-ing/decreasing) function of the contagion rate (the ratio between in-fectious and recovery probabilities). In particular, maximum uncertain-ty is attained at the “stable contagion threshold”, which represents the parameter conditions for which the endemic/steady state is reaching a plateau as a function of the contagion rate. The effect of the density of the net-work and the contagion rate are significant and quite similar, whereas the structure of the network, i.e., its amount of cluster-ing/randomness, has a mild effect on the contagion process. |
| Keywords: | contagion; uncertainty; functional data. |
| JEL: | C02 D80 |
| Date: | 2022 |
| URL: | http://d.repec.org/n?u=RePEc:pab:wpaper:22.12&r= |
| By: | Giovanni Carnazza; Pierluigi Vellucci |
| Abstract: | European Monetary Union continues to be characterised by significant macroeconomic imbalances. Germany has shown increasing current account surpluses at the expense of the other member states (especially the European periphery). Since the creation of a single currency has implied the impossibility of implementing competitive devaluations, trade imbalances within a monetary union can be considered unfair behaviour. We have modelled Eurozone trade flows in goods through a weighted network from 1995 to 2019. To the best of our knowledge, this is the first work that applies this methodology to this kind of data. Network analysis has allowed us to estimate a series of important centrality measures. A polarisation phenomenon emerges in relation to the growth of German dominance. The common currency has then not been capable to remove trade asymmetry, increasing the distance between surplus and deficit countries. This situation should be addressed with expansionary policies on the demand side at national and supranational level. |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2209.09837&r= |
| By: | Eri Yamada; Pierre-Alexandre Balland; Tetsu Kawakami; Jiro Nemoto |
| Abstract: | The literature on economic complexity has shown that the structure of the economy is a strong determinant of diversification, growth, innovation, inequality, and many other major socio- economic outcomes. Most of the empirical analyses, however, remain at a very macro level. It is not clear whether key features of the structure and dynamics of the macroeconomy also apply at a more meso, or micro-level. In this study, we deep dive into the automotive components industry in Japan and contribute to the literature by analyzing within-product category complexity and by taking a dynamic approach to the product space. To achieve this objective, we use unique survey data containing detailed information on each auto part supplier’s product baskets to uncover the industry’s productive structure and the process underlying structural change. We compute and visualize the auto parts product space and confirm properties found for international and domestic economies - suggesting the existence of fractals. These properties include power–laws, nestedness, and core- periphery structures. Moreover, this study develops exploratory and econometric approaches, unifying the measures of product relatedness and product complexity, explaining the productive structure’s dynamic process due to capability accumulation. The empirical analyses reveal that the events of new product appearance are not random but are instead significantly contingent on the network topology of the product space, which in turn shapes its structure. In particular, the effects of the network topology have a significant impact on the development of more complex products with sophisticated capabilities. |
| Keywords: | Product space, Relatedness, Economic complexity, Exploratory network analysis, Auto parts industry |
| JEL: | L62 O31 O33 |
| Date: | 2022–08 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2217&r= |
| By: | Ian Dew-Becker |
| Abstract: | This paper describes the response of the economy to large shocks in a nonlinear production network. While arbitrary combinations of shocks can be studied, it focuses on a sector's tail centrality, which quantifies the effect of a large negative shock to the sector – a measure of the systemic risk of each sector. Tail centrality is theoretically and empirically very different from local centrality measures such as sales share – in a benchmark case, it is measured as a sector's average downstream closeness to final production. The paper then uses the results to analyze the determinants of total tail risk in the economy. Increases in interconnectedness in the presence of complementarity can simultaneously reduce the sensitivity of the economy to small shocks while increasing the sensitivity to large shocks. Tail risk is strongest in economies that display conditional granularity, where some sectors become highly influential following negative shocks. |
| JEL: | D24 D57 E13 E32 |
| Date: | 2022–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:30479&r= |
| By: | Dirk Fornahl; Nils Grashof; Alexander Kopka |
| Abstract: | While innovations have been acknowledged as a key factor for economic growth, it appears that they are unique features of central actors. Recently, especially the outstanding opportunities arising from rather radical innovations have been highlighted. These kinds of innovations combine knowledge pieces that have not been combined before and consequently create something radically new. While the influence of firms' network position on innovativeness in general has already been investigated, it remains to be researched in the context of radical innovations. We address this research gap by empirically investigating the influence of firms' network position on the emergence and diffusion patterns of radical innovations. By analysing a unique dataset evidence is found that central firms are essential drivers of the emergence and diffusion of radical innovations. However, the results also indicate that under certain conditions (e.g. high knowledge diversity) also peripheral firms can contribute to the emergence of radical innovations. |
| Keywords: | Radical innovations, emergence, diffusion, core-periphery, firm-level |
| JEL: | O31 O33 R11 |
| Date: | 2021–02 |
| URL: | http://d.repec.org/n?u=RePEc:atv:wpaper:2102&r= |
| By: | Spyridon Tsangaris; Anastasios Xepapadeas; Athanasios Yannacopoulos |
| Abstract: | We incorporate the spatial dimension into a standard expanding variety growth model based on R&D. The spatial interaction is introduced through spatial production spillovers, knowledge diffusion across space, and the capability for spatial heterogeneity. Forward-looking agents who operate in a nite continuous geographic area choose how much to innovate at each point in time and space. We study the properties of equilibrium and optimal allocations and argue that the characteristics are different from those of the non-spatial model, which alter the appropriate policy measures. We show how spatial interactions may lead regions with spatial homogeneity to differ in their growth rates and areas with spatial heterogeneity to share the same growth rates in the long run. Finally, we present numerical examples to illustrate the different dynamic outcomes and stylized facts from the US economy. |
| Keywords: | endogenous growth, knowledge diffusion, R&D, scale effects, spatial development, spatial production externalities |
| Date: | 2022–10–03 |
| URL: | http://d.repec.org/n?u=RePEc:aue:wpaper:2225&r= |