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on Network Economics |
| By: | Vincent Boucher (Department of Economics, Université Laval, CRREP and CREATE); Yann Bramoullé (Aix-Marseille Univ, CNRS, AMSE, Marseille, France.) |
| Abstract: | Heckman and MaCurdy (1985) first showed that binary outcomes are compatible with linear econometric models of interactions. This key insight was unduly discarded by the literature on the econometrics of games. We consider general models of linear interactions in binary outcomes that nest linear models of peer effects in networks and linear models of entry games. We characterize when these models are well defined. Errors must have a specific discrete structure. We then analyze the models' game-theoretic microfoundations. Under complete information and linear utilities, we characterize the preference shocks under which the linear model of interactions forms a Nash equilibrium of the game. Under incomplete information and independence, we show that the linear model of interactions forms a Bayes-Nash equilibrium if and only if preference shocks are iid and uniformly distributed. We also obtain conditions for uniqueness. Finally, we propose two simple consistent estimators. We revisit the empirical analyses of teenage smoking and peer effects of Lee, Li, and Lin (2014) and of entry into airline markets of Ciliberto and Tamer (2009). Our reanalyses showcase the main interests of the linear framework and suggest that the estimations in these two studies suffer from endogeneity problems. |
| Keywords: | binary outcomes, linear probability model, peer effects, econometrics of games |
| JEL: | C31 C35 C57 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:2038&r=all |
| By: | Milad Abbasiharofteh; Dieter F. Kogler; Balazs Lengyel; ; |
| Abstract: | Novel combinations of technologies are generated from existing knowledge embedded in collaborative work. Albeit inventors tend to develop specialized skills and participate in specialized work, it is their collaboration with peers with varied experience that facilitates the production of radical novelty. While this is of key importance, we lack full understanding on how the evolution of inventor collaborations is related to the nature of technological combination. In this paper, we analyse how the role of technological specialization and variety in evolving co-inventor networks is related to the creation of ‘atypical’ inventions in European NUTS2 regions. By analysing the community structure of co-inventor networks in each region, we find that the share of atypical patents is growing where co-inventor communities are strongly specialized in certain technologies and these communities are also bridged by collaborations. Evidence suggests that linking communities of dissimilar technological profiles favours atypical knowledge production the most. Our work implies that to produce radical innovative outcomes, regions must support knowledge production in specialized inventor communities and sponsor the bridging of collaborations to induce diversity. |
| Keywords: | patents, novelty, network communities, technological similarity, network of places |
| JEL: | F23 D85 |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:egu:wpaper:2055&r=all |
| By: | Yifan Yu; Shan Huang; Yuchen Liu; Yong Tan |
| Abstract: | Social media-transmitted online information, particularly content that is emotionally charged, shapes our thoughts and actions. In this study, we incorporate social network theories and analyses to investigate how emotions shape online content diffusion, using a computational approach. We rigorously quantify and characterize the structural properties of diffusion cascades, in which more than six million unique individuals transmitted 387,486 articles in a massive-scale online social network, WeChat. We detected the degree of eight discrete emotions (i.e., surprise, joy, anticipation, love, anxiety, sadness, anger, and disgust) embedded in these articles, using a newly generated domain-specific and up-to-date emotion lexicon. We found that articles with a higher degree of anxiety and love reached a larger number of individuals and diffused more deeply, broadly, and virally, whereas sadness had the opposite effect. Age and network degree of the individuals who transmitted an article and, in particular, the social ties between senders and receivers, significantly mediated how emotions affect article diffusion. These findings offer valuable insight into how emotions facilitate or hinder information spread through social networks and how people receive and transmit online content that induces various emotions. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09003&r=all |
| By: | Vineeth S. Varma; Irinel-Constantin Morarescu; Samson Lasaulce; Samuel Martin |
| Abstract: | One of the key features of this paper is that the agents' opinion of a social network is assumed to be not only influenced by the other agents but also by two marketers in competition. One of our contributions is to propose a pragmatic game-theoretical formulation of the problem and to conduct the complete corresponding equilibrium analysis (existence, uniqueness, dynamic characterization, and determination). Our analysis provides practical insights to know how a marketer should exploit its knowledge about the social network to allocate its marketing or advertising budget among the agents (who are the consumers). By providing relevant definitions for the agent influence power (AIP) and the gain of targeting (GoT), the benefit of using a smart budget allocation policy instead of a uniform one is assessed and operating conditions under which it is potentially high are identified. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.08553&r=all |
| By: | Vineeth S. Varma; Samson Lasaulce; Julien Mounthanyvong; Irinel-Constantin Morarescu |
| Abstract: | In this paper, we consider a network of consumers who are under the combined influence of their neighbors and external influencing entities (the marketers). The consumers' opinion follows a hybrid dynamics whose opinion jumps are due to the marketing campaigns. By using the relevant static game model proposed recently in [1], we prove that although the marketers are in competition and therefore create tension in the network, the network reaches a consensus. Exploiting this key result, we propose a coopetition marketing strategy which combines the one-shot Nash equilibrium actions and a policy of no advertising. Under reasonable sufficient conditions, it is proved that the proposed coopetition strategy profile Pareto-dominates the one-shot Nash equilibrium strategy. This is a very encouraging result to tackle the much more challenging problem of designing Pareto-optimal and equilibrium strategies for the considered dynamical marketing game. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.09268&r=all |
| By: | Andrea Barón (Banco Central del Uruguay); María Victoria Landaberry (Banco Central del Uruguay); Rodrigo Lluberas (Banco Central del Uruguay); Jorge Ponce (Banco Central del Uruguay) |
| Abstract: | We build a commercial credit network, identify the most central economic sectors in terms of commercial debt, and provide a more complete idea of total indebtedness and financial interlinks between firms and banks in Uruguay. "Commerce", "manufacturing" and "transportation, storage, and communication" are the most central sectors in the commercial credit network. In a stress testing exercise, "transport, communication and storage" and "hotels and restaurants" are deeply affected in all cases. These sectors are the most exposed in terms of contagion. "Commerce" and "manufacturing" are central and have the highest level of indebtedness, but they have a large amount of liquid assets, that allows them to overcome shocks coming from other sectors. |
| Keywords: | commercial credit network, financial interlinks, financial contagion, financial stability |
| JEL: | G17 G32 G33 L14 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:bku:doctra:2020006&r=all |
| By: | Silvia Noirjean; Marco Mariani; Alessandra Mattei; Fabrizia Mealli |
| Abstract: | Nudging youths to visit historical and artistic heritage is a key goal pursued by cultural organizations. The field experiment we analyze is a clustered encouragement design (CED) conducted in Florence (Italy) and devised to assess how appropriate incentives assigned to high-school classes may induce teens to visit museums in their free time. In CEDs, where the focus is on causal effects for individuals, interference between units is generally unavoidable. The presence of noncompliance and spillover effects makes causal inference particularly challenging. We propose to deal with these complications by creatively blending the principal stratification framework and causal mediation methods, and exploiting information on interpersonal networks. We formally define principal natural direct and indirect effects and principal controlled direct and indirect effects, and use them to disentangle spillovers from other causal channels. The key insights are that overall principal causal effects for sub-populations of units defined by the compliance behavior combine encouragement, treatment and spillovers effects. In this situation, a synthesis of the network information may be used as a possible mediator, such that the part of the effect that is channeled by it can be attributed to spillovers. A Bayesian approach is used for inference, invoking latent ignorability assumptions on the mediator conditional on principal stratum membership. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.11023&r=all |
| By: | Yuan Tian; Maria Esther Caballero; Brian K. Kovak |
| Abstract: | We document the transmission of social distancing practices from the United States to Mexico along migrant networks during the early 2020 Covid-19 pandemic. Using data on pre-existing migrant connections between Mexican and U.S. locations and mobile-phone tracking data revealing social distancing behavior, we find larger declines in mobility in Mexican regions whose emigrants live in U.S. locations with stronger social distancing practices. We rule out confounding pre-trends and use a variety of controls and an instrumental variables strategy based on U.S. stay-at-home orders to rule out the potential influence of disease transmission and migrant sorting between similar locations. Given this evidence, we conclude that our findings represent the effect of information transmission between Mexican migrants living in the U.S. and residents of their home locations in Mexico. Our results demonstrate the importance of personal connections when policymakers seek to change fundamental social behaviors. |
| Keywords: | Social Learning, Migration, Mexico-U.S., Network, COVID-19 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:not:notgep:2020-12&r=all |
| By: | Jan Bietenbeck |
| Abstract: | I study how motivation shapes own and peers’ educational success. Using data from Project STAR, I find that academic motivation in early elementary school, as measured by a standardized psychological test, predicts contemporaneous and future test scores, high school GPA, and college-test taking over and above cognitive skills. Exploiting random assignment of students to classes, I find that exposure to motivated classmates causally affects contemporaneous reading achievement, a peer effect that operates over and above spillovers from classmates’ past achieve-ment and socio-demographic composition. However, peer motivation does not affect longer-term educational success, likely because it does not change own motivation. |
| Keywords: | motivation, personality, peer effects, Project STAR |
| JEL: | I21 J13 J24 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8696&r=all |
| By: | Menon, Ashish; Rajendran, Nithin K; Chandrachud, Anish |
| Abstract: | The objective of this paper is to study a treatment to social network analysis using the principles of statistical mechanics. After revisiting the popular models and random graph frameworks of complex networks, a formalism to statistical mechanism based on the conventional concepts like phase space, interactions and ensembles is devised. Specific machine learning techniques are employed for the purpose of figuring out the relevant phase-space equations. Thereafter, specific applications of the formalism is explored in the context of business partnership optimization and disease transmission. Several analogues with the statistical mechanics treatment of thermodynamics have also been made. |
| Date: | 2020–11–22 |
| URL: | http://d.repec.org/n?u=RePEc:osf:osfxxx:8we3v&r=all |
| By: | I. C. Morarescu; V. S. Varma; L. Busoniu; S. Lasaulce |
| Abstract: | We address formally the problem of opinion dynamics when the agents of a social network (e.g., consumers) are not only influenced by their neighbors but also by an external influential entity referred to as a marketer. The influential entity tries to sway the overall opinion as close as possible to a desired opinion by using a specific influence budget. We assume that the exogenous influences of the entity happen during discrete-time advertising campaigns; consequently, the overall closed-loop opinion dynamics becomes a linear-impulsive (hybrid) one. The main technical issue addressed is finding how the marketer should allocate its budget over time (through marketing campaigns) and over space (among the agents) such that the agents' opinion be as close as possible to the desired opinion. Our main results show that the marketer has to prioritize certain agents over others based on their initial condition, their influence power in the social graph and the size of the cluster they belong to. The corresponding space-time allocation problem is formulated and solved for several special cases of practical interest. Valuable insights can be extracted from our analysis. For instance, for most cases, we prove that the marketer has an interest in investing most of its budget at the beginning of the process and that budget should be shared among agents according to the famous water-filling allocation rule. Numerical examples illustrate the analysis. |
| Date: | 2020–11 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2011.08639&r=all |
| By: | M. Hashem Pesaran; Cynthia Fan Yang |
| Abstract: | This paper develops an individual-based stochastic network SIR model for the empirical analysis of the Covid-19 pandemic. It derives moment conditions for the number of infected and active cases for single as well as multigroup epidemic models. These moment conditions are used to investigate identification and estimation of recovery and transmission rates. The paper then proposes simple moment-based rolling estimates and shows them to be fairly robust to the well-known under-reporting of infected cases. Empirical evidence on six European countries match the simulated outcomes, once the under-reporting of infected cases is addressed. It is estimated that the number of reported cases could be between 3 to 9 times lower than the actual numbers. Counterfactual analysis using calibrated models for Germany and UK show that early intervention in managing the infection is critical in bringing down the reproduction numbers below unity in a timely manner. |
| Keywords: | Covid-19, multigroup SIR model, basic and effective reproduction numbers, rolling window estimates of the transmission rate, method of moments, calibration and counterfactual analysis. |
| JEL: | C13 C15 C31 D85 I18 J18 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8695&r=all |