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on Network Economics |
| By: | Yann Bramoullé (Aix-Marseille Univ, CNRS, EHESS, Ecole Centrale, AMSE, Marseille, France.); Habiba Djebbari (Aix-Marseille Univ, CNRS, EHESS, Ecole Centrale, AMSE, Marseille, France. and IZA); Bernard Fortin (Laval University (Economics Department), CRREP, CIRANO and IZA.) |
| Abstract: | We survey the recent, fast-growing literature on peer effects in networks. An important recurring theme is that the causal identification of peer effects depends on the structure of the network itself. In the absence of correlated effects, the reflection problem is generally solved by network interactions even in non-linear, heterogeneous models. By contrast, microfounda-tions are generally not identified. We discuss and assess the various approaches developed by economists to account for correlated effects and network endogeneity in particular. We classify these approaches in four broad categories: random peers, random shocks, structural endogeneity and panel data. We review an emerging literature relaxing the assumption that the network is perfectly known. Throughout, we provide a critical reading of the existing literature and identify important gaps and directions for future research. |
| Keywords: | social networks, peer effects, identification, causal effects, randomization, measurement errors |
| JEL: | C31 C21 C90 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:aim:wpaimx:1936&r=all |
| By: | Federica Alberti (Portsmouth Business School); Anna Conte (Sapienza University of Rome); Daniela T. Di Cagno (LUISS Guido Carli University); Emanuela Sciubba (Birkbeck University of London) |
| Abstract: | Our social lives are governed by trust. But how do we choose whom to trust? In this work, based on a laboratory experiment, we explore whether building relationships in a social network increases individuals' level of trust. We find that social interactions direct trust, but their impulse is not sufficiently strong to result beneficial. |
| Keywords: | Social network, Trust, Lab experiment |
| JEL: | C72 C91 C92 D82 D85 |
| Date: | 2020–01–24 |
| URL: | http://d.repec.org/n?u=RePEc:pbs:ecofin:2020-02&r=all |
| By: | Hamed Amini (J. Mack Robinson College of Business); Damir Filipović (Ecole Polytechnique Fédérale de Lausanne; Swiss Finance Institute); Andreea Minca (Cornell University) |
| Abstract: | We examine the effects on a financial network of clearing all contracts though a central node (CN) thereby transforming the original network into a star-shaped one. The CN is capitalized with external equity and a guaranty fund. We introduce a structural systemic risk measure that captures the shortfall of end users. We show that it is possible to simultaneously improve the expected surplus of the banks and the CN as well as decrease the shortfall of end users. We determine the CN's equity and guaranty fund policies as a Nash bargaining solution. We illustrate our findings on simulated Credit Default Swap networks compatible with aggregate market data. |
| Keywords: | Star-shaped Networks, Central Node, Market Design, Financial Network, Contagion, Systemic Risk, Credit Default Swap Markets |
| JEL: | C44 C54 C62 G01 G18 G32 |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:chf:rpseri:rp2004&r=all |
| By: | Matthew Elliott; Benjamin Golub; Matthew V. Leduc |
| Abstract: | We model the production of complex goods in a large supply network. Firms source several essential inputs through relationships with other firms. Relationships may fail, and given this idosyncratic risk, firms multisource inputs and make costly investments to make relationships with suppliers stronger (less likely to fail). We find that aggregate production is discontinuous in the strength of these relationships. This has stark implications for equilibrium outcomes. We give conditions under which the supply network is endogenously fragile, so that arbitrarily small negative shocks to relationship strength lead to a large, discontinuous drop in aggregate output. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.03853&r=all |
| By: | Bryan S. Graham |
| Abstract: | Many economic activities are embedded in networks: sets of agents and the (often) rivalrous relationships connecting them to one another. Input sourcing by firms, interbank lending, scientific research, and job search are four examples, among many, of networked economic activities. Motivated by the premise that networks' structures are consequential, this chapter describes econometric methods for analyzing them. I emphasize (i) dyadic regression analysis incorporating unobserved agent-specific heterogeneity and supporting causal inference, (ii) techniques for estimating, and conducting inference on, summary network parameters (e.g., the degree distribution or transitivity index); and (iii) empirical models of strategic network formation admitting interdependencies in preferences. Current research challenges and open questions are also discussed. |
| JEL: | C1 C23 C25 C31 D85 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26577&r=all |
| By: | Hossein Alidaee; Eric Auerbach; Michael P. Leung |
| Abstract: | Social network data can be expensive to collect. Breza et al. (2017) propose aggregated relational data (ARD) as a low-cost substitute that can be used to recover the structure of a latent social network when it is generated by a specific parametric random effects model. Our main observation is that many economic network formation models produce networks that are effectively low-rank. As a consequence, network recovery from ARD is generally possible without parametric assumptions using a nuclear-norm penalized regression. We demonstrate how to implement this method and provide finite-sample bounds on the mean squared error of the resulting estimator for the distribution of network links. Computation takes seconds for samples with hundreds of observations. Easy-to-use code in R and Python can be found at https://github.com/mpleung/ARD. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.06052&r=all |
| By: | Leonardo Bursztyn; Georgy Egorov; Ruben Enikolopov; Maria Petrova |
| Abstract: | We study the causal effect of social media on ethnic hate crimes and xenophobic attitudes in Russia using quasi-exogenous variation in social media penetration across cities. Higher penetration of social media led to more ethnic hate crimes, but only in cities with a high pre-existing level of nationalist sentiment. Consistent with a mechanism of coordination of crimes, the effects are stronger for crimes with multiple perpetrators. We implement a national survey experiment and show that social media persuaded young and low-educated individuals to hold more xenophobic attitudes, but did not increase respondents' openness to expressing these views. Our results are consistent with a simple model of social learning where penetration of social networks increases individuals' propensity to meet like-minded people. |
| JEL: | D7 H0 J15 |
| Date: | 2019–12 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26567&r=all |
| By: | Eyring, Henry; Narayanan, V.G. |
| Abstract: | We conduct a field experiment, based on a registered report accepted by the Journal of Accounting Research, to test performance effects of setting a high reference point for peer-performance comparison. Relative to providing the median as a reference point for online students to compare themselves to, providing the top quartile: damps performance for those below the median; boosts performance for those between the median and top quartile; and, in the case of outcome but not process comparison, boosts performance for those above the top quartile. We do not find that either reference point yields a greater average performance effect. However, providing the more effective reference point in each partition of initial performance yields a 40% greater performance effect than providing either reference point uniformly. Students access the online courses intermittently over the span of a year. Our effects derive from small portions of our treatment groups—5% in the case of process comparison and 26% in the case of outcome comparison—who accessed treatment and who were, on average, more active leading up to and during our intervention |
| Keywords: | Relative performance information; reference points; performance; social comparison |
| JEL: | C93 D91 I21 M41 |
| Date: | 2018–05–30 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:86732&r=all |
| By: | Gerxhani, Klarita; Kosyakova, Yuliya (Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]) |
| Abstract: | "Empirically identifying the causal effect of social networks on migrants' economic prospects is a challenging task due to the non-random residential sorting of migrants into locations with greater opportunities for (previous) connections. Our study addresses this selection-bias issue by using a unique natural-experimental dataset of refugees and other migrants that were exogenously allocated to their first place of residence by German authorities. The empirical results reveal a positive causal effect of social networks on migrants' transition rate to the first job, but only if the networks are mobilized for the job search." (Author's abstract, IAB-Doku) ((en)) |
| JEL: | F22 L14 J61 R23 |
| URL: | http://d.repec.org/n?u=RePEc:iab:iabdpa:202003&r=all |
| By: | Hyun Hak Kim (Department of Economics, Kookmin University); Hosung Jung (Economic Research Institute, Bank of Korea) |
| Abstract: | We investigate a network of financial institutions in Korea using the Korea Consumer Credit Panel (KCCP). The main contribution of this paper is that we construct the network of financial institution from the consumer credit level. We assume each consumer make a loan from multiple institutions so that those institutions share same risk from same consumer no matter of quality or type of loan. Then we construct the financial network between institutions and compute contagion index based on those multiple connection with a weight of default probability of individual borrowers. We found strong connection with banking institutions and credit card firms due to convenience in making small-amount loans with credit cards. However, when we give an weight with default probability to the linkage among institutions, connections of banking institution with savings bank, non-credit card finance corporation and merchant banking are stronger than others, while banking institution holds center position and has biggest amount of loans individually. Contagion index hit a peak in 2013Q1 and then fell rapidly, finally has been fluctuated in relatively low level from 2016 to 2017Q2. The result in our paper enables the authority to watch the systemic risk from consumer credit level with specific consumer type with their default probability. |
| Keywords: | Systemic risk, Financial network, Consumer credit, Financial stability |
| JEL: | C23 D14 G20 G21 G23 |
| Date: | 2019–09–17 |
| URL: | http://d.repec.org/n?u=RePEc:bok:wpaper:1923&r=all |
| By: | Lienert, Jeffrey; Koehly, Laura; Reed-Tsochas, Felix; Marcum, Christopher Steven (National Institutes of Health) |
| Abstract: | The triad census is an important approach to understand local structure in network science, providing The triad census is an important approach to understand local structure in network science, providing comprehensive assessments of the observed relational configurations between triples of actors in a network. However, researchers are often interested in combinations of relational and categorical nodal attributes. In this case, it is desirable to account for the label, or color, of the nodes in the triad census. In this paper, we describe an efficient algorithm for constructing the colored triad census, based, in part, on existing methods for the classic triad census. We evaluate the performance of the algorithm using empirical and simulated data for both undirected and directed graphs. The results of the simulation demonstrate that the proposed algorithm reduces computational time by approximately many-fold over the naive approach. We also apply the colored triad census to the Zachary karate club network dataset. We simultaneously show the efficiency of the algorithm, and a way to conduct a statistical test on the census by forming a null distribution from 1,000 realizations of a mixing-matrix conditioned graph and comparing the observed colored triad counts to the expected. From this, we demonstrate the method's utility in our discussion of results about homophily, heterophily, and bridging, simultaneously gained via the colored triad census. In sum, the proposed algorithm for the colored triad census brings novel utility to social network analysis in an efficient package.comprehensive assessments of the observed relational configurations between triples of actors in a network. However, researchers are often interested in combinations of relational and categorical nodal attributes. In this case, it is desirable to account for the label, or color, of the nodes in the triad census. In this paper, we describe an efficient algorithm for constructing the colored triad census, based, in part, on existing methods for the classic triad census. We evaluate the performance of the algorithm using empirical and simulated data for both undirected and directed graphs. The results of the simulation demonstrate that the proposed algorithm reduces computational time by approximately many-fold over the naive approach. We also apply the colored triad census to the Zachary karate club network dataset. We simultaneously show the efficiency of the algorithm, and a way to conduct a statistical test on the census by forming a null distribution from 1; 000 realizations of a mixing-matrix conditioned graph and comparing the observed colored triad counts to the expected. From this, we demonstrate the method's utility in our discussion of results about homophily, heterophily, and bridging, simultaneously gained via the colored triad census. In sum, the proposed algorithm for the colored triad census brings novel utility to social network analysis in an efficient package. |
| Date: | 2017–12–21 |
| URL: | http://d.repec.org/n?u=RePEc:osf:socarx:rd6kw&r=all |
| By: | Minoru Osawa; Jos\'e M. Gaspar |
| Abstract: | We consider an economic geography model with two inter-regional proximity structures, one due to trade linkages and the other due to social interactions. We investigate how the network structure of social interactions, or the social proximity structure, affects the timing of endogenous agglomeration and the spatial distribution of workers across regions. Endogenous agglomeration emerges when inter-regional trade and/or social interactions incur high transportation costs, and the uniform dispersion occurs when these costs become negligibly small (i.e., when distance dies). In many-region geography, the network structure of social proximity emerges as the determinant of the geographical distribution of workers when trade becomes freer. If social proximity is governed by geographical distance (as in ground transportation), a mono-centric concentration emerges. If geographically distant pairs of regions are ``socially close'' (due to, e.g., passenger transportation modes with strong distance economy such as regional airlines), then geographically multi-centric spatial distribution can be sustainable. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.05095&r=all |
| By: | Geert Ridder; Shuyang Sheng |
| Abstract: | This paper develops estimation methods for network formation models using observed data from a single large network. The model allows for utility externalities from friends of friends and friends in common, so the expected utility is nonlinear in the link choices of an agent. We propose a novel method that uses the Legendre transform to express the expected utility as a linear function of the individual link choices. This implies that the optimal link decision is that for an agent who myopically chooses to establish links or not to the other members of the network. The dependence between the agent's link choices is through an auxiliary variable. We propose a two-step estimation procedure that requires weak assumptions on equilibrium selection, is simple to compute, and has consistent and asymptotically normal estimators for the parameters. Monte Carlo results show that the estimation procedure performs well. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.03838&r=all |
| By: | Christopher J. Blackburn; Juan Moreno-Cruz |
| Abstract: | Industrial activity periodically experiences breakthrough innovations in energy efficiency, but the estimated impacts of these innovations on aggregate energy use are highly varied. We develop a general equilibrium model to investigate whether this variation is determined by the structure of the economy’s input-output network. Our results show sector-specific energy efficiency improvements affect aggregate energy use through adjustments in factor markets and commodity markets, and a process of structural transformation that alters the way energy is used and produced in the economy. We link the aggregate impact of these processes with new network centrality concepts that account for the capacity of a sector to transmit and respond to efficiency innovations. In a calibrated simulation, we find variation in these centrality concepts explains between 38 and 92 percent of variation in the aggregate impacts of energy efficiency, which suggests input-output structure is a critical determinant of the aggregate effects of energy efficiency. |
| Keywords: | energy efficiency, production network, input-output, rebound effect |
| JEL: | C67 D58 L23 O33 Q43 Q58 |
| Date: | 2019 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8007&r=all |
| By: | Paolo Bartesaghi; Gian Paolo Clemente; Rosanna Grassi |
| Abstract: | Community detection in a network plays a crucial role in the economic and financial contexts, specifically when applied to the World Trade Network. We provide a new perspective in which clusters of strongly interacting countries are identified by means of a specific distance criterion. We refer to the Estrada communicability distance and the vibrational communicability distance, which turn out to be particularly suitable for catching the inner structure of the economic network. The methodology is based on a varying distance threshold and it is effective from a computational point of view. It also allows an inspection of the intercluster and intracluster properties of the resulting communities. The numerical analyses highlight peculiar relationships between countries and provide a rich set of information that can hardly be achieved within alternative clustering approaches. |
| Date: | 2020–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2001.06356&r=all |