nep-net New Economics Papers
on Network Economics
Issue of 2023‒11‒27
ten papers chosen by
Alfonso Rosa García, Universidad de Murcia

  1. Collective Search in Networks By Niccolò Lomys
  2. Tractable Aggregation in Endogenous Network Formation Models By Jose M. Betancourt
  3. Identifying Network Ties from Panel Data: Theory and an application to tax competition By Imran Rasul; Pedro Souza; Aureo de Paula
  4. Empirical likelihood for network data By Matsushita, Yukitoshi; Otsu, Taisuke
  6. Monitoring Banking System Connectedness with Big Data By Hale, Galina; Lopez, Jose A
  7. The Making of a Developing Fiscal State: A New Historical Dataset and a Graphical Network Analysis for Greece, 1833-1939 By Franciscos Koutentakis
  8. Peer pressure and manager pressure in organisations By Diego Battiston; Jordi Blanes i Vidal; Tom Kirchmaier; Katalin Szemeredi
  9. Multimodal Information Fusion for the Prediction of the Condition of Condominiums By Miroslav Despotovic; David Koch; Matthias Zeppelzauer; Stumpe Eric; Simon Thaler; Wolfgang A. Brunauer
  10. FDI and superstar spillovers: Evidence from firm-to-firm transactions By Mary Amiti; Cedric Duprez; Jozef Konings; John Van Reenen

  1. By: Niccolò Lomys (CSEF and Università degli Studi di Napoli Federico II.)
    Abstract: I study the dynamics of collective search in networks. Bayesian agents act in sequence, observe the choices of their connections, and privately acquire information about the qualities of different actions via sequential search. If search costs are not bounded away from zero, maximal learning occurs in sufficiently connected networks where individual neighborhood realizations weakly distort agents’ beliefs about the realized network. If search costs are bounded away from zero, maximal learning is possible in several stochastic networks, including almost-complete networks, but generally fails otherwise. When agents observe random numbers of immediate predecessors, the learning rate, the probability of wrong herds, and long-run efficiency properties are the same as in the complete network. The density of indirect connections affects convergence rates. Network transparency has short-run implications for welfare and efficiency.
    Keywords: Networks; Bayesian Learning; Search; Speed and Efficiency of Social Learning.
    JEL: C7 D6 D8
    Date: 2023–10–18
  2. By: Jose M. Betancourt
    Abstract: This paper presents new conditions under which the stationary distribution of a stochastic network formation process can be characterized in terms of a generating function. These conditions are given in terms of a transforming function between networks: if the total transformation between two networks is independent of how these networks transform into each other (by adding or deleting links), then the process is reversible and a generating function can be constructed. When the network formation process is given by discrete choices of link formation, this procedure is equivalent to proving that the game with the associated utilities is a potential game. This implies that the potential game characterization is related to reversibility and tractability of network formation processes. I then use the characterized stationary distribution to study long-run properties of simple models of homophilic dynamics and international trade. The effects of adding forward-looking agents and switching costs are also discussed.
    Date: 2023–10
  3. By: Imran Rasul; Pedro Souza; Aureo de Paula
    Abstract: Social interactions determine many economic behaviors, but information on social ties does not exist in most publicly available and widely used datasets. We present results on the identification of social networks from observational panel data that contains no information on social ties between agents. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. While this result is relevant across different estimation strategies, we then describe how high-dimensional estimation techniques can be used to estimate the interactions model based on the Adaptive Elastic Net Generalized Method of Moments. We employ the method to study tax competition across US states. We find that the identified social interactions matrix implies tax competition differs markedly from the common assumption of competition between geographically neighboring states, providing further insights for the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our identification and application show that the analysis of social interactions can be extended to economic realms where no network data exists. JEL Codes: C31, D85, H71.
    Date: 2023–10–18
  4. By: Matsushita, Yukitoshi; Otsu, Taisuke
    Abstract: This article develops a concept of nonparametric likelihood for network data based on network moments, and proposes general inference methods by adapting the theory of jackknife empirical likelihood. Our methodology can be used not only to conduct inference on population network moments and parameters in network formation models, but also to implement goodness-of-fit testing, such as testing block size for stochastic block models. Theoretically we show that the jackknife empirical likelihood statistic for acyclic or cyclic subgraph moments loses its asymptotic pivotalness in severely or moderately sparse cases, respectively, and develop a modified statistic to recover pivotalness in such cases. The main advantage of our modified jackknife empirical likelihood method is its validity under weaker sparsity conditions than existing methods although it is computationally more demanding than the unmodified version. Supplementary materials for this article are available online.
    Keywords: Bootstrap/resampling; Goodness-of-fit methods; Nonparametric methods; Consolidator Grant (SNP 615882); T&F deal
    JEL: C1
    Date: 2023–08–23
  5. By: Mattia Filomena (Department of Economics and Social Sciences, Marche Polytechnic University); Matteo Picchio (Department of Economics and Social Sciences, Marche Polytechnic University)
    Abstract: We analyse how unemployment affects individuals' social networks, leisure activities, and the related satisfaction measures. Using the LISS panel, a representative longitudinal survey of the Dutch population, we estimate the effects by inverse propensity score weighting in a difference-in-differences design in order to deal with unobserved heterogeneity and unbalanced covariate distribution between treated and control units potentially associated with the dynamics of the outcome variables. We find that, after job loss, individuals increase their network size by strengthening their closest contacts within the family, spending more time with neighbors, and making more use of social media. Although they devote their extra leisure time mostly to private activities, our results do not support the hypothesis of social exclusion following unemployment.
    Keywords: Unemployment, job loss, social exclusion, leisure, social satisfaction, doubly robust difference-in-differences.
    JEL: I31 J01 J64
    Date: 2023–11
  6. By: Hale, Galina; Lopez, Jose A
    Abstract: The need to monitor aggregate financial stability was made clear during the global financial crisis of 2008-2009, and, of course, the need to monitor individual financial firms from a microprudential standpoint remains. However, linkages between financial firms cannot be observed or measured easily. In this paper, we propose a procedure that generates measures of connectedness between individual firms and for the system as a whole based on information observed only at the firm level; i.e., no explicit linkages are observed. We show how bank outcome variables of interest can be decomposed, including with mixed-frequency models, for how network analysis to measure connectedness across firms. We construct two such measures: one based on a decomposition of bank stock returns, the other based on a decomposition of their quarterly return on assets. Network analysis of these decompositions produces measures that could be of use in financial stability monitoring as well as the analysis of individual firms' linkages.
    Keywords: Economics, Banking, Finance and Investment, Applied Economics, Commerce, Management, Tourism and Services
    Date: 2023–10–29
  7. By: Franciscos Koutentakis (Department of Economics, University of Crete, Greece)
    Abstract: The paper studies the historical process of fiscal state-building in 19th and early 20th century Greece. A new public finances dataset, compiled from primary sources, is combined with international databases in a graphical network analysis revealing a rich set of dynamic interactions between economic (tax revenue, debt payments and GDP per capita) and institutional variables (army and representation). The emphasis is on two particular results closely related to the fiscal capacity literature: (a) the size of the army had a positive causal effect on tax revenues whereas (b) representation had a negative causal effect on tax revenue.
    Keywords: Economic History, Applied research
    JEL: N01 N20 N50
    Date: 2023–11–08
  8. By: Diego Battiston; Jordi Blanes i Vidal; Tom Kirchmaier; Katalin Szemeredi
    Abstract: We study the interaction between horizontal (peer) and vertical (manager) social factors in workers' motivation. In our setting, individuals work using open-plan desks. Using a natural experiment, we identify a sharp increase in workers' productivity following the occupation of adjacent desks. We link this peer pressure effect to two key aspects of the worker-manager relation. First, we find stronger peer pressure when managers monitor workers less. Second, we find stronger peer pressure among workers performance-evaluated by the same manager. In a set of counterfactual exercises, we illustrate how organisations could take advantage of these interdependencies to increase worker productivity.
    Keywords: social incentives, teamwork, peer pressure, monitoring, managers, peer effects, organisations, productivity
    Date: 2023–06–01
  9. By: Miroslav Despotovic; David Koch; Matthias Zeppelzauer; Stumpe Eric; Simon Thaler; Wolfgang A. Brunauer
    Abstract: Today's data analysis techniques allow for the combination of multiple different data modalities, which should also allow for more accurate feature extraction. In our research, we leverage the capacity of machine learning tools to build a model with shared neural network layers and multiple inputs that is more flexible and allows for more robust extraction of real estate attributes. The most common form of data for a real estate assessment is data structured in tables, such as size or year of construction, but also descriptions of the real estate. Other data that can also be easily found in real estate listings are visual data such as exterior and interior photographs. In the presented approach, we fuse textual information and variable quantity of interior photographs per condominium for condition assessment and investigate how multiple modalities can be efficiently combined using deep learning. We train and test the performance of a pre-trained convolutional neural network fine-tuned with variable quantity of interior views of selected condominiums. In parallel, we train and test the pre-trained bidirectional encoder-transformer language model using text data from the same observations. Finally, we build an experimental neural network model using both modalities for the same task and compare the performance with the models trained with a single modality. Our initial assumption that coupling both networks would lead to worse performance compared to fine-tuned single-modal models was not confirmed, as we achieved the better performance with the proposed multi-modal model despite the impairment of a very unbalanced dataset. The novelty here is the multimodal modeling of variable quantity of real estate-related attributes in a unified model that integrates all available modalities and can thus use their complementary information. With the presented approach, we intend to extend the existing information extraction methods for automated valuation models, which in turn would contribute to a higher transparency of valuation procedures and thus to more reliable statements about the value of real estate.
    Keywords: Avm; Computer vision; Hedonic Pricing; NLP
    JEL: R3
    Date: 2023–01–01
  10. By: Mary Amiti; Cedric Duprez; Jozef Konings; John Van Reenen
    Abstract: Using firm-to-firm transactions, we show that starting to supply a 'superstar' firm (large domestic firms, exporters, and multinationals) boosts productivity by 8% in the medium run. Placebos on starting relationships with smaller firms and novel identification strategies support a causal interpretation of "superstar spillovers". Consistent with a model of technology transfer, we find falls in markups and bigger treatment effects from technology intensive superstars. We also show that the increase in new buyers is particularly strong within the superstar firm's network, a "dating agency" effect. This suggests an important role for raising productivity through superstars' supply chains regardless of their multinational status.
    Keywords: productivity, FDI, spillovers
    Date: 2023–04–27

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