nep-net New Economics Papers
on Network Economics
Issue of 2023‒05‒15
ten papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Homophily and Transmission of Behavioral Traits in Social Networks By Palaash Bhargava; Daniel L. Chen; Matthias Sutter; Camille Terrier
  2. A Theory of Payments-Chain Crises By Saki Bigio
  3. Academic Migration and Academic Networks: Evidence from Scholarly Big Data and the Iron Curtain By Donia Kamel; Laura Pollacci
  4. Brexit and Canadadvent: An Application of Graphs and Hypergraphs to Recent International Trade Agreements By Michela Chessa; Arnaud Persenda; Dominique Torre
  5. Reconstructing firm-level input-output networks from partial information By Andrea Bacilieri; Pablo Austudillo-Estevez
  6. Bound by Ancestors: Immigration, Credit Frictions, and Global Supply Chain Formation By Jaerim Choi; Jay Hyun; Ziho Park
  7. A probabilistic method for reconstructing the foreign direct investments network in search of ultimate host economies By Nadia Accoto; Valerio Astuti; Costanza Catalano
  8. Theory and Evidence of Firm-to-firm Transaction Network Dynamics By Takafumi Kawakubo; Takafumi Suzuki
  9. GDP nowcasting with artificial neural networks: How much does long-term memory matter? By Krist\'of N\'emeth; D\'aniel Hadh\'azi
  10. Artificial neural networks and time series of counts: A class of nonlinear INGARCH models By Malte Jahn

  1. By: Palaash Bhargava (Max Planck Institute for Research on Collective Goods, Bonn); Daniel L. Chen (Max Planck Institute for Research on Collective Goods, Bonn); Matthias Sutter (Max Planck Institute for Research on Collective Goods, Bonn); Camille Terrier (Max Planck Institute for Research on Collective Goods, Bonn)
    Abstract: Social networks are a key factor of success in life, but they are also strongly segmented on gender, ethnicity, and other demographic characteristics (Jackson, 2010). We present novel evidence on an understudied source of homophily: behavioral traits. Behavioral traits are important determinants of life outcomes. While recent work has focused on how these traits are influenced by the family environment, or how they can be affected by childhood interventions, little is known about how these traits are related to social networks. Based on unique data collected using incentivized experiments on more than 2, 500 French high-school students, we find high levels of homophily across all ten behavioral traits that we study. Notably, the extent of homophily depends on similarities in demographic characteristics, in particular with respect to gender. Furthermore, the larger the number of behavioral traits that students share, the higher the overall homophily. Using network econometrics, we show that the observed homophily is not only an outcome of endogenous network formation, but is also a result of friends influencing each others’ behavioral traits. Importantly, the transmission of traits is larger when students share demographic characteristics, such as gender, have longer periods of friendship, or are friends with more popular individuals.
    Keywords: Homophily, social networks, behavioral traits, peer effects, experiments
    JEL: D85 C91 D01 D90
    Date: 2023–02
  2. By: Saki Bigio
    Abstract: This paper introduces an endogenous network of payments chains into a business cycle model. Agents order production in bilateral relations. Some payments are executed immediately. Other payments, chained payments, are delayed until other payments are executed. Because production starts only after orders are paid, chained payments induce production delays. In equilibrium, agents choose the amount of chained payments given interest rates and access to internal funds or credit lines. This choice determines the payments-chain network and aggregate total-factor productivity (TFP). The paper characterizes equilibrium dynamics and their innate inefficiencies. Agents internalize the direct costs of their payment delays, but do not internalize the costs induced onto others. This externality produces novel policy insights and rationalizes permanent reductions in TFP under excessive debt.
    Keywords: Payments, Networks, Business Cycles
    Date: 2023–04
  3. By: Donia Kamel; Laura Pollacci
    Abstract: Iron Curtain and Big Data are two words usually used to denote completely two different eras. Yet, the context the former offers and the rich data source the latter provides, enable the causal identification of the effect of networks on migration. Academics in countries behind the Iron Curtain were strongly isolated from the rest of the world. This context poses the question of the importance of academic networks for migration post the fall of the Berlin Wall and Iron Curtain. Using Microsoft Academic Knowledge Graph, a scholarly big data source, mapping of academics’ networks is possible and information about the size and quality of their co-authorships, by location is achieved. Focusing on academics from Eastern Europe (henceforth EE) from 1980-1988 and their academic networks (1980-1988), We investigate the effect of academic network characteristics, by location, on the probability to migrate post the fall of the Berlin Wall in 1989 and up to 2003, marking the year many EE countries held referendums or signed treaties to join the EU. The unique context ensures that there was no anticipation of the fall of the Eastern Bloc and together with the data that offers unique rich information, identification is achieved. Approximately 30k academics from EE were identified, from which 3% were migrants. The results could be explained by two channels, the cost and signalling channel. The cost channel is how the network characteristic reduces or increases the cost of migration and thus acting as a facilitator or a de-facilitator of migration. The signal channel on the other hand in which the network characteristic serves as a signal for the academic himself and his quality and his potential contribution and addition to the new host institution, thus also serving as a facilitator or a de-facilitator of migration. We find that mostly network size and quality results could be explained by the cost channel and signalling channel, respectively. Size of the network tends to be more important than the quality, which is a context-specific result. We find heterogeneous effects by fields of study that align with previous lines of research. Heterogeneous effects are explained by two things: threat of attention and arrest from KGB and the role of reputation, language, and network barriers.
    Keywords: networks, migration, academic networks, Big Data, brain drain, Iron Curtain, Eastern Europe
    JEL: C55 D85 F50 I20 I23 J24 N34 N44 O15
    Date: 2023
  4. By: Michela Chessa (Université Côte d'Azur, France; GREDEG CNRS); Arnaud Persenda (Université Côte d'Azur, France; GREDEG CNRS); Dominique Torre (Université Côte d'Azur, France; GREDEG CNRS; University of Bergamo)
    Abstract: This paper uses a network approach to study the relationship between trade agreements and trade flows. For the first time in the literature, hypergraphs are used to capture the topology of trade agreements, while theusual graphs are used to represent trade fows. For our analysis, we focused on a snapshot of 2017 data. This data did not consider CETA as an agreement already in force (the provisional application began only on September 21). It was also several years before Brexit. An analysis of modularity conducted on both the trade agreements and the trade flows shows an imperfect correspondence between the communities of countries found within the two networks. The results justify Brexit as a way to reconcile the networks of flows and agreements, and the CETA agreement as a confirmation that Canada already belonged to the module of countries including the EU community concerning trade agreements.
    Keywords: hypergraphs, trade agreements, networks, Brexit, CETA
    JEL: F10 F14 F15 C69
    Date: 2022–09
  5. By: Andrea Bacilieri; Pablo Austudillo-Estevez
    Abstract: There is a large consensus on the fundamental role of firm-level supply chain networks in macroeconomics. However, data on supply chains at the fine-grained, firm level are scarce and frequently incomplete. For listed firms, some commercial datasets exist but only contain information about the existence of a trade relationship between two companies, not the value of the monetary transaction. We use a recently developed maximum entropy method to reconstruct the values of the transactions based on information about their existence and aggregate information disclosed by firms in financial statements. We test the method on the administrative dataset of Ecuador and reconstruct a commercial dataset (FactSet). We test the method's performance on the weights, the technical and allocation coefficients (microscale quantities), two measures of firms' systemic importance and GDP volatility. The method reconstructs the distribution of microscale quantities reasonably well but shows diverging results for the measures of firms' systemic importance. Due to the network structure of supply chains and the sampling process of firms and links, quantities relying on the number of customers firms have (out-degrees) are harder to reconstruct. We also reconstruct the input-output table of globally listed firms and merge it with a global input-output table at the sector level (the WIOD). Differences in accounting standards between national accounts and firms' financial statements significantly reduce the quality of the reconstruction.
    Date: 2023–03
  6. By: Jaerim Choi; Jay Hyun; Ziho Park
    Abstract: This paper shows that the ancestry composition shaped by century-long immigration to the US can explain the current structure of global supply chain networks. Using an instrumental variable strategy, combined with a novel dataset that links firm-to-firm global supply chain information with a US establishment database and historical migration data, we find that the co-ethnic networks formed by immigration have a positive causal impact on global supply chain relationships between foreign countries and US counties. Such a positive impact not only exists in conventional supplier-customer relationships but also extends to strategic partnerships and trade in services. Examining the causal mechanisms, we find that the positive impact is stronger for counties in which more credit-constrained firms are located and that such a stronger effect becomes even more pronounced for foreign firms located in countries with weak contract enforcement. Collectively, the results suggest that co-ethnic networks serve as social collateral to overcome credit constraints and facilitate global supply chain formation.
    JEL: F14 F22 F36 F60 G30 J61 L14
    Date: 2023–04
  7. By: Nadia Accoto (Bank of Italy); Valerio Astuti (Bank of Italy); Costanza Catalano (Bank of Italy)
    Abstract: The Ultimate Host Economies (UHEs) of a given country are defined as the ultimate destinations of Foreign Direct Investment (FDI) originating in that country. Bilateral FDI statistics struggle to identify them due to the non-negligible presence of conduit jurisdictions, which provide attractive intermediate destinations for pass-through investments due to favorable tax regimes. At the same time, determining UHEs is crucial for understanding the actual paths followed by FDI among increasingly interdependent economies. In this paper, we first reconstruct the global FDI network through mirroring and clustering techniques, starting from data collected by the International Monetary Fund. Then we provide a method for computing an (approximate) distribution of the UHEs of a country by using a probabilistic approach to this network, based on Markov chains. More specifically, we analyze the Italian case.
    Keywords: foreign direct investment, ultimate host economies, Special Purpose Entities, network reconstruction, clustering, absorbing Markov chains
    JEL: C51 C60 F23 G15
    Date: 2023–04
  8. By: Takafumi Kawakubo; Takafumi Suzuki
    Abstract: How are supply chains formed and restructured over time? This paper investigates firm-to-firm transaction network dynamics from theoretical and empirical perspectives, exploiting large-scale firm-level transaction data from Japan. First, we provide basic facts which show substantial churning in supply chains over time, even after excluding the cases where either supplier or customer firms exit from the market. Second, we empirically find that productivity positive assortative matching between firms exists. Firms are more likely to keep trading with more productive firms and instead stop trading with less productive ones. Alternatively, more productive firms start new transactions with more productive business partners. Lastly, we build a theoretical framework to rationalize these findings. Both supplier and customer firms are heterogeneous and choose their trading partners with many-to-many matching setting. We derive the implications for supply chain formation and restructuring in response to productivity shocks.
    Date: 2023–04
  9. By: Krist\'of N\'emeth; D\'aniel Hadh\'azi
    Abstract: In our study, we apply different statistical models to nowcast quarterly GDP growth for the US economy. Using the monthly FRED-MD database, we compare the nowcasting performance of the dynamic factor model (DFM) and four artificial neural networks (ANNs): the multilayer perceptron (MLP), the one-dimensional convolutional neural network (1D CNN), the long short-term memory network (LSTM), and the gated recurrent unit (GRU). The empirical analysis presents the results from two distinctively different evaluation periods. The first (2010:Q1 -- 2019:Q4) is characterized by balanced economic growth, while the second (2010:Q1 -- 2022:Q3) also includes periods of the COVID-19 recession. According to our results, longer input sequences result in more accurate nowcasts in periods of balanced economic growth. However, this effect ceases above a relatively low threshold value of around six quarters (eighteen months). During periods of economic turbulence (e.g., during the COVID-19 recession), longer training sequences do not help the models' predictive performance; instead, they seem to weaken their generalization capability. Our results show that 1D CNN, with the same parameters, generates accurate nowcasts in both of our evaluation periods. Consequently, first in the literature, we propose the use of this specific neural network architecture for economic nowcasting.
    Date: 2023–04
  10. By: Malte Jahn
    Abstract: Time series of counts are frequently analyzed using generalized integer-valued autoregressive models with conditional heteroskedasticity (INGARCH). These models employ response functions to map a vector of past observations and past conditional expectations to the conditional expectation of the present observation. In this paper, it is shown how INGARCH models can be combined with artificial neural network (ANN) response functions to obtain a class of nonlinear INGARCH models. The ANN framework allows for the interpretation of many existing INGARCH models as a degenerate version of a corresponding neural model. Details on maximum likelihood estimation, marginal effects and confidence intervals are given. The empirical analysis of time series of bounded and unbounded counts reveals that the neural INGARCH models are able to outperform reasonable degenerate competitor models in terms of the information loss.
    Date: 2023–04

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