nep-net New Economics Papers
on Network Economics
Issue of 2020‒01‒06
seventeen papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Influencers and Communities in Social Networks By Chen, C. Y-H.; Härdle, W. K.; Klochkov, Y.
  2. Technology Adoption in Input-Output Networks By Xingtong Han; Lei Xu
  3. A Dynamic Default Contagion Model: From Eisenberg-Noe to the Mean Field By Zachary Feinstein; Andreas Sojmark
  4. Using Network Method to Measure Financial Interconnection By Ying Xu; Jennifer Corbett
  5. More (or Less) Economic Limits of the Blockchain By Joshua S. Gans; Neil Gandal
  6. Identifying network ties from panel data: theory and an application to tax competition By Áureo de Paula; Imran Rasul; Pedro CL Souza
  7. Peer effects in stock market participation: Evidence from immigration By Anastasia Girshina; Thomas Y. Mathä; Michael Ziegelmeyer
  8. Informative social interactions By Arrondel, Luc; Calvo-Pardo, Hector; Giannitsarou, Chryssi; Haliassos, Michael
  9. Intuitive Beliefs By Jawwad Noor
  10. Network Model Trees By Jones, Payton J.; Mair, Patrick; Simon, Thorsten; Zeileis, Achim
  11. Shocks to Supply Chain Networks and Firm Dynamics: An Application of Double Machine Learning By MIYAKAWA Daisuke
  12. EU Economic Modelling System By Olga Ivanova; d'Artis Kancs; Mark Thissen
  13. Global value chains, trade shocks and jobs: an application to Brexit By Hylke Vandenbussche; William Connell Garcia; Wouter Simons
  14. The Role of Prison in Recidivism By Kegon Teng Kok Tan; Mariyana Zapryanova
  15. Improving Access to Savings through Mobile Money: Experimental Evidence from African Smallholder Farmers By Batista, Catia; Vicente, Pedro C.
  16. Alpha Discovery Neural Network based on Prior Knowledge By Jie Fang; Zhikang Xia; Xiang Liu; Shutao Xia; Yong Jiang; Jianwu Lin
  17. Modeling market power on a constrained electricity network By Dahlke, Steven

  1. By: Chen, C. Y-H.; Härdle, W. K.; Klochkov, Y.
    Abstract: Integration of social media characteristics into an econometric framework requires modeling a high dimensional dynamic network with dimensions of parameter Θ typically much larger than the number of observations. To cope with this problem we introduce a new structural model which supposes that the network is driven by influencers. We additionally assume the community structure of the network, such that the users from the same community depend on the same influencers. An estimation procedure is proposed based on a greedy algorithm and LASSO. Through theoretical study and simulations, we show that the matrix parameter can be estimated even when the observed time interval is smaller than the size of the network. Using a novel dataset of 1069K messages from 30K users posted on the microblogging platform StockTwits during a 4-year period (01.2014-12.2018) and quantifying their opinions via natural language processing, we model their dynamic opinions network and further separate the network into communities. With a sparsity regularization, we are able to identify important nodes in the network.
    Keywords: Social Media, Network, Community, Opinion Mining, Natural Language Processing
    Date: 2019–12–17
  2. By: Xingtong Han; Lei Xu
    Abstract: We study how input-output networks affect the speed of technology adoption. In particular, we model the decision to adopt the programming language Python 3 by software packages. Python 3 provides advanced features but is not backward compatible with Python 2, which implies it comes with adoption costs. Moreover, packages are dependent on other packages, meaning one package’s adoption decision is affected by the adoption decisions of other packages because many packages are linked to each other. We build a dynamic model of technology adoption that incorporates an input-output network and estimate it using a complete dataset of Python packages. We are among the first to link the literature of dynamic discrete choice models to network analysis. We also contribute to the literature on technology adoption by showing the adverse effects that input-output networks can have on how technology is adopted in a dynamic setting. We show that a package’s adoption decision is significantly affected by the adoption decisions of its dependency packages. We conduct counterfactual analyses of cost subsidies that target a community level and show that network structure is crucial to determining an optimal policy of cost subsidy.
    Keywords: Economic models; Firm dynamics; Productivity
    JEL: C61 L23 L86 O14 O33
    Date: 2019–12
  3. By: Zachary Feinstein; Andreas Sojmark
    Abstract: In this work we introduce a model of default contagion that combines the approaches of Eisenberg-Noe interbank networks and dynamic mean field interactions. The proposed contagion mechanism provides an endogenous rule for early defaults in a network of financial institutions. The main result is to demonstrate a mean field interaction that can be found as the limit of the finite bank system generated from a finite Eisenberg-Noe style network. In this way, we connect two previously disparate frameworks for systemic risk, and in turn we provide a bridge for exploiting recent advances in mean field analysis when modelling systemic risk. The mean field limit is shown to be well-posed and is identified as a certain conditional McKean-Vlasov type problem that respects the original network topology under suitable assumptions.
    Date: 2019–12
  4. By: Ying Xu; Jennifer Corbett
    Abstract: This paper uses a new approach to measuring financial openness, highlighting interconnectedness in a network of financial flows. Applying an adapted version of eigenvector centrality, often used in network analysis, the new measure captures multidimensional and high-degree financial relations among countries. It provides a nuanced picture of financial integration and interconnectedness in the global and regional financial networks. The United Kingdom and the United States remain the ‘core’ in the global banking network, with all other countries scattered in the ‘periphery’. The application of the new measure of financial integration to the empirical analysis reveals the nonlinear relationship between financial integration and output volatility.
    JEL: F21 F36 G01
    Date: 2019–11
  5. By: Joshua S. Gans; Neil Gandal
    Abstract: This paper extends the blockchain sustainability framework of Budish (2018) to consider proof of stake (in addition to proof of work) consensus mechanisms and permissioned (where the number of nodes are fixed) networks. It is demonstrated that an economically sustainable network will involve the same cost regardless of whether it is proof of work or proof of stake although in the later the cost will take the form of illiquid financial resources. In addition, it is shown that regulating the number of nodes (as in a permissioned network) does not lead to additional cost savings that cannot otherwise be achieved via a setting of block rewards in a permissionless (i.e., free entry) network. This suggests that permissioned networks will not be able to economize on costs relative to permissionless networks.
    JEL: D00 E50 L10
    Date: 2019–12
  6. By: Áureo de Paula (Institute for Fiscal Studies and University College London); Imran Rasul (Institute for Fiscal Studies and University College London and IFS); Pedro CL Souza (Institute for Fiscal Studies)
    Abstract: 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 GMM method. We employ the method to study tax competition across US states. We find 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 the analysis of social interactions can be extended to economic realms where no network data exists.
    Date: 2019–10–21
  7. By: Anastasia Girshina; Thomas Y. Mathä; Michael Ziegelmeyer
    Abstract: This paper studies how peers’ financial behaviour affects individuals’ own investment choices. To identify the peer effect, we exploit the unique composition of the Luxembourg population and use the differences in stock market participation across various immigrant groups to study how they affect stock market participation of natives. We solve the reflection problem by instrumenting immigrants’ stock market participation with lagged participation rates in their countries of birth. We separate the peer effect from the contextual and correlated effects by controlling for neighbourhood and individual characteristics. We find that stock market participation of immigrant peers has sizeable effects on that of natives. We also provide evidence that social learning is one of the channels through which the peer effect is transmitted. However, social learning alone does not account for the entire effect and we conclude that social utility might also play an important role in peer effects transmission.
    Keywords: Peer effects, stock market participation, social utility, social learning
    JEL: D14 D83 G11 I22
    Date: 2019–12
  8. By: Arrondel, Luc; Calvo-Pardo, Hector; Giannitsarou, Chryssi; Haliassos, Michael
    Abstract: We design, field and exploit survey data from a representative sample of the French population to examine whether informative social interactions enter households'stockholding decisions. Respondents report perceptions about their circle of peers with whom they interact about financial matters, their social circle and the population. We provide evidence for the presence of an information channel through which social interactions influence perceptions and expectations about stock returns, and financial behavior. We also find evidence of mindless imitation of peers in the outer social circle, but this does not permeate as many layers of financial behavior as informative social interactions do.
    Keywords: Information networks,Social interactions,Subjective expectations,Peer effects,Portfolio choice
    JEL: D12 D83 D84 G11 C42
    Date: 2019
  9. By: Jawwad Noor (e Department of Economics, Boston University)
    Abstract: Beliefs are intuitive if they rely on associative memory, which can be described as a network of associations between events. A belief-theoretic characterization of the model is provided, its uniqueness properties are established, and the intersection with the Bayesian model is characterized. The formation of intuitive beliefs is modelled after machine learning, whereby the network is shaped by past experience via minimization of the difference from an objective probability distribution. The model is shown to accommodate correlation misperception, the conjunction fallacy, base-rate neglect/conservatism, etc.
    Keywords: Beliefs, Intuition, Associative memory, Boltzmann machine, Energy-Based Neural Networks, Non-Bayesian updating
    JEL: C45 D01 D90
    Date: 2019–12
  10. By: Jones, Payton J.; Mair, Patrick; Simon, Thorsten; Zeileis, Achim
    Abstract: In many areas of psychology, correlation-based network approaches (i.e., psychometric networks) have become a popular tool. In this paper we define a statistical model for correlation-based networks and propose an approach that recursively splits the sample based on covariates in order to detect significant differences in the network structure. We adapt model-based recursive partitioning and conditional inference tree approaches for finding covariate splits in a recursive manner. This approach is implemented in the networktree R package. The empirical power of these approaches is studied in several simulation conditions. Examples are given using real-life data from personality and clinical research.
    Date: 2019–07–30
  11. By: MIYAKAWA Daisuke
    Abstract: We examine the association between changes in supply chain networks and firm dynamics. To determine the causal relationship, first, using data on over a million Japanese firms, we construct machine learning-based prediction models for the three modes of firm exit (i.e., default, voluntary closure, and dissolution) and firm sales growth. Given the high performance in those prediction models, second, we use the double machine learning method (Chernozhukov et al. 2018) to determine causal relationships running from the changes in supply chain networks to those indexes of firm dynamics. The estimated nuisance parameters suggest, first, that an increase in global and local centrality indexes results in lower probability of exits. Second, higher meso-scale centrality leads to higher probability of exits. Third, we also confirm the positive association of global and local centrality indexes with sales growth as well as the negative association of a meso-scale centrality index with sales growth. Fourth, somewhat surprisingly, we found that an increase in one type of local centrality index shows a negative association with sales growth. These results reconfirm the already reported correlation between the centrality of firms in supply chain networks and firm dynamics in a causal relationship and further show the unique role of centralities measured in local and medium-sized clusters.
    Date: 2019–12
  12. By: Olga Ivanova; d'Artis Kancs; Mark Thissen
    Abstract: This is the first study that attempts to assess the regional economic impacts of the European Institute of Innovation and Technology (EIT) investments in a spatially explicit macroeconomic model, which allows us to take into account all key direct, indirect and spatial spillover effects of EIT investments via inter-regional trade and investment linkages and a spatial diffusion of technology via an endogenously determined global knowledge frontier with endogenous growth engines driven by investments in knowledge and human capital. Our simulation results of highly detailed EIT expenditure data suggest that, besides sizable direct effects in those regions that receive the EIT investment support, there are also significant spatial spillover effects to other (non-supported) EU regions. Taking into account all key indirect and spatial spillover effects is a particular strength of the adopted spatial general equilibrium methodology; our results suggest that they are important indeed and need to be taken into account when assessing the impacts of EIT investment policies on regional economies.
    Keywords: DSGE modelling, innovation, productivity, human capital, SCGE model, spatial spillovers.
    JEL: C68 D58 F12 R13 R30
    Date: 2019–10–10
  13. By: Hylke Vandenbussche; William Connell Garcia; Wouter Simons
    Abstract: We develop a network trade model with country-sector level input-output linkages with the objective of evaluating trade shocks. This framework includes (1) domestic and global value chain linkages between all country-sectors, (2) trade flows via domestic and foreign sectors to a final destination, (3) value added rather than gross trade flows. The model is applied to the sectoral World Input Output Database (WIOD) to predict the impact of Brexit for every individual EU country by aggregating up the country-sector effects. In contrast to other studies, we find EU-27 job losses to be substantially higher than hitherto believed as a result of the closely integrated EU network structure. Upstream country-sectors stand to lose more from Brexit due to their network centrality.
    Date: 2019–03–27
  14. By: Kegon Teng Kok Tan; Mariyana Zapryanova
    Abstract: Recidivism rates are a growing concern due to the high cost of imprisonment and the high rate of ex-prisoners returning back to prison. The factors leading to recidivism are multifaceted, but one policy-relevant and potentially important contributor is the composition of peer inmates. In this paper, we study the role of peer e?ects within a correctional facility using data on almost 80,000 individuals serving time in Georgia. We exploit randomness in peer-composition over time within prisons to identify e?ects of peers on recidivism rates. We ?nd no evidence of peer e?ects for property and drug-related crimes in the general prison population. However, we ?nd strong peer e?ects when we de?ne peer groups by race and age. Our ?ndings indicate that homophily plays a large part in determining the strength of peer exposure among prisoners in the same facility. Our ?ndings suggest that prison assignments can be a way to reduce recidivism for particular groups of prisoners.
    Keywords: crime, recidivism, peer effects, prisons
    Date: 2019–12
  15. By: Batista, Catia (Universidade Nova de Lisboa); Vicente, Pedro C. (Universidade Nova de Lisboa)
    Abstract: Investment in improved agricultural inputs is infrequent for smallholder farmers in Africa. One barrier may be limited access to formal savings. This is the first study to use a randomized controlled trial to evaluate the impact of using mobile money as a tool to promote agricultural investment. For this purpose, we designed and conducted a field experiment with a sample of smallholder farmers in rural Mozambique. This sample included a set of primary farmers and their closest farming friends. We work with two cross-randomized interventions. The first treatment gave access to a remunerated mobile savings account. The second treatment targeted closest farming friends and gave them access to the exact same interventions as their primary farmer counterparts. We find that the remunerated mobile savings account raised mobile savings, but only while interest was being paid. It also increased agricultural investment in fertilizer, although there was no change in investment in other complementary inputs that were not directly targeted by the intervention, unlike fertilizer. These results suggest that fertilizer salience in the remunerated savings treatment may have been important to focus farmers' (limited) attention on saving some of their harvest proceeds, rather than farmers being financially constrained by a lack of alternative ways to save. Our results also suggest that the network intervention where farming friends had access to non-remunerated mobile money accounts decreased incentives to save and invest in agricultural inputs, likely due to network free-riding because of lower transfer costs within the network. Overall this research shows that tailored mobile money products can be used effectively to improve modern agricultural technology adoption in countries with very low agricultural productivity like Mozambique.
    Keywords: mobile money, social networks, savings and agricultural investment, randomized field experiment, Mozambique, Africa
    JEL: D14 D85 Q12 Q14
    Date: 2019–11
  16. By: Jie Fang; Zhikang Xia; Xiang Liu; Shutao Xia; Yong Jiang; Jianwu Lin
    Abstract: In financial automatic feature construction task, genetic programming is the state-of-the-art-technic. It uses reverse polish expression to represent features and then uses genetic programming to simulate the evolution process. With the development of deep learning, there are more powerful feature extractors for option. And we think that comprehending the relationship between different feature extractors and data shall be the key. In this work, we put prior knowledge into alpha discovery neural network, combined with different kinds of feature extractors to do this task. We find that in the same type of network, simple network structure can produce more informative features than sophisticated network structure, and it costs less training time. However, complex network is good at providing more diversified features. In both experiment and real business environment, fully-connected network and recurrent network are good at extracting information from financial time series, but convolution network structure can not effectively extract this information.
    Date: 2019–12
  17. By: Dahlke, Steven
    Abstract: A closed electricity network with three markets is modeled to illustrate the impacts of transmission constraints and market power on prices and economic welfare. Four scenarios are presented, the first two assume perfect competition with and without transmission constraints, while the second two model market power with and without transmission constraints. The results show that transmission constraints reduce total surplus relative to the unconstrained case. When firms exercise market power their profits increase, while consumer surplus and total surplus decrease. Some results are counter intuitive, such as price exceeding the marginal cost of the most inefficient generator in a market with perfect competition, caused by transmission constraints and Kirchoff’s voltage law governing power flows. The GAMS code used to solve the models is included in the appendix. Next steps for research involve building the model to replicate a real-world market, to simulate impacts of proposed market restructuring or to identify areas of deregulated markets at high-risk of market power abuse.
    Date: 2019–05–28

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