nep-net New Economics Papers
on Network Economics
Issue of 2023‒08‒28
eleven papers chosen by
Alfonso Rosa García, Universidad de Murcia

  1. Measuring productivity in networks: A game-theoretic approach By Nizar Allouch; Luis A.Guardiola; A. Meca
  2. Functional Differencing in Networks By St\'ephane Bonhomme; Kevin Dano
  3. Raise your voice! Activism and peer effects in online social networks By Alejandra Agustina Martínez
  4. Social Connections and COVID-19 Vaccination By Basu, Arnab K.; Chau, Nancy H.; Firsin, Oleg
  5. Fast but multi-partisan: Bursts of communication increase opinion diversity in the temporal Deffuant model By Fatemeh Zarei; Yerali Gandica; Luis Enrique Correa Rocha
  6. The Interplay among Savings Accounts and Network-Based Financial Arrangements: Evidence from a Field Experiment By Comola, Margherita; Prina, Silvia
  7. On the mathematics of the circular flow of economic activity with applications to the topic of caring for the vulnerable during pandemics By Aziz Guergachi; Javid Hakim
  8. Social and individual learning in the Minority Game By Bryce Morsky; Fuwei Zhuang; Zuojun Zhou
  9. Scenario Sampling for Large Supermodular Games By Bryan S. Graham; Andrin Pelican
  10. Exploring the Bitcoin Mesoscale By Nicol\`o Vallarano; Tiziano Squartini; Claudio J. Tessone
  11. Why Personal Ties (Still) Matter: Referrals and Congestion By Mylius, F.

  1. By: Nizar Allouch; Luis A.Guardiola; A. Meca
    Abstract: Measuring individual productivity (or equivalently distributing the overall productivity) in a network structure of workers displaying peer effects has been a subject of ongoing interest in many areas ranging from academia to industry. In this paper, we propose a novel approach based on cooperative game theory that takes into account the peer effects of worker productivity represented by a complete bipartite network of in- teractions. More specifically, we construct a series of cooperative games where the characteristic function of each coalition of workers is equal to the sum of each worker intrinsic productivity as well as the productivity of other workers within a distance discounted by an attenuation factor. We show that these (truncated) games are balanced and converge to a balanced game when the distance of influence grows large. We then provide an explicit formula for the Shapley value and propose an alternative coalitionally stable distribution of productivity which is computationally much more tractable than the Shapley value. Lastly, we characterize this alternative distribution based on three sensible properties of a logistic network. This analysis enhances our understanding of game-theoretic analysis within logistics networks, offering valuable insights into the peer effects’ impact when assessing the overall productivity and its distribution among workers.
    Keywords: Productivity; peer effects; complete bipartite networks; cooperative games
  2. By: St\'ephane Bonhomme; Kevin Dano
    Abstract: Economic interactions often occur in networks where heterogeneous agents (such as workers or firms) sort and produce. However, most existing estimation approaches either require the network to be dense, which is at odds with many empirical networks, or they require restricting the form of heterogeneity and the network formation process. We show how the functional differencing approach introduced by Bonhomme (2012) in the context of panel data, can be applied in network settings to derive moment restrictions on model parameters and average effects. Those restrictions are valid irrespective of the form of heterogeneity, and they hold in both dense and sparse networks. We illustrate the analysis with linear and nonlinear models of matched employer-employee data, in the spirit of the model introduced by Abowd, Kramarz, and Margolis (1999).
    Date: 2023–07
  3. By: Alejandra Agustina Martínez
    Abstract: Do peers influence individuals’ involvement in political activism? To provide a quantitative answer, I study Argentina’s abortion rights debate through Twitter - the social media platform. Pro-choice and pro-life activists coexisted online, and the evidence suggests peer groups were not too polarized. I develop a model of strategic interactions in a network - allowing for heterogeneous peer effects. Next, I estimate peer effects and test whether online activism exhibits strategic substitutability or complementarity. I create a novel panel dataset - where links and actions are observable - by combining tweets’ and users’ information. I provide a reduced-form analysis by proposing a network-based instrumental variable. The results indicate strategic complementarity in online activism, both from aligned and opposing peers. Notably, the evidence suggests homophily in the formation of Twitter’s network, but it does not support the hypothesis of an echo-chamber effect.
    Keywords: political activism; Peer effects; Social networks; Social media
    Date: 2023
  4. By: Basu, Arnab K. (Cornell University); Chau, Nancy H. (Cornell University); Firsin, Oleg (Cornell University)
    Abstract: This paper unpacks the effects of social networks on monthly county-level COVID19 vaccinations in the US. To parse out short-term community-level externalities where people help each other overcome immediate access barriers, from learning spillovers regarding vaccine efficacy that naturally take time, we distinguish between the contemporaneous and dynamic network effects of vaccination exposure. Leveraging an extensive list of controls and network proxies including Facebook county-to-county links, we find evidence showing positive, stage-of-pandemic dependent contemporaneous friendship network effects. We also consistently find null dynamic network effect, suggesting that social exposure to vaccination has had limited effect on alleviating COVID vaccine hesitancy.
    Keywords: friendship network, COVID-19, vaccine uptake
    JEL: I12 D83 H12
    Date: 2023–07
  5. By: Fatemeh Zarei; Yerali Gandica; Luis Enrique Correa Rocha
    Abstract: Human interactions create social networks forming the backbone of societies. Individuals adjust their opinions by exchanging information through social interactions. Two recurrent questions are whether social structures promote opinion polarisation or consensus in societies and whether polarisation can be avoided, particularly on social media. In this paper, we hypothesise that not only network structure but also the timings of social interactions regulate the emergence of opinion clusters. We devise a temporal version of the Deffuant opinion model where pairwise interactions follow temporal patterns and show that burstiness alone is sufficient to refrain from consensus and polarisation by promoting the reinforcement of local opinions. Individuals self-organise into a multi-partisan society due to network clustering, but the diversity of opinion clusters further increases with burstiness, particularly when individuals have low tolerance and prefer to adjust to similar peers. The emergent opinion landscape is well-balanced regarding clusters' size, with a small fraction of individuals converging to extreme opinions. We thus argue that polarisation is more likely to emerge in social media than offline social networks because of the relatively low social clustering observed online. Counter-intuitively, strengthening online social networks by increasing social redundancy may be a venue to reduce polarisation and promote opinion diversity.
    Date: 2023–07
  6. By: Comola, Margherita (Paris School of Economics); Prina, Silvia (Northeastern University)
    Abstract: This paper studies how formal financial access affects network-based financial arrangements. We use a field experiment that granted access to a savings account to a random subset of households in 19 Nepalese villages. Exploiting a unique panel dataset that follows all bilateral informal financial transactions before and after the intervention, we show that households that were offered access to an account increased their loans and total transfers to others, independent of the treatment status of the receiver. The increase seemed to be driven by treatment households with more assets and greater financial inclusion at baseline.
    Keywords: financial access, savings, networks, financial arrangements
    JEL: C93 D14 G21 O16 O17
    Date: 2023–07
  7. By: Aziz Guergachi; Javid Hakim
    Abstract: We investigate, at the fundamental level, the questions of `why', `when' and `how' one could or should reach out to poor and vulnerable people to support them in the absence of governmental institutions. We provide a simple and new approach that is rooted in linear algebra and basic graph theory to capture the dynamics of income circulation among economic agents. A new linear algebraic model for income circulation is introduced, based on which we are able to categorize societies as fragmented or cohesive. We show that, in the case of fragmented societies, convincing wealthy agents at the top of the social hierarchy to support the poor and vulnerable will be very difficult. We also highlight how linear-algebraic and simple graph-theoretic methods help explain, from a fundamental point of view, some of the mechanics of class struggle in fragmented societies. Then, we explain intuitively and prove mathematically why, in cohesive societies, wealthy agents at the top of the social hierarchy tend to benefit by supporting the vulnerable in their society. A number of new concepts emerge naturally from our mathematical analysis to describe the level of cohesiveness of the society, the number of degrees of separation in business (as opposed to social) networks, and the level of generosity of the overall economy, which all tend to affect the rate at which the top wealthy class recovers its support money back. In the discussion on future perspectives, the connections between the proposed matrix model and statistical physics concepts are highlighted.
    Date: 2023–07
  8. By: Bryce Morsky; Fuwei Zhuang; Zuojun Zhou
    Abstract: We study the roles of social and individual learning on outcomes of the Minority Game model of a financial market. Social learning occurs via agents adopting the strategies of their neighbours within a social network, while individual learning results in agents changing their strategies without input from other agents. In particular, we show how social learning can undermine efficiency of the market due to negative frequency dependent selection and loss of strategy diversity. The latter of which can lock the population into a maximally inefficient state. We show how individual learning can rescue a population engaged in social learning from such inefficiencies.
    Date: 2023–07
  9. By: Bryan S. Graham; Andrin Pelican
    Abstract: This paper introduces a simulation algorithm for evaluating the log-likelihood function of a large supermodular binary-action game. Covered examples include (certain types of) peer effect, technology adoption, strategic network formation, and multi-market entry games. More generally, the algorithm facilitates simulated maximum likelihood (SML) estimation of games with large numbers of players, $T$, and/or many binary actions per player, $M$ (e.g., games with tens of thousands of strategic actions, $TM=O(10^4)$). In such cases the likelihood of the observed pure strategy combination is typically (i) very small and (ii) a $TM$-fold integral who region of integration has a complicated geometry. Direct numerical integration, as well as accept-reject Monte Carlo integration, are computationally impractical in such settings. In contrast, we introduce a novel importance sampling algorithm which allows for accurate likelihood simulation with modest numbers of simulation draws.
    Date: 2023–07
  10. By: Nicol\`o Vallarano; Tiziano Squartini; Claudio J. Tessone
    Abstract: The open availability of the entire history of the Bitcoin transactions opens up the possibility to study this system at an unprecedented level of detail. This contribution is devoted to the analysis of the mesoscale structural properties of the Bitcoin User Network (BUN), across its entire history (i.e. from 2009 to 2017). What emerges from our analysis is that the BUN is characterized by a core-periphery structure a deeper analysis of which reveals a certain degree of bow-tieness (i.e. the presence of a Strongly-Connected Component, an IN- and an OUT-component together with some tendrils attached to the IN-component). Interestingly, the evolution of the BUN structural organization experiences fluctuations that seem to be correlated with the presence of bubbles, i.e. periods of price surge and decline observed throughout the entire Bitcoin history: our results, thus, further confirm the interplay between structural quantities and price movements observed in previous analyses.
    Date: 2023–07
  11. By: Mylius, F.
    Abstract: The internet has reduced search costs significantly, making it much easier to apply for a large number of jobs. In spite of that, the share of jobs found through personal contacts has remained stable over the past decades. My theoretical framework explores a new channel that makes referred candidates favorable for firms: a higher likelihood to accept a job offer. This trait becomes particularly advantageous whenever firms face large uncertainty over whether their candidates would accept their job offer. As we see, if search barriers vanish and workers apply to more firms, a referred candidate expects to face more competitors. On the other hand, with more applications being sent out, workers are, on average, less interested in each firm they apply to, which makes referred candidates stand out more. This means the chances of getting a job offer through a referral can increase if competing workers send out more applications.
    Keywords: Matching theory, networks, winner’s curse, informal labor market
    JEL: C78 D83 D85 J46
    Date: 2023–08–07

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