nep-net New Economics Papers
on Network Economics
Issue of 2022‒11‒14
eight papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Structural importance and evolution: an application to financial transaction networks By Seabrook, Isobel; Barucca, Paolo; Caccioli, Fabio
  2. Sparse Production Networks By Andrew B. Bernard; Yuan Zi
  3. Online Versus Offline: Which Networks Spur Protests? By Niklas Potrafke; Felix Roesel
  4. Social Media and Newsroom Production Decisions By Julia Cagé; Nicolas Hervé; Béatrice Mazoyer
  5. Party On: The Labor Market Returns to Social Networks in Adolescence By Adriana Lleras-Muney; Matthew Miller; Shuyang Sheng; Veronica Sovero
  6. Linear Regression with Centrality Measures By Yong Cai
  7. Key stakeholders and actions to address Lake Beseka’s challenges in Ethiopia: A social network approach By Mekonnen, Dawit Kelemework; Tensay, Teferi M.; Yimam, Seid; Arega, Tiruwork; Beyene, Ephrem G.; Zhang, Wei; Ringler, Claudia
  8. Seller-buyer networks in NFT art are driven by preferential ties By Giovanni Colavizza

  1. By: Seabrook, Isobel; Barucca, Paolo; Caccioli, Fabio
    Abstract: A fundamental problem in the study of networks is the identification of important nodes. This is typically achieved using centrality metrics, which rank nodes in terms of their position in the network. This approach works well for static networks, that do not change over time, but does not consider the dynamics of the network. Here we propose instead to measure the importance of a node based on how much a change to its strength will impact the global structure of the network, which we measure in terms of the spectrum of its adjacency matrix. We apply our method to the identification of important nodes in equity transaction networks and show that, while it can still be computed from a static network, our measure is a good predictor of nodes subsequently transacting. This implies that static representations of temporal networks can contain information about their dynamics.
    Keywords: Node predictability; Spectral perturbation; Temporal network
    JEL: C1 F3 G3
    Date: 2022–12–01
  2. By: Andrew B. Bernard; Yuan Zi
    Abstract: Firm-to-firm connections in domestic and international production networks play a fundamental role in economic outcomes. Firm heterogeneity and the sparse nature of firm-to-firm connections implicitly discipline network structure. We find that a large group of well-established statistical relationships are not useful in improving our understanding of production networks. We propose an “elementary” model for production networks based on random matching and firm heterogeneity and characterize the families of statistics and data generating processes that may raise underidentification concerns in more complex models. The elementary model is a useful benchmark in developing “instructive” statistics and informing model construction and selection.
    Keywords: firm-to-firm networks, model selection, balls-and-bins, buyer-seller matching, underidentification
    JEL: F11 F14
    Date: 2022
  3. By: Niklas Potrafke; Felix Roesel
    Abstract: Does social media or offline social cohesion overcome collective action problems more effectively when both types of networks are prevalent? We investigate non-violent protests against a place-based economic reform in Austria—a country where one in two citizens uses Facebook but also one in two citizens is a member of a local club or civic organization. Our results show that protests spread more in places with strong offline networks as measured by real-life networks like village, folklore, or dialect clubs. We do not find that social media penetration intensifies local protests, a finding corroborated by microdata.
    Keywords: online and offline networks, social media, social cohesion, civic organizations, social capital, protest, economic reform, populism
    JEL: D71 D72 Z20
    Date: 2022
  4. By: Julia Cagé (ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique, CEPR - Center for Economic Policy Research - CEPR); Nicolas Hervé (INA - Institut National de l'Audiovisuel); Béatrice Mazoyer (Médialab - Médialab (Sciences Po) - Sciences Po - Sciences Po)
    Abstract: Social media affects not only the way we consume news, but also the way news is produced, including by traditional media outlets. In this paper, we study the propagation of information from social media to mainstream media, and investigate whether news editors' editorial decisions are influenced by the popularity of news stories on social media To do so, we build a novel dataset including a representative sample of all the tweets produced in French between August 1st 2018 and July 31st 2019 (1.8 billion tweets, around 70% of all tweets in French) and the content published online by 200 mainstream media outlets. We then develop novel algorithms to identify and link events on social and mainstream media. To isolate the causal impact of popularity, we rely on the structure of the Twitter network and propose a new instrument based on the interaction between measures of user centrality and "social media news pressure" at the time of the event. We show that story popularity has a positive effect on media coverage, and that this effect varies depending on the media outlets' characteristics, in particular on whether they use a paywall. Finally, we investigate consumers' reaction to a surge in social media popularity. Our findings shed new light on our understanding of how editors decide on the coverage for stories, and question the welfare effects of social media.
    Keywords: Internet,Information spreading,News editors,Network analysis,Social media,Twitter,Text analysis
    Date: 2022–05–31
  5. By: Adriana Lleras-Muney; Matthew Miller; Shuyang Sheng; Veronica Sovero
    Abstract: We investigate the returns to adolescent friendships on earnings in adulthood. Using data from the National Longitudinal Study of Adolescent to Adult Health, we document that individuals make investments to accumulate friends in addition to educational investments. Our model implies that these investments determine educational attainment and the number of friends, both of which generate returns in the labor market. Because both education and friendships are jointly determined, OLS estimates of their returns are biased. To estimate the causal returns to friendships, we implement a novel procedure that assumes the returns to schooling range from 5 to 15% (as the literature has documented), and instrument for friendships using homophily (similarity) measures to obtain bounds on the returns to friendships. We find that having one more friend in adolescence increases earnings between 7 and 12%. We also investigate which friendships matter and the mechanisms by which friendships affect earnings.
    Date: 2022–10
  6. By: Yong Cai
    Abstract: This paper studies the properties of linear regression on centrality measures when network data is sparse -- that is, when there are many more agents than links per agent -- and when they are measured with error. We make three contributions in this setting: (1) We show that OLS estimators can become inconsistent under sparsity and characterize the threshold at which this occurs, with and without measurement error. This threshold depends on the centrality measure used. Specifically, regression on eigenvector is less robust to sparsity than on degree and diffusion. (2) We develop distributional theory for OLS estimators under measurement error and sparsity, finding that OLS estimators are subject to asymptotic bias even when they are consistent. Moreover, bias can be large relative to their variances, so that bias correction is necessary for inference. (3) We propose novel bias correction and inference methods for OLS with sparse noisy networks. Simulation evidence suggests that our theory and methods perform well, particularly in settings where the usual OLS estimators and heteroskedasticity-consistent/robust t-tests are deficient. Finally, we demonstrate the utility of our results in an application inspired by De Weerdt and Deacon (2006), in which we consider consumption smoothing and social insurance in Nyakatoke, Tanzania.
    Date: 2022–10
  7. By: Mekonnen, Dawit Kelemework; Tensay, Teferi M.; Yimam, Seid; Arega, Tiruwork; Beyene, Ephrem G.; Zhang, Wei; Ringler, Claudia
    Abstract: Lake Beseka is a shallow, saline, endorheic lake in the East African Rift Valley of Ethiopia that has dramatically grown in size due to large-scale irrigation development in its catchment area. Recent artificial connections of the lake with the Awash River system to contain lake size have led to a series of changes and impacts on different water users, but are not reflected in lake and Awash River governance and institutions. Understanding who are the key actors affecting Lake Beseka and strengthening their linkages can help identify solutions that sustainably contain or reduce the lake’s size, improve its water quality, and address costs to nearby and downstream populations as well as the environment. Thus, this study analyzed qualitative data collected from net-mapping – a network analysis that identifies actors or stakeholders as well as linkages and relative power positions among stakeholders. The resulting network reflects the complexity of the water governance system including upstream actors who affect the size and quality of the lake as well as downstream actors who suffer from adverse consequences. The Awash Basin Development Authority, Metehara Sugar Factory, regional bureaus, and federal ministries were identified as the most influential actors affecting how Lake Beseka is used and managed. Actors most affected by the lake expansion and quality problems such as downstream communities currently have no role in the governance of the lake. Metehara Municipality, woreda offices, research institutes, and farmers were considered to have moderate influence. Stakeholders who participated in the net-mapping workshops identified flooding, salinity, water-related conflict, and health effects as the four main challenges of the lake. The study suggests that developing multi-stakeholder partnerships or platforms across most influential and most affected actors could support a more comprehensive understanding of the multiple challenges Lake Beseka is posing. It could also foster the development of more integrated solutions that support the different stakeholders in the lake catchment area and the Awash River Basin.
    Keywords: ETHIOPIA; EAST AFRICA; AFRICA SOUTH OF SAHARA; AFRICA; net-mapping; water governance system; Lake Beseka; Awash Basin; Awash River; water governance; governance; water quality; water
    Date: 2022
  8. By: Giovanni Colavizza
    Abstract: Non-Fungible Tokens (NFTs) have recently surged to mainstream attention by allowing the exchange of digital assets via blockchains. NFTs have also been adopted by artists to sell digital art. One of the promises of NFTs is broadening participation to the arts market, a traditionally closed and opaque system, to sustain a wider and more diverse set of artists and collectors. A key sign of this effect would be the disappearance or at least reduction in importance of seller-buyer preferential ties, whereby the success of an artist is strongly dependent on the patronage of a single collector. We investigate NFT art seller-buyer networks considering several galleries and a large set of nearly 40,000 sales for over 230M USD in total volume. We find that NFT art is a highly concentrated market driven by few successful sellers and even fewer systematic buyers. High concentration is present in both the number of sales and, even more strongly, in their priced volume. Furthermore, we show that, while a broader-participation market was present in the early phase of NFT art adoption, preferential ties have dominated during market growth, peak and recent decline. We consistently find that the top buyer accounts on average for over 80% of buys for a given seller. Similar trends apply to buyers and their top seller. We conclude that NFT art constitutes, at the present, a highly concentrated market driven by preferential seller-buyer ties.
    Date: 2022–10

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