nep-net New Economics Papers
on Network Economics
Issue of 2023‒10‒16
seven papers chosen by
Alfonso Rosa García, Universidad de Murcia

  1. Social Networks, Gender Norms and Labor Supply: Experimental Evidence Using a Job Search Platform By Afridi, Farzana; Dhillon, Amrita; Roy, Sanchari; Sangwan, Nikita
  2. The Expectations of Others By Ezequiel Garcia-Lembergman; Ina Hajdini; John Leer; Mathieu Pedemonte; Raphael Schoenle
  3. Exploring the network of individuals that influence the media's inflation message in South Africa By Katrien Smuts
  4. Combining Forecasts under Structural Breaks Using Graphical LASSO By Tae-Hwy Lee; Ekaterina Seregina
  5. Topology-Agnostic Detection of Temporal Money Laundering Flows in Billion-Scale Transactions By Haseeb Tariq; Marwan Hassani
  6. Frictions and Adjustments in Firm-to-Firm Trade By Francois Fontaine; Julien Martin; Isabelle Mejean
  7. Digital Disruptions & Inequality By Esteve Almirall; Steve Willmott; Ulises Cort\'es

  1. By: Afridi, Farzana (Indian Statistical Institute (Delhi) and IZA); Dhillon, Amrita (King’s College London and CAGE); Roy, Sanchari (King’s College London and CAGE); Sangwan, Nikita (Indian Statistical Institute)
    Abstract: This paper studies the role of job search frictions and peer effects in shaping female employment outcomes in developing countries. Motivated by a collective model of household decision-making, we conduct a randomized field experiment in Delhi, India where we randomly offer a hyper-local digital job search and matching service to married couples on their own (non-network treatment), together with the wife's peer network (network treatment), or not at all. Approximately one year later, we find no significant impact on wives' overall likelihood of working in either treatment group, but wives in the non-network treatment group reduce their work intensity and casual work, while those in the network treatment group increase their home-based self-employment. Strikingly, husbands' labor market outcomes also improved significantly in the network treatment group. We show theoretically and empirically that our findings can be explained by the home-bound structure of women’s social networks that reinforce (conservative) social norms about women's outside-of-home work.
    Keywords: social networks, social norms, gender, job-matching platforms, employment JEL Classification: J16, J21, J24, O33
    Date: 2023
  2. By: Ezequiel Garcia-Lembergman; Ina Hajdini; John Leer; Mathieu Pedemonte; Raphael Schoenle
    Abstract: Based on a framework of memory and recall that accounts for social networks, we provide conditions under which social networks can amplify expectations. We provide evidence for several predictions of the model using a novel dataset on inflation expectations and social network connections: Inflation expectations in the social network are statistically significantly, positively associated with individual inflation expectations; the relationship is stronger for groups that share common demographic characteristics, such as gender, income, or political affiliation. An instrumental variable approach further establishes causality of these results while also showing that salient information transmits strongly through the network. Our estimates imply that the influence of the social network overall amplifies but does not destabilize inflation expectations.
    Keywords: memory and recall; inflation expectations; social network
    JEL: E31 E71 C83
    Date: 2023–09–26
  3. By: Katrien Smuts
    Abstract: The main goal of this study—and its potential to add to the policy debate—is to cast light on the network of voices that influence the narrative about inflation and monetary policy in South Africa. To that end, this paper first identifies the main individuals (journalists, domestic policy makers, and financial analysts) that influence the inflation message in the news media. Using social network analysis, graph theory, and opinion leadership techniques, I describe the relationships and identify the most prominent persons in the network.
    Keywords: Inflation, Social networks, South Africa
    Date: 2023
  4. By: Tae-Hwy Lee (Department of Economics, University of California Riverside); Ekaterina Seregina (Colby College)
    Abstract: In this paper we develop a novel method of combining many forecasts based on a machine learning algorithm called Graphical LASSO (GL). We visualize forecast errors from different forecasters as a network of interacting entities and generalize network inference in the presence of common factor structure and structural breaks. First, we note that forecasters often use common information and hence make common mistakes,  which makes the forecast errors exhibit common factor structures. We use the Factor Graphical LASSO (FGL, Lee and Seregina (2023)) to separate common forecast errors from the idiosyncratic errors and exploit sparsity of the precision matrix of the latter. Second, since the network of experts changes over time as a response to unstable environments such as recessions, it is unreasonable to assume constant forecast combination weights. Hence, we propose Regime-Dependent Factor Graphical LASSO (RD-FGL) that allows factor loadings and idiosyncratic precision matrix to be regime-dependent. We develop its scalable implementation using the Alternating Direction Method of Multipliers (ADMM) to estimate regime-dependent forecast combination weights. The empirical application to forecasting macroeconomic series using the data of the European Central Bank’s Survey of Professional Forecasters (ECB SPF) demonstrates superior performance of a combined forecast using FGL and RD-FGL.
    Keywords: Common Forecast Errors, Regime Dependent Forecast Combination, Sparse Precision Matrix of Idiosyncratic Errors, Structural Breaks.
    JEL: C13 C38 C55
    Date: 2023–09
  5. By: Haseeb Tariq; Marwan Hassani
    Abstract: Money launderers exploit the weaknesses in detection systems by purposefully placing their ill-gotten money into multiple accounts, at different banks. That money is then layered and moved around among mule accounts to obscure the origin and the flow of transactions. Consequently, the money is integrated into the financial system without raising suspicion. Path finding algorithms that aim at tracking suspicious flows of money usually struggle with scale and complexity. Existing community detection techniques also fail to properly capture the time-dependent relationships. This is particularly evident when performing analytics over massive transaction graphs. We propose a framework (called FaSTMAN), adapted for domain-specific constraints, to efficiently construct a temporal graph of sequential transactions. The framework includes a weighting method, using 2nd order graph representation, to quantify the significance of the edges. This method enables us to distribute complex queries on smaller and densely connected networks of flows. Finally, based on those queries, we can effectively identify networks of suspicious flows. We extensively evaluate the scalability and the effectiveness of our framework against two state-of-the-art solutions for detecting suspicious flows of transactions. For a dataset of over 1 Billion transactions from multiple large European banks, the results show a clear superiority of our framework both in efficiency and usefulness.
    Date: 2023–09
  6. By: Francois Fontaine (Paris School of Economics); Julien Martin (University of Quebec in Montreal); Isabelle Mejean (Sciences Po)
    Abstract: We build a dynamic Ricardian model of trade with search frictions.The model generates an endogenous network of firm-to-firm trade relationships and price bargaining within and across relationships. Following a foreign shock, firms sourcing inputs from abroad have three options: absorb the shock, renegotiate with their current supplier or switch to a supplier in another country. The size of these adjustment margins depends on the interplay between Ricardian comparative advantages, search frictions and firms’ individual characteristics. We exploit French firm-to-firm trade data to estimate the model structurally and quantify the relative importance of these adjustment margins at sector-country level.
    Date: 2023–05
  7. By: Esteve Almirall; Steve Willmott; Ulises Cort\'es
    Abstract: Rising inequality is a critical concern for contemporary societies globally, to the extent that emerging high-growth economies such as China have identified common prosperity as a central goal. Yet, the role and mechanisms of digital disruptions as a cause of inequality and the effectiveness of existing remedies such as taxation, are poorly understood. Particularly the implications of the complex process of technological adoption that requires extensive social validation beyond weak ties. Similarly, the implications of globalization in the hyperconnected landscape of the 21st century remains unexplored. This study aims to shed light in these multifaceted issues. Our findings indicate that network connectivity and the presence of wide bridges between clusters are pivotal factors. We also show the limited utility of taxation as a countermeasure against inequality. Interestingly, the research reveals that the strategic injection of small cohorts of entrepreneurs can expedite technology adoption even when connectivity remains moderate, impacting inequality.
    Date: 2023–09

This nep-net issue is ©2023 by Alfonso Rosa García. It is provided as is without any express or implied warranty. It may be freely redistributed in whole or in part for any purpose. If distributed in part, please include this notice.
General information on the NEP project can be found at For comments please write to the director of NEP, Marco Novarese at <>. Put “NEP” in the subject, otherwise your mail may be rejected.
NEP’s infrastructure is sponsored by the School of Economics and Finance of Massey University in New Zealand.