|
on Network Economics |
| By: | Aristide Houndetoungan; Asad Islam; Michael Vlassopoulos; Yves Zenou |
| Abstract: | Social networks play an important role in various aspects of life. While extensive research has explored factors such as gender, race, and education in network formation, one dimension that has received less attention is the gender of one's child. Children tend to form friendships with same-gender peers, potentially leading their parents to interact based on their child's gender. Focusing on households with children aged 3-5, we leverage a rich dataset from rural Bangladesh to investigate the role of children's gender in parental network formation. We estimate an equilibrium model of network formation that considers a child's gender alongside other socioeconomic factors. Counterfactual analyses reveal that children's gender significantly shapes parents' network structure. Specifically, if all children share the same gender, households would have approximately 15% more links, with a stronger effect for families having girls. Importantly, the impact of children's gender on network structure is on par with or even surpasses that of factors such as income distribution, parental occupation, education, and age. These findings carry implications for debates surrounding coed versus single-sex schools, as well as policies that foster inter-gender social interactions among children. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.04474&r=net |
| By: | Stefano Pietrosanti (Bank of Italy); Edoardo Rainone (Bank of Italy) |
| Abstract: | We present a simple model of a credit market in which firms borrow from multiple banks and credit relationships are simultaneous and interdependent. In this environment, financial and real shocks induce credit reallocation across more and less affected lenders and borrowers. We show that the interdependence introduces a bias in the standard estimates of the effect of shocks on credit relationships. Moreover, we show that the use of firm fixed effects does not solve the issue, may magnify the problem and that the same bias contaminates fixed effects estimates. We propose a novel model that nests commonly used ones, uses the same information set, accounts for and quantifies spillover effects among credit relationships. We document its properties with Monte Carlo simulations and apply it to real credit register data. Evidence from the empirical application suggests that estimates not accounting for spillovers are indeed highly biased. |
| Keywords: | credit markets, shocks propagation, networks, identification |
| JEL: | C30 L14 G21 |
| Date: | 2023–12 |
| URL: | http://d.repec.org/n?u=RePEc:bdi:wptemi:td_1436_23&r=net |
| By: | Sahab Zandi; Kamesh Korangi; Mar\'ia \'Oskarsd\'ottir; Christophe Mues; Cristi\'an Bravo |
| Abstract: | Whereas traditional credit scoring tends to employ only individual borrower- or loan-level predictors, it has been acknowledged for some time that connections between borrowers may result in default risk propagating over a network. In this paper, we present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network, each layer reflecting a different source of network connection. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac, in which different types of connections arise from the geographical location of the borrower and their choice of mortgage provider. The proposed model considers both types of connections and the evolution of these connections over time. We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance. After testing multiple configurations, a model with GAT, LSTM, and the attention mechanism provides the best results. Empirical results demonstrate that, when it comes to predicting probability of default for the borrowers, our proposed model brings both better results and novel insights for the analysis of the importance of connections and timestamps, compared to traditional methods. |
| Date: | 2024–01 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.00299&r=net |
| By: | Effrosyni Adamopoulou; Jeremy Greenwood; Nezih Guner; Karen A. Kopecky |
| Abstract: | The role of friends in the US opioid epidemic is examined. Using data from the National Longitudinal Survey of Adolescent Health (Add Health), adults aged 25-34 and their high school best friends are focused on. An instrumental variable technique is employed to estimate peer effects in opioid misuse. Severe injuries in the previous year are used as an instrument for opioid misuse in order to estimate the causal impact of someone misusing opioids on the probability that their best friends also misuse. The estimated peer effects are significant: Having a best friend with a reported serious injury in the previous year increases the probability of own opioid misuse by around 7 percentage points in a population where 17 percent ever misuses opioids. The effect is driven by individuals without a college degree and those who live in the same county as their best friends. |
| Keywords: | opioid; peer-group effects; friends; instrumental variables; Add Health; severe injuries |
| JEL: | C26 D10 I12 J11 |
| Date: | 2024–02–20 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedcwq:97764&r=net |
| By: | Jochmans, Koen |
| Abstract: | This paper concerns the analysis of network data when unobserved node-specific heterogeneity is present. We postulate a weighted version of the classic stochastic block model, where nodes belong to one of a finite number of latent communities and the placement of edges between them and any weight assigned to these depend on the communities to which the nodes belong. A simple rank condition is presented under which we establish that the number of latent communities, their distribution, and the conditional distribution of edges and weights given community membership are all nonparametrically identified from knowledge of the joint (marginal) distribution of edges and weights in graphs of a fixed size. The identification argument is constructive and we present a computationally-attractive nonparametric estimator based on it. Limit theory is derived under asymptotics where we observe a growing number of independent networks of a fixed size. The results of a series of numerical experiments are reported on. |
| Keywords: | Heterogeneity; network; random graph; sorting; stochastic block model |
| Date: | 2024–02–26 |
| URL: | http://d.repec.org/n?u=RePEc:tse:wpaper:129137&r=net |
| By: | Hüser, Anne-Caroline; Lepore, Caterina; Veraart, Luitgard A. M. |
| Abstract: | We examine how the repo market operates during liquidity stress by applying network analysis to novel transaction-level data of the overnight gilt repo market including the COVID-19 crisis. We find that during this crisis the repo network becomes more connected, with most institutions relying on previously used counterparties. There are however important changes in the repo volumes and spreads during the stress relative to normal times. There is a significant increase in volumes traded with the central counterparties (CCPs) sector. At the same time non-banks, except hedge funds, decrease borrowing and face higher spreads in the bilateral segment. Overall, this evidence reflects a preference for dealers and banks to transact in the centrally cleared rather than the bilateral segment. Our results can inform the policy debate around the behaviour of banks and non-banks in recent liquidity stress and on widening participation in CCPs by non-banks. |
| Keywords: | repo market; liquidity risk; financial networks; non-banks; Covid-19; coronavirus |
| JEL: | G10 G33 |
| Date: | 2024–02–01 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:121347&r=net |