nep-net New Economics Papers
on Network Economics
Issue of 2023‒07‒24
six papers chosen by
Alfonso Rosa García
Universidad de Murcia

  1. Persecution and Escape By Sascha Becker; Volker Lindenthal; Sharun Mukand; Fabian Waldinger
  2. Optimal Public Transportation Networks: Evidence from the World's Largest Bus Rapid Transit System in Jakarta By Gabriel Kreindler; Arya Gaduh; Tilman Graff; Rema Hanna; Benjamin A. Olken
  3. Endogenous Heterogeneous Gender Norms and the Distribution of Paid and Unpaid Work in an Intra-Household Bargaining Model By Theresa Hager; Patrick Mellacher; Magdalena Rath
  4. Amortized Neural Networks for Agent-Based Model Forecasting By Denis Koshelev; Alexey Ponomarenko; Sergei Seleznev
  5. Regional diversification and labour market upgrading: Local access to skill-related high-income jobs helps workers escaping low-wage employment By Zoltán Elekes; Rikard Eriksson; Anna Baranowska-Rataj
  6. Deep calibration with random grids By Fabio Baschetti; Giacomo Bormetti; Pietro Rossi

  1. By: Sascha Becker (Monash University); Volker Lindenthal (LMU Munich); Sharun Mukand (University of Warwick); Fabian Waldinger (LMU Munich)
    Abstract: We study the role of professional networks in facilitating emigration of Jewish academics dismissed from their positions by the Nazi government. We use individual-level exogenous variation in the timing of dismissals to estimate causal eects. Academics with more ties to early émigrés (emigrated 1933-1934) were more likely to emigrate. Early émigrés functioned as “bridging nodes” that facilitated emigration to their own destination. We also provide evidence of decay in social ties over time and show that professional networks transmit information that is not publicly observable. Finally, we study the relative importance of three types (family, community, professional) of social networks.
    Keywords: professional networks; high-skilled emigration; Nazi Germany; Jewish academics; universities;
    JEL: I20 I23 I28 J15 J24 N30 N34 N40 N44
    Date: 2023–06–21
  2. By: Gabriel Kreindler; Arya Gaduh; Tilman Graff; Rema Hanna; Benjamin A. Olken
    Abstract: Designing public transport networks involves tradeoffs between extensive geographic coverage, frequent service on each route, and relying on interconnections as opposed to direct service. These choices, in turn, depend on individual preferences for waiting times, travel times, and transfers. We study these tradeoffs by examining the world's largest bus rapid transit system, in Jakarta, Indonesia, leveraging a large network expansion between 2016-2020. Using detailed ridership data and aggregate travel flows from smartphone data, we analyze how new direct connections, changes in bus travel time, and wait time reductions increase ridership and overall trips. We set up and estimate a transit network demand model with multi-dimensional travel costs, idiosyncratic heterogeneity induced by random wait times, and inattention, matching event-study moments from the route launches. Commuters in Jakarta are 2-4 times more sensitive to wait time compared to time on the bus, and inattentive to long routes. To study the implications for network design, we introduce a new framework to describe the set of optimal networks. Our results suggest that a less concentrated network would increase ridership and commuter welfare.
    JEL: L91 O18 R48
    Date: 2023–06
  3. By: Theresa Hager (Institute for Comprehensive Analysis of the Economy, Johannes Kepler University Linz, Austria); Patrick Mellacher (Schumpeter Centre, University of Graz, Austria); Magdalena Rath (Schumpeter Centre, University of Graz, Austria)
    Abstract: We study the impact of gender norms on the distribution of paid and unpaid labor between women and men in an intra-household bargaining model featuring endogenous social norms. In contrast to the previous literature, which assumes a homogeneous social norm, agents are connected via explicitly modeled social networks and accordingly face heterogeneous perceptions of gender norms. In our model, social pressure to conform to gender norms exacerbates gender inequalities in the distribution of paid and unpaid labor that may result from a gender pay gap or gender-specific preferences. However, we show that the behavior of agents connected in different standardized social networks is significantly closer to a situation in which agents face no social pressure than in a scenario in which the whole of society perceives homogeneous gender norms. This is particularly true if agents are more likely to form connections to other agents that have similar preferences.
    Date: 2023–06
  4. By: Denis Koshelev (Bank of Russia, Russian Federation); Alexey Ponomarenko (Bank of Russia, Russian Federation); Sergei Seleznev (Bank of Russia, Russian Federation)
    Abstract: In this paper, we propose a new procedure for unconditional and conditional forecasting in agent-based models. The proposed algorithm is based on the application of amortized neural networks and consists of two steps. The first step simulates artificial datasets from the model. In the second step, a neural network is trained to predict the future values of the variables using the history of observations. The main advantage of the proposed algorithm is its speed. This is due to the fact that, after the training procedure, it can be used to yield predictions for almost any data without additional simulations or the re-estimation of the neural network.
    Keywords: agent-based models, amortized simulation-based inference, Bayesian models, forecasting, neural networks.
    JEL: C11 C15 C32 C45 C53 C63
    Date: 2023–07
  5. By: Zoltán Elekes (Centre for Economic and Regional Studies, Umeå University); Rikard Eriksson (Umeå University); Anna Baranowska-Rataj (Umeå University)
    Abstract: This paper investigates how the evolution of local labour market structure enables or constrains workers as regards escaping low-wage jobs. Drawing on the network-based approach of evolutionary economic geography, we employ a detailed individual-level panel dataset to construct skill-relatedness networks for 72 functional labour market regions in Sweden. Subsequent fixed-effect panel regressions indicate that increasing density of skill-related high-income jobs within a region is conducive to low-wage workers moving to better-paid jobs, hence facilitating labour market upgrading through diversification. While metropolitan regions offer a premium for this relationship, it also holds for smaller regions, and across various worker characteristics.
    Keywords: skill-relatedness network; local labour market; low-wage workers; diversification and structural change; relatedness density
    JEL: J21 J31 R11 R23
    Date: 2023–06
  6. By: Fabio Baschetti (Scuola Normale Superiore); Giacomo Bormetti (University of Bologna); Pietro Rossi (University of Bologna; Prometeia S.p.A)
    Abstract: We propose a neural network-based approach to calibrating stochastic volatility models, which combines the pioneering grid approach by Horvath et al. (2021) with the pointwise two-stage calibration of Bayer and Stemper (2018). Our methodology inherits robustness from the former while not suffering from the need for interpolation/extrapolation techniques, a clear advantage ensured by the pointwise approach. The crucial point to the entire procedure is the generation of implied volatility surfaces on random grids, which one dispenses to the network in the training phase. We support the validity of our calibration technique with several empirical and Monte Carlo experiments for the rough Bergomi and Heston models under a simple but effective parametrization of the forward variance curve. The approach paves the way for valuable applications in financial engineering - for instance, pricing under local stochastic volatility models - and extensions to the fast-growing field of path-dependent volatility models.
    Date: 2023–06

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