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on Network Economics |
| By: | Luca Mungo; Alexandra Brintrup; Diego Garlaschelli; Fran\c{c}ois Lafond |
| Abstract: | Network reconstruction is a well-developed sub-field of network science, but it has only recently been applied to production networks, where nodes are firms and edges represent customer-supplier relationships. We review the literature that has flourished to infer the topology of these networks by partial, aggregate, or indirect observation of the data. We discuss why this is an important endeavour, what needs to be reconstructed, what makes it different from other network reconstruction problems, and how different researchers have approached the problem. We conclude with a research agenda. |
| Date: | 2023–09 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.00446&r=net |
| By: | Gary W. Cox; Jon H. Fiva; Max-Emil M. King |
| Abstract: | A vast and growing quantitative literature considers how social networks shape political mobilization but the degree to which turnout decisions are strategic remains ambiguous. Unlike previous studies, we establish personal links between voters and candidates and exploit discontinuous incentives to mobilize across district boundaries to estimate causal effects. Considering three types of network—families, co-workers, and immigrant communities—we show that a group member’s candidacy acts as a mobilizational impulse that propagates through the group’s network. In family networks, some of this impulse is non-strategic, surviving past district boundaries. However, the bulk of family mobilization is bound by the candidate’s district boundary, as is the entirety of the mobilizational effects in the other networks. |
| Keywords: | political participation, social networks, electoral geography |
| JEL: | D72 D85 C33 |
| Date: | 2023 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_10718&r=net |
| By: | Yuanrong Wang; Antonio Briola; Tomaso Aste |
| Abstract: | Modern portfolio optimization is centered around creating a low-risk portfolio with extensive asset diversification. Following the seminal work of Markowitz, optimal asset allocation can be computed using a constrained optimization model based on empirical covariance. However, covariance is typically estimated from historical lookback observations, and it is prone to noise and may inadequately represent future market behavior. As a remedy, information filtering networks from network science can be used to mitigate the noise in empirical covariance estimation, and therefore, can bring added value to the portfolio construction process. In this paper, we propose the use of the Statistically Robust Information Filtering Network (SR-IFN) which leverages the bootstrapping techniques to eliminate unnecessary edges during the network formation and enhances the network's noise reduction capability further. We apply SR-IFN to index component stock pools in the US, UK, and China to assess its effectiveness. The SR-IFN network is partially disconnected with isolated nodes representing lesser-correlated assets, facilitating the selection of peripheral, diversified and higher-performing portfolios. Further optimization of performance can be achieved by inversely proportioning asset weights to their centrality based on the resultant network. |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.14881&r=net |
| By: | John List; Fatemeh Momeni; Michael Vlassopoulos; Yves Zenou |
| Abstract: | This study explores the role of neighborhoods on human capital formation at an early age. We do so by estimating the spillover effects of an early childhood intervention on the educational attainment of a large sample of disadvantaged children in the United States. We document large spillover effects on the cognitive skills of children living near treated children, which amount to approximately 40% of the direct treatment effects. Interestingly, these spillover effects are localized and decrease with the spatial distance to treated neighbors. We do not find evidence of spillover effects on non-cognitive skills. Perhaps our most novel insight is the underlying mechanisms at work: the spillover effect on cognitive scores is very localized and seems to operate through the child's social network, mostly between treated kids. We do not find evidence that parents' or children's social networks are effective for non-cognitive skills. Overall, our results reveal the importance of public programs and neighborhoods on human capital formation at an early age, highlighting that human capital accumulation is fundamentally a social activity. |
| Date: | 2023 |
| URL: | https://d.repec.org/n?u=RePEc:feb:framed:00722&r=net |
| By: | Tamas David-Barrett |
| Abstract: | The practice of marriage is an understudied phenomenon in behavioural sciences despite being ubiquitous across human cultures. This modelling paper shows that replacing distant direct kin with in-laws increases the interconnectedness of the family social network graph, which allows more cooperative and larger groups. In this framing, marriage can be seen as a social technology that reduces free-riding within collaborative group. This approach offers a solution to the puzzle of why our species has this particular form of regulating mating behaviour, uniquely among pair-bonded animals. |
| Date: | 2023–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.05928&r=net |
| By: | Davide Viviano; Lihua Lei; Guido Imbens; Brian Karrer; Okke Schrijvers; Liang Shi |
| Abstract: | This paper studies the design of cluster experiments to estimate the global treatment effect in the presence of spillovers on a single network. We provide an econometric framework to choose the clustering that minimizes the worst-case mean-squared error of the estimated global treatment effect. We show that the optimal clustering can be approximated as the solution of a novel penalized min-cut optimization problem computed via off-the-shelf semi-definite programming algorithms. Our analysis also characterizes easy-to-check conditions to choose between a cluster or individual-level randomization. We illustrate the method's properties using unique network data from the universe of Facebook's users and existing network data from a field experiment. |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.14983&r=net |
| By: | Shijia Song; Handong Li |
| Abstract: | Bank crisis is challenging to define but can be manifested through bank contagion. This study presents a comprehensive framework grounded in nonlinear time series analysis to identify potential early warning signals (EWS) for impending phase transitions in bank systems, with the goal of anticipating severe bank crisis. In contrast to traditional analyses of exposure networks using low-frequency data, we argue that studying the dynamic relationships among bank stocks using high-frequency data offers a more insightful perspective on changes in the banking system. We construct multiple recurrence networks (MRNs) based on multidimensional returns of listed banks' stocks in China, aiming to monitor the nonlinear dynamics of the system through the corresponding indicators and topological structures. Empirical findings indicate that key indicators of MRNs, specifically the average mutual information, provide valuable insights into periods of extreme volatility of bank system. This paper contributes to the ongoing discourse on early warning signals for bank instability, highlighting the applicability of predicting systemic risks in the context of banking networks. |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.10283&r=net |
| By: | Nail Kashaev; Natalia Lazzati; Ruli Xiao |
| Abstract: | We develop a general model of discrete choice that incorporates peer effects in preferences and consideration sets. We characterize the equilibrium behavior and establish conditions under which all parts of the model can be recovered from a sequence of choices. We allow peers to affect only preferences, only consideration, or both. We exploit different types of variations to separate the peer effects in preferences and consideration sets. This allows us to recover the set (and type) of connections between the agents in the network. We then use this information to recover the random preferences and the attention mechanisms of each agent. These nonparametric identification results allow unrestricted heterogeneity across agents and do not rely on the variation of either covariates or the set of available options (or menus). We apply our results to model expansion decisions by coffee chains and find evidence of limited consideration. We simulate counterfactual predictions and show how limited consideration slows down competition. |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.12272&r=net |
| By: | Emmet Hall-Hoffarth |
| Abstract: | Recently a number of papers have suggested using neural-networks in order to approximate policy functions in DSGE models, while avoiding the curse of dimensionality, which for example arises when solving many HANK models, and while preserving non-linearity. One important step of this method is to represent the constraints of the economic model in question in the outputs of the neural-network. I propose, and demonstrate the advantages of, a novel approach to handling these constraints which involves directly constraining the neural-network outputs, such that the economic constraints are satisfied by construction. This is achieved by a combination of re-scaling operations that are differentiable and therefore compatible with the standard gradient descent approach used when fitting neural-networks. This has a number of attractive properties, and is shown to out-perform the penalty-based approach suggested by the existing literature, which while theoretically sound, can be poorly behaved practice for a number of reasons that I identify. |
| Date: | 2023–10 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2310.13436&r=net |
| By: | Xie, Gang; Wang, Bing; Manyweathers, Jennifer |
| Abstract: | The ultimate goal of statistical modelling is to best represent the information contained in a set of sample data. A sampling subject (i.e., an experimental unit or an observational unit on which measurements were taken) in a data collection scheme can be a person, a plant, an animal, or even an event, etc. In this study, Bayesian Network (BN) models were fitted to four data sets to explore the potential for ideal representation of a data set. BN models following both the conventional regression approach and the novel sampling-subject-oriented approach were built for comparison of the model fitting and predictive performance, using both research project and textbook data sets. The sampling-subject-oriented approach treated the sampling subject as the target variable for specification of the optimal model structure via Tree Augmented Naive Bayes algorithm. The results showed clear superiority of the sampling-subject-oriented approach models. A BN model following the sampling-subject-oriented approach had great potential for quantifying interrelationships between variables of mixed types in a complex model, hence it was particularly suitable for analysing data sets from a survey study with large numbers of numeric and categorical interrelated variables. |
| Date: | 2023–10–27 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:gqud3&r=net |