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on Network Economics |
| By: | Arthur Lewbel (Boston College); Xi Qu (Antai College of Economics and Management, Shanghai Jiao Tong University); Xun Tang (Department of Economics, Rice University) |
| Abstract: | We propose an adjusted 2SLS estimator for social network models when the network links reported in samples are subject to two-sided misclassification errors (due, e.g., to recall errors by survey respondents, or lapses in data input). Misclassified links make all covariates endogenous, and add a new source of correlation between the structural errors and peer outcomes (in addition to simultaneity), thus invalidating conventional estimators used in the literature. We resolve these issues by adjusting endogenous peer outcomes with estimates of the misclassification rates and constructing new instruments that exploit properties of the noisy network measures. Simulation results confirm our adjusted 2SLS estimator corrects the bias from a naive, unadjusted 2SLS estimator which ignores misclassification and uses conventional instruments. We apply our method to study peer effects in household decisions to participate in a microfinance program in Indian villages. |
| Keywords: | Social Network, Link Misclassification |
| JEL: | D11 D13 |
| Date: | 2024–08–31 |
| URL: | https://d.repec.org/n?u=RePEc:boc:bocoec:1079 |
| By: | Shan Huang; Yuan Yuan; Yi Ji |
| Abstract: | The diffusion of novel information through social networks is essential for dismantling echo chambers and promoting innovation. Our study examines how two major types of viral channels, specifically Direct Messaging (DM) and Broadcasting (BC), impact the well-known "strength of weak ties" in disseminating novel information across social networks. We conducted a large-scale empirical analysis, examining the sharing behavior of 500, 000 users over a two-month period on a major social media platform. Our results suggest a greater capacity for DM to transmit novel information compared to BC, although DM typically involves stronger ties. Furthermore, the "strength of weak ties" is only evident in BC, not in DM where weaker ties do not transmit significantly more novel information. Our mechanism analysis indicates that the content selection by both senders and recipients, contingent on tie strength, contributes to the observed differences between these two channels. These findings expand both our understanding of contemporary weak tie theory and our knowledge of how to disseminate novel information in social networks. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.03579 |
| By: | Lukas Rosenberger; W. Walker Hanlon; Carl Hallmann |
| Abstract: | How did Britain sustain faster rates of economic growth than comparable European countries, such as France, during the Industrial Revolution? We argue that Britain possessed an important but underappreciated innovation advantage: British inventors worked in technologies that were more central within the innovation network. We offer a new approach for measuring the innovation network using patent data from Britain and France in the late-18th and early-19th century. We show that the network influenced innovation outcomes and demonstrate that British inventors worked in more central technologies within the innovation network than French inventors. Drawing on recently developed theoretical tools, and using a novel estimation strategy, we quantify the implications for technology growth rates in Britain compared to France. Our results indicate that the shape of the innovation network, and the location of British inventors within it, explains an important share of the more rapid technological change and industrial growth in Britain during the Industrial Revolution. |
| JEL: | N13 O30 |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:32875 |
| By: | Nikolas Michael; Mihai Cucuringu; Sam Howison |
| Abstract: | We propose a novel data-driven network framework for forecasting problems related to E-mini S\&P 500 and CBOE Volatility Index futures, in which products with different expirations act as distinct nodes. We provide visual demonstrations of the correlation structures of these products in terms of their returns, realized volatility, and trading volume. The resulting networks offer insights into the contemporaneous movements across the different products, illustrating how inherently connected the movements of the future products belonging to these two classes are. These networks are further utilized by a multi-channel Graph Convolutional Network to enhance the predictive power of a Long Short-Term Memory network, allowing for the propagation of forecasts of highly correlated quantities, combining the temporal with the spatial aspect of the term structure. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.05659 |
| By: | Tobias Wand; Oliver Kamps; Hiroshi Iyetomi |
| Abstract: | Granger causality can uncover the cause and effect relationships in financial networks. However, such networks can be convoluted and difficult to interpret, but the Helmholtz-Hodge-Kodaira decomposition can split them into a rotational and gradient component which reveals the hierarchy of Granger causality flow. Using Kenneth French's business sector return time series, it is revealed that during the Covid crisis, precious metals and pharmaceutical products are causal drivers of the financial network. Moreover, the estimated Granger causality network shows a high connectivity during crisis which means that the research presented here can be especially useful to better understand crises in the market by revealing the dominant drivers of the crisis dynamics. |
| Date: | 2024–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2408.12839 |