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on Network Economics |
| By: | Marco Battaglini; Forrest W. Crawford; Eleonora Patacchini; Sida Peng |
| Abstract: | In this paper, we propose a new approach to the estimation of social networks and we apply it to the estimation of productivity spillovers in the U.S. Congress. Social networks such as the social connections among lawmakers are not generally directly observed, they can be recovered only using the observable outcomes that they contribute to determine (such as, for example, the legislators’ effectiveness). Moreover, they are typically stable for relatively short periods of time, thus generating only short panels of observations. Our estimator has three appealing properties that allows it to work in these environments. First, it is constructed for “small” asymptotic, thus requiring only short panels of observations. Second, it requires relatively nonrestrictive sparsity assumptions for identification, thus being applicable to dense networks with (potentially) star shaped connections. Third, it allows for heterogeneous common shocks across subnetworks. The application to the U.S. Congress gives us new insights about the nature of social interactions among lawmakers. We estimate a significant decrease over time in the importance of productivity spillovers among individual lawmakers, compensated by an increase in the party level common shock over time. This suggests that the rise of partisanship is not affecting only the ideological position of legislators when they vote, but more generally how lawmakers collaborate in the U.S. Congress. |
| JEL: | D7 D72 D85 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27557&r=all |
| By: | Jean-Baptiste Hasse (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique - AMU - Aix Marseille Université) |
| Abstract: | We propose a new measure of systemic risk based on interconnectedness, defined as the level of direct and indirect links between financial institutions in a correlation-based network. Deriving interconnectedness in terms of risk, we empirically show that within a financial network, indirect links are strengthened during systemic events. The relevance of our measure is illustrated at both local and global levels. Our framework offers policymakers a useful toolbox for exploring the real-time topology of the complex structure of dependencies in financial systems and for measuring the consequences of regulatory decisions. |
| Keywords: | Financial networks,Interconnectedness,Systemic risk,Spillover |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:hal:wpaper:halshs-02893780&r=all |
| By: | Baumöhl, Eduard; Bouri, Elie; Hoang, Thi-Hong-Van; Shahzad, Syed Jawad Hussain; Výrost, Tomáš |
| Abstract: | Over the last few decades, large banks worldwide have become more interconnected. As a result, the failure of one can trigger the failure of many. In finance, this phenomenon is often known as financial contagion, which can act like a domino effect. In this paper, we show an unprecedented increase in bank interconnectedness during the outbreak of the Covid-19 pandemic. We measure how extreme negative stock market returns from one bank can spill over to the other banks within the network. Our contribution relies on the establishment of a new systemic risk index based on the cross-quantilogram approach of Han et al. (2016). The results indicate that the systemic risk and the density of the spillover network among 83 banks in 24 countries have never been as high as during the Covid-19 pandemic – much higher than during the 2008 global financial crisis. Furthermore, we find that US banks are the most important risk transmitters, and Asian banks are the most important risk receivers. In contrast, European banks were strong risk transmitters during the European sovereign debt crisis. These findings may help investors, portfolio managers and policymakers adapt their investment strategies and macroprudential policies in this context of uncertainty. |
| Keywords: | Systemic risk,Banks,Covid-19 pandemic,Cross-quantilogram,Financial networks |
| JEL: | G01 G15 G21 G28 C21 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:zbw:esprep:222580&r=all |
| By: | Marina Azzimonti-Renzo; Alessandra Fogli; Fabrizio Perri; Mark Ponder |
| Abstract: | We develop an ECON-EPI network model to evaluate policies designed to improve health and economic outcomes during a pandemic. Relative to the standard epidemiological SIR set-up, we explicitly model social contacts among individuals and allow for heterogeneity in their number and stability. In addition, we embed the network in a structural economic model describing how contacts generate economic activity. We calibrate it to the New York metro area during the 2020 COVID-19 crisis and show three main results. First, the ECON-EPI network implies patterns of infections that better match the data compared to the standard SIR. The switching during the early phase of the pandemic from unstable to stable contacts is crucial for this result. Second, the model suggests the design of smart policies that reduce infections and at the same time boost economic activity. Third, the model shows that reopening sectors characterized by numerous and unstable contacts (such as large events or schools) too early leads to fast growth of infections. |
| Keywords: | Complex networks; COVID-19; Epidemiology; Social distance; SIR |
| JEL: | D85 E23 E65 I18 |
| Date: | 2020–08–19 |
| URL: | http://d.repec.org/n?u=RePEc:fip:fedmsr:88604&r=all |
| By: | Alessandro Barattieri; Matteo Cacciatore |
| Abstract: | Using monthly data on temporary trade barriers (TTBs), we estimate the dynamic employment effects of protectionism through vertical production linkages. First, exploiting procedural details of TTBs and high-frequency data, we identify movements in protectionism exogenous to economic fundamentals. We then use input-output tables to construct measures of protectionism affecting downstream producers. Finally, we estimate panel local projections using the identified trade-policy shocks. Protectionism has small and insignificant beneficial effects in protected industries. In contrast, the effects in downstream industries are negative, sizable, and significant. The employment decline follows an increase in intermediate-inputs and final goods prices. |
| JEL: | F13 F14 F62 |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:27630&r=all |
| By: | Brown, Mark; Dar-Brodeur, Afshan; Tweedle, Jesse |
| Abstract: | While the presence of provincial border effects—the relative weakness of inter-provincial trade compared to intra-provincial trade—is well established, it remains unclear what underlies them. Parsing out the sources of the border effect is important, because it provides policy makers with much more information on where to direct their efforts. This paper takes a step in this direction by asking whether part of the border effect can be attributed to how multi-unit firms organize their production within and across provincial borders. Networks of operating units controlled by the same enterprise lower the cost of trade by shipping goods between units as value is added through the production chain or via the use of common upstream and downstream supply chains. Higher costs of operating these networks in multiple provinces may act as a barrier to firm networks. By combining measures of regional trade and firm networks over a nine-year period (2004 to 2012), the study tests these propositions. |
| Keywords: | Economic regions, Domestic trade, Domestic shipping |
| Date: | 2019–04–02 |
| URL: | http://d.repec.org/n?u=RePEc:stc:stcp3e:2019009e&r=all |
| By: | Tatsushi Oka; Wei Wei; Dan Zhu |
| Abstract: | Governments around the world have implemented preventive measures against the spread of the coronavirus disease (COVID-19). In this study, we consider a multivariate discrete-time Markov model to analyze the propagation of COVID-19 across 33 provincial regions in China. This approach enables us to evaluate the effect of mobility restriction policies on the spread of the disease. We use data on daily human mobility across regions and apply the Bayesian framework to estimate the proposed model. The results show that the spread of the disease in China was predominately driven by community transmission within regions and the lockdown policy introduced by local governments curbed the spread of the pandemic. Further, we document that Hubei was only the epicenter of the early epidemic stage. Secondary epicenters, such as Beijing and Guangdong, had already become established by late January 2020, and the disease spread out to connected regions. The transmission from these epicenters substantially declined following the introduction of human mobility restrictions across regions. |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2008.06051&r=all |