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on Network Economics |
| By: | Morad Zekhnini (Michigan State University) |
| Abstract: | Network analysis has become critical to the study of social sciences. While several Stata programs are available for analyzing network structures, programs that execute regression analysis with a network structure are currently lacking. We fill this gap by introducing the nwxtregress command. Building on spatial econometric methods (LeSage and Pace 2009), nwxtregress uses MCMC estimation to produce estimates of endogenous peer effects, as well as own-node (direct) and cross-node (indirect) partial effects, where nodes correspond to cross-sectional units of observation, such as firms, and edges correspond to the relations between nodes. Unlike existing spatial regression commands (for example, spxtregress), nwxtregress is designed to handle unbalanced panels of economic and social networks as in Grieser et al. (2021). Networks can be directed or undirected with weighted or unweighted edges, and they can be imported in a list format that does not require a shapefile or a Stata spatial weight matrix set by spmatrix. Finally, the command allows for the inclusion or exclusion of contextual effects. To improve speed, the command transforms the spatial weighting matrix into a sparse matrix. Future work will be targeted toward improving sparse matrix routines, as well as introducing a framework that allows for multiple networks. |
| Date: | 2021–08–07 |
| URL: | http://d.repec.org/n?u=RePEc:boc:scon21:17&r= |
| By: | Marisa Miraldo; Carol Propper; Christiern Rose |
| Abstract: | This paper provides new identification results for panel data models with contextual and endogenous peer effects, respectively operating through time-invariant individual heterogeneity and outcomes. The results apply for general network structures governing peer interactions, and hinge on a conditional mean restriction requiring exogenous mobility of individuals between groups over time. Some networks preclude identification, in which case we propose additional conditional variance restrictions. We apply our method to surgeon-hospital-year data to study take-up of keyhole surgery, finding a positive effect of the average individual heterogeneity of peers. This effect is equally due to endogenous and contextual effects. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.11545&r= |
| By: | Juan Estrada (Emory University) |
| Abstract: | I present the netivreg command, which implements the generalized three-stage least-squares (G3SLS) estimator for the endogenous linear-in-means model developed in Estrada et al. (2020, “On the Identification and Estimation of Endogenous Peer Effects in Multiplex Networks"). The G3SLS procedure utilizes full observability of a two-layered multiplex network data structure using Stata 16's new multiframes capabilities and Python integration. Implementations of the command utilizing simulated data as well as three years' worth of data on peer-reviewed articles published in top general-interest journals in economics in Estrada et al. (2020) are also included. |
| Date: | 2021–08–07 |
| URL: | http://d.repec.org/n?u=RePEc:boc:scon21:16&r= |
| By: | Pichler, Anton; Farmer, J. Doyne |
| Abstract: | Natural and anthropogenic disasters frequently affect both the supply and demand side of an economy. A striking recent example is the Cover-19 pandemic which has created severe industry-specific disruptions to economic output in most countries. Since firms are embedded in production networks, these direct shocks to supply and demand will propagate downstream and upstream. We show that existing input-output models which allow for binding demand and supply constraints yield infeasible solutions when applied to pandemic shocks of three major European countries (Germany, Italy, Spain). We then introduce a mathematical optimisation procedure which is able to determine best-case feasible market allocations, giving a lower bound on total shock propagation. We find that even in this best-case scenario network effects substantially amplify the initial shocks. To obtain more realistic model predictions, we study the propagation of shocks bottom-up by imposing different rationing rules on firms if they are not able to satisfy the emergence of input bottlenecks, making the rationing assumption a key variable in predicting adverse economic impacts. We further establish that the magnitude of initial shocks and network density heavily influence model predictions. |
| Keywords: | Covid-19, production networks, input-output models, rationing, linear programming, economic shocks, shock propagation, economic impact |
| JEL: | C61 C67 D57 E23 |
| Date: | 2021–01 |
| URL: | http://d.repec.org/n?u=RePEc:amz:wpaper:2021-05&r= |
| By: | Subhadip Chakrabarti; Loyimee Gogoi; Robert P Gilles; Surajit Borkotokey; Rajnish Kumar |
| Abstract: | A network game assigns a level of collectively generated wealth to every network that can form on a given set of players. A variable network game combines a network game with a network formation probability distribution, describing certain restrictions on network formation. Expected levels of collectively generated wealth and expected individual payoffs can be formulated in this setting. We investigate properties of the resulting expected wealth levels as well as the expected variants of well-established network game values as allocation rules that assign to every variable network game a payoff to the players in a variable network game. We establish two axiomatizations of the Expected Myerson Value, originally formulated and proven on the class of communication situations, based on the well-established component balance, equal bargaining power and balanced contributions properties. Furthermore, we extend an established axiomatization of the Position Value based on the balanced link contribution property to the Expected Position Value. |
| Date: | 2021–08 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2108.07047&r= |
| By: | Pichler, Anton; Pangallo, Marco; del Rio-Chanona, R. Maria; Lafond, François; Farmer, J. Doyne |
| Abstract: | We analyse the economics and epidemiology of different scenarios for a phased restart of the UK economy. Our economic model is designed to address the unique features of the COVID-19 pandemic. Social distancing measures affect both supply and demand, and input-output constraints play a key role in restricting economic output. Standard models for production functions are not adequate to model the short-term effects of lockdown. A survey of industry analysts conducted by IHS Markit allows us to evaluate which inputs for each industry are absolutely necessary for production over a two month period. Our model also includes inventory dynamics and feedback between unemployment and consumption. We demonstrate that economic outcomes are very sensitive to the choice of production function, show how supply constraints cause strong network effects, and find some counter-intuitive effects, such as that reopening only a few industries can actually lower aggregate output. Occupation-specific data and contact surveys allow us to estimate how different industries affect the transmission rate of the disease. We investigate six different re-opening scenarios, presenting our best estimates for the increase in R0 and the increase in GDP. Our results suggest that there is a reasonable compromise that yields a relatively small increase in R0 and delivers a substantial boost in economic output. This corresponds to a situation in which all non-consumer facing industries reopen, schools are open only for workers who need childcare, and everyone who can work from home continues to work from home. |
| Keywords: | COVID-19, production networks, economic growth, epidemic spreading |
| JEL: | C61 C67 D57 E00 E23 I19 O49 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:amz:wpaper:2020-12&r= |
| By: | Pichler, Anton; Pangallo, Marco; del Rio-Chanona, R. Maria; Lafond, François; Farmer, J. Doyne |
| Abstract: | Economic shocks due to Covid-19 were exceptional in their severity, suddenness and heterogeneity across industries. To study the upstream and downstream propagation of these industry-specific demand and supply shocks, we build a dynamic input-output model inspired by previous work on the economic response to natural disasters. We argue that standard production functions, at least in their most parsimonious parametrizations, are not adequate to model input substitutability in the context of Covid-19 shocks. We use a survey of industry analysts to evaluate, for each industry, which inputs were absolutely necessary for production over a short time period. We calibrate our model on the UK economy and study the economic effects of the lockdown that was imposed at the end of March and gradually released in May. Looking back at predictions that we released in May, we show that the model predicted aggregate dynamics very well, and sectoral dynamics to a large extent. We discuss the relative extent to which the model's dynamics and performance was due to the choice of the production function or the choice of an exogenous shock scenario. To further explore the behavior of the model, we use simpler scenarios with only demand or supply shocks, and find that popular metrics used to predict a priori the impact of shocks, such as output multipliers, are only mildly useful. |
| Keywords: | Covid-19, production networks, epidemic spreading |
| JEL: | C61 C67 D57 E00 E23 I19 O49 |
| Date: | 2020–05 |
| URL: | http://d.repec.org/n?u=RePEc:amz:wpaper:2021-18&r= |