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on Network Economics |
| By: | Rapanos, Theodoros; Sommer, Marc; Zenou, Yves |
| Abstract: | We develop a simple Bayesian network game in which players, embedded in a network of social interactions, bear a cost from deviating from the social norm of their peers. All agents face uncertainty about the private benefits and the private and social costs of their actions. We prove the existence and uniqueness of a Bayesian Nash equilibrium and characterize players' optimal actions. We then show that denser networks do not necessary increase agents' actions and welfare. We also find that, in some cases, it is optimal for the planner to affect the payoffs of selected individuals rather than all agents in the network. We finally show that having more information is not always beneficial to agents and can, in fact, reduce their welfare. We illustrate all our results in the context of criminal networks in which offenders do not know with certitude the probability of being caught and do not want to be different from their peers in terms of criminal activities. |
| Keywords: | Bayesian games; beliefs; Conformism; crime; networks; value of information |
| JEL: | C72 D82 D85 K42 |
| Date: | 2019–10 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:14087&r=all |
| By: | Gandy, Axel; Veraart, Luitgard A. M. |
| Abstract: | We develop a modelling framework for estimating and predicting weighted network data. The edge weights in weighted networks often arise from aggregating some individual relationships be- tween the nodes. Motivated by this, we introduce a modelling framework for weighted networks based on the compound Poisson distribution. To allow for heterogeneity between the nodes, we use a regression approach for the model parameters. We test the new modelling framework on two types of financial networks: a network of financial institutions in which the edge weights represent exposures from trading Credit Default Swaps and a network of countries in which the edge weights represent cross-border lending. The compound Poisson Gamma distributions with regression fit the data well in both situations. We illustrate how this modelling framework can be used for predicting unobserved edges and their weights in an only partially observed network. This is for example relevant for assessing systemic risk in financial networks. |
| Keywords: | eighted directed networks; compound Poisson distribution; regression; subnetwork prediction; financial networks; systemic risk |
| JEL: | C02 C46 C53 D85 G32 |
| Date: | 2020–05–29 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:104185&r=all |
| By: | Hellerstein, Judith K. (University of Maryland); Neumark, David (University of California, Irvine) |
| Abstract: | One definition of social capital is the "networks of relationships among people who live and work in a particular society, enabling that society to function effectively". This definition of social capital highlights two key features. First, it refers to connections between people, shifting our focus from characteristics of individuals and families to the ties between them. Second, it emphasizes that social capital is present not simply because individuals are connected, but rather when these network relationships lead to productive social outcomes. In that sense, social capital is productive capital, in the same way that economists think of physical capital or human capital as productive capital. Social capital, under this definition, is still very broad. Networks can be formed along many dimensions of society in which people interact – neighborhoods, workplaces, extended families, schools, etc. We focus on networks whose existence fosters social capital in one specific way: by facilitating the transfer of information that helps improve the economic wellbeing of network members, especially via better labor market outcomes. We review evidence showing that networks play this important role in labor market outcomes, as well as in other outcomes related to economic wellbeing, paying particular attention to evidence of how networks can help less-skilled individuals. We also discuss the measurement of social capital, including new empirical methods in machine learning that might provide new evidence on the underlying connections that do – or might – lead to productive networks. Throughout, we discuss the policy implications of what we know so far about networks and social capital. |
| Keywords: | social capital, networks |
| JEL: | J1 J8 |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13413&r=all |
| By: | Ferrari, Massimo Minesso |
| Abstract: | In this paper, I incorporate a complex network model into a state of the art stochastic general equilibrium framework with an active interbank market. Banks exchange funds one another generating a complex web of interbanking relations. With the tools of network analysis it is possible to study how contagion spreads between banks and what is the probability and size of a cascade (a sequence of defaults) generated by a single initial episode. Those variables are a key component to understand systemic risk and to assess the stability of the banking system. In extreme scenarios, the system may experience a phase transition when the consequences of one single initial shock affect the entire population. I show that the size and probability of a cascade evolve along the business cycle and how they respond to exogenous shocks. Financial shocks have a larger impact on contagion probability than real shocks that, however, are long lasting. Additionally I find that monetary policy faces a trade off between financial stability and macroeconomic stabilization. Government spending shocks, on the contrary, have smaller effects on both. JEL Classification: E44, E32, E52, E58, D85 |
| Keywords: | contagion, DSGE, heterogenous agents, interbank market, network analysis |
| Date: | 2020–06 |
| URL: | http://d.repec.org/n?u=RePEc:ecb:ecbwps:20202432&r=all |
| By: | M. Keith Chen; Judith A. Chevalier; Elisa F. Long |
| Abstract: | Nursing homes and other long term-care facilities account for a disproportionate share of COVID-19 cases and fatalities worldwide. Outbreaks in U.S. nursing homes have persisted despite nationwide visitor restrictions beginning in mid-March. An early report issued by the Centers for Disease Control and Prevention identified staff members working in multiple nursing homes as a likely source of spread from the Life Care Center in Kirkland, Washington to other skilled nursing facilities. The full extent of staff connections between nursing homes---and the crucial role these connections serve in spreading a highly contagious respiratory infection---is currently unknown given the lack of centralized data on cross-facility nursing home employment. In this paper, we perform the first large-scale analysis of nursing home connections via shared staff using device-level geolocation data from 30 million smartphones, and find that 7 percent of smartphones appearing in a nursing home also appeared in at least one other facility---even after visitor restrictions were imposed. We construct network measures of nursing home connectedness and estimate that nursing homes have, on average, connections with 15 other facilities. Controlling for demographic and other factors, a home's staff-network connections and its centrality within the greater network strongly predict COVID-19 cases. Traditional federal regulatory metrics of nursing home quality are unimportant in predicting outbreaks, consistent with recent research. Results suggest that eliminating staff linkages between nursing homes could reduce COVID-19 infections in nursing homes by 44 percent. |
| Date: | 2020–07 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2007.11789&r=all |
| By: | Diemer, Andreas |
| Abstract: | There is little evidence on the relevance of social networks in the aggregate spatial diffusion of localised economic shocks. This paper uses novel data on the universe of online friendships in the US to uncover how plausibly exogenous surges in the local demand for jobs in the oil and gas industry can affect the economy of spatially distant but socially proximate places. Although most of the diffusion is limited to geographically proximate areas, social networks matter too. According to 2SLS estimates, a million dollar per capita increase in oil and gas extraction raises per capita wages by over 5,000 dollars for workers reporting their incomes in counties located as far as 1,200 km away from the drilling site, but strongly socially connected to it. This effect is likely explained by the relocation of transient workers within the industry, providing new aggregate evidence in support of the literature on job information networks. |
| Keywords: | social networks; fracking; spatial diffusion; job search |
| JEL: | J61 J64 L71 Q33 R12 R23 Z13 |
| Date: | 2020–08 |
| URL: | http://d.repec.org/n?u=RePEc:ehl:lserod:105868&r=all |