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on Network Economics |
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Issue of 2017‒11‒05
four papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Brian J. Asquith; Judith K. Hellerstein; Mark J. Kutzbach; David Neumark |
| Abstract: | We explore the links between social capital and labor market networks at the neighborhood level. We harness rich data taken from multiple sources, including matched employer-employee data with which we measure the strength of labor market networks, data on behavior such as voting patterns that have previously been tied to social capital, and new data – not previously used in the study of social capital – on the number and location of non-profits at the neighborhood level. We use a machine learning algorithm to identify potential social capital measures that best predict neighborhood-level variation in labor market networks. We find evidence suggesting that smaller and less centralized schools, and schools with fewer poor students, foster social capital that builds labor market networks, as does a larger Republican vote share. The presence of establishments in a number of non-profit oriented industries are identified as predictive of strong labor market networks, likely because they either provide public goods or facilitate social contacts. These industries include, for example, churches and other religious institutions, schools, country clubs, and amateur or recreational sports teams or clubs. |
| JEL: | J01 J64 R23 |
| Date: | 2017–10 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23959&r=net |
| By: | Konstantin Büchel; Maximilian von Ehrlich |
| Abstract: | Social interactions are considered pivotal to agglomeration economies. We explore a unique dataset on mobile phone calls to examine how distance and population density shape the structure of social interactions. Exploiting an exogenous change in travel times, we show that distance is highly detrimental to interpersonal exchange. Despite distance-related costs, we find no evidence that urban residents benefit from larger networks when spatial sorting is accounted for. Higher density rather generates a more efficient network in terms of matching and clustering. These differences in network structure capitalize into land prices, corroborating the hypothesis that agglomeration economies operate via network efficiency. |
| Keywords: | social interactions, agglomeration externalities, network analysis, spatial sorting |
| JEL: | R10 R23 D83 D85 Z13 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_6568&r=net |
| By: | Michela Maria Tincani |
| Abstract: | Using a theoretical model where students care about achievement rank, I study effort choices in the classroom and show that rank concerns generate peer effects. The model’s key empirical prediction is that the effect on own achievement of increasing the dispersion in peer cost of effort is heterogeneous, depending on a student’s own cost of effort. To test this, I construct a longitudinal multi-cohort dataset of students, with data on the geographic propagation of building damages from the Chilean 2010 earthquake. I find that higher dispersion in home damages among one’s classmates led, on average, to lower own Mathematics and Spanish test scores. To be able to test the theory, I develop a novel nonlinear difference-in-differences model that estimates effect heterogeneity and that relates observed damages to unobserved cost of effort. I find that some students at the tails of the predicted cost of effort distribution benefit from higher dispersion in peer cost of effort, as predicted by the theoretical model. This finding suggests that observed peer effects on test scores are, at least partly, governed by rank concerns. |
| Keywords: | ability peer effects, rank preferences, semiparametric model |
| JEL: | C51 I29 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_6331&r=net |
| By: | Spiros Bougheas |
| Abstract: | We study the formation of networks in environments where agents derive benefits from other agents directly linked to them but suffer losses through contagion when any agent on a path connected to them is hit by a shock. We first consider networks with undirected links (e.g. epidemics, underground resistance organizations, trade networks) where we find that stable networks are comprised of completely connected disjoint subnetworks. Then, we consider networks with directed links and we find that the completely connected network is stable, although, its exact structure, and thus contagion implications, is sensitive to parameter values for costs and benefits. Lastly, we introduce aggregate externalities (e.g. fire sales for the case of financial networks) and we find that stable networks can be asymmetric, connected but not completely connected, thus capturing the main features of inter-industry and financial networks. |
| Keywords: | network formation, stability, contagion |
| JEL: | C72 D85 |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_6682&r=net |