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on Network Economics |
| By: | List, John A. (University of Chicago); Momeni, Fatemeh (University of Chicago); Zenou, Yves (Monash University) |
| Abstract: | The behavioral revolution within economics has been largely driven by psychological insights, with the sister sciences playing a lesser role. This study leverages insights from sociology to explore the role of neighborhoods on human capital formation at an early age. We do so by estimating the spillover effects from a large-scale early childhood intervention on the educational attainment of over 2,000 disadvantaged children in the United States. We document large spillover effects on both treatment and control children who live near treated children. Interestingly, the spillover effects are localized, decreasing with the spatial distance to treated neighbors. Perhaps our most novel insight is the underlying mechanisms at work: the spillover effect on non-cognitive scores operate through the child's social network while parental investment is an important channel through which cognitive spillover effects operate. Overall, our results reveal the importance of public programs and neighborhoods on human capital formation at an early age, highlighting that human capital accumulation is fundamentally a social activity. |
| Keywords: | early education, social activity, neighborhood, field experiment, spillover effects, non-cognitive skills |
| JEL: | C93 I21 R1 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13966&r=all |
| By: | Michael Bailey; Drew Johnston; Martin Koenen; Theresa Kuchler; Dominic Russel; Johannes Stroebel |
| Abstract: | We explore how social network exposure to COVID-19 cases shapes individuals’ social distancing behavior during the early months of the ongoing pandemic. We work with de-identified data from Facebook to show that U.S. users whose friends live in areas with worse coronavirus outbreaks reduce their mobility more than otherwise similar users whose friends live in areas with smaller outbreaks. The effects are quantitatively large: a one standard deviation increase in friend-exposure to COVID-19 cases early in the pandemic results in a 1.2 percentage point increase in the probability that an individual stays home on a given day. As the pandemic progresses, changes in friend-exposure drive changes in social distancing behavior. Given the evolving nature and geography of the pandemic—and hence friend-exposure — these results rule out many alternative explanations for the observed relationships. We also analyze data on public posts and membership in groups advocating to “reopen” the economy to show that our findings can be explained by friend-exposure raising awareness about the risks of the disease and inducing individuals to participate in mitigating public health behavior. |
| Keywords: | social networks, peer effects, Covid-19, social distancing |
| JEL: | I00 D83 D85 H00 |
| Date: | 2020 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_8771&r=all |
| By: | Parsons, Christopher (University of Western Australia); Reysenbach, Tyler (Productivity Commission); Wahba, Jackline (University of Southampton) |
| Abstract: | Immigrants' social networks exert considerable influence over their labor market opportunities and yet the pre-sorting of co-nationals by ability and across space, endures as a key challenge for empiricists attempting to establish causal network effects. To surmount this issue, we leverage the chaotic dispersal of Vietnamese refugees across the U.S. in 1975, which was demonstrably exogenous in both initial network size and quality, in tandem with an absence of pre-existing networks of co-nationals, to causally identify the effects of network size and network quality on refugees': occupational outcomes, skill intensity and skill upgrading. Our administrative data provide refugee's precise initial locations and pre-placement characteristics in Vietnam, which we uniquely employ as additional controls, as well as longitudinal information about their locations and occupations six years hence. We construct instruments from the initial quasi-random refugee allocations of network size and quality and leverage refugees' geo-locations to insulate our results from the Reflection Problem. Overall, network quality is a far more important determinant of refugees' labor market outcomes when compared to network size, one interpretation of which is that the type of referrals network members receive are more important than the overall number of referrals. Blue-collar networks: increase the probability of refugees' working in blue-collar jobs, draw additional workers into more manual and less complex intensive employment and serve to up-skill individuals along the manual skill dimension. Given the protracted circumstances under which the Viet Kieu entered the U.S., the composition of their networks played a pivotal role in their ultimate success. |
| Keywords: | networks, refugees, migration, labor markets |
| JEL: | F22 J61 |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:iza:izadps:dp13952&r=all |
| By: | Th\'eo Dessertaine; Jos\'e Moran; Michael Benzaquen; Jean-Philippe Bouchaud |
| Abstract: | We study the conditions under which input-output networks can dynamically attain competitive equilibrium, where markets clear and profits are zero. We endow a classical firm network model with simple dynamical rules that reduce supply/demand imbalances and excess profits. We show that the time needed to reach equilibrium diverges as the system approaches an instability point beyond which the Hawkins-Simons condition is violated and competitive equilibrium is no longer realisable. We argue that such slow dynamics is a source of excess volatility, through accumulation and amplification of exogenous shocks. Factoring in essential physical constraints, such as causality or inventory management, we propose a dynamically consistent model that displays a rich variety of phenomena. Competitive equilibrium can only be reached after some time and within some region of parameter space, outside of which one observes periodic and chaotic phases, reminiscent of real business cycles. This suggests an alternative explanation of the excess volatility that is of purely endogenous nature. Other regimes include deflationary equilibria and intermittent crises characterised by bursts of inflation. Our model can be calibrated using highly disaggregated data on individual firms and prices, and may provide a powerful tool to describe out-of-equilibrium economies. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.05202&r=all |
| By: | Emanuele Ciola (Department of Economics, Universitat Jaume I, Castellón, Spain and Department of Economics and Social Sciences, Università Politecnica delle Marche, Ancona-Italy); Gabriele Tedeschi (Department of Economics, Universitat Jaume I, Castellón, Spain) |
| Abstract: | In this paper, we develop a macroeconomic model with heterogeneous interacting agents to study the effects of different configurations of the interbank network on the overall performance of the economy. Specifically, we implement a simple decentralized matching model in which deposit, credit and interbank relations evolve endogenously via a fitness measure. Our findings confirm the importance of the interbank market as an indisputable source of economic stability able to counterbalance deposit withdrawal and stabilize the credit allocation. However, when highly centralized, this market can amplify the effects of shocks in the economy due to coordination failures of core banks. |
| Keywords: | Interbank network; Business Fluctuations; Financial crises |
| JEL: | C63 E44 E32 G01 |
| Date: | 2021 |
| URL: | http://d.repec.org/n?u=RePEc:jau:wpaper:2021/01&r=all |
| By: | Toru Kitagawa; Guanyi Wang |
| Abstract: | How to allocate vaccines over heterogeneous individuals is one of the important policy decisions in pandemic times. This paper develops a procedure to estimate an individualized vaccine allocation policy under limited supply, exploiting social network data containing individual demographic characteristics and health status. We model spillover effects of the vaccines based on a Heterogeneous-Interacted-SIR network model and estimate an individualized vaccine allocation policy by maximizing an estimated social welfare (public health) criterion incorporating the spillovers. While this optimization problem is generally an NP-hard integer optimization problem, we show that the SIR structure leads to a submodular objective function, and provide a computationally attractive greedy algorithm for approximating a solution that has theoretical performance guarantee. Moreover, we characterise a finite sample welfare regret bound and examine how its uniform convergence rate depends on the complexity and riskiness of social network. In the simulation, we illustrate the importance of considering spillovers by comparing our method with targeting without network information. |
| Date: | 2020–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2012.04055&r=all |