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on Network Economics |
| By: | Raúl Duarte; Frederico Finan; Horacio Larreguy; Laura Schechter |
| Abstract: | Throughout much of the developing world, politicians rely on political brokers to buy votes prior to elections. We investigate how social networks help facilitate vote-buying exchanges by combining village network data of brokers and voters with broker reports of vote buying. We show that networks diffuse politically-relevant information about voters to brokers who leverage it to target voters. In particular, we find that brokers target reciprocal voters who are not registered to their party and about whom they can hear more information through their social network. These results highlight the importance of information diffusion through social networks for vote buying and ultimately for political outcomes. |
| JEL: | D72 O1 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26241&r=all |
| By: | Tiziano Arduini; Alberto Bisin; Onur Özgür; Eleonora Patacchini |
| Abstract: | We study risky behavior of adolescents. Concentrating on smoking and alcohol use, we structurally estimate a dynamic social interaction model in the context of students' school networks included in the National Longitudinal Study of Adolescent Health (Add Health). The model allows for forward-looking behavior of agents, addiction effects, and social interactions in the form of preferences for conformity in the social network. We find strong evidence for forward looking dynamics and addiction effects. We also find that social interactions in the estimated dynamic model are quantitatively large. A misspecified static model would fit data substantially worse, while producing a much smaller estimate of the social interaction effect. With the estimated dynamic model, a temporary shock to students' preferences in the 10th grade has effects on their behavior in grades 10, 11, 12, with estimated social multipliers 1:53, 1:03, and 0:76, respectively. The multiplier effect of a permanent shock is much larger, up to 3:7 in grade 12. Moreover (semi-) elasticities of a permanent change in the availability of alcohol or cigarettes at home on child risky behavior implied by the dynamic equilibrium are 25%, 63%, and 79%, in grades 10, 11, 12, respectively. |
| JEL: | C18 C33 C62 C63 C73 I12 |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:26223&r=all |
| By: | Ayden Higgins; Federico Martellosio |
| Abstract: | This paper explores the estimation of a panel data model with cross-sectional interaction that is flexible both in its approach to specifying the network of connections between cross-sectional units, and in controlling for unobserved heterogeneity. It is assumed that there are different sources of information available on a network, which can be represented in the form of multiple weights matrices. These matrices may reflect observed links, different measures of connectivity, groupings or other network structures, and the number of matrices may be increasing with sample size. A penalised quasi-maximum likelihood estimator is proposed which aims to alleviate the risk of network misspecification by shrinking the coefficients of irrelevant weights matrices to exactly zero. Moreover, controlling for unobserved factors in estimation provides a safeguard against the misspecification that might arise from unobserved heterogeneity. The estimator is shown to be consistent and selection consistent as both $n$ and $T$ tend to infinity, and its limiting distribution is characterised. Finite sample performance is assessed by means of a Monte Carlo simulation, and the method is applied to study the prevalence of network spillovers in determining growth rates across countries. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.02823&r=all |
| By: | Krishna Dasaratha; Kevin He |
| Abstract: | We conduct a sequential social learning experiment where subjects guess a hidden state after observing private signals and the guesses of a subset of their predecessors. A network determines the observable predecessors, and we compare subjects' accuracy on sparse and dense networks. Later agents' accuracy gains from social learning are twice as large in the sparse treatment compared to the dense treatment. Models of naive inference where agents ignore correlation between observations predict this comparative static in network density, while the result is difficult to reconcile with rational-learning models. |
| Date: | 2019–09 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1909.02220&r=all |