|
on Network Economics |
|
Issue of 2018‒03‒26
four papers chosen by Pedro CL Souza Pontifícia Universidade Católica do Rio de Janeiro |
| By: | Tim Conley; Nirav Mehta; Ralph Stinebrickner; Todd Stinebrickner |
| Abstract: | We develop and estimate a model of student study time on a social network. The model is designed to exploit unique data collected in the Berea Panel Study. Study time data allow us to quantify an intuitive mechanism for academic social interactions: own study time may depend on friend study time in a heterogeneous manner. Social network data allow us to embed study time and resulting academic achievement in an estimable equilibrium framework. We develop a specification test that exploits the equilibrium nature of social interactions and use it to show that novel study propensity measures mitigate econometric endogeneity concerns. |
| Keywords: | social networks, peer effects, homophily, time-use |
| JEL: | C52 C54 I20 |
| Date: | 2018 |
| URL: | http://d.repec.org/n?u=RePEc:ces:ceswps:_6896&r=net |
| By: | Carlo Piccardi; Lucia Tajoli |
| Abstract: | Trade networks, across which countries distribute their products, are crucial components of the globalized world economy. Their structure is strongly heterogeneous across products, given the different features of the countries which buy and sell goods. By using a diversified pool of indicators from network science and product complexity theory, we quantitatively confirm the intuition that, overall, products with higher complexity -- i.e., with larger technological content and number of components -- are traded through a more centralized network -- i.e., with a small number of countries concentrating most of the export flow. Since centralized networks are known to be more vulnerable, we argue that the current composition of production and trading is associated to high fragility at the level of the most complex -- thus strategic -- products. |
| Date: | 2018–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1802.08575&r=net |
| By: | Angelo Antoci; Fabio Sabatini |
| Abstract: | There is growing evidence that face-to-face interaction is declining in many countries, exacerbating the phenomenon of social isolation. On the other hand, social interaction through online networking sites is steeply rising. To analyze these societal dynamics, we have built an evolutionary game model in which agents can choose between three strategies of social participation: 1) interaction via both online social networks and face-to-face encounters; 2) interaction by exclusive means of face-to-face encounters; 3) opting out from both forms of participation in pursuit of social isolation. We illustrate the dynamics of interaction among these three types of agent that the model predicts, in light of the empirical evidence provided by previous literature. We then assess their welfare implications. We show that when online interaction is less gratifying than offline encounters, the dynamics of agents’ rational choices of interaction will lead to the extinction of the sub-population of online networks users, thereby making Facebook and similar platforms disappear in the long run. Furthermore, we show that the higher the propensity for discrimination of those who interact via online social networks and via face-to-face encounters (i.e., their preference for the interaction with agents of their same type), the greater the probability will be that they all will end up choosing social isolation in the long run, making society fall into a “social poverty trap”. |
| Keywords: | Social networks; segregation; dynamics of social interaction; social media, social networking sites. |
| JEL: | C73 D85 O33 Z13 |
| Date: | 2018–03–01 |
| URL: | http://d.repec.org/n?u=RePEc:eei:rpaper:eeri_rp_2018_01&r=net |
| By: | Áureo de Paula (University College London); Imran Rasul (University College of London); Pedro Souza (PUC-Rio) |
| Abstract: | It is almost self-evident that social interactions can determine economic behavior and outcomes. Yet, information on social ties does not exist in most publicly available and widely used datasets. We present methods to recover information on the entire structure of social networks from observational panel data that contains no information on social ties between individuals. In the context of a canonical social interactions model, we provide sufficient conditions under which the social interactions matrix, endogenous and exogenous social effect parameters are all globally identified. We describe how high dimensional estimation techniques can be used to estimate the model based on the Adaptive Elastic Net GMM method. We showcase our method in Monte Carlo simulations using two stylized and two real world network structures. Finally, we employ our method to study tax competition across US states. We find the identified network structure of tax competition differs markedly from the common assumption of tax competition between geographically neighboring states. We analyze the identified social interactions matrix to provide novel insights into the long-standing debate on the relative roles of factor mobility and yardstick competition in driving tax setting behavior across states. Most broadly, our method shows how the analysis of social interactions can be usefully extended to economic realms where no network data exists. |
| Keywords: | social interactions, panel data, high dimensional estimation, GMM, adaptive elastic net |
| JEL: | C18 C31 D85 H71 |
| Date: | 2018–03 |
| URL: | http://d.repec.org/n?u=RePEc:hka:wpaper:2018-013&r=net |