Angelo Mele

A Structural Model of Segregation in Social Networks

In this paper, I develop and estimate a dynamic model of strategic network formation with heterogeneous agents. While existing models have multiple equilibria, I prove the existence of a unique stationary equilibrium, which characterizes the likelihood of observing a specific network in the data. As a consequence, the structural parameters can be estimated using only one observation of the network at a single point in time. The estimation is challenging because the exact evaluation of the likelihood is computationally infeasible. To circumvent this problem, I propose a Bayesian Markov Chain Monte Carlo algorithm that avoids direct evaluation of the likelihood. This method drastically reduces the computational burden of estimating the posterior distribution and allows inference in high dimensional models. I present an application to the study of segregation in school friendship networks, using data from Add Health containing the actual social networks of students in a representative sample of US schools. My results suggest that for white students, the value of a same-race friend decreases with the fraction of whites in the school. The opposite is true for African American students. The model is used to study how different desegregation policies may affect the structure of the network in equilibrium. I find an inverted u-shaped relationship between the fraction of students belonging to a racial group and the expected equilibrium segregation levels. These results suggest that desegregation programs may decrease the degree of interracial interaction within schools.

A Structural Model of Dense Network Formation

This paper proposes an empirical model of network formation, combining strategic and random networks features. Payoffs depend on direct links, but also link externalities. Players meet sequentially at random, myopically updating their links. Under mild assumptions, the network formation process is a potential game and converges to an exponential random graph model (ERGM), generating directed dense networks. I provide new identification results for ERGMs in large networks: if link externalities are nonnegative, the ERGM is asymptotically indistinguishable from an Erdős–Renyi model with independent links. We can identify the parameters only when at least one of the externalities is negative and sufficiently large. However, the standard estimation methods for ERGMs can have exponentially slow convergence, even when the model has asymptotically independent links. I thus estimate parameters using a Bayesian MCMC method. When the parameters are identifiable, I show evidence that the estimation algorithm converges in almost quadratic time.

Poisson indices of segregation

Existing indices of residential segregation are based on a partition of the city in neighborhoods: given a spatial distribution of racial groups, the index measures different segregation levels for different partitions. I propose a spatial approach, which estimates segregation at the individual level and produces the entire spatial distribution of segregation. This method provides different rankings of cities in terms of segregation and new insights on the effect of segregation on socioeconomic outcomes. Using Census data and controlling for endogeneity using instrumental variables, I show that reduced form estimates of the impact of segregation on socioeconomic outcomes are not robust to the spatial approach.

Approximate Variational Estimation for a Model of Network Formation

We study an equilibrium model of sequential network formation with heterogeneous players. The payoffs depend on the number and composition of direct connections, but also the number of indirect links. We show that the network formation process is a potential game and in the long run the model converges to an exponential random graph (ERGM). Since standard simulation-based inference methods for ERGMs could have exponentially slow convergence, we propose an alternative deterministic method, based on a variational approximation of the likelihood. We compute bounds for the approximation error for a given network size and we prove that our variational method is asymptotically exact, extending results from the large deviations and graph limits literature to allow for covariates in the ERGM. A simple Monte Carlo shows that our deterministic method provides more robust estimates than standard simulation based inference.

Approximate Variational Inference for a Model of Social Interactions

This paper proposes approximate variational inference methods for estimation of a strategic model of social interactions. Players interact in an exogenous network and sequentially choose a binary action. The utility of an action is a function of the choices of neighbors in the network. I prove that the interaction process can be represented as a potential game and it converges to a unique stationary equilibrium distribution. However, exact inference for this model is infeasible because of a computationally intractable likelihood, which cannot be evaluated even when there are few players. To overcome this problem, I propose variational approximations for the likelihood that allow approximate inference. This technique can be applied to any discrete exponential family, and therefore it is a general tool for inference in models with a large number of players. The methodology is illustrated with several simulated datasets and compared with MCMC methods.

Viral Altruism? A Natural Field Experiment of Social Contagion in On-Line Networks

We present preliminary results from a small-scale natural field experiment aimed at exploring online social contagion, with an application to charitable giving. We worked in partnership with Heifer International, a non-profit organization aimed at fighting poverty in developing countries, and HelpAttack!, the developer of a Facebook application that facilitates donations to charities while broadcasting such activities to the donors’ Facebook contacts. We ran a series of marketing campaigns, and randomized the broadcasting of users’ pledges, thereby creating exogenous variation in the information that users’ contacts were receiving. Although our campaigns reached as many as about 13 million Facebook users, 6,000 users clicked on the ad and only 18 pledges were made, without any subsequent pledge from these users’ contacts. We offer potential explanations for this finding on the absence of network effects, and outline our plans for future developments of this on-going project.

Novelty and Market Success: Evidence from the Movie Industry

Does novelty mean commercial success? We examine this question in the context of movie industry where creativity is assumed to be the key to success. The paper uses the movie data from 1911 to 2016...

Poisson Indices of Segregation

Existing indices of residential segregation are based on a partition of the city in neighborhoods: given a spatial distribution of racial groups, the index measures different segregation levels for different partitions. I propose a spatial approach, which estimates segregation at the individual level and produces the entire spatial distribution of segregation. This method provides different rankings of cities in terms of segregation and new insights on the effect of segregation on socioeconomic outcomes. Using Census data and controlling for endogeneity using instrumental variables, I show that reduced form estimates of the impact of segregation on socioeconomic outcomes are not robust to the spatial approach.