Social networks and labor market inequality between ethnicities and races

Toomet, Ott; Leij, Marco van der; Rolfe, Meredith

doi:10.1017/nws.2013.20

Cited by 6 publications

(2 citation statements)

References 48 publications

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“…A recent study focusing on the US and Estonia finds that racial wage differentials are larger in areas where people are more likely to choose friends from the same racial background [11]. However, besides being related to co-ethnic networks, this may also be the result of social class [6].…”

Section: Possible Determinants Of Racial Wage Differentialsmentioning

confidence: 99%

Racial wage differentials in developed countries

Longhi¹

2017

izawol

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Section: Possible Determinants Of Racial Wage Differentialsmentioning

confidence: 99%

Racial wage differentials in developed countries

Longhi¹

2017

izawol

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“…Fortin and Yazbeck (2015) and references therein -to labour-force participation -see, e.g. Toomet et al (2013), Patacchini and Zenou (2012) and Mourifié and Siow (2014). 1 However, as most social networks are endogenously S15 formed, the estimated influence of peers is likely to be biased.…”

Section: Introductionmentioning

confidence: 99%

My friend far, far away: a random field approach to exponential random graph models

Boucher

Mourifié

2017

The Econometrics Journal

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We explore the asymptotic properties of strategic models of network formation in very large populations. Specifically, we focus on (undirected) exponential random graph models. We want to recover a set of parameters from the individuals' utility functions using the observation of a single, but large, social network. We show that, under some conditions, a simple logit-based estimator is coherent, consistent and asymptotically normally distributed under a weak version of homophily. The approach is compelling as the computing time is minimal and the estimator can be easily implemented using pre-programmed estimators available in most statistical packages. We provide an application of our method using the Add Health database.Exponential random graph models S15 formed, the estimated influence of peers is likely to be biased. 2 An understanding of how the networks are formed could allow us to control for this endogeneity, and it might suggest policy instruments that would help to influence network formation processes.In this paper, we use the network formation model of Mele (2017), for which the joint distribution is an exponential random graph model (ERGM). The estimation of ERGMs is typically challenging because of the intractable denominator of the joint distribution. We use random field theory to provide a simple pseudo maximum likelihood estimator (MLE), as in Besag (1975) and Strauss and Ikeda (1990), based on the product of the conditional distributions. To our knowledge, this paper is the first to apply random field theory to empirical models of network formation. The approach is promising and has been recently employed by Leung (2014) in a similar context. Our approach only requires the observation of a single, potentially very large, network.Our model allows for a large set of admissible preferences, which are characterized by intuitive conditions. Specifically, we show that our estimator is consistent and asymptotically normally distributed, provided that individuals' preferences exhibit a weak version of homophily. Homophily is one of the most robust empirical characteristics of social networks. It formalizes the observation that similar individuals are more likely to interact with each other. As homophily is featured in both theoretical -see, e.g. Boucher (2015), Bramoullé et al. (2012) and Currarini et al. (2009, 2010) -and empirical -see, e.g. Mele (2017) and Christakis et al. ( 2010) models of network formation, our methodology is applicable to many existing models of network formation. We apply this new methodology to the formation of friendship networks by US teenagers.A fundamental challenge in estimating a network formation process is the highly dependent nature of most socio-economic relationships. Consider a friendship: the probability that Alice and Bob are friends depends on their individual characteristics. However, it can also depend on Bob's friendship with Charlotte (who perhaps does not like Alice). The probability that Alice and Bob are friends might then depend on Charlotte's individual c...

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