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The authors generalize the standard linear-in-means model to allow for multiple types with between and within-type interactions. The authors provide a set of identification conditions of peer effects and consider a two-stage least squares estimation approach. Large sample properties of the proposed estimators are derived. Their performance in finite samples is investigated using Monte Carlo simulations.

This article aims to analyze the impact that the inclusion of students with disabilities has on the achievement of their schoolmates and to analyze the impact that this inclusion has on the achievement of the students with disabilities themselves.

The author begins investigating how the inclusion of students with disabilities in regular schools affects achievement of schoolmates. To answer this research question, the author explores the natural variation in time in the number of students with disabilities and use data from National Exam of Upper Secondary Education (Enem). Then, the author investigates how the inclusion affects achievement of the students with disabilities themselves and uses propensity score matching methodologies and, again, data from Enem.

The results show that an additional percentage point in the proportion of students with disabilities would reduce schoolmates' writing scores by a 0.0031 standard deviation. In other subjects, the author finds weak or none evidence of a significant peer effect. In addition, using Propensity Score Matching methodologies, the results show that the mean scores are up to 44% of a standard deviation, which is higher among students with disabilities enrolled in regular schools compared to those who are enrolled in special schools. In summary, the evaluation is that inclusion policies achieve the goal of improving the performance of students with disabilities but such policies have a small and adverse side effect.

For this reason, the present study proposes to fill this gap in the literature by analyzing the impact of the inclusion of students with disabilities on both groups. In addition, this paper contributes to the empirical literature of peer effect with an analysis of the peer effect of students with disabilities per competency. Finally, the article is important given the existence of few articles in Brazil on the topic of education and people with disabilities.

This chapter develops a set of two-step identification methods for social interactions models with unknown networks, and discusses how the proposed methods are connected to the identification methods for models with known networks. The first step uses linear regression to identify the reduced forms. The second step decomposes the reduced forms to identify the primitive parameters. The proposed methods use panel data to identify networks. Two cases are considered: the sample exogenous vectors span Rn (long panels), and the sample exogenous vectors span a proper subspace of Rⁿ (short panels). For the short panel case, in order to solve the sample covariance matrices’ non-invertibility problem, this chapter proposes to represent the sample vectors with respect to a basis of a lower-dimensional space so that we have fewer regression coefficients in the first step. This allows us to identify some reduced form submatrices, which provide equations for identifying the primitive parameters.

This chapter is concerned with the estimation of spillover effects when outcomes arise as a consequence of bilateral interactions instead of from individual actions. In this type of environments, outcomes are generated on links instead of on nodes of a network, like bilateral prices in over-the-counter markets. The author proposes a link-based spatial autoregressive (SAR) model and discusses identification conditions and a two step least square estimation procedure. The author shows analytically that using a standard node-based SAR, which models nodes instead of links’ outcomes, produces misleading results when the data generating process is link-based. The methodology is illustrated using Monte Carlo experiments and real data from an interbank network.

The purpose of this paper is to examine peer-to-peer sharing platform business models, their sources of competitive advantage, and the roles, motivations and behaviors of key actors in their ecosystems.

This paper uses a conceptual approach that is rooted in the service, tourism and hospitality, and strategy literature.

First, this paper defines key types of platform business models in the sharing economy anddescribes their characteristics. In particular, the authors propose the differentiation between sharing platforms of capacity-constrained vs capacity-unconstrained assets and advance five core properties of the former. Second, the authors contrast platform business models with their pipeline business model counterparts to understand the fundamental differences between them. One important conclusion is that platforms cater to vastly more heterogeneous assets and consumer needs and, therefore, require liquidity and analytics for high-quality matching. Third, the authors examine the competitive position of platforms and conclude that their widely taken “winner takes it all” assumption is not valid. Primary network effects are less important once a critical level of liquidity has been reached and may even turn negative if increased listings raise friction in the form of search costs. Once a critical level of liquidity has been reached, a platform’s competitive position depends on stakeholder trust and service provider and user loyalty. Fourth, the authors integrate and synthesize the literature on key platform stakeholders of platform businesses (i.e. users, service providers, and regulators) and their roles and motivations. Finally, directions for further research are advanced.

This paper helps platform owners, service providers and users understand better the implications of sharing platform business models and how to position themselves in such ecosystems.

This paper integrates the extant literature on sharing platforms, takes a novel approach in delineating their key properties and dimensions, and provides insights into the evolving and dynamic forms of sharing platforms including converging business models.

Social media have attracted attention as an information channel for content generated in heterogeneous internet services. Focusing on social media platforms, the purpose of this paper is to examine the factors behind social transmission with content crossover from other services through hypertext link (URL). The authors investigate the effects of source influence and peer referrals on diffusion outcome and address their variations in the case of content crossover.

The authors use a Poisson regression model due to the discrete nature of the dependent variable. The authors conduct an empirical study using 233 million real transaction data generated by 1,203,196 Korean users of Twitter.

Source influence and peer referral have a positive impact on cascade size in the content dissemination process. In the case of content crossover, the impact of source influence decreases. However, the impact of peer referrals increases in the process of external content dissemination.

The authors demonstrate source and peer effects on content diffusion and that these effects vary when shared content is linked from an external service by a URL.

The findings indicate that firms that wish to diffuse information through social media or enter the social media with new services to provide new ways of creating and sharing content should understand the nature of the social transmission process.

Given the growing popularity of social media, particularly SNSs with online social networks as information channels, the authors first consider online social transmission as a user-driven diffusion process. Based on social factors in the diffusion process, the authors derive source and peer effects on the social transmission process.