Model Of Strategic Network Formation Research Articles

Collaborative Production in Science: An Empirical Analysis of Coauthorships in Economics

Abstract This paper studies productivity and preferences in scientific research. Collaboration is increasingly important for innovation in science and other domains, but we have limited understanding of the factors researchers use to choose their collaborators and the projects they work on. Here, we use a model of strategic network formation and a recently developed econometric method to examine this question in the context of economics researchers. We learn that research teams with more collaborators tend to produce papers with higher impact, and without increasing individual costs of communication and coordination. This suggests the trend toward larger research teams in economics will continue.

Identification and inference of network formation games with misclassified links

This paper considers a network formation model when links are potentially misclassified. We focus on a game-theoretical model of strategic network formation with incomplete information, in which the linking decisions depend on agents’ exogenous attributes and endogenous network characteristics. In the presence of link misclassification, we derive moment conditions that characterize the identified set for the preference parameters associated with homophily and network externalities. Based on the moment equality conditions, we provide an inference method that is asymptotically valid when a single network of many agents is observed. Finally, we apply our misclassification-robust method to study the preference parameters of a lending network in rural villages in southern India.

Collaborative production networks among unequal actors

AbstractWe develop a model of strategic network formation of collaborations to analyze the consequences of an understudied but consequential form of heterogeneity: differences between actors in the form of their production functions. We also address how this interacts with resource heterogeneity, as a way to measure the impact actors have as potential partners on a collaborative project. Some actors (e.g., start-up firms) may exhibit increasing returns to their investment into collaboration projects, while others (e.g., established firms) may face decreasing returns. Our model provides insights into how actor heterogeneity can help explain well-observed collaboration patterns. We show that if there is a direct relation between increasing returns and resources, start-ups exclude mature firms and networks become segregated by types of production function, portraying dominant group architectures. On the other hand, if there is an inverse relation between increasing returns and resources, networks portray core-periphery architectures, where the mature firms form a core and start-ups with low-resources link to them.

A Structural Model of Firm Collaborations with Unobserved Heterogeneity

We develop and estimate a structural model of strategic network formation to study the determinants of firms collaborations for patenting new technology in the medical device industry. Our aim is to bridge the strategy literature on interorganizational networks and the economic literature on structural estimation of network models. In our model, firms have payoffs that depend on linking costs and benefits, as well as externalities from common partners and popular partners. Firms are characterized by observed and unobserved characteristics, that affect both their opportunity and their willingness to form links. The equilibrium networks are sparse and match the aggregate clustering levels observed in the data. We use the network of patent collaborations among medical device firms, to estimate the structural parameters using a Bayesian approach. Our results show that firms tend to partner domestically and collaborate with companies in similar markets, perhaps due to technological complementarities or regulation effects. Unobserved heterogeneity matters: we find that firms' payoffs vary by type. Finally we show that the estimated model including unobserved heterogeneity provides a better fit of crucial features of the data.

The common enemy effect under strategic network formation and disruption

Social psychology studies the “common enemy effect”, the phenomenon that members of a group work together when they face an opponent, although they otherwise have little in common. An interesting scenario is the formation of an information network where group members individually sponsor costly links. Suppose that ceteris paribus, an outsider appears who aims to disrupt the information flow within the network by deleting some of the links. The question is how the group responds to this common enemy. We address this question for the homogeneous connections model of strategic network formation, with two-way flow of information and without information decay. For sufficiently low linkage costs, the external threat can lead to a more connected network, a positive common enemy effect. For very high but not prohibitively high linkage costs, the equilibrium network can be minimally connected and efficient in the absence of the external threat whereas it is always empty and inefficient in the presence of the external threat, a negative common enemy effect. For intermediate linkage costs, both connected networks and the empty network are Nash for certain cost ranges.

A Structural Model of Firm Collaborations with Unobserved Heterogeneity

We develop and estimate a structural model of strategic network formation to study the determinants of firms collaborations for patenting new technology in the medical device industry. Our aim is to bridge the strategy literature on interorganizational networks and the economic literature on structural estimation of network models. In our model, firms have payoffs that depend on linking costs and benefits, as well as externalities from common partners and popular partners. Firms are characterized by observed and unobserved characteristics, that affect both their opportunity and their willingness to form links. The equilibrium networks are sparse and match the aggregate clustering levels observed in the data. We use the network of patent collaborations among medical device firms, to estimate the structural parameters using a Bayesian approach. Our results show that firms tend to partner domestically and collaborate with companies in similar markets, perhaps due to technological complementarities or regulation effects. Unobserved heterogeneity matters: we find that firms’ payoffs vary by type. Finally we show that the estimated model including unobserved heterogeneity provides a better fit of crucial features of the data.

A unifying model of strategic network formation

We provide a model that merges two basic models of strategic network formation and incorporates them as extreme cases: Jackson and Wolinsky’s connections model based on bilateral formation of links, and Bala and Goyal’s two-way flow model, where links can be unilaterally formed. In our model a link can be created unilaterally, but when it is only supported by one of the two players the flow through it suffers some friction or decay, but more than when it is supported by both players. When the friction in singly-supported links is maximal (i.e. there is no flow) we have Jackson and Wolinsky’s connections model, while when flow in singly-supported links is as good as in doubly-supported links we have Bala and Goyal’s two-way flow model. In this setting, a joint generalization of the results relative to efficiency and stability in both seminal papers is achieved, and the robustness in both models is tested with positive results.

A Structural Model of Homophily and Clustering in Social Networks

Social networks display homophily and clustering, and are usually sparse. I develop and estimate a structural model of strategic network formation with heterogeneous players and latent community structure, whose equilibrium networks are sparse and exhibit homophily and clustering. Each player belongs to a community unobserved by the econometrician. Players' payoffs vary by community and depend on the composition of direct links and common neighbors, allowing preferences to have a bias for similar people. Players meet sequentially and decide whether to form bilateral links, after receiving a random matching shock. The probability of meeting people in different communities is smaller than the probability of meeting people in the same community, and it decreases with the size of the network. The model converges to an exponential family random graph, with weak dependence among links. As a consequence the equilibrium networks are sparse and the sufficient statistics of the network are asymptotically normal. The posterior distribution of structural parameters and unobserved heterogeneity is estimated with school friendship network data from Add Health, using a Bayesian exchange algorithm. The estimates detect high levels of racial homophily, and heterogeneity in both costs of links and payoffs from common friends. The posterior predictions show that the model is able to replicate the homophily levels and the aggregate clustering of the observed network, in contrast with standard exponential family network models.

Peer effects in endogenous networks

This paper presents a model of strategic network formation with local complementarities in effort levels and positive local externalities. Results are obtained for a general class of payoff functions, which subsumes the linear-quadratic specification frequently used in theoretical and applied work. We assume homogeneous agents and characterize equilibria for two-sided and one-sided link formation. (Pairwise) Nash equilibrium networks are nested split graphs, which are a strict subset of core-periphery networks. We highlight the relevance of the convexity of the value function for obtaining these structures. More central agents are shown to exert more effort and obtain higher gross payoffs in equilibrium. However, net of linking cost, central agents may obtain strictly lower net payoffs. The curvature of the value function is also important for efficiency considerations. These findings are relevant for many social and economic phenomena, such as educational attainment, criminal activity, labor market participation, and R&D expenditures of firms.

Strategic Network Formation and Performance in the Venture Capital Industry

I investigate the network structure of venture capitalists based on co-investments, and the effects of network structure on investment performance. As venture capitalists select their partners, network structure is endogenously determined in equilibrium. Using comprehensive data on venture capital firms in the U.S., I jointly estimate a model of strategic network formation and a performance equation, taking endogeneity of network structure into account. In the estimation of the strategic network formation model, instead of imposing an equilibrium selection rule, I exploit the partial identification approach. My estimation strategy relies only on the necessary conditions of pairwise stability and is computationally feasible. I find that the network of venture capitalists tends to be homophilous in terms of asset size, but anti-homophilous in terms of investment experience and industry expertise. Moreover, I fi nd that not taking the endogeneity into account results in signi ficant overestimates of the effects of the network structure on investment performance. Lastly, I conduct a counterfactual policy experiment in which the government makes direct investment into the market by establishing a venture capital firm. I examine the effects of the additional venture capital fi rm on equilibrium network structure and investment performance.

Peer Effects in Endogenous Networks

This paper presents a model of strategic network formation with local complementarities in effort levels and positive local externalities. Results are obtained for a general class of payoff functions, which subsumes the linear-quadratic specification frequently used in the literature. We assume homogeneous agents and characterize equilibria for two-sided and one-sided link formation. (Pairwise) Nash equilibrium networks are shown to be nested split graphs, which are a strict subset of core-periphery networks. The relevance of the convexity of the value function in obtaining these structures is highlighted. In equilibrium more central agents exert more effort and obtain higher gross payoffs. However, net of linking cost, central agents may obtain strictly lower net payoffs. Under additional assumptions on payoffs we show that the only efficient network structures are also nested split graphs. These findings are relevant for a wide range of social and economic phenomena, such as educational attainment, criminal activity, labor market participation, and R&D expenditures of firms.

Network extension

In a model of strategic network formation, the endogenously formed network is built around a pre-existing network. We envisage that the pre-existing or core network is publicly provided. Strategic network formation is decentralized: Players act in their private interest and bear the costs when adding links to the pre-existing network. We study how the pre-existing network affects existence of Nash equilibria and efficiency of Nash equilibrium outcomes: It can foster or prohibit existence of Nash equilibria. It can improve or worsen equilibrium welfare. Special attention is paid to an insider–outsider model where society is partitioned into several groups and links within a group (between insiders) are much cheaper than links across groups (between outsiders). We also present equilibrium existence results and examine the effect of Pareto optimal publicly provided networks.

Network Formation with Heterogeneous Agents and Absolute Friction

We present a model of strategic network formation with absolute friction and heterogeneous agents. The individual payoffs from a given network are determined by the difference of an agent specific utility function that depends on the number of her direct links and the sum of her link-costs. These link-costs decompose in a symmetric function that represents the social distance between the two agents and an agent specific function representing the partner’s quality. From a theoretical point of view, we analyze strong pairwise stable networks (Belleflamme and Bloch, Int Econ Rev 45(2):387–411, 2004) and show that our model always has a unique strong pairwise stable network which is also strongly stable (Dutta and Mutuswami, J Econ Theory, 76:322–344, 1997). From a practical point of view, we provide an algorithm that reproduces this stable network from information on the individual payoff structure. We illustrate the use of this algorithm by applying it to an informal risk sharing network data set from the village of Nyakatoke in rural Tanzania.

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 specic 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 dierent

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.

(Non-)existence and Scope of Nash Networks

For the connections model of strategic network formation, with two-way flow of information and without information decay, specific parameter configura- tions are given for which Nash networks do not exist. Moreover, existence and the scope of Nash network architectures are briefly discussed.