Linear Social Interactions Models

Blume, Lawrence E.; Brock, William A.; Durlauf, Steven N.; Jayaraman, Rajshri

doi:10.1086/679496

Cited by 215 publications

(192 citation statements)

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“…Since bank i is excluded,ȳ −i,j,t varies not only across countries and over time, but also across banks within each country-year combination. The contextual effects inX −i,j,t−1 capture the propensity of bank i to change its liquidity transformation policy in response to changes in other characteristics of the peer group such as capital or profitability (e.g., Blume, Brock, Durlauf, and Jayaraman, 2015). Peer-, bank-, and country-level controls are lagged by one period to mitigate concerns of reverse causality.…”

Section: Identification Strategymentioning

confidence: 99%

Strategic Liquidity Mismatch and Financial Sector Stability

Silva

2019

The Review of Financial Studies

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This paper examines whether banks strategically incorporate their competitors’ liquidity mismatch policies when determining their own and the impact of these collective decisions on financial stability. Using a novel identification strategy exploiting the presence of partially overlapping peer groups, I show that banks’ liquidity transformation activity is driven by that of their peers. These correlated decisions are concentrated on the asset side of riskier banks and are asymmetric, with mimicking occurring only when competitors take more risk. Accordingly, this strategic behavior increases banks’ default risk and overall systemic risk, highlighting the importance of regulating liquidity risk from a macroprudential perspective. ReceivedMay 4, 2016; editorial decision January 1, 2019 by Editor Philip Strahan. Author has furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

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Section: Identification Strategymentioning

confidence: 99%

Strategic Liquidity Mismatch and Financial Sector Stability

Silva

2019

The Review of Financial Studies

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“…Another method that has been proposed (Blume et al ., ) and implemented in recent work is the control function. Endogenous linking decisions create selectivity bias in social effect estimates.…”

Section: Dealing With Endogeneity Of Network Formationmentioning

confidence: 99%

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Advani

Malde

2018

Journal of Economic Surveys

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Understanding whether and how connections between agents (networks) such as declared friendships in classrooms, transactions between firms, and extended family connections, influence their socio-economic outcomes has been a growing area of research within economics. Early methods developed to identify these social effects assumed that networks had formed exogenously, and were perfectly observed, both of which are unlikely to hold in practice. A more recent literature, both within economics and in other disciplines, develops methods that relax these assumptions. This paper reviews that literature. It starts by providing a general econometric framework for linear models of social effects, and illustrates how network endogeneity and missing data on the network complicate identification of social effects. Thereafter, it discusses methods for overcoming the problems caused by endogenous formation of networks. Finally, it outlines the stark consequences of missing data on measures of the network, and regression parameters, before describing potential solutions.CREDIBLY IDENTIFYING SOCIAL EFFECTS 1017 unobserved dimensions could influence within-group interactions, and through this the actual outcome. Ignoring variation in interactions within such groups can lead to misleading conclusions and policy design, as shown in recent work by Carrell et al. (2013).More recently, a growing body of research within empirical economics uses data which directly measure interactions between pairs of agents (network data hereon) to sidestep these issues. This growth has been spurred by the increasing availability of such data, as well as the development of methods to identify and estimate social effects with such data. Starting with Bramoullé et al. (2009) andDe Giorgi et al. (2010), methods have been developed to overcome the reflection problem. They show how information on network structure can be used to break the simultaneity, and obtain the necessary exclusion restrictions for parameter identification. These methods, reviewed in detail by Advani and Malde (2014), Topa and Zenou (2015) and Boucher and Fortin (2015), impose strong restrictions on the network formation process and the quality of the data.In particular, the network is assumed to be exogenous conditional on observed agent-and network-level characteristics, and to be fully and perfectly observed by the researcher. Both assumptions are unlikely to hold in practice. In a schooling context, for example, personality traits which are rarely observed by a researcher might influence both a child's choice of friends and her schooling performance. Estimates of the influence of a child's friends' outcomes on her outcomes will be biased if her choice of friends is not accounted for. Similarly, accurately collecting fine-grained information on all connections is very costly and logistically challenging, making it rare to observe a complete, perfectly measured network. This has important implications for identification of social effects using restrictions based on the network struct...

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“…We derive a …rst order approximation of expression (8) around k i = 0. Rewriting expression (8) as (10), and rearranging, we obtain:…”

Section: B Econometric Specificationmentioning

confidence: 99%

Informative Social Interactions

et al. 2018

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We design, …eld and exploit survey data from a representative sample of the French population to examine whether informative social interactions enter households'stockholding decisions. Respondents report perceptions about their circle of peers with whom they interact about …nancial matters, their social circle and the population. We provide evidence for the presence of an information channel through which social interactions in ‡uence perceptions and expectations about stock returns, and …nancial behavior. We also …nd evidence of mindless imitation of peers in the outer social circle, but this does not permeate as many layers of …nancial behavior as informative social interactions do.

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Linear Social Interactions Models

Cited by 215 publications

References 50 publications

Strategic Liquidity Mismatch and Financial Sector Stability

Strategic Liquidity Mismatch and Financial Sector Stability

Credibly Identifying Social Effects: Accounting for Network Formation and Measurement Error

Informative Social Interactions

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