Resolving the Identification Problem in Linear Social Interactions Models: Modeling with Between-Group Spillovers

The linear-in-means model has been a theoretical and empirical workhorse of the social interactions field. As was noted by Manski (1993), the collinearity between group-level 'contextual' and 'endogenous' effects leads to an inability to identify the structural parameters of this model. Manski called this the 'reflection' problem. This paper suggests that Manksi’s reflection problem is unique to a special case of a more general context in which agents care about multiple reference groups. Specifically, the identification problem is resolved through a model generalization to include between-group and within-group effects.Social Interactions, Identification, Linear-in- Means Model

Monetary policy and capital regulation in the US and Europe

From the onset of the 2007-2009 crisis, the Federal Reserve and the European Central Bank have aggressively lowered interest rates. Both sets of changes are at odds with an anti-inflationary stance of monetary policy; indeed, as the crisis began in August 2007 inflation expectations were high and rising, particularly in the United States. We have two additions to the literature. One, we present a model economy with a leveraged and regulated financial sector. Two, we find optimal Taylor rules for our economy that are consistent with a strong pro-inflationary reaction during financial crisis while maintaining a standard output-inflation mandate. We have three interpretations of our results. One, because the Federal Reserve has partial control over bank regulation it can exercise regulatory lenience. Two, the Fed’s stronger output orientation means that it will potentially respond more quickly when faced with constrained banks. Three, our results support procyclical capital regulation. JEL Classification: E52, E58, G18, G28capital regulation, crisis, monetary policy

Welfare Stigma or Information Sharing? Decomposing Social Interactions Effects in Social Benefit Use

Empirical research has shown that social interactions affect the use of public benefits, thus providing evidence in favor of the idea of “welfare cultures.” In this paper we take the next crucial step by separately identifying the role of social stigma and information sharing in welfare participation, using Census data. We argue that the stigma vs. information distinction has possibly important consequences. Separate identification exploits the asymmetry between association and mere spatial proximity: we asume that while information is transmitted within groups, stigma works across groups as well. We also allow for heterogeneity of social effects across different race-ethnic groups and find non-trivial differences. We find that while the information channel is more important than stigma, White Americans appear to perceive stigma more from otherWhite Americans than by other races, and Black and Hispanic Americans appear to respond principally to stigma from external groupssocial interactions, neighborhood effects, welfare stigma

The Balance Sheet Channel

In this paper, we study the role of the credit channel of monetary policy in the context of a DSGE model. Through the use of a regulated banking sector subject to a regulatory capital constraint on lending, we provide alternative interpretations that can potentially explain differences in the implementation of monetary policy without appealing to ad-hoc central bank preferences. This is accomplished through the characterization of the external finance premium as a function of bank leverage and systemic aggregate risk.

Systemic Risk and Network Formation in the Interbank Market

We propose a novel mechanism to facilitate understanding of systemic risk in financial markets. The literature on systemic risk has focused on two mechanisms, common shocks and domino-like sequential default. Our approach is a formal model that provides an intellectual combination of the two by looking at how shocks propagate through a network of interconnected banks. Transmission in our model is not based on default. Instead, we provide a simple microfoundation of banks’ profitability based on classic competition incentives. As competitors lending quantities change, both for closely connected ones and the whole market, banks adjust their own lending decisions as a result, generating a ‘transmission’ of shocks through the system. We provide a unique equilibrium characterization of a static model, and embed this model into a full dynamic model of network formation with n agents. Because we have an explicit characterization of equilibrium behavior, we have a tractable way to bring the model to the data. Indeed, our measures of systemic risk capture the propagation of shocks in a wide variety of contexts; that is, it can explain the pattern of behavior both in good times as well as in crisis.Financial networks; interbank lending; interconnections; network centrality; spatial autoregressive models

Detecting implausible social network effects in acne, height, and headaches: longitudinal analysis

Objective To investigate whether “network effects” can be detected for health outcomes that are unlikely to be subject to network phenomena

In Noise we Trust? Optimal Monetary Policy with Random Targets

We show that a monetary policy in which the central bank commits to a randomized inflation target allows for potentially faster-expectations convergence than with a fixed target. The randomized target achieves faster convergence in particular in transition environments: those demonstrating either particularly high or low inflation

The "Unfriending" Problem: The Consequences of Homophily in Friendship Retention for Causal Estimates of Social Influence

An increasing number of scholars are using longitudinal social network data to try to obtain estimates of peer or social influence effects. These data may provide additional statistical leverage, but they can introduce new inferential problems. In particular, while the confounding effects of homophily in friendship formation are widely appreciated, homophily in friendship retention may also confound causal estimates of social influence in longitudinal network data. We provide evidence for this claim in a Monte Carlo analysis of the statistical model used by Christakis, Fowler, and their colleagues in numerous articles estimating "contagion" effects in social networks. Our results indicate that homophily in friendship retention induces significant upward bias and decreased coverage levels in the Christakis and Fowler model if there is non-negligible friendship attrition over time.Comment: 26 pages, 4 figure