Empirical Challenges with Peers-of-Peers Instruments in the Linear-In-Means Model

In the linear-in-means model, endogeneity arises naturally due to the reflection problem.

A common solution is to use Instrumental Variables (IVs) based on higher-order network links, such as using friends-of-friends' characteristics.

In this paper, we show that such instruments are unlikely to work well in many applied settings due to a specific sparse/dense-network mechanism: in extremely sparse networks, friends-of-friends instruments may become degenerate, while in denser networks they may still provide too little first-stage information.

This implies that the IVs may be weak or that the first-stage estimand is undefined.

We use random graph theory to characterize the rates at which these issues arise for a benchmark class of random graphs.

This allows us to link network topology to first-stage information accumulation and to identify when such instruments are likely to perform well.

We show how existing weak-IV robust inference can be adapted to this environment, and how scaling the network provides an alternative specification that can mitigate some of these challenges.

We provide extensive Monte Carlo simulations and revisit empirical applications, showing the prevalence of such issues in empirical practice, and how our results apply.