Pith. sign in

REVIEW 1 cited by

Spillovers of Program Benefits with Missing Network Links

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2009.09614 v3 pith:WRYXVTVV submitted 2020-09-21 econ.EM

classification econ.EM
keywords networklinksmethodmissingbenefitsdegreeseffectsissue
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

The issue of missing network links in partially observed networks is frequently neglected in empirical studies. This paper addresses this issue when investigating the spillovers of program benefits in the presence of network interactions. Our method is flexible enough to account for non-i.i.d. missing links. It relies on two network measures that can be easily constructed based on the incoming and outgoing links of the same observed network. The treatment and spillover effects can be point identified and consistently estimated if network degrees are bounded for all units. We also demonstrate the bias reduction property of our method if network degrees of some units are unbounded. Monte Carlo experiments and a naturalistic simulation on real-world network data are implemented to verify the finite-sample performance of our method. We also re-examine the spillover effects of home computer use on children's self-empowered learning.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Estimating Peer Effects Using Partial Network Data

    econ.EM 2025-09 conditional novelty 6.0 of 10

    A new SGMM and a Bayesian estimator recover peer effects from partially observed networks, and show that Add Health data errors bias the estimated peer effect downward by roughly a third.

Pith tools