Testing for Differences in Stochastic Network Structure

Eric Auerbach*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


How can one determine whether a treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogs of a two-sample Kolmogorov–Smirnov test, widely used in the literature to test the null hypothesis of no treatment effects, for network data. It first specifies a testing problem in which the null hypothesis is that two networks are drawn from the same random graph model. It then describes two randomization tests based on the magnitude of the difference between the networks' adjacency matrices as measured by the 2 → 2 and ∞ → 1 operator norms. Power properties of the tests are examined analytically, in simulation, and through two real-world applications. A key finding is that the test based on the ∞ → 1 norm can be much more powerful for the kinds of sparse and degree-heterogeneous networks common in economics.

Original languageEnglish (US)
Pages (from-to)1205-1223
Number of pages19
Issue number3
StatePublished - May 2022


  • endogenous link formation
  • heterogeneous treatment effects
  • matrix norms
  • network externalities
  • Networks
  • panel data
  • randomization inference
  • semidefinite programming

ASJC Scopus subject areas

  • Economics and Econometrics


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