arXiv 26 Mar 2019 · Econometrics · publishedEconometrica (2022) · 1 citations (OpenAlex)
arXiv:1903.11117 · PDF · DOI · OpenAlex · Extracted main text
How can one determine whether a community-level treatment, such as the introduction of a social program or trade shock, alters agents' incentives to form links in a network? This paper proposes analogues 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 $2\to2$ and $\infty\to1$ 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 $\infty\to1$ norm can be substantially more powerful than that based on the $2\to2$ norm for the kinds of sparse and degree-heterogeneous networks common in economics.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Banerjee, A., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2013) The diffusion of microfinance | 1.000 | 5 | 3 | 100% |
| 2 | Banerjee, A. V., A. G. Chandrasekhar, E. Duflo, and M. O. Jackson (2018) Changes in social network structure in response to exposure to formal credit markets | 0.843 | 3 | 3 | 100% |
| 3 | Lehmann, E. L. and J. P. Romano (2006) Testing statistical hypotheses | 0.737 | 3 | 2 | 100% |
| 4 | Jackson, M. O. and B. W. Rogers (2007) Meeting strangers and friends of friends: How random are social networks? | 0.644 | 2 | 2 | 100% |
| 5 | Alon, N. and A. Naor (2006) Approximating the cut-norm via grothendieck's inequality | 0.511 | 2 | 2 | 50% |
| 6 | Aronow, P. M (2012) A general method for detecting interference between units in randomized experiments | 0.405 | 1 | 1 | 100% |
| 7 | Athey, S., D. Eckles, and G. W. Imbens (2018) Exact p-values for network interference | 0.405 | 1 | 1 | 100% |
| 8 | Calvó-Armengol, A., E. Patacchini, and Y. Zenou (2009) Peer effects and social networks in education | 0.405 | 1 | 1 | 100% |
| 9 | Fafchamps, M. and F. Gubert (2007) Risk sharing and network formation | 0.405 | 1 | 1 | 100% |
| 10 | Ghoshdastidar, D., M. Gutzeit, A. Carpentier, and U. von Luxburg (2017) Two-sample hypothesis testing for inhomogeneous random graphs | 0.405 | 1 | 1 | 100% |
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