arXiv 21 Sep 2020 · Econometrics
arXiv:2009.09614 · PDF · DOI · OpenAlex · Extracted main text
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.
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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 | Yingyao Hu (2008) Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution | 0.874 | 6 | 5 | 67% |
| 2 | Michael P Leung (2020) Treatment and spillover effects under network interference | 0.874 | 6 | 4 | 67% |
| 3 | Diether W Beuermann, Julian Cristia, Santiago Cueto, Ofer Malamud, a… (2015) One laptop per child at home: Short-term impacts from a randomized experiment in Peru | 0.874 | 6 | 2 | 100% |
| 4 | Arun G. Chandrasekhar and Matthew O. Jackson (2021) A network formation model based on subgraphs | 0.737 | 3 | 3 | 67% |
| 5 | Han Hong, Aprajit Mahajan, and Denis Nekipelov (2015) Extremum estimation and numerical derivatives | 0.737 | 3 | 2 | 100% |
| 6 | Whitney K Newey (1994) Kernel estimation of partial means and a general variance estimator | 0.737 | 3 | 2 | 100% |
| 7 | Jing Cai, Alain De Janvry, and Elisabeth Sadoulet (2015) Social networks and the decision to insure | 0.644 | 2 | 2 | 100% |
| 8 | Michael Carter, Rachid Laajaj, and Dean Yang (2021) Subsidies and the african green revolution: Direct effects and social network spillovers of randomized input subsidies in mozamb… | 0.644 | 2 | 2 | 100% |
| 9 | Arthur Lewbel (2007) Estimation of average treatment effects with misclassification | 0.644 | 2 | 2 | 100% |
| 10 | Aprajit Mahajan (2006) Identification and estimation of regression models with misclassification | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 72 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.
| Citing paper | Intensity | Mentions | Sections | |
|---|---|---|---|---|
| 1 | Estimating peer effects in noisy, low-rank networks via network smoothing | 0.405 | 1 | 1 |