Arthur Lewbel, Xi Qu, Xun Tang
arXiv 9 Sep 2025 · Econometrics
arXiv:2509.07343 · PDF · DOI · OpenAlex · Extracted main text
We propose an adjusted 2SLS estimator for social network models when reported binary network links are misclassified (some zeros reported as ones and vice versa) due, e.g., to survey respondents' recall errors, or lapses in data input. We show misclassification adds new sources of correlation between the regressors and errors, which makes all covariates endogenous and invalidates conventional estimators. We resolve these issues by constructing a novel estimator of misclassification rates and using those estimates to both adjust endogenous peer outcomes and construct new instruments for 2SLS estimation. A distinctive feature of our method is that it does not require structural modeling of link formation. Simulation results confirm our adjusted 2SLS estimator corrects the bias from a naive, unadjusted 2SLS estimator which ignores misclassification and uses conventional instruments. We apply our method to study peer effects in household decisions to participate in a microfinance program in Indian villages.
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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 | 19 | 4 | 100% |
| 2 | Hu, Y (2008) Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution | 0.843 | 3 | 3 | 100% |
| 3 | Bramoullé, Y., H. Djebbari, and B. Fortin (2009) Identification of peer effects through social networks | 0.737 | 3 | 2 | 100% |
| 4 | Bollinger, C. R (1996) Bounding mean regressions when a binary regressor is mismeasured | 0.644 | 2 | 2 | 100% |
| 5 | Boucher, V. and A. Houndetoungan (2020) Estimating peer effects using partial network data | 0.644 | 2 | 2 | 100% |
| 6 | Chandrasekhar, A. and R. Lewis (2011) Econometrics of sampled networks | 0.644 | 2 | 2 | 100% |
| 7 | Lewbel, A (2007) Estimation of average treatment effects with misclassification self | 0.644 | 2 | 2 | 100% |
| 8 | Mahajan, A (2006) Identification and estimation of regression models with misclassification | 0.644 | 2 | 2 | 100% |
| 9 | Griffith, A (2022) Name your friends, but only five? the importance of censoring in peer effects estimates using social network data | 0.511 | 2 | 1 | 100% |
| 10 | Hausman, J. A., J. Abrevaya, and F. M. Scott-Morton (1998) Misclassification of the dependent variable in a discrete-response setting | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 32 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 | 0.585 | 3 | 1 |
| 2 | 1 Linear Regression with Centrality Measures | 0.405 | 1 | 1 |
| 3 | Individualized Treatment Allocation in Sequential Network Games | 0.405 | 1 | 1 |
| 4 | Heterogeneity in peer effects for binary outcomes | 0.405 | 1 | 1 |
| 5 | Flexible Imputation of Incomplete Network Data | 0.405 | 1 | 1 |