Francis J. DiTraglia, Camilo Garcia-Jimeno, Rossa O'Keeffe-O'Donovan, Alejandro Sanchez-Becerra
arXiv 13 Nov 2020 · Econometrics · publishedJournal of Econometrics (2023) · 4 citations (OpenAlex)
arXiv:2011.07051 · PDF · DOI · OpenAlex · Extracted main text
This paper shows how to use a randomized saturation experimental design to identify and estimate causal effects in the presence of spillovers--one person's treatment may affect another's outcome--and one-sided non-compliance--subjects can only be offered treatment, not compelled to take it up. Two distinct causal effects are of interest in this setting: direct effects quantify how a person's own treatment changes her outcome, while indirect effects quantify how her peers' treatments change her outcome. We consider the case in which spillovers occur within known groups, and take-up decisions are invariant to peers' realized offers. In this setting we point identify the effects of treatment-on-the-treated, both direct and indirect, in a flexible random coefficients model that allows for heterogeneous treatment effects and endogenous selection into treatment. We go on to propose a feasible estimator that is consistent and asymptotically normal as the number and size of groups increases. We apply our estimator to data from a large-scale job placement services experiment, and find negative indirect treatment effects on the likelihood of employment for those willing to take up the program. These negative spillovers are offset by positive direct treatment effects from own take-up.
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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 | Crépon, B., Duflo, E., Gurgand, M., Rathelot, R., Zamora, P (2013) Do labor market policies have displacement effects? Evidence from a clustered randomized experiment | 0.967 | 21 | 8 | 90% |
| 2 | Imai, K., Jiang, Z., Malani, A (2020) Causal inference with interference and noncompliance in two-stage randomized experiments | 0.874 | 6 | 2 | 100% |
| 3 | Kang, H., Imbens, G (2016) Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects | 0.811 | 4 | 2 | 100% |
| 4 | Manski, C.F (2013) Identification of treatment response with social interactions | 0.644 | 2 | 2 | 100% |
| 5 | Hudgens, M.G., Halloran, M.E (2008) Toward causal inference with interference | 0.644 | 2 | 2 | 100% |
| 6 | Masten, M.A., Torgovitsky, A (2016) Identification of instrumental variable correlated random coefficients models | 0.644 | 2 | 2 | 100% |
| 7 | Vazquez-Bare, G (2021) Causal spillover effects using instrumental variables | 0.585 | 3 | 1 | 100% |
| 8 | Baird, S., Bohren, J.A., McIntosh, C., Özler, B (2018) Optimal design of experiments in the presence of interference | 0.511 | 2 | 1 | 100% |
| 9 | Miguel, E., Kremer, M (2004) Worms: identifying impacts on education and health in the presence of treatment externalities | 0.511 | 2 | 1 | 100% |
| 10 | Akram, A.A., Chowdhury, S., Mobarak, A.M (2018) Effects of emigration on rural labor markets | 0.405 | 1 | 1 | 100% |
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