arXiv 16 Aug 2019 · Statistics — Methodology · 2 citations (OpenAlex)
arXiv:1908.05811 · PDF · DOI · OpenAlex · Extracted main text
The LATE monotonicity assumption of Imbens and Angrist (1994) precludes "defiers," individuals whose treatment always runs counter to the instrument, in the terminology of Balke and Pearl (1993) and Angrist et al. (1996). I allow for defiers in a model with a binary instrument and a binary treatment. The model is explicit about the randomization process that gives rise to the instrument. I use the model to develop estimators of the counts of defiers, always takers, compliers, and never takers. I propose separate versions of the estimators for contexts in which the parameter of the randomization process is unspecified, which I intend for use with natural experiments with virtual random assignment. I present an empirical application that revisits Angrist and Evans (1998), which examines the impact of virtual random assignment of the sex of the first two children on subsequent fertility. I find that subsequent fertility is much more responsive to the sex mix of the first two children when defiers are allowed.
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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 | Angrist, J. D. and W. N. Evans (1998) Children and their parents' labor supply: Evidence from exogenous variation in family size | 1.000 | 18 | 5 | 100% |
| 2 | Angrist, J. D., G. W. Imbens, and D. B. Rubin (1996) Identification of causal effects using instrumental variables | 1.000 | 8 | 4 | 100% |
| 3 | Imbens, G. W. and J. D. Angrist (1994) Identification and estimation of local average treatment effects | 1.000 | 5 | 3 | 100% |
| 4 | Balke, A. and J. Pearl (1993) Nonparametric bounds on causal effects from partial compliance data | 0.644 | 2 | 2 | 100% |
| 5 | Angrist, J. D. and I. Fernandez-Val (2013) ExtrapoLATE-ing: External validity and overidentification in the LATE framework | 0.511 | 2 | 1 | 100% |
| 6 | Imbens, G. W. and D. B. Rubin (1997) Estimating outcome distributions for compliers in instrumental variables models | 0.511 | 2 | 1 | 100% |
| 7 | Kowalski, A (2019) A model of a randomized experiment with an application to the PROWESS clinical trial self | 0.511 | 2 | 1 | 100% |
| 8 | Angrist, J. D. and J.-S. Pischke (2009) Mostly Harmless Econometrics: An Empiricists' Companion | 0.405 | 1 | 1 | 100% |
| 9 | Sahinidis, N. V (2018) BARON 18.8.23: Global Optimization of Mixed-Integer Nonlinear Programs, User's Manual | 0.405 | 1 | 1 | 100% |
| 10 | Holland, P. W (1986) Statistics and causal inference | 0.405 | 1 | 1 | 100% |
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