arXiv 24 Nov 2021 · Econometrics · publishedThe Review of Economics and Statistics (2024) · 3 citations (OpenAlex)
arXiv:2111.12258 · PDF · DOI · OpenAlex · Extracted main text
Researchers using instrumental variables to investigate ordered treatments often recode treatment into an indicator for any exposure. We investigate this estimand under the assumption that the instruments shift compliers from no treatment to some but not from some treatment to more. We show that when there are extensive margin compliers only (EMCO) this estimand captures a weighted average of treatment effects that can be partially unbundled into each complier group's potential outcome means. We also establish an equivalence between EMCO and a two-factor selection model and apply our results to study treatment heterogeneity in the Oregon Health Insurance Experiment.
appendix boundary found by appendix_command · 54% of the source is main text. Read the extracted text to check this.
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 and Imbens (1995) Two-Stage Least Squares Estimation of Average Causal Effects in Models with Variable Treatment Intensity | 0.961 | 9 | 3 | 89% |
| 2 | Finkelstein, Taubman, Wright, Bernstein, Gruber, Newhouse, Allen, Ba… (2012) The Oregon Health Insurance Experiment: Evidence from the First Year | 0.953 | 15 | 4 | 87% |
| 3 | Abadie (2003) Semiparametric instrumental variable estimation of treatment response models | 0.928 | 5 | 3 | 80% |
| 4 | Andresen and Huber (2021) Instrument-based estimation with binarised treatments: issues and tests for the exclusion restriction | 0.894 | 7 | 3 | 71% |
| 5 | Heckman and Vytlacil (2005) Structural Equations, Treatment Effects, and Econometric Policy Evaluation | 0.843 | 4 | 4 | 75% |
| 6 | Heckman, Urzua and Vytlacil (2006) Understanding Instrumental Variables in Models with Essential Heterogeneity | 0.843 | 3 | 3 | 100% |
| 7 | Rose and Shem-Tov (2021) How does incarceration affect reoffending? Estimating the dose-response function | 0.811 | 4 | 2 | 100% |
| 8 | Vytlacil (2006) Ordered Discrete-Choice Selection Models and Local Average Treatment Effect Assumptions: Equivalence, Nonequivalence, and Repres… | 0.811 | 4 | 2 | 100% |
| 9 | Imbens and Rubin (1997) Estimating Outcome Distributions for Compliers in Instrumental Variables Models | 0.737 | 3 | 2 | 100% |
| 10 | Vytlacil (2002) Independence, Monotonicity, and Latent Index Models: An Equivalence Result | 0.737 | 3 | 2 | 100% |
Showing the top 10 of 56 scored citations.
arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.