EconBase
← All papers

Sharp Testable Implications of Encouragement Designs

Yuehao Bai, Shunzhuang Huang, Max Tabord-Meehan

arXiv 14 Nov 2024 · Econometrics

arXiv:2411.09808 · PDF · DOI · OpenAlex · Extracted main text

Abstract

This paper studies a potential outcome model with a continuous or discrete outcome, a discrete multi-valued treatment, and a discrete multi-valued instrument. We derive sharp, closed-form testable implications for a class of restrictions on potential treatments where each value of the instrument encourages towards at most one unique treatment choice; such restrictions serve as the key identifying assumption in several prominent recent empirical papers. Borrowing the terminology used in randomized experiments, we call such a setting an encouragement design. The testable implications are inequalities in terms of the conditional distributions of choices and the outcome given the instrument. Through a novel constructive argument, we show these inequalities are sharp in the sense that any distribution of the observed data that satisfies these inequalities is compatible with this class of restrictions on potential treatments. Based on these inequalities, we propose tests of the restrictions. In an empirical application, we show some of these restrictions are violated and pinpoint the substitution pattern that leads to the violation.

Citation extraction

50
references
134
in-text mentions
50
distinct cited
2
self-citations
12,788
main-text words

appendix boundary found by appendix_command · 67% of the source is main text. Read the extracted text to check this.

Most heavily cited references

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.

ReferenceIntensityMentionsSectionsMain text
1Fang, Zheng and Santos, Andres and Shaikh, Azeem M. and Torgovitsky,… (2023) Inference for Large-Scale Linear Systems With Known Coefficients1.000133100%
2Kitagawa, Toru (2015) A Test for Instrument Validity1.00075100%
3Kirkeboen, Lars J and Leuven, Edwin and Mogstad, Magne (2016) Field of study, earnings, and self-selection1.00074100%
4Kline, Patrick and Walters, Christopher R (2016) Evaluating public programs with close substitutes: The case of Head Start0.97413492%
5Behaghel, Luc and Crépon, Bruno and Gurgand, Marc (2014) Private and Public Provision of Counseling to Job Seekers: Evidence from a Large Controlled Experiment0.96911691%
6Behaghel, Luc and Crépon, Bruno and Gurgand, Marc (2013) Robustness of the encouragement design in a two-treatment randomized control trial0.94112683%
7Mourifié, Ismael and Wan, Yuanyuan (2017) Testing Local Average Treatment Effect Assumptions0.9209578%
8Chernozhukov, Victor and Lee, Sokbae and Rosen, Adam M (2013) Intersection Bounds: Estimation and Inference0.9098475%
9Balke, Alexander and Pearl, Judea (1997) Bounds on Treatment Effects from Studies with Imperfect Compliance0.73732100%
10Balke, Alexander A and Pearl, Judea (1997) Probabilistic Counterfactuals: Semantics, Computation, and Applications0.73732100%

Showing the top 10 of 50 scored citations.

Cited by, within the corpus

arXiv econ.EM papers that cite this one, ranked by how heavily they lean on it.

Citing paperIntensityMentionsSections
1Testing Exclusion and Shape Restrictions in Potential Outcomes Models1.00073
2Treatment Effects with Targeting Instruments0.92843
3On the falsification of instrumental variable models for heterogeneous treatment effects0.73754
4Inference for Treatment Effects Conditional on Generalized Principal Strata using Instrumental Variables0.64422
5Identification in Multiple Treatment Models under Discrete Variation0.40511
6Testing identifying assumptions in Tobit models0.40511
7On the Identifying Power of Generalized Monotonicity for Average Treatment Effects0.00011