Bounding Program Benefits When Participation is Misreported

Tommasi, Denni; Zhang, Lína

doi:10.2139/ssrn.3643194

Cited by 12 publications

(26 citation statements)

References 51 publications

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“…My framework can be adapted to a binary instrument, i.e., Z = {0, 1} . This case, that bounds the LATE parameter, is detailed in Appendix E and complements the work developed byUra (2018),Calvi et al (2019),Tommasi and Zhang (2020) andAcerenza et al (2021).…”

mentioning

confidence: 55%

“…Similarly to my work, four recent papers focused on identifying treatment effect parameters allowing for heterogeneous effects, endogenous selection into treatment and misclassification of the treatment variable: Ura (2018), Calvi et al (2019), Tommasi and Zhang (2020) and Acerenza et al (2021). While the first three papers focus on discrete instruments and the LATE parameter (Imbens and Angrist, 1994), Acerenza et al (2021) and I focus on continuous instruments and the MTE function.…”

Section: Datementioning

confidence: 94%

“…Similarly to the comparison between the LATE and the Wald Estimator when the treatment variable is misclassified (Calvi et al, 2019;Tommasi and Zhang, 2020), there is a scaling factor connecting the LIV estimand and the MTE function.…”

Section: Partial Identification Of the Mte With A Misclassified Treat...mentioning

confidence: 99%

“…Moreover, these conditions are stated with specific empirical contexts to illustrate the broad applicability of my theoretical framework to many applied problems. At the end, I follow Tommasi and Zhang (2020) and propose alternative theoretical restrictions that imply Assumption 5.…”

Section: Simple Sufficient Conditions For Assumptions 5-7mentioning

confidence: 99%

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Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification

Possebom¹

2021

Preprint

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Using data from criminal cases in the State of São Paulo, Brazil, I analyze whether alternative sentences -e.g., fines or community services -decrease recidivism. To do so, I leverage the random assignment of judges within a court district as a source of exogenous variation in the probability of punishment to identify the effect of alternative sentences in comparison to the no-punishment counterfactual. Initially, I show that the usual identification strategy fails to identify the correct treatment effect parameter if it uses the trial judge's sentence, because the trial judge's decision may misclassify the final sentence due to the appeals process. To avoid this measurement error problem, I follow two approaches. First, I propose a novel partial identification strategy to identify the marginal treatment effect (MTE) with a misclassified treatment. This method explores restrictions on the relationship between the misclassified treatment and the correctly measured treatment, and allows for dependence between the instrument and the measurement error. Second, I collect data on Appeals Court's decisions and estimate the MTE based on the correctly measured final sentence. This last exercise is used as a benchmark for the set identification method that I propose.This draft is preliminary. While its theoretical part is complete, its empirical application is not ready yet.

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mentioning

confidence: 55%

Section: Datementioning

confidence: 94%

Section: Partial Identification Of the Mte With A Misclassified Treat...mentioning

confidence: 99%

Section: Simple Sufficient Conditions For Assumptions 5-7mentioning

confidence: 99%

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Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification

Possebom¹

2021

Preprint

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“…Such a task requires different set of assumptions compared to the ones used to recover either directly AT E, LAT E or M T E with continuous instruments. We complement Ura (2018), Kreider et al (2012 and Tommasi and Zhang (2020) because we are interested in identifying M T E (which can then be used to achieve identification of LAT E and AT E) instead of the LAT E and AT E. It is also complementing Acerenza et al (2021) since their analysis relies on the continuity of the instrument. It is worth noticing that it is more common to observe discrete (mostly binary) instruments such as random selection to receive treatment like in medical studies, or random selection to receive a treatment conditional on covariates in social sciences ( e.g.…”

Section: Introductionmentioning

confidence: 99%

Partial Identification of Marginal Treatment Effects with discrete instruments and misreported treatment

Acerenza¹

2021

Preprint

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Identifying program benefits when participation is misreported

Tommasi,

Zhang

2024

J of Applied Econometrics

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SummaryIn cases of noncompliance with an assigned treatment, estimates of causal effects typically rely on instrumental variables (IV). However, when participation is also misreported, the IV estimand may become a nonconvex combination of local average treatment effects that fails to satisfy even a minimal condition for being causal. The aim of our paper is to generalize the MR‐LATE approach. This is an alternative IV estimand that is more robust in cases of noncompliance and nondifferential misclassification of the treatment variable. Our generalization is threefold: First, we incorporate discrete and multiple‐discrete instrument(s); second, we consider the use of instrument(s) under a weaker, partial monotonicity condition; third, we provide a general inferential procedure. Under relatively stringent assumptions, MR‐LATE is either identical to the IV estimand or less biased than the naïve IV estimand. Under less stringent assumptions, the MR‐LATE estimand can identify the sign of the IV estimand. We conclude with the use of a dedicated Stata command, ivreg2m, to assess the return on education in the United Kingdom.

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Bounding Program Benefits When Participation is Misreported

Cited by 12 publications

References 51 publications

Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification

Crime and Mismeasured Punishment: Marginal Treatment Effect with Misclassification

Partial Identification of Marginal Treatment Effects with discrete instruments and misreported treatment

Identifying program benefits when participation is misreported

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