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Changes-in-Changes for Ordered Choice Models: Too Many "False Zeros"?

Daniel Gutknecht, Cenchen Liu

arXiv 1 Jan 2024 · Econometrics

arXiv:2401.00618 · PDF · DOI · OpenAlex · Extracted main text

Abstract

In this paper, we develop a Difference-in-Differences model for discrete, ordered outcomes, building upon elements from a continuous Changes-in-Changes model. We focus on outcomes derived from self-reported survey data eliciting socially undesirable, illegal, or stigmatized behaviors like tax evasion or substance abuse, where too many "false zeros", or more broadly, underreporting are likely. We start by providing a characterization for parallel trends within a general threshold-crossing model. We then propose a partial and point identification framework for different distributional treatment effects when the outcome is subject to underreporting. Applying our methodology, we investigate the impact of recreational marijuana legalization for adults in several U.S. states on the short-term consumption behavior of 8th-grade high-school students. The results indicate small, but significant increases in consumption probabilities at each level. These effects are further amplified upon accounting for misreporting.

Citation extraction

29
references
62
in-text mentions
29
distinct cited
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self-citations
11,487
main-text words

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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
1Athey, S. and Imbens, G (2006) Identification and inference in nonlinear difference-in-differences models1.000136100%
2Brown, Sarah and Harris, Mark N and Srivastava, Preety and Zhang, Xi… (2018) Modelling illegal drug participation0.84333100%
3Chernozhukov, Victor and Fernández-Val, Iván and Melly, Blaise and W… (2020) Generic inference on quantile and quantile effect functions for discrete outcomes0.84333100%
4Masten, Matthew A. and Poirier, Alexandre (2020) Inference on breakdown frontiers0.81142100%
5Hollingworth, A. and Wing, C. and Bradford, A.C (2022) Comparative effects of recreational and medical marijuana laws on drug use among adults and adolescents0.73732100%
6Anderson, D. M. and Hansen, B. and Rees, D. I (2015) Medical marijuana laws and teen marijuana use0.64422100%
7Cerdá, Magdalena and Wall, Melanie and Feng, Tianshu and Keyes, Kath… (2017) Association of state recreational marijuana laws with adolescent marijuana use0.64422100%
8Fang, Z. and Santos, A (2019) Inference on Directionally Differentiable Functions0.64422100%
9Greene, William and Harris, Mark N. and Srivastava, Preety and Zhao,… (2018) Misreporting and econometric modelling of zeros in survey data on social bads: An application to cannabis consumption0.64422100%
10Mondal, Orville and Wang, Rui (2024) Partial identification of binary choice models with misreported outcomes0.64422100%

Showing the top 10 of 29 scored citations.