Measurement Error in Self-Reported Health Variables

Butler, J. S.; Burkhauser, Richard V.; Mitchell, Jean M.; Pincus, Theodore

doi:10.2307/1935959

“…We argue that our results have ample applications in settings where the dependent variable suffers from non-random measurement error Mathiowetz, 2001, Butler, Burkhauser, Mitchell, andPincus, 1987) and where administrative records are not a data source alternative. As a general result, we review the implications of systematic misreporting on the estimation of causal effects.…”

Section: Introductionmentioning

confidence: 81%

“…For example, Butler, Burkhauser, Mitchell, and Pincus (1987) show evidence of nonclassical error in the measurement of arthritis while Johnston, Propper, and Shields (2009) unbiased causal effects of a particular characteristic or attribute, especially if the latter is correlated with misreporting behavior. For example, a well-known puzzle in the development economics literature is that of an inverse plot size-productivity relationship.…”

Section: Misreporting In Sensitive Survey Questionsmentioning

confidence: 99%

Misreporting in Sensitive Health Behaviors and its Impact on Treatment Effects: An Application to Intimate Partner Violence

Agüero

¹

,

Frisancho

²

2017

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Inter-American Development Bank. This work is licensed under a Creative Commons IGO 3.0 AttributionNonCommercial-NoDerivatives (CC-IGO BY-NC-ND 3.0 IGO) license (http://creativecommons.org/licenses/by-nc-nd/3.0/igo/ legalcode) and may be reproduced with attribution to the IDB and for any non-commercial purpose, as provided below. No derivative work is allowed. Terms of use: Documents inAny dispute related to the use of the works of the IDB that cannot be settled amicably shall be submitted to arbitration pursuant to the UNCITRAL rules. The use of the IDB's name for any purpose other than for attribution, and the use of IDB's logo shall be subject to a separate written license agreement between the IDB and the user and is not authorized as part of this CC-IGO license.Following a peer review process, and with previous written consent by the Inter-American Development Bank (IDB), a revised version of this work may also be reproduced in any academic journal, including those indexed by the American Economic Association's EconLit, provided that the IDB is credited and that the author(s) receive no income from the publication. Therefore, the restriction to receive income from such publication shall only extend to the publication's author(s). With regard to such restriction, in case of any inconsistency between the Creative Commons IGO 3.0 Attribution-NonCommercial-NoDerivatives license and these statements, the latter shall prevail.Note that link provided above includes additional terms and conditions of the license.The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Inter-American Development Bank, its Board of Directors, or the countries they represent.http://www.iadb.org 2017 AbstractA growing literature seeks to identify policies that could reduce intimate partner violence. However, in the absence of reliable administrative records, this violence is often measured using self-reported data from health surveys. In this paper, an experiment is conducted comparing data from such surveys against a methodology that provides greater privacy to the respondent. Non-classical measurement error in health surveys is identified as college-educated women, but not the less educated, underreport physical and sexual violence. The paper provides a low-cost solution to correct the bias in the estimation of causal effects under non-classical measurement error in the dependent variable.

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“…Hence, we can work with five complete datasets where all missings are replaced by imputed values. 3 These datasets differ slightly with respect to the imputed variables and reflect the uncertainty about the true values of the missing attributes. For all datasets, five repetitions are used to generate each imputed dataset.…”

Section: Data and Estimation Methodsmentioning

confidence: 99%

I feel good! Gender differences and reporting heterogeneity in self-assessed health

Schneider

¹

,

Pfarr

²

,

Schneider

³

et al. 2011

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For empirical analysis and policy-oriented recommendation, the precise measurement of individual health or well-being is essential. The problem with variables based on questionnaires such as selfassessed health is that the answer may depend on individual reporting behaviour. Moreover, if individual"s health perception varies with certain attitudes of the respondent reporting heterogeneity may lead to index or cut-point shifts of the health distribution, causing estimation problems. We analyse the reporting behaviour of individuals on their self-assessed health status, a five-point categorical variable. We explore observed heterogeneity in categorical variables and include unobserved individual heterogeneity using German panel data. Estimation results show different impacts of socioeconomic and health related variables on the five subscales of self-assessed health.Moreover, the answering behaviour varies between female and male respondents, pointing to gender specific perception and assessment of diseases. Reporting behaviour on self-assessed health questions in surveys is problematic due to a possible heterogeneity. Hence, in case of reporting heterogeneity, using self-assessed measures in empirical studies may be misleading or at least ambiguous. JEL-Classification: I12, C21

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“…A subset of the non-core variables is used as conditioning variables or predictors for the current imputation step. 3 Only variables on age and gender contain no missing values. 4 Because foreigners are under-represented in the dataset, we concentrate on German citizens only.…”

Section: Data and Estimation Methodsmentioning

confidence: 99%

I Feel Good! Gender Differences and Reporting Heterogeneity in Self-Assessed Health

Pfarr

¹

,

Schneider

²

,

Schneider

³

et al. 2010

SSRN Journal

1

0

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For empirical analysis and policy-oriented recommendation, the precise measurement of individual health or well-being is essential. The problem with variables based on questionnaires such as selfassessed health is that the answer may depend on individual reporting behaviour. Moreover, if individual"s health perception varies with certain attitudes of the respondent reporting heterogeneity may lead to index or cut-point shifts of the health distribution, causing estimation problems. We analyse the reporting behaviour of individuals on their self-assessed health status, a five-point categorical variable. We explore observed heterogeneity in categorical variables and include unobserved individual heterogeneity using German panel data. Estimation results show different impacts of socioeconomic and health related variables on the five subscales of self-assessed health.Moreover, the answering behaviour varies between female and male respondents, pointing to gender specific perception and assessment of diseases. Reporting behaviour on self-assessed health questions in surveys is problematic due to a possible heterogeneity. Hence, in case of reporting heterogeneity, using self-assessed measures in empirical studies may be misleading or at least ambiguous. JEL-Classification: I12, C21

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Measurement Error in Self-Reported Health Variables

Cited by 166 publications

References 15 publications

Misreporting in Sensitive Health Behaviors and its Impact on Treatment Effects: An Application to Intimate Partner Violence

Misreporting in Sensitive Health Behaviors and its Impact on Treatment Effects: An Application to Intimate Partner Violence

I feel good! Gender differences and reporting heterogeneity in self-assessed health

I Feel Good! Gender Differences and Reporting Heterogeneity in Self-Assessed Health

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