State Dependence and Heterogeneity in Health Using a Bias‐corrected Fixed‐effects Estimator

Carro, Jesús M.; Traferri, Alejandra

doi:10.1002/jae.2301

Cited by 21 publications

(21 citation statements)

References 22 publications

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“…() provides an excellent motivation of these covariates, so an extensive review of covariates is not repeated here. These variables are consistent with those used in other studies of self‐assessed health (Jones and Wildman ; Powdthavee ; Carro and Traferri ). For parsimony, we keep the choice of variables limited to these socio‐demographic variables.…”

Section: The Data and Self‐assessed Health Variablesupporting

confidence: 86%

Heterogeneity in Ordered Choice Models: A Review With Applications to Self‐assessed Health

Greene

Harris

Hollingsworth

et al. 2012

Journal of Economic Surveys

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Abstract. Discrete variables that have an inherent sense of ordering across outcomes are commonly found in large data sets available to many economists, and are often the focus of research. However, assumptions underlying the standard ordered probit (which is usually used to analyse such variables) are not always justified by the data. This study provides a review of the ways in which the ordered probit might be extended to account for additional heterogeneity. Differing from other reviews in scope, application and relevance in economic settings, a series of issues pertaining to choices of variables, and the economic assumptions underlying each model are discussed in the context of measuring the underlying health of respondents. The models are applied to a wave of the household, income and labour dynamics in Australia survey, in order to check the appropriateness of such assumptions in an applied context.

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Section: The Data and Self‐assessed Health Variablesupporting

confidence: 86%

Heterogeneity in Ordered Choice Models: A Review With Applications to Self‐assessed Health

Greene

Harris

Hollingsworth

et al. 2012

Journal of Economic Surveys

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“…In such models the boundary parameters embedded in the ordered choice model are functions of observed personal characteristics (although we note that the strict approach of Pudney and Shields, 2000, is just one of many ways in which heterogeneity can be introduced into the boundary parameters; see Greene and Hensher, 2010). Schurer (2011) andCarro andTrafferi (2012) both develop elaborate models of heterogeneity involving a correlated random effect in the broader health satisfaction index model and a conventional fixed effect in the inherent boundaries of the model. These studies examine heterogeneity in broad terms, rather than as a symptom of 'mis-reporting.…”

Section: Mis-reporting In Empirical Studies Of Healthmentioning

confidence: 99%

Inflated Responses in Measures of Self-Assessed Health

Greene

Harris

Hollingsworth

2015

American Journal of Health Economics

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Abstract/SummaryThis paper focuses on the self-reported responses given to survey questions of the form In general how would you rate your health? with typical response items being on a scale ranging from poor to excellent. Usually, the overwhelming majority of responses fall in either the middle category or the one immediately to the "right" of this (in the above example, good and very good). However, based on a wide range of other medical indicators, such favourable responses appear to paint an overly rosy picture of true health. The hypothesis here is that these "middle" responses have been, in some sense, inflated. That is, for whatever reason, a significant number of responders inaccurately report into these categories. We find a significant amount of inflation into these categories. Adjusted responses to these questions could lead to significant changes in policy, and should be reflected upon when analysing and interpreting these scales. Keywords: Self-assessed health, inflated outcomes, mis-reporting, ordered probit, panel data.JEL codes: C3, C5, I1. 1 We are grateful to the Australian Research Council to help fund this research. We are also grateful to Michael Shields, David Johnson, Nigel Rice, John Wildman and Andrew Jones for useful comments and suggestions, and also to seminar participants at

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“…Our approach also relates more broadly to other strands of the health economics literature. For instance, Carro and Traferri (2014) use a biascorrected estimator for a model of self-assessed health and considers the implied distribution of two sets of individual effects. We focus on two advantages of our approach that are likely to be important to the study of persistence in health care utilization.…”

Section: Doctor Visitsmentioning

confidence: 99%

Predicting Individual Effects in Fixed Effects Panel Probit Models

Kunz

Staub

Winkelmann

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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Many applied settings in empirical economics require estimation of a large number of individual effects, like teacher effects or location effects; in health economics, prominent examples include patient effects, doctor effects or hospital effects. Increasingly, these effects are the object of interest of the estimation, and predicted effects are often used for further descriptive and regression analyses. To avoid imposing distributional assumptions on these effects, they are typically estimated via fixed effects methods. In short panels, the conventional maximum likelihood estimator for fixed effects binary response models provides poor estimates of these individual effects since the finite sample bias is typically substantial. We present a bias-reduced fixed effects estimator that provides better estimates of the individual effects in these models by removing the first-order asymptotic bias. An additional, practical advantage of the estimator is that it provides finite predictions for all individual effects in the sample, including those for which the corresponding dependent variable has identical outcomes in all time periods over time (either all zeros or ones); for these, the maximum likelihood prediction is infinite. We illustrate the approach in simulation experiments and in an application to health care utilization.

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State Dependence and Heterogeneity in Health Using a Bias‐corrected Fixed‐effects Estimator

Cited by 21 publications

References 22 publications

Heterogeneity in Ordered Choice Models: A Review With Applications to Self‐assessed Health

Heterogeneity in Ordered Choice Models: A Review With Applications to Self‐assessed Health

Inflated Responses in Measures of Self-Assessed Health

Predicting Individual Effects in Fixed Effects Panel Probit Models

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