Biased health perceptions and risky health behaviors—Theory and evidence

Patrick P Arni, Davide Dragone, Lorenz Goette, Nicolas Ziebarth*

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)

Abstract

This paper investigates the role of biased health perceptions as a potential driving force of risky health behaviors. We define absolute and relative health perception biases, illustrate their measurement in surveys and provide evidence on their relevance. Next, we decompose the theoretical effect into its extensive and intensive margin: When the extensive margin dominates, people (wrongly) believe they are healthy enough to “afford” unhealthy behavior. Finally, using three population surveys, we provide robust empirical evidence that respondents who overestimate their health are less likely to exercise and sleep enough, but more likely to eat unhealthily and drink alcohol daily.
Original languageEnglish
Article number102425
Number of pages26
JournalJournal of Health Economics
Volume76
Early online date22 Jan 2021
DOIs
Publication statusPublished - 1 Mar 2021

Bibliographical note

Funding Information:
We would like to thank Teresa Bago d'Uva, Emily Beam, Kitt Carpenter, John Cawley, Davide Cesarini, Resul Cesur, Owen O'Donnell, Fabrice Etil?, Osea Giuntella, Glenn Harrison, Ben Hansen, Hendrik J?erges, Jonathan Ketcham, Nadine Ketel, Gaurav Khanna, Audrey Laporte, Fabian Lange, Nathalie Mathieu-Bolh, Taryn Morrissey, Robert Nuscheler, Reto Odermatt, Ricardo Perez-Truglia, Gregor Pfeifer, Pia Pinger, Aldo Rustichini, Joe Sabia, Luis Santos Pinto, Tom Siedler, Rodrigo Soares, Sara Solnick, Pascal St-Amour, Alois Stutzer, Justin Sydnor, Harald Tauchmann, Erdal Tekin, Christian Traxler, Gerard van den Berg, Ben Vollaard, Justin White, and V?ra Zabrodina. In particular, we thank our discussants Matt Harris and Nathan Kettlewell for excellent comments and suggestions. Moreover, we thank conference participants at the the 2019 iHEA World Congress in Basel, the 2019 Workshop on the Economics of Risky Behavior in Bologna, the 2018 ASHEcon meetings in Atlanta, 2017 Bristol Workshop on Economic Policy Interventions and Behaviour, the 2017 Risky Health Behaviors Workshop in Hamburg, the 2014 iHEA/ECHE conference in Dublin as well as seminar participants at the University of Basel, the University of Reno, the University of Vermont, the Berlin Network of Labor Market Research (BeNA), and The Institute on Health Economics, Health Behaviors and Disparities at Cornell University. We take responsibility for all remaining errors in and shortcomings of the article. This article uses data from the Berlin Aging Study II (BASE-II) which has been supported by the German Federal Ministry of Education and Research under grant numbers 16SV5537/16SV55837/16SV5538/16SV5536K/01UW0808. We also would like to thank Peter Eibich and Katrin Schaar for excellent support with the BASE-II data, David Richter for his excellent support with the SOEP-IP data as well as Gert Wagner for his overall support of this research and with the BASE-II and SOEP-IP data. The research reported in this paper is not the result of a for-pay consulting relationship. The responsibility for the contents of this publication lies with its authors. Our employers do not have a financial interest in the topic of the paper that might constitute a conflict of interest. The project has been exempt from Cornell IRB review under ID #1309004122.

Funding Information:
We would like to thank Teresa Bago d’Uva, Emily Beam, Kitt Carpenter, John Cawley, Davide Cesarini, Resul Cesur, Owen O’Donnell, Fabrice Etilé, Osea Giuntella, Glenn Harrison, Ben Hansen, Hendrik Jüerges, Jonathan Ketcham, Nadine Ketel, Gaurav Khanna, Audrey Laporte, Fabian Lange, Nathalie Mathieu-Bolh, Taryn Morrissey, Robert Nuscheler, Reto Odermatt, Ricardo Perez-Truglia, Gregor Pfeifer, Pia Pinger, Aldo Rustichini, Joe Sabia, Luis Santos Pinto, Tom Siedler, Rodrigo Soares, Sara Solnick, Pascal St-Amour, Alois Stutzer, Justin Sydnor, Harald Tauchmann, Erdal Tekin, Christian Traxler, Gerard van den Berg, Ben Vollaard, Justin White, and Véra Zabrodina. In particular, we thank our discussants Matt Harris and Nathan Kettlewell for excellent comments and suggestions. Moreover, we thank conference participants at the the 2019 iHEA World Congress in Basel, the 2019 Workshop on the Economics of Risky Behavior in Bologna, the 2018 ASHEcon meetings in Atlanta, 2017 Bristol Workshop on Economic Policy Interventions and Behaviour, the 2017 Risky Health Behaviors Workshop in Hamburg, the 2014 iHEA/ECHE conference in Dublin as well as seminar participants at the University of Basel, the University of Reno, the University of Vermont, the Berlin Network of Labor Market Research (BeNA), and The Institute on Health Economics, Health Behaviors and Disparities at Cornell University. We take responsibility for all remaining errors in and shortcomings of the article. This article uses data from the Berlin Aging Study II (BASE-II) which has been supported by the German Federal Ministry of Education and Research under grant numbers 16SV5537/16SV55837/16SV5538/16SV5536K/01UW0808 . We also would like to thank Peter Eibich and Katrin Schaar for excellent support with the BASE-II data, David Richter for his excellent support with the SOEP-IP data as well as Gert Wagner for his overall support of this research and with the BASE-II and SOEP-IP data. The research reported in this paper is not the result of a for-pay consulting relationship. The responsibility for the contents of this publication lies with its authors. Our employers do not have a financial interest in the topic of the paper that might constitute a conflict of interest. The project has been exempt from Cornell IRB review under ID #1309004122.

Publisher Copyright:
© 2021 Elsevier B.V.

Structured keywords

  • ECON CEPS Health
  • ECON Applied Economics

Keywords

  • Health bias
  • Health perceptions
  • Subjective beliefs
  • Overconfidence
  • Overoptimism
  • Risky behaviour
  • Smoking
  • Obesity
  • Exercising
  • SF12
  • SAH
  • BASE-II
  • SOEP-IP

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