<abstract xmlns="http://www.w3.org/1999/xhtml"><p> Longitudinal surveys follow people over time and some of these people will die during the life of the panel. Through fieldwork effort, some deaths will be reported or known, but others will be unobserved due to sample members no longer being issued to field or having inconclusive fieldwork outcomes (such as a noncontact that is not followed by a contact at a later wave). The coverage of deaths identified among sample members has flow-on implications to nonresponse correction. Using the Household, Income and Labour Dynamics in Australia (HILDA) Survey, four methods are used to examine the extent of missing death reports. The first method matches the sample to the national death register. The second method uses life-expectancy tables to extrapolate the expected number of deaths among the sample with unknown eligibility. The third method is similar but models deaths from data internal to the survey. The fourth method models deaths as part of the attrition process of a longitudinal survey. The last three methods are compared to the first method and the implications for the construction of balanced panel weights and subsequent population inference are explored.</p></abstract>

Longitudinal surveys follow people over time and some of these people will die during the life of the panel. Through fieldwork effort, some deaths will be reported or known, but others will be unobserved due to sample members no longer being issued to field or having inconclusive fieldwork outcomes (such as a noncontact that is not followed by a contact at a later wave). The coverage of deaths identified among sample members has flow-on implications to nonresponse correction. Using the Household, Income and Labour Dynamics in Australia (HILDA) Survey, four methods are used to examine the extent of missing death reports. The first method matches the sample to the national death register. The second method uses life-expectancy tables to extrapolate the expected number of deaths among the sample with unknown eligibility. The third method is similar but models deaths from data internal to the survey. The fourth method models deaths as part of the attrition process of a longitudinal survey. The last three methods are compared to the first method and the implications for the construction of balanced panel weights and subsequent population inference are explored.

Dead or Alive? Dealing with Unknown Eligibility in Longitudinal Surveys

Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Level 5, 111 Barry St, University of Melbourne Vic 3010,

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

{"article-title":"Dead or Alive? Dealing with Unknown Eligibility in Longitudinal Surveys"}

Longitudinal surveys follow people over time and some of these people will die during the life of the panel. Through fieldwork effort, some deaths will be...

Dead or Alive? Dealing with Unknown Eligibility in Longitudinal Surveys

Article Category: Articles

Published Online: Nov 23, 2016

Page range: 987 - 1010

Received: Mar 01, 2015

Accepted: Aug 01, 2016

DOI: https://doi.org/10.1515/jos-2016-0052

Keywords
HILDA Survey, death register, life-expectancy tables, survival model, weighting methods

© by Nicole Watson

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Dead or Alive? Dealing with Unknown Eligibility in Longitudinal Surveys

Article Category: Articles

Published Online: Nov 23, 2016

Page range: 987 - 1010

Received: Mar 01, 2015

Accepted: Aug 01, 2016

DOI: https://doi.org/10.1515/jos-2016-0052

KeywordsHILDA Survey, death register, life-expectancy tables, survival model, weighting methods

© by Nicole Watson

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Keywords
HILDA Survey, death register, life-expectancy tables, survival model, weighting methods