<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Incomplete unemployment data is a fundamental problem when evaluating labour market policies in several countries. Many unemployment spells end for unknown reasons; in the Swedish Public Employment Service’s register as many as 20 percent. This leads to an ambiguity regarding destination states (employment, unemployment, retired, etc.). According to complete combined administrative data, the employment rate among dropouts was close to 50 for the years 1992 to 2006, but from 2007 the employment rate has dropped to 40 or less. This article explores an imputation approach. We investigate imputation models estimated both on survey data from 2005/2006 and on complete combined administrative data from 2005/2006 and 2011/2012. The models are evaluated in terms of their ability to make correct predictions. The models have relatively high predictive power.</p></abstract>

Incomplete unemployment data is a fundamental problem when evaluating labour market policies in several countries. Many unemployment spells end for unknown reasons; in the Swedish Public Employment Service’s register as many as 20 percent. This leads to an ambiguity regarding destination states (employment, unemployment, retired, etc.). According to complete combined administrative data, the employment rate among dropouts was close to 50 for the years 1992 to 2006, but from 2007 the employment rate has dropped to 40 or less. This article explores an imputation approach. We investigate imputation models estimated both on survey data from 2005/2006 and on complete combined administrative data from 2005/2006 and 2011/2012. The models are evaluated in terms of their ability to make correct predictions. The models have relatively high predictive power.

An Imputation Model for Dropouts in Unemployment Data

Research and Evaluation, Swedish Public Employment Service, SE-113 99 Stockholm

Journal of Official Statistics

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"An Imputation Model for Dropouts in Unemployment Data"}

Incomplete unemployment data is a fundamental problem when evaluating labour market policies in several countries. Many unemployment spells end for unknown...

An Imputation Model for Dropouts in Unemployment Data

Online veröffentlicht: 23. Sept. 2016

Seitenbereich: 719 - 732

Eingereicht: 01. Jan. 2015

Akzeptiert: 01. Apr. 2016

DOI: https://doi.org/10.1515/jos-2016-0036

Schlüsselwörter
Follow-up study, multiple regression imputation, register data

© 2016 Petra Nilsson, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

An Imputation Model for Dropouts in Unemployment Data

Online veröffentlicht: 23. Sept. 2016

Seitenbereich: 719 - 732

Eingereicht: 01. Jan. 2015

Akzeptiert: 01. Apr. 2016

DOI: https://doi.org/10.1515/jos-2016-0036

SchlüsselwörterFollow-up study, multiple regression imputation, register data

© 2016 Petra Nilsson, published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
Follow-up study, multiple regression imputation, register data