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  • Articles and reports: 75F0002M2014002
    Description:

    Statistics that depict the movements in the bottom end of the income distribution, such as the proportion of low-income persons exiting low income from one year to the next, provide important information for developing policy on poverty and income inequality. Since the mid 1990s, these statistics have been generated using data from the Survey of Labour and Income Dynamics (SLID). The longitudinal component of the SLID was discontinued in 2010. This paper examines new and alternative time series on low income dynamics that can be created using the Longitudinal Administrative Databank (LAD).

    Release date: 2014-12-19

  • Articles and reports: 11-522-X201300014291
    Description:

    Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occuptions available from recent studies as training data. We asses the feasibility of these methods of evaluating performance and quality of the algorithms.

    Release date: 2014-10-31
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Articles and reports (2)

Articles and reports (2) ((2 results))

  • Articles and reports: 75F0002M2014002
    Description:

    Statistics that depict the movements in the bottom end of the income distribution, such as the proportion of low-income persons exiting low income from one year to the next, provide important information for developing policy on poverty and income inequality. Since the mid 1990s, these statistics have been generated using data from the Survey of Labour and Income Dynamics (SLID). The longitudinal component of the SLID was discontinued in 2010. This paper examines new and alternative time series on low income dynamics that can be created using the Longitudinal Administrative Databank (LAD).

    Release date: 2014-12-19

  • Articles and reports: 11-522-X201300014291
    Description:

    Occupational coding in Germany is mostly done using dictionary approaches with subsequent manual revision of cases which could not be coded. Since manual coding is expensive, it is desirable to assign a higher number of codes automatically. At the same time the quality of the automatic coding must at least reach that of the manual coding. As a possible solution we employ different machine learning algorithms for the task using a substantial amount of manually coded occuptions available from recent studies as training data. We asses the feasibility of these methods of evaluating performance and quality of the algorithms.

    Release date: 2014-10-31
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