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MDG 3.2: Share of women in wage employment in the non-agricultural sector National and global data PowerPoint PPT Presentation


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MDG 3.2: Share of women in wage employment in the non-agricultural sector National and global data. Introduction. ILO data gathering Definitions Data sources Data availability at international level Possible sources of discrepancies

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MDG 3.2: Share of women in wage employment in the non-agricultural sector National and global data

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Mdg 3 2 share of women in wage employment in the non agricultural sector national and global data l.jpg

MDG 3.2: Share of women in wage employment in the non-agricultural sector National and global data

Workshop on MDG, Bangkok, 14-16 Jan.2009


Introduction l.jpg

Introduction

ILO data gathering

Definitions

Data sources

Data availability at international level

Possible sources of discrepancies

Treatment of missing values (use of proxy indicators and imputations)

Regional and Global estimates

Future challenges

Workshop on MDG, Bangkok, 14-16 Jan.2009


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ILO data gathering

  • Annual questionnaire, websites, NSP

  • Questionnaires pre-filled with the statistics provided in the previous years (last 10 years)

  • Meta data collected as well

  • Consistency checks, validations

  • Clarifications with the countries

  • Dissemination (YB of Labour Statistics, http://laborsta.ilo.org/, KILM)

  • Clear international standards, ILO Resolutions

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Data sources and their limitations

  • Labour Force Surveys

  • Establishment surveys

  • Official estimates

  • Administrative records

  • Insurance records

  • Censuses

  • Other surveys

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Problems of comparability across countries and over time within countries

  • Methodological and conceptual differences: definitions, coverage of the reference population, coverage of the sectors, classifications used, sources, etc

    (e.g. only public sector, excl. enterprises with less than 5 employees, excl. informal sector, etc)

  • international comparisons difficult

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Data availability by country

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Data availability by year

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Workshop on MDG, Bangkok, 14-16 Jan.2009


Slide9 l.jpg

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Estimated values for MDG 11

Estimations based on auxiliary variables

- Total paid employment

- Total employment in non-agriculture

- Employees

- Total employment

- Economically Active Population in non-agriculture

Sensitivity analysis conducted on a selected number of countries: there is strong correlation between the indicator and the auxiliary variable.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Data availability

10 countries do not provide data but the information on the economically active population is used instead as a proxy.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Sources of discrepancies between national and global data

  • different sources,

  • different series from the same source,

  • changes in the definitions and classifications over time (in the same source),

  • estimates when the national data are not available for a particular year,

  • imputations.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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An illustrative example

Workshop on MDG, Bangkok, 14-16 Jan.2009


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An illustrative example

Workshop on MDG, Bangkok, 14-16 Jan.2009


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An illustrative example

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Multiple series/sources

  • Where data from multiple sources are available, the selection of the most appropriate one is based on a number of criteria, incl.

  • consistency of concepts, definitions and classifications with the international standards,

  • quality of data,

  • availability of data/source over time, etc.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Conceptual variation

  • Discrepancies may also exist because of different definitions and classifications.

    • for employment status, especially for part-time workers, students, members of the armed forces, and household or contributing family workers;

    • classifications over time;

    • geographical and population coverage |incl.changes over time).

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Treatment of missing values

Imputations for missing values is unavoidable in any aggregation process.

Assuming that, if there no data, the value of the indicator is zero results in biased regional and global estimates

Imputations:

Implicit: assuming the value of the indicator is the same as the average for the countries with available data

Explicit: (i) carry forward the last observed value; (ii) use the value of the indicator for a country with similar characteristics, (iii) predict the value by statistical modelling

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Treatment of missing values in MDG 3.2

In process of producing regional and global aggregates for MDG11, ILO uses a methodology for explicit imputation for missing values

The sole purpose of these imputations is to produce the regional and global aggregates and may not be best-fitted for national reports.

The national imputations are best produced through methodologies that take directly into account the local specificities of the country concerned.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Modelled values for MDG 3.2

Separate two-level models developed for each region. The models take into account

  • between-countries variation over time,

  • within-country variation over time.

    Predicted values are based on the assumption that the data that are available for a given country are representative of that country’s deviation from the average trend across time in its region.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Modelled values for MDG 3.2

5 different models developed and their properties tested.

The data available for the latest year omitted from the dataset and imputed by using different models. The modelled data then compared with the actual observed values.

The quality of the modelled data assessed based on several criteria (i) mean deviation, (ii) standard deviation, (iii) maximum positive and negative deviations.

.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Modelled values for MDG 3.2

The quality of the predicted values

  • is proportional to the number of years for which the indicators is available;

  • depends on the quality of the observed values for a given country and the quality of the data for the corresponding region.

    → Careful checking is required (outliers, unusual trends, sources, etc.)

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Workshop on MDG, Bangkok, 14-16 Jan.2009


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Observed, estimated and modelled data for MDG 3.2

Methodological descriptions of the series disseminated available on the ILO Bureau of Statistics website.

The estimated values based on proxy indicators are disseminated on the MDG website. All values which are estimated are clearly identified. The modelled data are not disseminated as their sole purpose is to produce the regional and global aggregates.

The ILO is making its methodology for imputing missing values in the process of producing regional and global aggregates publicly available.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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Future work

The ILO will continue to work with countries and other partners to

(a) enhance the national statistical capacity of countries to produce the data needed for estimating the indicator;

(b) develop national analytical capacity to produce good quality imputed country values for use by countries in their monitoring of the MDGs and other dev.programmes;

(c) ensure that all data available at national level are collected in a way that will be of least burden to countries.

(d) Cooperation by the countries much needed.

Workshop on MDG, Bangkok, 14-16 Jan.2009


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