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MDG 3.2: Share of women in wage employment in the non-agricultural sector Sources of discrepancies between the data at national and international level. Sources of discrepancies between national and global data. different sources, different series from the same source,

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MDG 3.2: Share of women in wage employment in the non-agricultural sector Sources of discrepancies between the data at national and international level

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

sources of discrepancies between national and global data
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.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

an illustrative example
An illustrative example

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

an illustrative example1
An illustrative example

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

an illustrative example2
An illustrative example

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

multiple series sources
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.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

conceptual variation
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).

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

treatment of missing values
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

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

treatment of missing values in mdg 3 2
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.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

modelled values for mdg 3 2
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.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

modelled values for mdg 3 21
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.

.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

modelled values for mdg 3 22
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.)

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

observed estimated and modelled data for mdg 3 2
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.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009

future work
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.

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009