Expert Group Meeting on MDG, Astana, 5-8 Oct.2009 MDG 3.2: Share of women in wage employment in the non-agricultural sector Sources of discrepancies between

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

MDG 3.2: Share of women in wage employment in the MDG 3.2: Share of women in wage employment in the non-agricultural sector non-agricultural sector

Sources of discrepancies between the data at national and Sources of discrepancies between the data at national and international levelinternational level


Sources of discrepancies between Sources of discrepancies between

national and global datanational 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.


An illustrative exampleAn illustrative example

Estimating missing values

31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0

Paid employment in NA,observed (ES)

#REF!

Total employment, observed(LFS)




31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0


#REF!

Paid employment in NA,estimated




31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0


Paid employment in NA,estimated

Total employment,observed (LFS)


Multiple series/sourcesMultiple series/sources

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

1. consistency of concepts, definitions and classifications with the international standards,

2. quality of data,

3. availability of data/source over time, etc.


Conceptual variationConceptual 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).


Treatment of missing valuesTreatment 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 dataExplicit: (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


Treatment of missing values in MDG 3.2Treatment 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.


Modelled values for MDG 3.2Modelled 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.



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.

.



The quality of the predicted values (i) is proportional to the number of years for which

the indicators is available;(ii) 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.)


Observed and impputed values, % (Bangladesh)

0

5

10

15

20

25

30

35

Observed valuesLow er predictedUpper predicted

Observed and impputed values, % (Kyrgyzistan)

0

10

20

30

40

50

60


Observed and impputed values, % (Tanzania)

0

5

10

15

20

25

30

35

40



Observed, estimated and modelled data for Observed, estimated and modelled data for MDG 3.2MDG 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.


Future workFuture 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.

Documents

Expert Group Meeting on MDG, Astana, 5-8 Oct.2009 MDG 3.2: Share of women in wage employment in the non-agricultural sector Sources of discrepancies between