Valentina Stoevska ILO Department of Statistics

1. Regional and global estimates and imputation of missing values: An example of MDG 3.2 Share of women in wage employment in the non-agricultural sector Valentina Stoevska ILO Department of Statistics Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

2. Introduction ILO data gathering Data sources Problems: • data availability • data comparability Treatment of missing values • use of proxy indicators • imputations Regional and Global estimates Future challenges Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

3. ILO data gathering • Annual questionnaire, websites, NSP • Meta data collected as well • Consistency checks, validations • Clarifications with the countries • Dissemination (http://laborsta.ilo.org/, KILM) • Clear international standards, ILO Resolutions Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

4. Data sources and their limitations • Labour Force Surveys • Establishment surveys • Official estimates • Administrative records (incl. insurance records) • Censuses • Other surveys Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

5. 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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

6. Data availability by country Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

7. No. of values for the period 1990-2010 Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

8. Data availability by year Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

9. Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

10. Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

11. Estimated values for MDG 11:Use of proxy indicators Estimations based on auxiliary variables • Total paid employment • Employees • Total employment in non-agriculture • 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 variables (a and b). Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

12. INDICATOR AND ITS PROXIES Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

13. Use of proxy indicators:An illustrative example Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

14. Use of proxy indicators:An illustrative example Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

15. Use of proxy indicators:An illustrative example Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

16. Use of proxy indicators:An illustrative example Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

17. Treatment of missing values: Use of imputations Imputations for missing values-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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

18. Treatment of missing values: Use of imputations In process of producing regional and global aggregates for MDG 3.2, 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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

19. 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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

20. 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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

21. 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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

22. MDG 3.2: Observed and imputed values, % • Yemen: Jordan United Arab Emirates Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

23. MDG 3.2: Observed and imputed values, % Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

24. MDG 3.2: Observed and imputed values, % Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

25. MDG 3.2: Observed and imputed values, % Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

26. MDG 3.2: Observed and imputed values, % Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

27. Observed, estimated and modelled data for MDG 3.2 Methodological descriptions of the sources of data disseminated at http://laborsta.ilo.org/ . • The estimated values based on proxy indicators are disseminated on the MDG website (note: estimated). • 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 Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

28. Estimating regional and global averages Iiis the indicator for country i wiis the share of countryiin the total economically active population in non-agricultural sector in the world Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

29. MDG 3.2: Share of women in wage employment in the non-agricultural sector, ILO, April 2012 ESCWA member states Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012

30. MDG 3.2: Share of women in wage employment in the non-agricultural sector, ILO, April 2012 ESCWA member states Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012