Valentina Stoevska ILO Department of Statistics Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

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, July

Annual questionnaire, websites, NSP Meta data collected as well Consistency checks, validations Clarifications with the countries Dissemination ( KILM) Clear international standards, ILO Resolutions 3

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012 Labour Force Surveys Establishment surveys Official estimates Administrative records (incl. insurance records) Censuses Other surveys 4

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012  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 5

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012  6

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 20127

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Estimations based on auxiliary variables a)Total paid employment b)Employees c)Total employment in non-agriculture d)Total employment e)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, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

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, July

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, July

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, July

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, July

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.) Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

 Yemen : Jordan Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012 United Arab Emirates 22

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July

Methodological descriptions of the sources of data disseminated at  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, July

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012 I i is the indicator for country i w i is the share of country i in the total economically active population in non-agricultural sector in the world 28

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012 ESCWA member states 29

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, July 2012 ESCWA member states 30