ILO Modelled Estimates (ILOEST database)

Conducting labour force surveys is a complicated and costly task which some countries are unable to do on a systematic basis. Consequently, significant data gaps remain in most international labour statistics databases. To be able to produce reliable global and regional estimates of key labour indicators, the ILO has developed statistical models that produce estimates for countries in years for which no data have been reported. These models have been tested for statistical accuracy and allow the ILO to forecast changes in key labour market indicators as well as to produce global and regional aggregates. The end result of these models is a complete set of national labour statistics alongside the global and regional aggregates. In the interest of transparency, the ILO publishes the resulting country-level and global and regional estimates in the ILO modelled estimates series.

Not all countries submit statistically comparable data. Before running the models to obtain the estimates, ILO labour market information specialists evaluate country-reported data and select only those observations deemed sufficiently comparable across countries. The recent efforts by the ILO to produce harmonized indicators from country-reported microdata have greatly increased the comparability of the observations. Nonetheless, it is still necessary to select the data on the basis of the following four criteria: (a) type of data source; (b) geographical coverage; (c) age-group coverage; and (d) presence of methodological breaks or outliers.

Our models also include country-level data on population, economic growth, poverty and other economic indicators from the following sources:

• United Nations World Population Prospects
• IMF/World Bank data on macroeconomic indicators
• World Bank poverty estimates from the Poverty and Inequality Platform (PIP) database

The estimates are produced using a series of models, which establish statistical relationships between observed labour market indicators and explanatory variables. These relationships are used to impute missing observations and to make projections for the indicators.

There are many potential statistical relationships, also called “model specifications” that could be used to predict labour market indicators. The key to obtaining accurate and unbiased estimates is to select the best model specification in each case. The ILO modelled estimates generally rely on a procedure called cross-validation, which is used to identify those models that minimize the expected error and variance of the estimation. This procedure involves repeatedly computing a number of candidate model specifications using random subsets of the data: the missing observations are predicted and the prediction error is calculated for each iteration. This makes it possible to identify the statistical relationship that provides the best estimate of a given labour market indicator.

The ILO modelled estimates aim is to provide a complete set (without missing observations) of internationally comparable labour statistics. In order to achieve complete and comparable estimates several harmonisation processes are carried out, which can result in estimates differing from nationally reported data. The following procedures are the most common source of differences:

• Benchmarking the working-age population to the estimates of the United Nations World Population Prospects.
• Application of the standards adopted by the International Conference of Labour Statisticians to produce internationally comparable figures.
• Adjustment of classifications for standardization purposes. For instance, adjusting data to account for differences in age coverage.
• Internal consistency procedures. Each edition of the ILO modelled estimates is internally consistent by construction. This entails ensuring that components add up to the total in every observation across all related indicators. Such normalization procedures can produce differences with respect to national sources.
• Mitigation of time series breaks. In order to produce statistics that are comparable over time, in certain instances it is necessary to adjust figures within a time series to account for changes in methodology, coverage or other relevant dimensions.

The ILO modelled estimates cover a wide variety of indicators. Hence, input updates and methodological improvements are implemented in a staggered manner. The time stamp indicates the production date of the estimates, also referred to as the edition. The production date is important because it indicates approximately the cut-off date for inclusion of nationally reported observations as input into the models. Additionally, estimates with the same production date have undergone normalization to ensure that they are internally consistent. For instance, the sum of employment across all economic sectors will equal the sum across all occupations. Nonetheless, for estimates with different production dates, this will not be the case.

We are constantly improving the ILO modelled estimates. Revisions usually happen for one of three reasons:

• Countries make new data available. The ILOSTAT database is constantly updated as new national labour statistics become available. In some cases, this may only happen after a significant delay, requiring the ILO to replace estimates for that year with the reported statistics.
• Revisions are made to other databases used by our statistical model. The ILO’s econometrics models use databases maintained by other international organizations such as the UN’s World Population Prospects and the IMF’s World Economic Outlook. These databases are periodically subject to their own revisions, which can lead to revisions in the ILO modelled estimates.
• Historical data needs to be revised. Periodically, data from prior years needs to be revised as new information emerges.

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