ILO modelled estimates and projections
Data considerations and methodological approach
The ILO modelled estimates series provides a complete set of internationally comparable labour statistics, including both nationally-reported observations and imputed data for countries with missing data. The imputations are produced through a series of econometric models maintained by the ILO. The resulting complete datasets enable the ILO to produce regional and global aggregates of key labour market indicators with consistent country coverage. Despite the use of all available information, estimates for countries with very limited labour market information have a high degree of uncertainty. Therefore, the estimation methodology ensures that changes in global and regional aggregates are mostly driven by countries with real information. This also means that estimates of labour market indicators for countries with limited nationally reported data should not be considered as “true” data, and great care needs to be applied when using these data for analysis, especially at the country level.
Data selection for ILO modelled estimates
The ILO modelled estimates series includes indicators related to the labour force, unemployment and other forms of labour underutilization, employment (including disaggregation by status, economic activity, occupation and economic class), labour productivity, and labour income, among others. For each indicator, the output of the underlying econometric models is a complete matrix of data for up to 189 countries. The country-level data are then aggregated to produce regional and global estimates of indicators such as the unemployment rate and the employment-to-population ratio.
Prior to running the models, ILO labour market information specialists evaluate existing country-reported data and select only those observations deemed sufficiently comparable across countries using criteria including:
- Type of data source
In order for data to be included in the model, they must be derived from either a labour force survey, other sufficiently comparable household survey, or a population census. National labour force surveys are generally similar across countries, and the data derived from these surveys are more readily comparable than data obtained from other sources. A strict preference is given to labour force survey-based data in the selection process. However, many developing countries which lack the resources to carry out a labour force survey do report labour market information based on other types of household surveys or population censuses. Consequently, due to the need to balance the competing goals of data comparability and data coverage, some other survey-based estimates and population census-based data are included.
- Geographic coverage
Only nationally representative (i.e. not prohibitively geographically limited) labour market indicators are included. Observations that correspond to only urban or rural areas are not included, as large differences typically exist between rural and urban labour markets, and using only rural or urban data would not be consistent with benchmark data such as GDP.
- Age group coverage
The age groups covered by the observed data must be sufficiently comparable across countries. Countries report labour market information for a variety of age groups and the age group selected can have an influence on the observed value of a given labour market indicator.
The data used to prepare the World Employment and Social Outlook: Trends are gathered by countries which regularly submit employment statistics to the ILO. This is a complicated and costly task which some countries are unable to do on a systematic basis. To compensate, the ILO has developed a statistical model in order to fill the gaps for countries in years for which no data have been reported. This model has proven statistically accurate and allows us to forecast changes in employment.
Not all countries submit statistically comparable data. Before deciding whether to add a country’s dataset, the ILO assesses the source (i.e. national labour force survey or population census), whether it’s nationally representative or localized to urban areas, and if it includes data for comparable age groups.
Our models also include country-level data on population, employment, growth, and poverty and economic indicators from the following sources:
- United Nations World Population Prospects
- ILO Labour Force Estimates and Projections (LFEP)
- IMF/World Bank data on macroeconomic indicators
- World Bank poverty estimates from the PovcalNet database.
Our estimates are based on the historical relationship between unemployment rates and GDP growth. They also take into account the regional context, as countries within the same geographic area exhibit similar labour market characteristics.
Countries are asked to follow the guidelines of the International Standards of Labour Statistics when reporting their data. However, some countries choose to apply different definitions of the indicators when reporting data nationally and, in some of these cases, statisticians at the ILO process the micro data files to produce internationally comparable figures which differ from these national figures.
We are constantly improving the ILO modelled estimates. This usually happens for one of three reasons:
- Countries make new data available. The ILO’s labour statistics database is kept constantly up to date as new national labour force surveys are released. In some cases, this may only happen after a significant delay, requiring the ILO to replace its estimates for that year with the statistics reported.
- Revisions are made to other databases used by our statistical model. As mentioned above, our Trends Econometrics Models uses 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 our model must take into account.
- Historical data needs to be revised. Periodically, data from prior years needs to be revised as new information emerges about it that can affect how ILO interprets that data in its model.
Estimates of labour market indicators
The ILO’s econometric models produce estimates of labour indictators to fill in missing values in the countries and years for which country-reported data are unavailable. For example, for unemployment rates, multivariate regressions are run separately for different regions in the world in which unemployment rates, broken down by age and sex (youth male, youth female, adult male, adult female), are regressed on GDP growth rates. Weights are used in the regressions to correct for biases that may result from the fact that countries that report unemployment rates tend to differ (in statistically important respects) from countries that do not report unemployment rates.1For instance, if simple averages of unemployment rates in reporting countries in a given region are used to estimate the unemployment rate in that region, and the countries that do not report unemployment rates differ from reporting countries with respect to unemployment rates, without such a correction mechanism the resulting estimated regional unemployment rate would be biased. The “weighted least squares” approach adopted in the ILO’s models corrects for this potential problem. For the current year, a preliminary estimate is produced, using quarterly and monthly information available up to the time of production of the estimates. The ILO estimates employment by status using similar techniques to impute missing values at the country level. In addition to GDP growth rates, the variables used as explanatory variables include the value added shares of the three broad sectors in GDP, per capita GDP and the share of people living in urban areas. Additional econometric models are used to produce global and regional estimates of working poverty and employment by economic class.2See Kapsos and Bourmpoula, 2013.
Youth labour market indicators
Labour market indicators for the sub-populations youth-female, youth-male, adult-female and adult-male have been estimated using the same regression techniques as the aggregate indicators. However, the estimates are adjusted using the shares in the population implied by the labour force survey estimates so that the implied sum of the sub-populations equals the aggregate rate. This means that country data on subpopulations could differ from reported rates in other sources when the underlying shares of the subpopulation in the labour force differ from the ILO’s estimates.
Short-term projection model
For a subset of countries, the preliminary unemployment estimate for the current year and the projection for the following year are based on results from a country-specific short-term projection model. The ILO maintains a database on monthly and quarterly unemployment flows that contains information on inflow and outflow rates of unemployment, estimated on the basis of unemployment by duration. A multitude of models are specified that either project the unemployment rate directly or determine both inflow and outflow rates, using ARIMA, VARX and combined forecast techniques. All estimated models are evaluated on an eight-quarter ahead rolling pseudo out-of-sample forecasting evaluation starting in Q1 2009, among which five models are selected using a weighting of the mean and maximum forecast error. The top five model forecasts are then averaged.
Labour Force Estimates and Projections (LFEP)
The ILO programme on labour force estimates and projections is part of a larger international effort on demographic estimates and projections to which several UN agencies contribute. Estimates and projections of the total population and its components by sex and age group are produced by the UN Population Division, the employed, unemployed and related populations by the ILO, the agricultural population by FAO and the school attending population by UNESCO.
The main objective of the ILO programme is to provide member States, international agencies and the public at large with the most comprehensive, detailed and comparable estimates and projections of the labour force for countries and territories, the world as a whole and its main geographical regions.
Labour income share and distribution
The Labour Income Share and Distribution dataset covers 189 countries as well as global and regional aggregates. The data are based on the ILO Harmonized Microdata collection. In order to produce consistent time series for all countries, statistical models are used to extrapolate and impute missing data points. The dataset contains two key indicators: the labour income share and the labour income distribution, following the recommendation of the ILO Global Commission on the Future of Work to develop new distributional indicators. Furthermore, the new internationally comparable labour share data will be used to monitor progress towards the United Nations’ Sustainable Development Goals.
Wage growth rates
The methodology to estimate global and regional wage trends was developed by the ILO for the previous editions of the Global Wage Report (GWR) in collaboration between technical departments and the Department of Statistics, following four peer reviews conducted by five independent experts. The appendix of the GWR describes the methodology adopted as a result of this process.
In 2015, the ILO developed a methodology for generating global and regional estimates of international migrant workers and issued the first edition of ILO global estimates on migrant workers: Results and methodology, including global and regional estimates of international migrant workers and international migrant domestic workers, with reference year 2013.