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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. Introduction. ILO data gathering Data sources Problems: data availability

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valentina stoevska ilo department of statistics

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

introduction
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

ilo data gathering
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

data sources and their limitations
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

problems of comparability across countries and over time within countries
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

data availability by country
Data availability by country

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

no of values for the period 1990 2010
No. of values for the period 1990-2010

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut,

12-13 July 2012

data availability by year
Data availability by year

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

estimated values for mdg 11 use of proxy indicators
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

indicator and its proxies
INDICATOR AND ITS PROXIES

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

use of proxy indicators an illustrative example
Use of proxy indicators:An illustrative example

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

use of proxy indicators an illustrative example1
Use of proxy indicators:An illustrative example

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

use of proxy indicators an illustrative example2
Use of proxy indicators:An illustrative example

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

use of proxy indicators an illustrative example3
Use of proxy indicators:An illustrative example

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

treatment of missing values use of imputations
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

treatment of missing values use of imputations1
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

modelled values for mdg 3 2
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

modelled values for mdg 3 21
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

modelled values for mdg 3 22
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

mdg 3 2 observed and imputed values
MDG 3.2: Observed and imputed values, %
  • Yemen:

Jordan

United Arab Emirates

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

mdg 3 2 observed and imputed values1
MDG 3.2: Observed and imputed values, %

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

mdg 3 2 observed and imputed values2
MDG 3.2: Observed and imputed values, %

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

mdg 3 2 observed and imputed values3
MDG 3.2: Observed and imputed values, %

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

mdg 3 2 observed and imputed values4
MDG 3.2: Observed and imputed values, %

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

observed estimated and modelled data for mdg 3 2
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

estimating regional and global averages
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

mdg 3 2 share of women in wage employment in the non agricultural sector ilo april 2012
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

mdg 3 2 share of women in wage employment in the non agricultural sector ilo april 20121
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