MDG 3.2: Share of women in wage employment in the non-agricultural sector National and global data Workshop on MDG, Bangkok, 14-16 Jan.2009

Introduction

ILO data gathering Definitions Data sources Data availability at international level Possible sources of discrepancies Treatment of missing values (use of proxy indicators and imputations) Regional and Global estimates Future challenges Workshop on MDG, Bangkok, 14-16 Jan.2009

ILO data gathering

 Annual questionnaire, websites, NSP  Questionnaires pre-filled with the statistics provided in the previous years (last 10 years)  Meta data collected as well  Consistency checks, validations  Clarifications with the countries  Dissemination (YB of Labour Statistics, http://laborsta.ilo.org/ , KILM) 

Clear international standards, ILO Resolutions

Workshop on MDG, Bangkok, 14-16 Jan.2009

Data sources and their limitations

 Labour Force Surveys  Establishment surveys    Insurance records  Official estimates Administrative records Censuses  Other surveys Workshop on MDG, Bangkok, 14-16 Jan.2009

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, Bangkok, 14-16 Jan.2009

 80 70 60 50 40 30 20 10 0

Data availability by country

Num ber of countries by No. of points per tim e serie (1990-2006)

62 42 21 21 1 pt 2-5 pts 6-9 pts 10-13 pts 72 14-17 pts Workshop on MDG, Bangkok, 14-16 Jan.2009

Data availability by year

No. of countries reportiing data per year

140  120 100 94 90 80 86 91 93 101 109 105 104 114 121 120 117 114 108 100 81 60 40 20 0 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Workshop on MDG, Bangkok, 14-16 Jan.2009

Data availability per region

90.0% 80.0% 70.0% 60.0% 50.0% 40.0% 30.0% 20.0% 10.0% 0.0% 49.0% CIS 77.1% 76.5% 56.3% 50.6% 13.8% 43.8% 55.6% 16.2% 42.4% Developed East Asia Latin America North Africa Oceania South Asia South-East Asia Sub Saharan Africa West Asia Workshop on MDG, Bangkok, 14-16 Jan.2009

Sources of data

120 100 80 60 40 20 0 7 Administrative and Insurance Records 2 Household Survey 106 34 28 Labour Force Survey Labour-related Establishment Survey and Census Official Estimates 30 Population Census 2 Other Workshop on MDG, Bangkok, 14-16 Jan.2009

Estimated values for MDG 11

Estimations based on auxiliary variables - Total paid employment - Total employment in non-agriculture - Employees - 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 variable.

Workshop on MDG, Bangkok, 14-16 Jan.2009

 122

Data availability

Indicator and proxy: No. of countries reporting data

27 45 Total empl Total empl in non-agri Total paid empl Total paid empl in non-agri 14 10 countries do not provide data but the information on the economically active population is used instead as a proxy.

Workshop on MDG, Bangkok, 14-16 Jan.2009

Sources of discrepancies between national and global data

 different sources,  different series from the same source,  changes in the definitions and classifications over time (in the same source),  estimates when the national data are not available for a particular year,  imputations.

Workshop on MDG, Bangkok, 14-16 Jan.2009

An illustrative example

Estimating missing values

39.0

38.0

37.0

36.0

35.0

34.0

33.0

32.0

31.0

19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 Paid employment in NA, observed (ES) #REF!

Total employment, observed (LFS) Workshop on MDG, Bangkok, 14-16 Jan.2009

An illustrative example

Estimating missing values

39.0

38.0

37.0

36.0

35.0

34.0

33.0

32.0

31.0

19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 Paid employment in NA, observed (ES) #REF!

Paid employment in NA, estimated Workshop on MDG, Bangkok, 14-16 Jan.2009

An illustrative example

Estimating missing values

39.0

38.0

37.0

36.0

35.0

34.0

33.0

32.0

31.0

19 90 19 92 19 94 19 96 19 98 20 00 20 02 20 04 20 06 Paid employment in NA, observed (ES) Paid employment in NA, estimated Total employment, observed (LFS) Workshop on MDG, Bangkok, 14-16 Jan.2009

Multiple series/sources

 1.

2.

3.

Where data from multiple sources are available, the selection of the most appropriate one is based on a number of criteria, incl.

consistency of concepts, definitions and classifications with the international standards, quality of data, availability of data/source over time, etc.

Workshop on MDG, Bangkok, 14-16 Jan.2009

Conceptual variation

 Discrepancies may also exist because of different definitions and classifications. – for employment status, especially for part-time workers, students, members of the armed forces, and household or contributing family workers; – – classifications over time; geographical and population coverage |incl.changes over time).

Workshop on MDG, Bangkok, 14-16 Jan.2009

Treatment of missing values Imputations for missing values is 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, Bangkok, 14-16 Jan.2009

Treatment of missing values in MDG 3.2

In process of producing regional and global aggregates for MDG11, 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, Bangkok, 14-16 Jan.2009

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, Bangkok, 14-16 Jan.2009

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, Bangkok, 14-16 Jan.2009

Modelled values for MDG 3.2

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, Bangkok, 14-16 Jan.2009

Workshop on MDG, Bangkok, 14-16 Jan.2009

Observed, estimated and modelled data for MDG 3.2

Methodological descriptions of the series disseminated available on the ILO Bureau of Statistics website.

The estimated values based on proxy indicators are disseminated on the MDG website. All values which are estimated are clearly identified. 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, Bangkok, 14-16 Jan.2009

Future work The ILO will continue to work with countries and other partners to (a) enhance the national statistical capacity of countries to produce the data needed for estimating the indicator; (b) develop national analytical capacity to produce good quality imputed country values for use by countries in their monitoring of the MDGs and other dev.programmes; (c) ensure that all data available at national level are collected in a way that will be of least burden to countries.

(d) Cooperation by the countries much needed.

Workshop on MDG, Bangkok, 14-16 Jan.2009