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MDG Labour Indicators: Measurement, availability and discrepancies of data

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Title: Regional and Global Estimates for MDG 11: (Observed, Estimated and Modeled Values) Author: Valentina Stoevska Last modified by: Valentina Created Date – PowerPoint PPT presentation

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Title: MDG Labour Indicators: Measurement, availability and discrepancies of data


1
MDG Labour Indicators Measurement,
availability and discrepancies of data
  • MDG 3.2 Share of women in wage employment in the
    non-agricultural sector

ILO Dept. of Statistics
2
Introduction
  • ILO data gathering mechanisms
  • 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

3
ILO data gathering mechanisms
  • Annual standardised questionnaire, websites, NSP
  • Questionnaires pre-filled with the statistics
    reported in the previous years (last 9 years)
  • Meta data collected as well
  • Consistency checks, validations, footnoting
  • Clarifications with the countries
  • Dissemination (YB of Labour Statistics,
    http//laborsta.ilo.org/, KILM)
  • Clear international standards, ILO Resolutions

4
ILO data gathering mechanisms
  • Data collected on EAP, Employment, Unemployment,
    Hours of Work, Wages, CPI, Occupational Injuries,
    Strikes and lockouts.
  • Disaggregation by sex, age, economic activity,
    occupation, education, status in employment
    (where relevant)
  • These row data are used to calculate various
    indicators, incl. MDG

5
MDG labour indicators
  • MDG1 Eradicate extreme poverty and hunger
  • Target 1B Achieve full and productive
    employment and decent work for all, including for
    women and young people
  • Employment-to-population ratios for persons
    aged 15 and youth (15-24) by sex
  • Vulnerable employment rate by sex
  • Working poverty rate
  • Labour productivity growth rate
  • MDG3 Promote gender equality and empower women
  • Share of women in wage employment in the
    non-agricultural sector

6
MDG labour indicators
All five employment indicators (under MDG1b and
MDG3) are explained in detail in the Guide to the
new Millennium Development Goals Employment
Indicators, which is available at http//www.il
o.org/public/english/employment/docu/index.htm Th
e Guide includes definitions, data sources,
calculations and analytical examples. The Guide
also includes the full set of Decent Work
Indicators, which allows for comprehensive
monitoring of decent work.
7
Employment-to-population ratios for persons aged
15 and youth (15-24) by sex
  • The EPR measures the proportion of a countrys
    working age population that is employed
  • EPR Total employment/working age population
    100
  • Source of data labour force survey or other
    household survey/population census with data on
    population and employment
  • There is no single correct employment-to-populat
    ion ratio, but national EPRs are typically
    between 50-75 per cent
  • Disaggergation by sex, age - to identify possible
    imbalances and dependency rates, and changes over
    time

8
Vulnerable employment rate for persons aged 15
by sex
  • Indication about the proportion of employed that
    are in the more vulnerable statuses of employment
  • Vulnerable employment rate (number of
    own-account workers number of contributing
    family workers)/total employment 100
  • The rate is typically related to level of GDP per
    capita
  • Source of data labour force survey or other
    household surveys with data on status in
    employment

9
Vulnerable employment rate, selected countries
ranked by GDP per capita
10
Working poverty rate for persons aged 15
  • The working poor are defined as employed persons
    living in a household whose members are estimated
    to be below the nationally-defined poverty line
  • Working poverty rate working poor/total
    employment 100
  • Source of data household surveys with both
    employment and income expenditure data if data
    on employment and poverty come from different
    sources, an approximation may be used to arrive
    at the working poverty rate

11
Growth rate of labour productivity
  • Labour productivity represents the amount of
    output achieved per unit of labour input
  • Labour productivity GDP measured at constant
    market prices in national currency/ total
    employment
  • Labour productivity growth rate is measured as
    the annual change in GDP per person employed
  • Labour productivity can be used to assess to
    which extent the economic environment allows for
    the creation of decent employment opportunities
  • Source of data labour force survey or other
    household survey/population census with data on
    employment, in combination with data from
    national accounts

12
Share of women in wage employment in the
non-agricultural sector
  • Measures (i) the degree to which women have
    equally access to paid employment, (ii) the
    degree to which labour markets are open to women
    in industry and services sectors (iii) the
    flexibility of the labour market and the
    economys capacity to adapt to changes over time.
  • Does not (i) cover employment within the
    agricultural sector, (ii) reveal any differences
    in the quality of the different types of non-ag.
    wage employment regarding earning, conditions or
    work, or the legal and social protection which
    they offer.
  • The indicator is calculated by dividing the total
    number of women in paid employment in the
    industrial and service sectors by the total
    number of people in paid employment in that same
    sector.

13
Share of women in wage employment in the
non-agricultural sector ESCWA-averages by country
14
Share of women in wage employment in the
non-agricultural sector ESCWA-trends over time
15
Data sources at the national level, and their
limitations
  • Labour Force Surveys
  • Establishment surveys
  • Official estimates
  • Administrative records
  • Insurance records
  • Censuses
  • Other household surveys

16
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17
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

18
Data availability by country

19
Data availability by year

20
ESCWA Bahrain, Egypt, Jordan, Palestine,
Morocco, Oman
21
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.

22
Data availability (use of proxy indicators)
  • 10 countries do not provide data but the
    information on the economically active population
    is used instead as a proxy.

23
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.

24
An illustrative example
25
An illustrative example
26
An illustrative example
27
Multiple series/sources
  • 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.

28
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).

29
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

30
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.

31
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 countrys deviation
    from the average trend across time in its region.

32
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.
  • .

33
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.)

34
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35
Observed, estimated and modelled data for MDG 3.2
  • Methodological descriptions of the series
    disseminated available on the ILO Dept. 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.

36
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.
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