valentina stoevska ilo department of statistics

Valentina StoevskaILO Department of Statistics

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

July 2012 1

ILO data gatheringData sourcesProblems: ◦ data availability◦ data comparabilityTreatment of missing values◦ use of proxy indicators ◦ imputationsRegional and Global estimatesFuture challenges

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 20122


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

3


Labour Force Surveys Establishment surveys Official estimates Administrative records (incl. insurance records) Censuses Other surveys

4


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

5


Number of countries by No. of points per time serie (1990-2006)

62

42

21 21

72

0

10

20

30

40

50

60

70

80

1 pt 2-5 pts 6-9 pts 10-13 pts 14-17 pts

No.

of c

ount

ries

6

Workshop on MDG Data Reconciliation: Employment Indicators, Beirut, 12-13 July 2012 7


No. of countries reportiing data per year

9490

8691 93

101109

105 104

114121 120 117 114

108100

81

0

20

40

60

80

100

120

140

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006

8

Data availability per region

49.0%

77.1% 76.5%

56.3%50.6%

13.8%

43.8%

55.6%

16.2%

42.4%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

CIS Developed East Asia LatinAmerica

North Africa Oceania South Asia South-EastAsia

Sub-Saharan

Africa

West Asia

% o

f av

aila

ble

dat

a



Sources of data

72

106

3428 30

2

0

20

40

60

80

100

120

Administrativeand Insurance

Records

HouseholdSurvey

Labour ForceSurvey

Labour-relatedEstablishment

Survey andCensus

OfficialEstimates

PopulationCensus

Other

No

. of

cou

ntr

ies

10

Estimations based on auxiliary variablesa) Total paid employmentb) Employeesc) Total employment in non-agricultured) Total employmente) 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).


Indicator and proxy: No. of countries reporting data

27

45

14

122

Total empl

Total empl in non-agri

Total paid empl

Total paid empl in non-agri


Estimating missing values

31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0

1990

1992

1994

1996

1998

2000

2002

2004

2006

Paid employment in NA,observed (ES)

#REF!

Total employment, observed(LFS)



31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0

1990

1992

1994

1996

1998

2000

2002

2004

2006


#REF!

Paid employment in NA,estimated



31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0

1990

1992

1994

1996

1998

2000

2002

2004

2006


#REF!




31.0

32.0

33.0

34.0

35.0

36.0

37.0

38.0

39.0

1990

1992

1994

1996

1998

2000

2002

2004

2006



Total employment,observed (LFS)


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


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.


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.


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.

.


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.)


Yemen: Jordan


United Arab Emirates

22

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.



July 2012

Ii is the indicator for country i

wi is the share of country i in the total economically active

population in non-agricultural sector in the world

28


July 2012

ESCWA member states

29


July 2012

ESCWA member states

30

valentina stoevska ilo department of statistics

Documents

mdg data reconciliation

employment indicators

nspmeta data

ilo resolutions

reference population

auxiliary variablestotal

informal sector

public sector