Transcript Uses of fertility and mortality statistics

2007 International Conference on
Millennium Development Goals Statistics
Manila, 1 – 3 October 2007
Data gaps in international
databases
Francesca Coullare
United Nations Statistics Division
Overview
1
 Global monitoring and the Inter-agency and
Expert Group on MDGs indicators:


Working modalities,
Millennium Development Goals Indicators database
mdgs.un.org
2
 How international agencies “adjust” country data
3
 Current mechanisms/initiatives to improve
to obtain regional estimates and/or address data
gaps issues
international data series used to monitor progress
towards the MDGs
1
•Global monitoring of MDGs
indicators
1

The Inter-Agency and Expert Group (IAEG) on
MDG Indicators (2 meetings per year)



International Monitoring Efforts
Coordinated by UN Statistics Division/DESA
Composed of representatives from:



25 specialized agencies,
regional commissions,
NSOs
Thematic sub-groups of the IAEG






Gender
Employment
Health
Poverty and hunger
Environment
Slums
International Monitoring Efforts
IAEG is responsible for:
(a)compiling data and undertaking analysis to
monitor progress towards the MDGs at the
global and regional levels;
(b)reporting on status of annual progress through
printed reports, progress charts, CD-roms and
internet;
(c)reviewing and preparing guidelines on
methodologies and technical issues related to
the indicators;
(d)helping define priorities and strategies to
support countries in data collection, analysis
and reporting on MDGs.
Type of indicator/series
Agency
MDGs
Others
Total
FAO
2
1
3
ILO
4
9
13
IPU
1
3
4
ITU
3
(a)compiling
data for the3
OECD
8
7
global/regional monitoring
of MDGs
6
12
UNAIDS
1
4
5
UNEP-Ozone
1
1
2
UNEP-WCMC
1
1
2
UNESCO
9
7
16
UNFCCC (CDIAC)
2
2
4
UN-HABITAT
1
1
2
UNICEF
17
8
25
UNPD
2
2
3
WB
7
4
11
WHO
8
1
9
WTO
5
0
5
(a) Data compilation: data flow
International
agency
country
office
Line
Ministry in
country
National
Statistical
Office in
country
Agency
Headquarters
e.g. UNICEF
Agency
Headquarters
e.g. UNESCO
Agency
Headquarters
e.g. ILO
MDG Indicators
database
48+ indicators
192 Member States
1990-2006
mdgs.un.org
•Adjustment of country data
by international agencies to
ensure international
comparability and address
data gaps
2
Data gaps for MDG 3
in international databases
Percentage of countries with at least 2 data points since 1990
(excluding modeled data), by indicator and MDG region
Ind. 9
Enrolment
Primary Secondary Tertiary
Ind.
10
Ind. 11
Ind.12
Youth
Literacy
Employment
Parliament
Developing Regions
86
85
68
-
55
81
Northern Africa
83
83
50
-
67
67
Sub-Saharan Africa
94
92
82
-
28
96
Latin America &
Caribbean
83
83
57
-
76
72
Eastern Asia
83
83
83
-
83
67
Southern Asia
89
89
78
-
67
100
South-eastern Asia
82
82
82
-
73
82
Western Asia
100
100
93
-
80
87
Oceania
65
60
30
-
25
60
Source: UNSD-MDGs database, access on June 2007
The example of UNESCO
Indicator 6. Net enrolment ratio in
primary education
UNESCO Steps:
(a) An adjustment to account for over- or underreporting:
i. To include enrolments in private schools and/or
geographical areas left out
ii. To exclude pupils of other programmes than
primary (i.e. adult education)
(b) An estimate of the number of enrolments in the
official age group for primary education
(when only total enrolments in primary education is
reported, using reliable source for age distribution)
The example of UNESCO
Indicator 6. Net enrolment ratio in
primary education
UNESCO Steps (cont.):
(c) A redistribution of enrolments of unknown age
(across known ages - only if more than 5% of tot.
enrolments)
(d) An estimate of the population in the official age
group for primary education (if neither UNPD nor the
country itself can provide estimates of their own)
Treatment of missing values : When missing data for a variable, use:
(a) previous years submissions,
(b) other correlated variable or
(c) similar countries (never published-only used in regional aggregates)
The example of ILO-Gender
Indicator 11. Share of women in wage
employment in the non-agricultural sector
ILO-Gender: Estimated values vs. Predicted values
a)
i.
ii.
iii.
iv.
v.
Estimations based on auxiliary variables
Total paid employment
Total employment in non-agriculture
Employees
Total employment
Economically Active Population in nonagriculture
Empirical analysis shows that strong correlation exits
between the indicator and the auxiliary variable.
The example of ILO-Gender
Indicator 11. Share of women in wage employment
in the non-agricultural sector
ILO-Gender: Estimated values vs. Predicted values
b) Predictions based on statistical models
i.
Only for producing regional and global
aggregates
ii.
Separate two-level models developed for each
of the 5 regions, considering:
i. between-countries variation over time,
ii. within-country variation over time.
iii. Based on the assumption that available data are
representative of a country’s deviation from the
average trend in its region, across time .
3
•Improving international
data series used to monitor
progress towards the MDGs
(a) Strengthening
country statistical capacity
• 2006 ECOSOC Resolution
• Renewed commitment on the importance of sound statistical
systems to produce evidence-based policies
• 2004 Marrakech Action Plan for Statistics
• Blue print identifying 6 steps for achieving better statistics for
better monitoring policies:
1. NSDS = national strategy
2. Increased budget allocated to Statistics
3. 2010 Round of Population and Housing Census
4. Better support for Household Surveys - (IHSN)
5. Quick and better data in key areas such as MDGs – (ADP)
6. Increased accountability and better coordination among
international statistical partners
• PARIS21 = Partnership in Statistics for Development
in the 21st Century
• Promoting a culture of “Evidence-based decision making and
implementation”
(b) Improving mechanisms for data
transfer and consultation with countries
• Within countries: among different stakeholders
producing data in the national statistical system
• Between countries and international agencies
• Role of Regional Commissions
• Establishing a central repository of data
• Between international agencies and UNSD
• SDMX initiative :
• in pilot in 3 SADC countries
• Work in progress in IAEG on MDGs indicators
(c) Enhance transparency in
MDGs Global database
• UNSD MDG database to present metadata information at
the “cell” level for country-level estimates
• Showing data source, reference period, …, pointing out
possible discrepancies between international and
national figures
(c) Enhance transparency in
MDGs Global database
Revised metadata for MDG Indicators
in the IAEG MDG Database
• UNSD MDG database to
present more detailed
indicator-level metadata
CONTACT POINT in international
agency
DEFINITION
METHODS OF COMPUTATION
• Explaining in details
methodology used to
calculate indicators and
presenting contact details
for users to contact to
obtain additional
information
COMMENTS AND LIMITATIONS
SOURCES OF DISCREPANCIES
BETWEEN GLOBAL AND NATIONAL
FIGURES
PROCESS OF OBTAINING DATA
TREATMENT OF MISSING VALUES
DATA AVAILABILITY
REGIONAL AND GLOBAL ESTIMATES
EXPECTED TIME OF RELEASE
http://mdgs.un.org