Discrepancies BetweenNational and International

Data

By Shelton Kanyanda(Chief Statistician Malawi)

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Outline of this presentation

• forms of data available – indicators 2.1 and 2.3

• information on the national data sources used;

• consultation with countries;

• method of computation and/or adjustments

• challenges

• suggestions

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forms of data available

• Country Data

• Country Adjusted

• Estimated

• Modeled

• Global Monitoring Data

• Non-relevant

• Not Available

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• Country Data– The figure is the one produced and disseminated by the country

(including data adjusted BY THE COUNTRY to meet international standards)

• Country Adjusted– The figure is the one produced and provided by the country, but

adjusted by the international agency for international comparability—that is to comply with internationally agreed standards, definitions and classifications (age group, ISCED, etc)

• Estimated– The figure is estimated by the international agency, when

corresponding country data on a specific year or set of years are not available, or when multiple sources exist, or there are issues of data quality. Estimates are based on national data, such as surveys or administrative records, or other sources but on the same variable being estimated.

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• Modeled– The figure is modeled by the agency when there is a complete lack of data on

the variable being estimated. The model is based on a set of covariates—other variables for which data are available and that can explain the phenomenon (example: maternal mortality or annual poverty from LSMS)

• Global Monitoring Data– The figure is regularly produced by the designated agency for the global

monitoring, based on country data. However, there is no corresponding figure at the country level, because the indicator is defined for international monitoring only (example: population below 1$ a day)

• Non-relevant– The figure is not available because the indicator—as defined for the global

monitoring—does not apply to the circumstances of the specific country, and therefore is not reported

• Not Available– A figure is not provided, or the method by which the figure was calculated is

unknown

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information on the national data sources used

• Indicator 2.1 Net Enrolment Ratio (Primary)

– In most cases, Ministries of Education provid data through EMIS

– Other countries also use publications from Statistical Offices

_______________________________________– Surveys help to check over reporting from schools

through net attendance ratios

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Net enrolment (primary)

0

10

20

30

40

50

60

70

80

90

100

1991 1999 2000 2001 2002 2003 2004 2005 2006

Botswana

Cameroon

Cape Verde

Ethiopia

Ghana

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mauritius

Mozambique

Namibia

Sierra Leone

South Africa

Swaziland

Uganda

United Republic of Tanzania

Zambia

Zimbabwe

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Net enrolment (primary)

0

10

20

30

40

50

60

70

80

90

100

1991 1999 2000 2001 2002 2003 2004 2005 2006

Botswana

Cameroon

Cape Verde

Ethiopia

Ghana

Kenya

Lesotho

Liberia

Madagascar

Malawi

Mauritius

Mozambique

Namibia

Sierra Leone

South Africa

Swaziland

Uganda

United Republic of Tanzania

Zambia

Zimbabwe

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• Net enrolment ratio (primary)

– The principal UIS questionnaire respondents at the national ministries of education and the official UNESCO National Commissions for Education are consulted prior to the dissemination of all related indicators

list of consultation with countries

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method of computation and/or adjustments

• Indicator 2.1 Net enrolment ratio (Primary)

• Number of students enrolled in Primary who are of the official primary school age (including those of Primary school age students enrolled in Secondary) divided by the population for the same age-group and multiply the result by 100.

• Example: LESOTHO 2004, NER = 76.2 because the number of students enrolled in Primary who are of the official primary school age (including those of Primary school age students enrolled in Secondary) was 280,884, which was then divided by the population for the same age-group (368,486) and multiplied by 100.

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• Indicator 2.3 Literacy rate (15-24 yr olds)

Census : Botwana (’91); Ethiopia (’94); Ghana (’00); Malawi (’98) (08);Mauritius (’90) (’00); Mozambique (’97) (07); Zambia (’90);

• Surveys: Botwana (’03); Ethiopia (’04); Kenya (’00); Madagascar (’00); Zambia (’99); Malawi (’98) (’05)

• Estimates: Liberia (’94) (’04); • Rarely from administrative data

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• Literacy rate (15-24 yr olds)

Population aged 15-24 years literate X 100

Total population 15-24 years

• Adjustments• Not specified literacy status not known/

specified population removed from the calculation– Literate (15-24 yr) + Literate<>Illiterate(15-24 yr) + Illiterate (15-

24) = 100

Computation Literacy rate (15-24 yr olds)

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• Literacy rate (15-24 yr olds)

Population aged 15-24 years literate X 100

Total population 15-24 years

• Adjustments• Not specified literacy status not known/

specified population removed from the calculation– Literate (15-24 yr) + Literate<>Illiterate(15-24 yr) + Illiterate (15-

24) = 100

Computation Literacy rate (15-24 yr olds)

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list of consultation with countries

• In most cases consultations with Statistical Offices

• Interactions are also done with countries/ institutions during data collection and/or processing

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Challenges

• Data Gaps/Discrepancies– Lack of baseline data (i.e., 1990) to track the

progress at the national and sub national

– Lack of consistency of nationally generated data

– Lack of sustainability of some surveys (which are

sources of MDG statistics) as some of these are dependent on international communities

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• Dissemination– Problems in timely dissemination of data– Poor electronic devices for disseminating

information/data– Multiple sources of the same indicator

• Coordination– Weak coordination among the statistical

activities in generating MDG indicators– Weak (or just developing) National Statistical

System (coordination between and among statistical office and other statistical units on the one hand, and data producers and data users on the other, and international development intervention)

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• Human Resource

• Lack of manpower especially in the area of statistics to monitor the MDGs

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Suggestions• Data Gaps

– There is a need to establish linkage/strengthen institutional capacity and networking of NSOs and other line ministries which are sources of data.

• Needs financial and technical assistance on this concern.

– Appropriate/best country consultation mechanism should be explored to validate country data or reconcile country and internationally-estimated data

– Advocacy on the use of international estimation procedures/ process, including the reasons for the differences should be strengthened

– Better documentation of approaches adopted/methodologies applied should be undertaken

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• Dissemination– Need to acquire appropriate electronic devices– Need for timely processing of data

• Coordination– Need for sound development and implementation

of National Statistical Development Plan (NSDP)– Strengthen the role of the NSOs in the collection

and compilation of MDG indicators

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END OF MY PRESENTATION

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