MDG data at the sub-national level:

relevance, challenges and IAEG recommendations

Workshop on MDG Monitoring Workshop on MDG Monitoring

United Nations Statistics Division

Kampala, 5-8 May 2008

Contents

● IAEG recommendations

● Relevance and challenges of sub-national data

● Examples

● Data sources

● Combining data sources

● GIS

● Conclusion

IAEG recommendations

● The Inter-agency and Expert Group Meeting on MDG Indicators

– Recognized that sub-national data are needed for showing differences within countries and for helping countries to better allocate their resources.

– In order to improve the availability of reliable sub-national data, recommended the following:• To draw up recommendations regarding the use of

censuses to localize the MDGs as well as the use of small area estimation when data are not available;

• To investigate the availability of sub-national data.

Sub-national data

Relevant

● Key to identify disparities within the country; discrepancies can be substantial (e.g. urban/rural)

● Helps countries to better allocate their resources;

● Permits identifying areas which should be prioritized in policy interventions.

Challenging

● More resources needed

– Statistical capacity– Cost

● Methodological difficulties

– Sample design– Variability of

estimates

Sub-national data – Example 1

Relevant

● Literacy total population: 93.3%

– National average masks variation within the country

– Population density drives the national average

Sub-national data – Example 2

Challenging for health indicators (deaths, disease cases)

● Areas with very high rates are very close to areas with very small rates

● Are these dramatic contrasts real?

Sub-national data – Example 2

Counting of random events (like deaths, disease cases)

● Observation behave like Poisson distributions because their counts of random events

Nr infant deaths ~ Poisson (Nr births Infant mortality rate)

● Feature of Poisson distribution: mean = variance. This implies that variance (nr infants deaths/nr births) is inversely proportional to the number of births

● Thus, the lower the number of births the higher the variability of the infant mortality rate

● Statistical artifact: Areas with the smaller number of births are those with the lowest/highest rates infant mortality rates

Sub-national data – Example 2

Infant mortality in Portuguese counties

0

5

10

15

20

25

30

0 2000 4000 6000 8000

Number of births

Infantsmortality

rate

The lower the number of births the higher the variability of the infant mortality rate

Policy makers should be aware of this statistical artifact

Sub-national data – Example 2

How to cope with this statistical artifact

● Aggregate area with very small number of births, so that all areas have approximately the same number of births.

● Use smoothing methods, which produce estimates for small areas taking into account the Poisson variability.

Data sources

● Censuses

– Universal coverage permits to obtain data for very small areas (as long as confidentiality is not compromised);

● Administrative records

– Sometimes have a close to universal coverage (e.g. civil registration);

● Surveys

– Larger sample sizes are required to provide estimates for small areas (cost can be prohibitive). Defining prior to survey the small areas needed in essential if sample sizes are not too large.

Sub-national data

Census SurveysPros

Universal coverage permits to obtain data for very small areas (as long as confidentiality is not compromised);

Pros

Topics covered in more detail;More frequent;Less expensive.

Cons

Infrequent (usually, every ten years);

Few topics covered and with little detail;

Costly.

Cons

Can not be used for small areas unless the sample sizes are large and planning of small areas is done in advance of survey taking.

Combining data from censuses and surveys

Combining data from censuses and surveysPros

Universal coverage permits to obtain data for very small areas (as long as confidentiality is not compromised);

Pros

Topics covered in more detail;More frequent;Less expensive.

Combining data sources - Example

Poverty maps

● Use survey data to:

– Fit (regression) a model of logarithm of household consumption/income with independent variables which are common to the census and the survey (national level).

● Use census data to:

– Use the model above to predict for each small area the logarithm of household consumption/income with independent variables which are common to the census and the survey (for each small area).

Combining data sources - Example

Use survey data to:

● Estimate a and b in the model:

Income/consumption = a + b f(x) + e,

where x are common variables between census and survey which are good predictors of income.

Combining data sources - Example

Use census data to:

● Predict income/consumption for each small area, using the estimated values of a and b and the model:

Income/consumption = a + b f(x) + e,

Census data

Estimate of household income/consumption for

small area

Combining data from censuses and surveys

● Can provide small area estimates for topics not included in the census.

However,

● There may be lack of consistency between the definitions used in the surveys and those used in the censuses. The impact of this should be carefully assessed.

● Census and surveys may not be synchronized: they may be conducted at periods quite distant in time.

Geographic information systems

● In order to present the disaggregated information on maps, one needs to have some kind of geographic location coordinate for each observation.

● Geographic information systems (GISs) are useful computer software programs to handle geographically referenced data as they use geographic location as a reference for each database record.

Geographic information systems

● These systems are used to integrate information from:

– Very different sources (e.g. surveys, census, administrative data, satellite images, etc.) into a single platform, where each observation is matched with the identifier of the area it covers.

– Data observed at different levels. For instance, poverty status might be observed at the district level while climate is recorded at the level of agro-climatic zones.

Conclusions

● Small area estimates may be costly to produce from surveys. They require larger samples sizes and preplanning of small areas prior to survey taking.

● Combining surveys with other data sources with universal coverage (like census) may be an option. Administrative sources can also be useful if they have a good coverage.

● Maps with small area statistics can be misleading for health indicators such as deaths and number of disease cases. High and low rates may be a consequence of areas with small population.

● GIS is an useful tool to handle geographic data and to produce small area estimates.

THANKS!