1. PACIFIC STATISTICS DEVELOPMENT PARTNERSHIP PROGRAM & FIVE-YEAR NATIONAL STATISTICS COLLECTION PLAN
Pacific Statistics Standing Committee meeting,
Nadi, Fiji,
1 November, 2018
Michael Sharp
Statistics for Development Division

2. Purpose and content Present to PSSC the gap in the figures
Present to PSSC the 5-year national statistics collection plan
Present to PSSC opportunities where efficiencies can be gained
Present recommendation to PSSC

3. Indicator gaps: heavy reliance on national statistical collections
Under the 2030 Agenda for Sustainable Development – and through global, regional, national, there is unprecedented demand for timely and reliable statistics. Meeting the demand for disaggregated population and related data is highly dependent on core national statistical collections. For example, 95 of the 231 indicators under the Sustainable Development Goals (SDGs), distributed among 14 of the 17 goals, require high quality disaggregated population data generated from the census; a further 71 are household-based and mainly informed through household survey data.
In the case of the Pacific, a combination of DHS or MICS and HIES will result in the production of data for around 65 of the 132 Pacific subset of indicators under the Sustainable Development Goals. This is evidenced when looking at PNG, who have a recent DHS and HIES. The gaps presented in the table will be closed if the Pacific can effectively execute the 5-year national statistics collection plan.

4. 5-year national statistics collection plan

5. 5-year national statistics collection plan, by collection

6. 5-year national statistics collection plan: Census schedule
Yellow shaded = UN Member

7. 5-year national statistics collection plan: HIES schedule
Yellow shaded = IDA Member

8. 5-year national statistics collection plan: MICS, DHS (MICS-DHS; DHS-MICS; SIS) schedule
Yellow shaded = UN Member

9. 5-year national statistics collection plan: “Other” schedule
Yellow shaded = UN Member

10. Efficiencies can be gained

11. Throughout the statistical collection cycle
Planning and budgeting
Sample design and operation plan
Questionnaire, data capture, instruments
Manuals, pilot test and training
Field operations
Data processing
Reporting and metadata documentation
Dissemination
Capacity transfer

12. Integrate documentation
Planning and budgeting
Sample design and operation plan
Questionnaire, data capture, instruments
Manuals, pilot test and training
Field operations
Data processing
Reporting and metadata documentation
Dissemination
Metadata documentation written at design phase
Populates part of the report
Populates metadata documentation
Pre defined sample design template
Only do it once
Capacity transfer

13. Standardised methodologies
Regionally standardised methodologies
Field operation schedule
Modulated questionnaire to meet data needs
Classifications
Data capture systems
Training and resource materials
Data processing methodologies
Reporting
Metadata documentation
Standardised (harmonised) data structures
Technology and software
Planning and budgeting
Sample design and operation plan
Questionnaire, data capture, instruments
Manuals, pilot test and training
Field operations
Data processing
Reporting and metadata documentation
Dissemination
Capacity transfer

14. Use what we have before we collect more
Planning and budgeting
Sample design and operation plan
Questionnaire, data capture, instruments
Manuals, pilot test and training
Field operations
Data processing
Reporting and metadata documentation
Dissemination
Use what we have before we collect more
Do the data already exist (e.g., labour force, education, disability, agriculture)
Drop modules
Exploit administrative data
Let the data user drive the need (demand driven, not supply driven)
Hold users accountable
Capacity transfer

15. Align collections for regional roll out
Time
PICT A
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°
PICT B
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°
PICT C
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°
PICT D
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°

16. Align collections for regional roll out
Time
Workshop 1
Workshop 2
Workshop 3
PICT A
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°
PICT B
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°
Example: regional MICS
PICT C
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°
PICT D
Plan
Sample
Instruments
Training
Field work
Data processing
Report and dissem°

17. Optimised collection schedules
The schedule of statistical collections demonstrates that efficient and integrated statistical collection cycles are not always followed. The figure on the slide presents an integrated 10-year collection cycle and work plan that is considered to be efficient and takes advantage of master sampling frames derived from censuses.
The advantages of adopting such a collection cycle are:
A cost-effective approach that achieves economies of scale through one-off investment in certain activities or goods for the collection cycle, such a conducting the household listing or purchasing tablets for data capture. This will reduce the per unit (marginal) cost as fixed costs will be spread over an increased number of collections.
It allows for the establishment of core enumeration teams, or collection specialists within NSOs, who benefit from regular employment and who specialise in statistical collections, which will improve data quality and reduce the requirement for repetitive training before each collection.
The use of a master sampling frame in a structured collection cycle allows for linking of datasets through the use of common household and/or individual identifiers, which presents a significant opportunity for exploiting various components of datasets to make more targeted and informed policy interventions.
A fixed schedule of collections allows for all stakeholders to coordinate and plan their contribution to the collection.
A fixed collection cycle allows for a modulated approach to collections, whereby specific modules can be added to or removed from different collections as needed. This allows for regular updating of key indicators over time or for the reduction in questionnaire size when modules can be removed as it’s been recently collected.

18. Other areas where efficiencies can be gained
Integrated collections to avoid stand-alone surveys and associated cost (e.g., LFS ICSE; agriculture/fisheries; KI integrated HIES)
Modulated approach to survey design to only collect what is needed
South-South and/or resident advisors
CAPI S-S
Different technical assistance provision for “large” and “small” NSOs
Don’t move until our member is ready
Adopt new technology and conduct research
RMI HIES experiment; drones/satellite imagery for listing
Hold data users accountable (demand vs supply driven)

19. Coordination of NSOs, partners and donors

20. Examples of partnerships
World Bank
TFSCB: improving data access and use
RMI HIES experiment: optimising the collection of consumption information
IDA project: statistical leadership and HIES
UNICEF – PCA and 17-month work programme
Collect and mainstream childhood, gender and disability statistics
Analyse and utilize new and existing data to report and monitor the situation of children, women and people with disabilities
Disseminate statistics, engage stakeholders and advocate for the collection and use of statistics in policy formulation
MICS collaboration
UNFPA – IPA and annual work plan
Kiribati CAPI household listing
Kiribati Social Indicator Survey (with UNICEF)
University of Wollongong and WorldFish
Secondary data use (HIES and trade) for food systems oriented policy formation
FAO – MOU
P-SPAFS; GIS training; POU Regional Project
ILO – MOU
Data analysis and harmonisation

21. Need for coordination mechanisms
Detailed country collection plan
NSDS
Regional pool of resources
Funding
Technical assistance
Tablets
South-South
Resident advisors

22. Recommendation
PSSC is invited to consider the following recommendations:
the Pacific Statistics Development Partnership Program (PSDPP) and Five-Year National Statistics Collection Plan guide donors, development partners and Pacific national statistics offices (NSOs) on all technical support programs and resource mobilisation initiatives related to national official data collection activities;
members of the Donor-Development Partner Group (DDPG) support the PSDPP and Five Year Collection Plan through their technical support and funding program;
the Secretariat continues to work with Pacific NSOs to ensure an optimal data collection plan is development for each of their main data collection activities; and
the Secretariat continues to coordinate, update and disseminate this document to Pacific NSOs, donors and development partners on a regular basis.

23. Thank you