Presentation to Pacific Statistics Methods Board Household Statistics Model for Pacific Island Countries and Territories Presentation to Pacific Statistics Methods Board

Purpose In line with section 2a and 2c of the Terms of reference Identify best practice for household survey methodology Determine priorities for Board advice and recommendations on aspects of household survey methodology Determine priorities for the development of tools or for capability building to support best practice methods Provide a framework to facilitate discussion on drivers of regular statistical production in terms of methods A discussion-starter, not a final proposal 6/11/2018

Vision – where are we going and what do we build to get there? 20 year plan Population Spine Link Census and administrative data Allows household and person level analysis Uses models and data fusion techniques to populate with survey data Encompass all PICTs Detailed content may vary from country to country Common core content (eg for SDGs) Long-term goal of continuous collection? Avoids set-up costs, maintains the capability and corporate knowledge 6/11/2018

Statistical tools to build and strengthen capability “How to” guides and manuals “Plain vanilla” survey model Standard toolkit of methods Excel or other tools Can be used “off the shelf” Can be tailored and modified to meet country or survey needs Use HIES as the starting point for a general model? 6/11/2018

Statistical infrastructure Population register and frames Initialised through Census Census used to produce a sampling frame for post-Census surveys Concentrate on household level initially Future registers or frames could potentially be expanded to include people nested under households (e.g. to identify subpopulations) 6/11/2018

Area-based sampling Multi-stage area sampling framework Use HIES framework? 2 stages: Census Enumeration Area (EA); Dwelling cluster from within each EA There may be benefits in a third stage, PSUs formed by amalgamating EAs EAs (or PSUs) assigned to an area classification / stratification Area classification to reflect costs / level of difficulty in surveying It may be desirable to have further area stratification to reflect population types (socio-economic; e.g. % of low income hh in area) May not be an initial priority 6/11/2018

Cluster sizes Cluster size set for each area type Choice of cluster size determined to optimise HIES results Aims to give the greatest accuracy per dollar cost Set these cluster sizes as standard clusters Allow flexibility through use of half cluster surveys, or double cluster surveys to cater for surveys with different cost or variance structures May be desirable to set cluster sizes as multiples of 4 6/11/2018

Libraries of tools and methods Libraries / Manuals of methods Data collection methods Applying coverage rules and other operational procedures Data processing rules Aim for a standard set, allowing tailoring for country or survey needs 6/11/2018

Program of surveys following Census Aim to take advantage of the Census to aid the design of upcoming surveys and target key subpopulations Identify EAs/PSUs for oversampling of key subpopulations Determine if it necessary to consider socioeconomic stratification for upcoming surveys Initially agree on HIES being ‘first cab off the rank’ after Census? 6/11/2018

Sample design Look at rollover designs initially Potential to use Census area-level information to better control representivity of samples Modifications may be needed to reflect new / emerging statistical requirements Build in an evaluation stage post-survey to consider how sample design can be improved (targeting of sub-populations) Continuous improvement cycle after cycle Knowledge management is key Stagger surveys across countries so e.g. learning from one countries HIES is then applied to next countries HIES 6/11/2018

Estimation Standard benchmarking / post-stratification for household weights Consider need for / benefits of person weighting vs household weighting Census benchmarking could help with subpopulations at risk of being missed (e.g. education, income, household size) Gradually build capability and weighting ability, aiming for small but continual improvement cycle to cycle Modelling to build towards long-term vision of data fusion and small area estimation techniques (i.e. one country drawing strength from other countries in the region) Modelling may be driven or conducted by a central agency 6/11/2018

Reporting systems Can processes and tools used in one country be rolled over to the next country This should include training tools Gradually move towards standard reporting 6/11/2018

Issues for discussion What elements of this model offer the greatest potential benefit? What are our priorities for strengthening statistical infrastructure and capability? – long-term and short-term (quick wins and big payoff) How can we resource this work? 6/11/2018