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Capacity building and reform of Statistics Indonesia

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Andrej Kveder Cora Mezger Marian Guest Matthew Powell Matthew Shearing Patrick Ward

The main driver behind quality improvements is the transformation of the Indonesian statistical office (BPS) from an organisation where different subject matter areas work in silos into an integrated organisation using standard processes, tools and systems. This is through the STATCAP-Cerdas programme.

We will specify the blueprint of the future organisation; develop and support the introduction of standardised infrastructure, processes, and systems; and redesign six priority statistical series (trade, manufacturing, living standards and labour force surveys, producer prices, horticulture) as well as national accounts.

Challenges

BPS is facing an increase in demand for more, better and timelier statistics, while operating in a challenging context. Statistics have to be provided at both the national and regional levels, against a backdrop of declining survey response rates and greater demand for budgetary efficiency. Inefficiencies are compounded by the current organisational structure, which promotes duplication of work in some cases.

The STATCAP-Cerdas programme aims to address these challenges through the implementation of comprehensive, integrated statistical and methodological improvements to the entire statistical business process. To achieve this, the team will make use of opportunities arising from new technology and a larger variety of data sources, including administrative data.

The ultimate aim to make the BPS a more integrated organisation, with a strengthened role within the National Statistical System, is in line with modernisation efforts in other statistical offices around the world, in particular in middle- and higher-income countries.

Our approach

Our multi-sectoral team includes both resident and international specialist advisers in areas including official statistics, statistical reform and organisational re-engineering.

We are supporting the BPS with the following:

  • The development of the Statistical Business Framework and Architecture, including implications for statistical legislation and the organisational structure; and a roadmap and implementation plan that outlines recommendations for the transition phase provides a risk management plan
  • The design of statistical infrastructure needed to support the future business processes. These include, for instance, systems for the design, collection, processing, analysis and dissemination of different types of data from various sources
  • The design of cross-cutting processes such as quality assurance, application of common statistical standards and compilation of metadata, an improved use of geospatial information and tools and strengthened engagement with data providers and data users
  • The development of redesign plans for national accounts and six priority statistical series (trade, manufacturing, living standards and labour force surveys, producer prices, horticulture)
  • The production of statistical training curricula and content to support the skill development required for the reform
  • The provision of additional statistical capacity-building on methodological aspects of statistical series, such as improved sampling designs; and estimation methodologies.

In addition to the design activities, we will provide support to the implementation of new statistical processes and the IT infrastructure, human resource policy and organisation change requirements needed to underpin this.

Outcomes

This project is supporting the development of integrated statistical production processes that improve accuracy, timeliness and coherence of outputs. Organisational structures and IT will also support new, efficient ways of working, alongside improved technical and managerial human resource capacity.

Ultimately, by making statistics more relevant, more accessible and more timely for their users, the project will contribute to improving the evidence base for decision-making in Indonesia.

UNECE documentation on statistical modernisation