In the fast-moving, tech-heavy world of the data revolution, statistical capacity building has a reputation as a slow-paced, unwieldy, and somewhat archaic endeavor that could somehow be bypassed through investments in smart devices and big data analytics. We found that this reputation is misguided and unjustified.


I am a fan of the Economist's "Daily Chart" section. In one chart, with appealing visualization, the magazine provides a "window" onto the world around us. The other day, six compelling graphs were taking the pulse of the "world's wellbeing" with global data on new cases of Malaria and HIV, maternal mortality and poverty. As development evaluators and practitioners, we consume data daily, marveling or deploring trends. Yet, we hardly think about what it takes to produce and share data.

Let's think for a minute about the magnitude of the effort. For any data point on one of these charts, countries need to collect and share their data following internationally-agreed standards. This is not a trivial endeavor. It requires among other things, having a coordinated network of data producers following strict procedures to record, process and share administrative information (in health centers, schools, townhalls, etc.) and to collect household and enterprise survey data.

More fundamentally, underneath the production of each data point lies a set of technical, administrative, legal, and, governance challenges. To put it simply, statistical capacity building is institution building.

In our evaluation we assessed the World Bank's results in supporting its client countries in this endeavor over the past decade.

The case of Tanzania is quite enlightening. Back in 2007, the National Bureau of Statistics (NBS) had limited autonomy, technical ability and scarce human resources. Consequently, there were delays in data collection, limited quality control, and ultimately data gaps. Since then, a concerted effort between the government, the World Bank (with no fewer than six data interventions) and its partners have yielded significant progress. Today, surveys are more timely, cover a wider range of topics, and adhere to quality standards. The NBS is even making headways in using geospatial data. Tanzania has moved up 15 points on the scrutinized Statistical Capacity Indicator, positioning the country well above the regional average.

Data for Development: Perspectives on the World Bank’s Role and Contribution
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Similar patterns emerged from other cases such as Rwanda and Ghana: reforms that paired improvements in the institutional and legal environment for data production with investments in the training of staff, equipment of offices and improvement of IT systems helped improve data reliability and timeliness, in turn making countries more willing to share their data. This required long-term support from the Bank to build trust, and alignment with other development partners, including through pooled funding mechanisms.

Despite steady change in some countries, progress remains slow and uneven, many countries are still data deprived, continue to have weak data systems, especially at the sub-national level. In Tanzania, capacity remains weaker in the other parts of the national statistical systems, in ministries and at the local level, which have a key role to play in the production and use of routine administrative and results data.

In the fast-moving, tech-heavy world of the data revolution, statistical capacity building has a reputation as a slow-paced, unwieldy, and somewhat archaic endeavor that could somehow be bypassed through investments in smart devices and big data analytics. We found that this reputation is misguided and unjustified. Technological "fixes" cannot be useful without the right institution and proper skills--the core of building statistical capacity. Undoubtedly the World Bank initiatives have had high transaction costs, and have been slow to show results. However, this is somewhat characteristic of this type of intervention, which seeks change at the system level.

Country clients need continued support from the World Bank that is more coordinated and long-term, especially in strengthening their administrative data systems and building data capacity beyond national statistical offices. This is key, not for my daily enjoyment of the Economist's "Daily chart" but because data production, sharing and use is fundamental to achieving the SDGs.

Read the Evaluation - Data for Development: An Evaluation of World Bank Support for Data and Statistical Capacity



Data for Development (D4D) is crucial and cannot be overemphasized. One needs data to be able to track and measure development progress, through tracking development indicators against your targets overtime at various level of results, whether it be at activity, output, outcome, impact or goal level. Data needs and sources will also vary relative to the results level that one may want to track and measure. It will also depend on the emphasis and focus of monitoring and reporting, whether one conducts output monitoring, outcome monitoring or impact monitoring. However to be more strategic and serve the needs and requirements of donors and development partners, leaders and politicians and making a difference in communities, it is important to conduct outcome and impact monitoring and reporting as these stakeholders are really interested in higher level development results or development outcomes.

In terms of sources and avaialbility of data, I think in the Pacific Islands Countries, there is a lot of data, statistics and information that are being collected by the national statistics office and through reports (annual reports, reviews, assessments, evaluation) that are sitting and gathering dust in their registries and data bank - NSOs, administrative data, civil registration and vital statistics data and community data. The challenge lies in the construct and development of indicators on global commitments such as the MDGs and now the SDGs. First, attempt should be made to first know and understand the current status of data and information wealth in developing countries, thence develop/constuct indicators to measure output and outcome or impact level results that we know NSOs and organizations in developing countries are already collecting. It does not add value if at the global level indicators are developed and developing countries are not collecting data for these indicators. If the indicator is crucial to track directly the progress towards a relative outcome or impact,than include the indicators, however, the UN and other institutions must stand ready to provide technical assistance for strengthen NSOs and other institutions to be able to collect data for those key indicators. Secondly, the other option is to examine the potential for developing and constructing proxy indicators for outcome and impact level results for which countries are already collecting data. Thirdly, support institutional strengtheing to NSOs and organizations for the collection of key indicators through one off surveys or periodic surveys.

The other challenge and option is the standardization and alignment of global results framework and indicators to regional and national level results framework and a minimum indicator set for tracking global and regional results framework which is then aligned to the data capacity at regional and national levels. This would then identify and prioritise data and survey needs for results and indicators that needs to be tracked and measured and the costs asssociated with it and provide relevant survey and data collections TAs.

Having said that, support should be provided to NSOs in developing and strengthening their national statistical and data base system that is closely linked to ministries/departments statistical and data base system and the whole of Government M&E and database system. NSOs statistical and database system should feed into the whole of Government M&E and data base system, for which the latter is crucial for managing and reporting for results or development outomes to Cabinet, Parliament and donors and development partners. From my perspective, for developing countries such as Fiji or the PICs to better track, measure and report on development outcomes in the SDGs and other global commitments, there is indeed three areas of support or institutional strengthening that would be required; (i) NSOs Statistical and Database Systems (systems and apps, HR and staff capacity building, survey/data collection tools) (ii) Whole of Government M&E and Data Base System (systems, M&E results and indicator framework, tools, apps, templates, web-based reporting system, HR Capacity Building training on RBM and M&E, LFA, Programme/Project Design and Results based Reporting) (iii) M&E Leadership/M&E Focal Points and National M&E or Evaluation Associations - Strengthening M&E culture.

The challenge of tracking/monitoring, measuring and reporting on development outcomes is to ensure that no one is left behind from a development intervention whether it be at programme or project level at national, subnational (regional) district (subregional ) and community or village level. This highlights the importance of data collection and availability of data and tracking and measuring indicators and reporting not only at national level but also at regional, subregional, community and village levels. Therefore the construct of indicators is vitally important to be able to reach that far in measuring and tracking indicators at regional and subregional levels and reporting on them. In Fiji and other PICs, these seems to be a major problem. When tracking and reporting on national indicators on MDGs and global targets, at the national level, it may indicate that Fiji and other PICs are tracking well in terms of meeting the national targets, however, as one tracks these indicators at provincial or district levels than there is wide disaparity in terms of how well these provinces or districts are tracking. The challenge is to be able to track and report on whats working and whats not working and prioritise and focus and replicate and allocate more resources on whats working. This an area that may need support as we transit and move towards the SDGs where tracking/monitoring, reporting, prioritization and decision making is vitally importantly.

Thus, to end, a national statistical and database system and Whole of Government M&E System must be able to reach out and respond in terms of data collection and reporting on development outcomes at district and village levels and provincial levels and contribute to achievements and performance at national levels. This also supports good governance and social justice on a targetted approach to development intervention and transparency and accountability on management for results and reporting, decision making and resource allocations.

Dear Jone, Thank you very much for taking interest in our evaluation and for your extensive comments. Your diagnosis of the challenges echoes very much what we found in our report, and your ideas for deepening and strengthening data work and statistical capacity building, M&E culture and data use resonate with the recommendations that were embedded in this and other IEG reports. We just had a very interesting conversations around these very issues with a panel of experts from OECD-DAC and AidData. You may want to take a look at their reports as well: and…

Dear Estelle, Thank you so much for acknowledging my comments and the link provided for additional information and discussions. I will certainly follow up and access information from the link that you have provided. Best regards.


Hi Estelle! Good of you to draw attention to this important - and often neglected - part of capacity building. Governments need the statistical offices (and survey capacity) for many things, most of all for informed decision making! As you and Jone point out, an important aspect of this work is also for government agencies, CSO, and other groups to be able - and see value in - engaging with the data. The Centers for Learning in Evaluation And Results (CLEAR) ( are one of the groups that do just that - themselves and with their partners - through direct mobilisation and engagement. Worthwhile another blog? Warmest, Monika

Happy New Year Monika! Thank you for keeping abreast of our progress and for highlighting the work of CLEAR. As we stress in the report, capacity-building for data use is as important as capacity-building for data production, but has received less attention from the international community. There are many avenues to do better, as shown in a new study from Aid Data - - on the topic. CLEAR is a key player in this long-term effort to foster data ecosystems and promote data use. I agree with you a blog post on its unique model would be worthwhile!

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