⯇ WEBINAR SERIES
Connection Details
16 November 2018; 3:00-4:00 pm CET
Registration – (now closed)
Webinar ID – 655-187-499
Context
Capacity development has become a buzzword in the development sector, including in the data and statistics field. Long-term objectives will only be achieved if countries take ownership over their own development needs through dedicated capacity building programmes. The sector has become the holy grail of policy makers and development experts in their pursuit of long-term success in achieving goals such as those expressed in the 2030 Agenda and the UN Cape Town Global Action Plan for Sustainable Development.
Yet, after more than a decade of efforts, one could argue that capacity development shows mixed results at best; both in general terms and in development terms, in particular for data and statistics. In fact, the capacity development indicator of the World Bank – which tracks the ability of countries to produce more and better data – shows slow improvements. Many countries do not abide by international standards and lack basic statistics on their respective populations. Additionally, the quality and timeliness of data are still major problems for both national policy making and international monitoring. Several statistical offices face challenges when it comes to funding, modernization and defining their role in the new emerging complex data ecosystem. How is this happening?
Has capacity development shown any sign of success up until now? Should we simply de-prioritize capacity development efforts? Or is there a light ahead, with emerging approaches that could yield better results? What can we learn from capacity development so far, and what new practices can we envision for the future?
Regarding data and statistics, PARIS21, along with a number of partners, has developed a new framework over the past two years – Capacity Development 4.0 – that aims to be both relevant for the modernization efforts of national statistical systems and its offices as well as for providers/aid agencies.
Questions to be addressed
- What has been the impact so far of capacity development, and in particular as it relates to the area of data and statistics? Have there been any tangible results? What are the lessons we can learn?
- What improvements are needed and what new approaches are emerging? What does Capacity Development 4.0 mean and how does it address current weaknesses?
- Are there any promising concrete projects and how could those be scaled up?
Featured speakers
- Johannes Jütting, Manager, PARIS21
- Shaida Badiee, Managing Director, Open Data Watch
- Barbara Baredes, Policy Analyst, PARIS21
- Lisa Denney, Research Associate, Overseas Development Institute
Video – Beyond the Numbers – Webinar 1
16/11/2018
Followup Questions
Following Webinar 1, participants had a few questions that are addressed below:
Q: Is there a strategy to introduce politics to the importance of capacity development for statistics?
A: (from Lisa Denney) – There are a few ‘movements’ or communities of practice that seek to do this. The thinking and working politically community of practice, doing development differently community, and the problem-driven, iterative and adaptive approach are all good examples. Adaptive programming (for instance, USAID’s Collaborate, Learn, Adapt approach) is another example. These are not about capacity development specifically, but about how development assistance can better engage with politics, and the political drivers of dysfunction.
A: (from Shaida Badiee) – While there is not one universal strategy to involve politics into the importance of capacity development in statistics, there was hope that including the statistical capacity-related SDG indicators (17.18.2, 17.18.3, 17.19.1) in the SDG framework would create a political buy-in for statistical capacity building and increase the resources allocated to it. There are also initiatives at the international, regional, and national level to involve politics in statistical capacity development. At the international level, the United Nations Statistics Divisions has placed efforts to bridge technical and political communities together through the United Nations World Data Forum. This conference marks an opportunity to raise the political profile of data and statistics by involving different government departments or ministries and policy-makers. This often happens at the regional level as seen at the African Open Data Conference specifically and events held by UN Economic Commissions more broadly. At the national level, there have been successes through partnerships between data producers and users. This means involving end-users (usually the government policy-makers) in data conversations early on in the process. Showing policy-makers what they can accomplish with data creates the incentives to not only use the data for policy-making but to build support (political and financial) and demand for statistical outputs from the national statistical office. Here is a good example of this playing out in practice.
Q: Is it possible or useful to conduct an impact evaluation study to extract concrete/actionable learnings from capacity development interventions at a more aggregate level? (Hearkening to Lisa’s initial point on lack of enough learning taking place in the field overall due to a lack of innovation and actors employment of rather homogenous models across sectors etc.)
A: (from Lisa Denney) – So many studies of capacity development and its limitations have been done (see EDCPM’s excellent work on this, for instance) that I am not sure more reports and lessons learning exercises are the answer! I think the question to ask is why, despite all the studies and lessons learned that show our tried and tested models produce limited results, do we keep doing the same thing? I think this speaks to the political economy of the wider aid industry:
- The push for tangible, easily quantifiable outputs that we can put in log frames and results frameworks;
- A fundamental belief that more knowledge or skills will change behaviour when we know that our theories of change need to be more nuanced than that.
Without addressing those things, we are likely to keep doing the same thing. In the SLRC synthesis report, we argued that we need a re-politicisation of capacity development. We now think of CD as very much a technical issue – but it’s not. It’s about a process of social change that we hope will produce development outcomes from which some people will win and others stand to lose (at least in the short term). I think we need to re-engage that with politics and put it front and centre a bit more.
Q: Do the panellists think that the SDGs provide a good hook/momentum for capacity development for statistics?
A: (from Shaida Badiee) – Yes, the Sustainable Development Goals (SDGs) provide a good hook for capacity development for statistics. From the beginning, the SDGs have been closely tied to the United Nations Data Revolution and the 2030 Agenda is underpinned by the need for high-quality data. There was also a lot that was learned from the Millennium Development Goals, the predecessor to the SDGs. Moving into the next (far more ambitious) global agenda, the SDGs, it was well understood that without the right international support to increase statistical capacity, monitoring the SDGs would be almost impossible. Not only are data needed to measure and track progress towards each of the individual targets, the national capacity for statistics is found within indicators: Indicator 17.18.2: Number of countries that have a national statistical legislation that complies with the Fundamental Principles of Official Statistics. Indicator 17.18.3: Number of countries with a national statistical plan that is fully funded and under implementation, by source of funding. Indicator 17.19.1: Dollar value of all resources made available to strengthen statistical capacity in developing countries. There is a consensus that the SDGs place a tremendous demand on the international and national statistical systems. Alongside this consensus is the understanding that not all countries have the capacity to collect, produce, and publish the data necessary to meet those demands. Besides traditional methods of data collection such as census, household surveys, or civil registration systems, the SDGs also put pressure on statistical systems to produce new data using new sources, implement new standards, and develop new methodologies to provide missing information for critical indicators. Strengthening capacity for statistics is critical to close the gap and produce the necessary inputs for sustainable development. There is a saying that you may hear echoing within the walls of conferences and panel sessions: Leave no national statistical office behind. It means that as the leave no one behind agenda promises to ensure all individuals – no matter what race, ethnicity, age, sex, disability status – benefit from the gains of sustainable development; this must also be the case for all national statistical offices – no matter the country, capacity level, or income level. If the development community is serious about achieving the SDGs and understanding our progress towards them, we must also get serious about capacity development for statistics and the SDGs are one entry point to do this.
Q: Barbara’s Swiss/Albanian story is wonderful but how could you apply the lessons with National Statistics? Could you have a programme that said we are going to improve statistics but we can’t say which statistics we will be able to improve until one or one and a half years into the programme?
A: (from Barbara Baredes) – The main question before designing a programme or intervention is “what do we need statistics for?” The answer that we all have in mind is “to inform decision-making” (for citizens, researchers, the media, private companies, the government, international organizations and the public at large). Evaluations of statistical capacity development interventions-such as World Bank’s 2017 Data for Development: An Evaluation of World Bank Support for Data and Statistical Capacity and UN’s 2016 Evaluation of the contribution of the United Nations development system to strengthening national capacities for statistical analysis– acknowledge that one of the main shortcomings of current programmes is the lack of engagement with users (especially from the government) to promote effective use of data for decision-making. We are currently focusing on producing statistics, but we are less concerned with understanding how users can access them and what purposes they will serve (e.g. research, monitoring policies).
Indeed, waiting for a year to find out what data is needed by national or municipal stakeholders and how they plan to use it seems too time consuming, and includes no concrete results. However, as we propose in the Capacity Development 4.0 Framework (and in line with the recommendations of such evaluations), relationships between stakeholders of the National (or Municipal) Statistical Systems are capabilities in themselves. Following this train of thought, the full year of consultation counts as part of the project because it contributes to developing statistical capacity –as much as providing technical support or training statisticians do.
Q: It is interesting to hear from all panelists a focus on leadership, political willingness, and political/contextual analysis as key indicators of success for capacity development for statistics. What advice do you have for those working on statistics of populations groups/topics of high political sensitivity? For example, we work on statistics of refugees and IDPs as part of an Expert Group which is why I am asking this question.
A: (from Barbara Baredes) – Indeed, there are topics that are more politically sensitive than others. Migration is one of them, since there has been an upsurge of anti-immigration (and refugee) groups that threaten the stability of governments. Even more, this has been the main focus of several (successful) political campaigns in the past few years. In this sense, it is important to understand the particularities of the context (and the subject) in which we are intervening before proposing any intervention. First of all, analysing how various actors in the country would use the data on refugees or IDPs, in order to outline a strategy to outweigh identified negative effects. Second, leveraging on the work of other actors, such as that of the International Organization on Migration–together with UNSD- for the 2010 Census Round. These organizations recommended to identify individuals’ country of birth, citizenship and year of arrival. As a result, 87% of 149 countries that sent data to UNSD collected at least one question on the matter.Finally, working together with the national government to build consensus on what needs to be done, and focusing on small wins to build up from there.
Additional Resources
Byfuglien, J. (2014). Coordination of the production of official statistics: good practices, mechanisms and tools. UNDA 9th tranche: Kick-off meeting 10 – 11 December 2014, Almaty, Kazakhstan. UNECE. Retrieved February 26, 2018, from https://www.unece.org/fileadmin/DAM/stats/documents/technical_coop/2014/mtg3/S2_Jan_Byfuglien.pdf
Denney, L. and Mallett, R. with Benson, M. S. (2017), “Service delivery and state capacity: findings from the Secure Livelihoods Research Consortium”, Secure Livelihoods Research Consortium, London, https://securelivelihoods.org/publication/service-delivery-and-state-capacity-findings-from-the-secure-livelihoods-research-consortium
Eurostat (2014). Statistics in development cooperation – improving statistical capacity. [online] Retrieved November 22, 2017, from https://ec.europa.eu/eurostat/statistics-explained/index.php/Statistics_in_development_cooperation_-_improving_statistical_capacity
Fonteneau, F. Baredes, B. and Mayard, C. (2018), Measuring Statistical Capacity Development: A review of current practices and ideas for the future – moving towards Statistical Capacity 4.0 (Working paper No. 11). Paris: PARIS21. Retrieved from http://www.paris21.org/sites/default/files/inlinefiles/Measuring-Statistical-Capacity-Development_draft.pdf
Government Statistical Service (2016). The government statistician group competency framework. Retrieved January 30, 2018, from https://gss.civilservice.gov.uk/wp-content/uploads/2016/03/GSG-Competency-Framework_March2016-1.pdf
IEAG (2014). A World that Counts: Mobilising the Data Revolution for sustainable development, Independent Expert Advisory Group on a Data Revolution for Sustainable Development
Keijzer, N. & Klingebiel, S. (2017), Realising the Data Revolution for Sustainable Development: Towards Capacity Development 4.0 (Working paper No. 9). Paris: PARIS21. Retrieved from http://paris21.org/sites/default/files/201710/CapacityDevelopment4.0_FINAL.pdf
Ngaruko, F. (2008), “The World Bank’s framework for statistical capacity measurement: strengths, weaknesses, and options for improvement”, The African Statistical Journal, 7.
OECD (2015). Recommendation of the OEDC Council on good statistical practice. Retrieved February 26, 2018, from http://www.oecd.org/statistics/good-practice-toolkit/Brochure-Good-Stat-Practices.pdf
PARIS21. (2014, July 26). NSDS Guidelines. Retrieved November 26, 2017, from http://nsdsguidelines.paris21.org
PARIS21. (2010). Advocating for the National Strategy for the Development of Statistics Country-level Toolkit.
PARIS21 (2018a), Proposing a Framework for Capacity Development 4.0, PARIS21, Paris, www.paris21.org/results-cd40-task-team
PARIS21 (2018, February 23), Draft report on responses to the joint survey on New approaches to Capacity Development and Future priorities (Rep.). Paris: PARIS21. Retrieved http://paris21.org/sites/default/files/inline-files/DRAFT%20-%20Report%20on%20joint%20survey%20results%20for%20UNSC.pdf
Taylor, M., (2016), The Political Economy of Statistical Capacity: A Theoretical Approach. Inter-American Development Bank Discussion Paper IDB-DP-471, https://publications.iadb.org/bitstream/handle/11319/7794/ThePolitical-Economy-of-Statistical-Capacity-A-Theoretical-Approach.pdf?sequence=2
UN Secretary General (UNSG) (2016) Evaluation of the contribution of the United Nations development system to strengthening national capacities for statistical analysis and data collection to support the achievement of the Millennium Development Goals and other internationally agreed development goals, A/71/430, online: http://www.un.org/ga/search/view_doc.asp?symbol=A/71/431
UNDP (2009) Capacity Development: a UNDP primer. http://www.undp.org/content/dam/aplaws/publication/en/publications/capacity-development/capacity-development-a-undp-primer/CDG_PrimerReport_final_web.pdf
World Bank (2017), Data on Statistical Capacity, The World Bank, Washington DC, http://datatopics.worldbank.org/statisticalcapacity , accessed 22 November 2017.
