As a leading source of development data, the World Bank plays an important role in helping countries access the data they need to make informed policy decisions. The Sustainable Development Goals require countries to invest in monitoring and interpreting their progress towards achieving a host of development targets. This, along with recent technological shifts has further underscored the importance of data in development. As a result, many client countries are increasingly turning to the World Bank and other development partners for support in building their national statistical capacity and strengthening data use and sharing.

Experts from the World Bank, OECD, and AidData explored the latest trends, opportunities and challenges in enhancing the role of data in development. Panelists will discuss how to increase collaboration at the Bank-level, strengthen global data partnerships, and scale up investments in national data capacity building. The event will also highlight findings from IEG’s recent evaluation on Data for Development and recent reports by these organizations.

Continue the conversation on twitter at #Data4Dev

Watch the re-play of the live event

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


Caroline Heider
Director-General, IEG, and
Senior Vice President
World Bank Group

Perspectives on the Future of Data for Development

Shantayanan Devarajan
Senior Director, Development Economics
World Bank


Brenda Barbour
Manager, Knowledge & Communications
Independent Evaluation Group
World Bank Group



Samantha Custer
Director of Policy Analysis, Policy Analysis Team

Haishan Fu
Director, Development Data Group
World Bank Group

Rasmus Heltberg
Lead Evaluation Officer, IEG Corporate and Human Development Unit
Independent Evaluation Group
World Bank Group

Ida McDonnell
Senior Policy Analyst
Team Leader OECD Development Co-operation Report
OECD Development Co-operation Directorate

See also:



In Slovenia we had pretty well developed statistics and data thay were collecting. umerous data waspublically avalable for different uses. After 2008 (economic crisis) the public availability fo data decreased enormously in tersm of diveristy of data and its quality in temrs of periods of collection, level of collection. Is there any correlation among development level of the country and the public data availabilty?


How can we Regenerate & Revive JOBS in huge billion agricultural & Non Farm Enterprises / Farmers in this year very Bad Drought & repeated Tsunami floodconditions in South India couples with Demonetisation cash crunch for year Long 2016-17..?


What percentage of a development project's cost should be allotted to data collection and evaluation (ballpark figures). What is the current average for Bank projects and is that expected to change in the next five years?


While working in statistical systems of developing countries, I witnessed numerous cases where donors supported operations didn't conform with the national statistics and other data acts or regulations. I know nationals accepted this, for various reasons.

My only question to the panellists: is what should be the general rule?

My own position is that the national regulations should be abided by, and advises on improvements given when relevant.


In my field (climate change), there is an urgent need for data among very diverse user groups, ranging from deputy ministers to farmers. While the World Bank has amassed an impressive repository of climate data and information on the internet, many potential users do not use the internet for climate change information and in fact may not use the internet at all. As the Bank has supported a number of innovative off-line approaches, such as text messaging, community risk mapping, etc., my question is as follows: Do you have plans to incorporate off-line data proliferation into your efforts to increase data use, and what are your thoughts on data use strategies in situations where target users are not obtaining information from the internet?


Unquestionably believe that that you stated. Your favourite reason appeared to be at the net the easiest thing to be mindful of. I say to you, I certainly get irked at the same time as other folks think about issues that they just don't understand about. You managed to hit the nail upon the highest as smartly as outlined out the entire thing with no need side effect , other folks can take a signal. Will probably be back to get more. Thank you!


On the one hand, aggregate data is very useful for evidence-based policy making and we want it to be publicly available; on the other hand, individual data could threaten the privacy of individual people. Should we offer more help to countries in managing data (e.g., tax data) to protect the confidentiality of the individual? In particular, "disaggregated" data might inadvertantly reveal too much (e.g., a medium-sized manufacturing company in a particular locality might be easily guessed by anyone familiar with that locality).


Can you comment on the status on gender disaggregated data? Also what are the on going efforts to have Governments produce information that can support the case for gender parity/equality?


Working in developing countries of West Africa, where resources are not enough and are mostly used for priorities only. The national data systems are very week and the collection teams are not well trained to collect effectively, and the collection costs are high.
In reality, these countries use their scared resources in other development areas such as road building, health rather in data collection and use. My question is, how could we get to sensitize the decision makers of these countries that data financing is not a waste of resources and to make it a priority.


Big Data sources, such as mobile operator data, have great potential to support the improvement of national statistics in low- and middle-income countries and cover the data gaps that currently exist there. How do we guarantee the quality of the statistics obtained from these new sources, such that they can be accepted (particularly by governments) as acceptable shoe-ins for those gold standard statistics derived from traditional data sources? It is also important to assist national statistical office's in capacity building around understanding how to use the outputs of those new statistics derived from Big Data sources in an effective manner.


Instead of thinking about "what percent of the project budget should be allocated for M&E", shouldn't we ask "How much should we invest in building capacity of client governments to be able to do their own M&E"?

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