The Challenge of Accessing Quality Data in the Post-Crisis World

1. The Challenge of Accessing Quality Data in the Post-Crisis World Abdullateef Bello (Ph.D) Islamic Development Bank 7 December 2010

2. Crisis and data The 2008 financial and economic crisis exposed the weaknesses in the tools and systems of Statisticians • Global statistical systemfailed to capture: • key statistical indicators relevant to the crisis such as: • Non-bank financial institutions • Interlinkages across financial institutions • Derivatives • risks associated with measuring and understanding international financial system • Statistical modelsfailed to predict the crisis All this depends on data availability and quality!

3. Why Data Quality matters “Data quality played a significant role in the mispricing and business intelligence errors that caused the crisis” (Casualty Actuarial Society E-forum, Spring 2010) “Without strong data, policymakers cannot manage effectively and business leaders may be left in the dark, unable to spot emerging trends and danger signals” (IMF’s F&D, Vol. 46, No. 1, March 2009) “…accurately measuring progress towards the MDGs is sometimes difficult when precise data are not available or come with a long time lag” (UN S-G report on MDGs, p. 3, 12/2/2010) • “… economic policymaking is hindered by low frequency and long publication lags associated with key … finance and spending data” (Alan Krueger, US Treasury Dept, Feb. 2010)

4. What are the attributes of quality data? Statistics Canada’s Quality Assurance Framework 2002 defines 6 dimensions of quality data as follows: • Relevance • Accuracy • Timeliness • Accessibility • Interpretability • Coherence

5. Challenges • Capacity to go beyond traditional approaches to statistical production in order to respond to emerging issues • Statistical methodologies for the 21st Century • Technical skills and expertise beyond the traditional knowledge of statistics. E.g. “Data Scientist” (a la Economist magazine, 5/3/2010) • Financial resources E.g. in 1998 during Asian Financial Crisis: The ADB reported that Indonesia CBS suffered 21% budget cut, and Malaysia’s Dept. of Statistics 15% (Source, ADB, 1998)

6. Challenges • Brain drain • Donor Fatigue • Institutional capacity “For an international organization, the quality of statistics disseminated depends on two dimensions: the quality of national (or external) statistics it receives and the quality of its internal processes for collection, processing, analysis, and dissemination of data and metadata” (Source: Quality Framework for OECD Statistics)

7. Way Forward • Strengthen 3Cs – Coordination, Cooperation and Collaboration at three levels : NSOs, donors and the data users • Transform the traditional statistical production system into modern approach • Deepen statistical methodological approaches • Invest more in statistical capacity • Explore new innovative financing of STATCAP (e.g. philanthropy)

8. Way Forward • New skills and new statistical indicators for post-crisis needs • Enhance knowledge sharing and transfers among data users and producers • Incentives to NSOs with best performance (introduce NSO Data Quality Index) • Enhance communication between NSOs, donors and data users • Encourage Knowledge transfers and not international consultants all the time.

9. Thank You