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The Challenge of Accessing Quality Data in the Post-Crisis World . Abdullateef Bello (Ph.D) Islamic Development Bank 7 December 2010. Crisis and data. The 2008 financial and economic crisis exposed the weaknesses in the tools and systems of Statisticians

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The challenge of accessing quality data in the post crisis world

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

Abdullateef Bello (Ph.D)

Islamic Development Bank

7 December 2010


Crisis and data
Crisis and data World

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!


  • Why data quality matters
    Why Data Quality matters World

    “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)


    What are the attributes of quality data
    What are the attributes of quality data? World

    Statistics Canada’s Quality Assurance Framework 2002 defines 6 dimensions of quality data as follows:

    • Relevance

    • Accuracy

    • Timeliness

    • Accessibility

    • Interpretability

    • Coherence


    Challenges
    Challenges World

    • 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)


    Challenges1
    Challenges World

    • 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)


  • Way forward
    Way Forward World

    • 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)


    Way forward1
    Way Forward World

    • 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.


    Thank You World


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