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Structural Vulnerability Assessments

Structural Vulnerability Assessments. An example of theory-informed, data-driven research to support for the formulation of actionable policy options. March 2014. Approach.

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Structural Vulnerability Assessments

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  1. Structural Vulnerability Assessments An example of theory-informed, data-driven research to support for the formulation of actionable policy options March 2014

  2. Approach • Specify the goal (e.g. to minimize violent conflict) as measured by a third party measure (Heidelberg’s Conflict Barometer) – the target • Select a broad range of comparable structural indicators that are suggested to escalate the level of conflict – the drivers • Compile a structural indicator data set with country profiles for the world, continent and region over at least a decade – the profiles • Train & Test the model based on the historical data and test its performance, refining and iterating until optimized – the model • Forecast levels of conflict going into the future, identifying common and country-specific drivers for diagnosis – the “input” • Formulate options to promote the prevention or escalation of conflict (i.e. to promote peace)– the recommended “options”

  3. Assumptions & Requirements • The choice of Target measure is guided by the Institution’s Mandate • History Continuity and Learning • Model can be trained on the association of past profiles & levels of conflict, with measurable performance, but • Beware of novelties and discontinuities • Statistical method must be tolerant of indicator collinearity, data noise and missing values • Tools must be customizable, interactive and able to track performance metrics Recall = TP / (TP + FN) <<< most important for early intervention Precision = TP / (TP + FP) F Score = harmonic mean of recall & precision - BEST OVERALL MEASURE(TP = true positives, FP = false positives, TN = true negative, FN = false negatives)

  4. Data Criteria: in descending order of importance • Mandated Focus & Scope (e.g. gender) • Available, at least 50% non-missing data • Actionable (e.g. terrain versus education) • Theory-informed (linkage to target variable) • Open source, with regular updates • Comparable, national, continental & global • Replicable, with full documentation • Historical, a decade plus for training

  5. Data Sources: Preferred, Usable and Others • Member States – preferred • International Governmental Organizations • AU, RECs, UN, World Bank, AfDB, ILO, etc. (especially African) • Nongovernmental Organizations • Academic Centers (e.g. Uppsala, Heidelberg, George Mason) • Research Units (e.g. CRED, IISS, SIPRI) • Advocacy (World Economic Forum, Freedom House) • Ad hoc and internally generated, particularly intra-regional, for example • Informal cross-border trade internally generated • Country-specific measures limited to a region

  6. Data Challenges: Missing Data and Alternate Measures • 5,814 indicators across five databases (Africa, Development, Education, Gender & HPN), but • 4,485 unique indicators • 1,940 indicators with more than 50% non-missing data (Only 43% usable for global comparisons) • With numerous variations of each: • 219 individual measures with a GDP component • 98 individual measures on school enrollment • 21 measures on education (mostly spending) Based on June 2012 World Bank data

  7. Data Challenges:Two More Examples • Unemployment and Employment • 56 indicators on unemployment, by age, education, gender & sector, but none with more than 46% non-missing data • 145 indicators on employment, with 15 having non-missing data greater than 78% • Distribution of Income (HDI as alternative) • 22 GINI-related measures, none with more than 14 % non-missing data • 12 income share-related measures, none with more than 14 % non-missing data

  8. Analysis Process:Transparent & Participatory Approach • Engage stakeholders in discussions of target, indicators & data sources, with several iterations of each • Test & re-test both DV and IV operationalizations • Solicit regional expert ideas on the indicators • Explore national sources first, then IGOs and others • Communicate progress on intermediate results, especially on their communication & ultimate use • Ongoing collaboration, review & validation (national and IGO officials, regional experts and academics) is required • Focus on drivers, both common and country-specific • Calculate discrimination value & distributions • Emphasize interpretation & formulation of options

  9. Interpretation:Guidelines • Applies to both Common & Country-Specific Drivers • Consider • Performance Metrics • Distribution • Triangulation • Missing Drivers (if indicator is missing, it cannot discriminate ) • Indicator utility in specific contexts, including • Indicator relevance, and • A “Smell Test” of intuition and grounded expertise

  10. Formulation of Options: Guidelines • Within the Institutional Mandate • Actionable Prevention and/or Mitigation • Consider Intermediate Requirements • Define Progress Milestones • Specify linkage and expected outcomes

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