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A Sustainable Poverty Monitoring System for Policy Decisions

A Sustainable Poverty Monitoring System for Policy Decisions. Bjørn K. G. Wold, Astrid Mathiassen and Geir Øvensen Division for Development Cooperation, Statistics Norway IAOS October, 2008. Two parts:.

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A Sustainable Poverty Monitoring System for Policy Decisions

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  1. A Sustainable Poverty Monitoring System for Policy Decisions Bjørn K. G. Wold, Astrid Mathiassen and Geir Øvensen Division for Development Cooperation, Statistics Norway IAOS October, 2008

  2. Two parts: • Describing the Household Survey System Model, including the Poverty Monitoring System; Case Malawi • Testing the Poverty Model; Case Uganda

  3. Part 1:The Household Survey System Model

  4. Growing need for statistics for policy decisions • Recent initiatives since Millenium Development Goals • Paris21: ”Scaling up” • World Bank: ”Better statistics for better results” • Three major challenges remain • Design short questionnaire for fast monitoring of MDG and PRSP indicators • Method for easy and accurate measurement of money-metric poverty • Household survey system with annual core for MDG and PRSP, and a rotating program of specialized sector surveys

  5. Suggested solution • Identify indicators to measure progress on MDG and PRSP • Household Budget Survey for initial poverty line • A poverty model for monitoring in non-HBS years • Annual, rotating sector surveys with common core • Household Survey Program to cover all topics in 5-10 years • Statistical tools such as seasonal adjustment for consistent trends, and small areas estimation • Fast and easily accessible results • Active dialogue between donors and to ensure that all agencies accept integration of ”their” surveys in program

  6. Model evaluation Precision? Est. HC Est. HC Est. HC Est. HC Est. HC District Headcount Consumption aggregate Consumption aggregate Poverty line Poverty line Headcount 15 Indicators WMS07 Nacal WMS05 WMS08 WMS06 IHS3 Same question for each indicator as used in HBS1 Selection based on statistical correlation and theory 15 Indicators 2008 Timeline: 2004 2006 2009 2005 2007 The Malawi Poverty Prediction Sequence IHS2

  7. ? ? ? ? ? ? ? ? ? Malawi: Large data gaps if use HBS only! • 1998: IHS1 Budget Survey, (with data problems) • 2004: IHS2 • 2009(?): IHS 3

  8. ? ? ? ? ? ? ? ? 60 Malawi ? ? 50 Rural ? ? 40 Urban 30 20 10 0 1998 2004 2005 2006 2007 2008 2009 Malawi II: Complement with Poverty Estimates from “light” Surveys! • 1998: IHS1 Budget Survey, with data problems • 2004: IHS2 • Estimates from (light) WMS 2005, 2006, 2007 • 2009(?): IHS 3

  9. Part 2:Testing the Poverty model on Ugandan Surveys

  10. Testing the poverty models’ predictive ability • Test the predictive ability of the poverty model Compare model’s poverty estimates relative to poverty estimated directly from consumption aggregates • Use 7 comparable household expenditure surveys from Uganda from 1993 to 2006 • Comparable consumption aggregates and sufficiently number of (exactly) identical indicators • Calculate urban/rural poverty models from each survey • Cross-testing models from each survey onto the other surveys

  11. Model 1995 Cons.agg 1995 Cons.agg 1993 Cons.agg 1994 Cons.agg 1997 Cons.agg 2002 Cons.agg 2005 Example: Pairwise testing from 1995 survey onto itself, and the 6 other surveys Expenditure Survey 1995

  12. Uganda: Comparing actual poverty level predictions from RURAL model • Models capture most, but not all of reduction • All models have similar patterns of changes • Less capture of variability within trend  Biases related to factors specific for specific years? (e.g, omitted variables)

  13. Uganda: Comparing actual poverty level predictions from URBAN model • Better capture of variability than rural • Low poverty in base year  low urban predictions • 1999 survey bad base for urban models • Combination of long time elapsed and large fall in poverty seriously shake the model? (ref. 2005 predictions)

  14. Uganda: Take out two most problematic surveys in urban and rural models • Predictions now much more in line with trend • Also good predictions at sub-regional level • Lower ability to capture sudden changes  Add new types of variables?

  15. Conclusion • Predictive ability on general trend proven, but: • Models”carry on” their base year poverty level • Difficult to capture sudden changes • Challenge with two individual surveys (survey issues) If two HBS available, and both <10 years old, use average of both in predictions • Possible improvements: Number and level of assets and locality-level explanatory variables • Statistically, a second best solution  never perfect

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