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BAYESIAN ANALYSIS OF POVERTY RATES: THE CASE OF VIETNAMESE PROVINCES

BAYESIAN ANALYSIS OF POVERTY RATES: THE CASE OF VIETNAMESE PROVINCES. Dominique Haughton and Nguyen Phong Bentley College, USA and General Statistical Office, Vietnam. T.P. Hå CHÝ MINH URBAN: DATA. T.P. Hå CHÝ MINH URBAN: sampled communes.

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BAYESIAN ANALYSIS OF POVERTY RATES: THE CASE OF VIETNAMESE PROVINCES

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  1. BAYESIAN ANALYSIS OF POVERTY RATES: THE CASE OF VIETNAMESE PROVINCES Dominique Haughton and Nguyen Phong Bentley College, USA and General Statistical Office, Vietnam

  2. T.P. Hå CHÝ MINH URBAN: DATA

  3. T.P. Hå CHÝ MINH URBAN: sampled communes

  4. T.P. Hå CHÝ MINH URBAN: frequentist (weighted) computations

  5. PRIOR 1: LOOSELY BASED ON BAULCH AND MINOT POVERTY MAPPING

  6. NANDRAM AND SEDRANSK (1993)

  7. NANDRAM AND SEDRANSK (1993)

  8. NANDRAM AND SEDRANSK (1993)

  9. NANDRAM AND SEDRANSK (1993)

  10. NANDRAM AND SEDRANSK (1993)

  11. NANDRAM AND SEDRANSK (1993)

  12. NANDRAM AND SEDRANSK (1993)

  13. PRIOR 1: LOOSELY BASED ON BAULCH AND MINOT POVERTY MAPPING MEANS = /  = 200

  14. TYPICAL COMPUTATIONS

  15. NANDRAM & SEDRANSK CLOSE FORM FORMULAS: RESULTS

  16. WINBUGS SIMULATIONS: RESULTS

  17. HCMC URBAN PRIOR 2: SAME /, HIGHER STANDARD DEVIATIONS,  = 80

  18. NANDRAM & SEDRANSK CLOSE FORM FORMULAS: RESULTS

  19. HCMC URBAN PRIOR 3: EXPERT OPINION, P.R. BETWEEN .01 AND .03 (95%),  = 80

  20. NANDRAM & SEDRANSK CLOSE FORM FORMULAS: RESULTS

  21. WINBUGS SIMULATIONS: RESULTS

  22. HCMC URBAN PRIOR 4: DIFFUSE PRIOR,  = 80

  23. NANDRAM & SEDRANSK CLOSE FORM FORMULAS: RESULTS

  24. NGHÖAN RURAL: DATA

  25. NGHÖAN RURAL: sampled communes

  26. NGHÖAN RURAL : frequentist (weighted) computations

  27. PRIOR 1: LOOSELY BASED ON BAULCH AND MINOT POVERTY MAPPING  = 40

  28. NANDRAM & SEDRANSK CLOSE FORM FORMULAS: RESULTS

  29. WINBUGS SIMULATIONS: RESULTS

  30. NghÖ An PRIOR 2: MOLISA-BASED PRIOR,  = 30

  31. NANDRAM & SEDRANSK CLOSE FORM FORMULAS: RESULTS

  32. WINBUGS SIMULATIONS: RESULTS

  33. NOW FOR NOT SO GOOD NEWS: • Poverty lines are very noisy, among other things because of high uncertainty in prices • So households are classified into poor/non-poor with some misclassification • In this case, samples sizes needed for a given accuracy will in general need to be higher (Rahme, Joseph and Gyorkos, 1999)

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