1 / 13

Poverty Maps for Sri Lanka

Poverty Maps for Sri Lanka. Nobuo Yoshida Economist The World Bank. Idea of poverty mapping method. Household survey (HIES 2002): Detailed information about living standards but small sample size (16,840 HHs used for analysis)

adie
Télécharger la présentation

Poverty Maps for Sri Lanka

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Poverty Maps for Sri Lanka Nobuo Yoshida Economist The World Bank

  2. Idea of poverty mapping method • Household survey (HIES 2002): Detailed information about living standards but small sample size (16,840 HHs used for analysis) • Population Census (Census 2001): Lack of information on consumption expenditure but large sample size (around 4 million HHs) • Poverty mapping method: By combining the strengths of both, estimate poverty indices at a remarkably disaggregated level

  3. How to combine the strengths of both census and household survey: • To take advantage of a large sample size of the Census 2001, “impute” per capita consumption expenditures for each census household from information available in the Census • Using the imputed expenditures, estimate poverty statistics at DS/GN division levels • Accuracy of the imputation process is a key • Using the HIES 2002 (& GIS database), find the appropriate imputation model

  4. Review of Methodology 1 Estimate the following equation using HIES 2002 and GIS info

  5. Review of Methodology 2

  6. Review of Methodology 3 • Using the predicted log of per capita consumption expenditures , we calculate poverty indices for each of small geographical areas • By drawing from the estimated distributions 100 times, we can calculate the standard errors of estimates of poverty indices

  7. Validating Assumptions • Consumption model (1) estimated in the HIES2002 is an accurate imputation model for CENSUS households if • Properties of X (right hand side variables) are the same between HIES and Census • The impacts of X and Z, i.e., β and γ, are the same between HIES and Census • This has been one of the most time consuming tasks in this exercise

  8. Comparability between CENSUS and HIES • Not a long interval between CENSUS and HIES • Common variables (which are included in both Census and HIES) should have similar distributions • Check the questions of both HIES and CENSUS • Compare the summary statistics of these variables • Location IDs should be the same; otherwise, either of them should be revised

  9. Domains for regressions • Domains: sub-sample used for regressions (1) • This is necessary since different districts might have very different coefficients β, γ • It should be better to create different domains for each sector/district (estimate imputation models separately) • HIES2002 has an enough sample size to create the following 26 domains

  10. Results • The imputation models are reasonably accurate • Adjusted R-squares: 0.44-0.60 (urban); 0.29-0.72 (rural/estate) • Papua New Guinea: 0.34; Madagascar: 0.24-0.64; Ecuador: 0.46-0.74 • Standard errors of districts and DS divisions are reasonably small • For DS divisions, the standard errors of HCRs range between 0.6% and 6.8%. • Estimates of poverty headcount ratios based on the SL poverty mapping method are statistically close enough to those based on HIES 2002

  11. Comparison in estimated poverty headcount ratios from PovMap with those from HIES 2002

  12. Toward updating poverty maps • Combining poverty maps with other GIS info are recommended for expanding benefits of poverty maps • Expanding HIES will improve the accuracy of imputation models • This might enable us to produce statistically reliable poverty estimates at GN division level

More Related