1. Poverty Maps for Sri Lanka
I am honored to present the technical aspects of the Sri Lanka Poverty Mapping Exercise today. This exercise has been undertaken in very close collaboration between the Department of Census and Statistics and the World Bank. We would like to acknowledge all the staff in the DCS for their supports and dedication to this exercise.
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
First, I would like to briefly explain the idea of a poverty mapping exercise.
Household survey (HIES 2002) is characterized by … Population census (Census 2001) is characterized by…
As a result, both data sets cannot provide statistically reliable poverty estimates at small geographical unit levels

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
How to combine the strengths of both household survey and the census
In summary, the role of CENSUS is to provide its large sample size; the role of HIES is to provide an accurate imputation model to estimate census households’ consumption expenditures.

4. Review of Methodology 1
Estimate the following equation using HIES 2002 and GIS info
Next, we would like to review the methodology more formally.
Estimate the following imputation model using the HIES 2002 (explain variables)
By regress yi on Xi and Zgn, we estimate beta, gamma, and the distributions of myu and e

5. Review of Methodology 2 Assuming…
We impute/estimate per capita consumption expenditure for census households, which are missing, by combining the estimated beta, gamma, dist of myu and e with x and z in census 2001

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 1.
2. Simulations for estimating the standard errors for poverty estimates are calculated using a software developed by the World Bank.

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
In the following, I will show you the process

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
Remember that the imputation model (1) estimated in HIES2002 is accurate if CENSUS and HIES have a similar relationship between consumption expenditure and poverty correlates (x, z)
For that,…, as is the case in SL
Common variables (i.e., variables included in the imputation model)

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
1.2
3. Because of that, it should be better to create different domains for some sectors and districts; and estimate the imputation model (1) for each domain separately
4. HIES 2002 has an enough sample size to create the following 26 domains

11. Results The imputation models are reasonably accurate
Adjusted R-squares: (urban); (rural/estate)
Papua New Guinea: 0.34; Madagascar: ; Ecuador:
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
We use adjusted R-squares to measure the accuracy of the imputation models. According to the adjusted R-sq, the imputation models in SL poverty mapping exercise are reasonably accurate.
definition of rural areas or consumption behaviors are heterogenous in the rural areas

12. Comparison in estimated poverty headcount ratios from PovMap with those from HIES 2002
95% confidence intervals of poverty headcount ratios for each district: the true HCRs should be in the 95% CI with a probability of 95%.
Two observations:
95%CI from HIES 2002 are much larger than those from PovMap
The 95% CIs from PovMap do not contradict those from HIES 2002.

13. 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
Also improving the accuracy of the imputation model (1)