Updating Bangladesh Poverty

Maps

Nobuo Yoshida

Dissemination Workshop

April 2nd, 2009

Power of disaggregationPoverty estimates of 2005 at the Division,

Zila, Upazila levels

What is poverty mapping?

Poverty mapping – Statistically Reliable Disaggregation of Poverty Estimates

It is called “mapping” since the results are often illustrated in a form of “Map”

Traditional methodology can estimate poverty rates of Zila or Upazila from HIES 2005 data.

However, the estimates are not statistically reliable since the HIES data include too few households for Zilas and Upazilas

Poverty mapping – take advantage of strengths of both Census and HIES

How to estimate poverty headcount rates:

Traditional Approach

Poverty headcount rates are estimated using household expenditure data from HIES

First, calculate per capita monthly household expenditure (PCEXP) from HIES dataSecond, a Household is defined as poor if PCEXP < Poverty Line Third, after counting the number of poor households in HIES data, the poverty headcount rate (proportion of poor population) is calculated

The accuracy of this estimate depends on the number of hhlds in HIES data

The national poverty rate estimate is very accurate since HIES 2005 data include around 10,000 hhldsThe poverty rate of Sylhet division is less accurate since HIES 2005 data include only around 1000 hhldsThe Zila/Upazila level poverty estimate is far less accurate since HIES 2005 data include very few hhlds

Poverty Mapping approach: Use Census data (all hhlds but no PCEXP)Predict PCEXP from information included in Census (like literacy of hhead; dependency ratio) for each Census householdUsing the predicted expenditure (instead of true expenditure), identify who is poor and estimate poverty headcount rates for small areasThe estimates have no sampling error but might have large prediction errorHIES data are used to reduce the prediction error

The role of HIES data changedTraditional approach uses HIES data for directly estimating poverty rates Poverty mapping uses HIES data to reduce the prediction error

How to estimate poverty headcount rates:

Poverty Mapping

Bangladesh Poverty Maps

Two previous poverty mapping exercises –producing poverty maps for 2000/01

BBS-WFP: Poverty and Food insecurity map

BBS-LGED-IRRI: Rural Bangladesh Poverty Map

The objective of our exercise is to update the poverty maps using

All of unit-record Census 2001 data

HIES 2005 data

What is new?

Main challengeLong interval between Census 2001 and HIES 2005

Some new remedies were attempted

Validation Exercises Creative way of testing whether the long interval remain an issue after the remedies

Perception Survey Analysis: Perception vs. Objective data

Capacity Building processWorld Bank provided poverty mapping training for the BBS staff

BBS opened this training opportunity to other government officials and researchers

The 2005

Poverty Map

at the

Upazila level(based on upper

poverty lines of

2005)

Poverty Headcount Rates vs. Poor Population

Poverty Map and Extreme Poverty

Map

Poverty Map:

Upper Poverty

Lines

Extreme

Poverty Map:

Lower Poverty

Lines

Poverty and Inequality

.1.2

.3.4

0 .5 1 0 .5 1 0 .5 1

Rural Urban SMA

Gini Fitted values

Gin

i C

oeff

HCR

Graphs by region

High Poverty

and low

inequality

But, quite a

large variation

as well

Use of

geographic

targeting for

poor areas with

low inequality

Uses of poverty maps in poverty alleviation

programs: International Experience

Nicaragua: Used poverty map to guide expansion of health services in especially poor areas

South Africa: Used poverty map along with maps on safe water and on cholera outbreak (2001) to identify high risk areas and devise protection mechanisms

Brazil: Used poverty map with other local level data to inform the needs and outcomes of poverty reduction initiatives (educational programs, providing safe water and sanitation for schools, establishing health care teams, etc.)

Guatemala and Panama: Used poverty map with road network data to devise a road strategy and identify need for roads in poorest districts

Cambodia: Used the map to guide food aid (World Food Program food aid (2001-02)) to alleviate food insecurity

Next Step and Future

Next Step: DisseminationDissemination of poverty maps is criticalDistrict level disseminationPreparation of a technical note summarizing all results and analyses

Future: Make poverty mapping a regular monitoring exercise• Speed up the population Census data entry• Data Collection in the middle of Census years

Creation of a meta database as monitoring and planning instruments• Linking poverty maps, agro-climatic data, EMIS/HMIS, Public Expenditure data, Infrastructure data could be very useful

Market Access and Travel time to

Dhaka•Travel time to Dhaka is

estimated from the road

network data

•Travel time depends on the

existence and quality of road

network

•Travel time is often different

from distance to Dhaka

•Travel time to Dhaka can be

seen as a measure of market

access given the importance of

Dhaka city

Market Accessibility Index

•There are many other markets

than Dhaka city

•Market accessibility index

measures access to all major

cities