The geographical distribution of poverty and food security in Bangladesh: Implications for the design of policy & agricultural R&D&E interventions IRRI – ISNAR/WAU - BARC POVERTY & FOOD INSECURITY MAPPING CASE STUDIES WORKSHOP 6-9 March 2002

Bangladesh the country Land area of 144,600 km 2 Population of 120 million Population density = 830 persons/km 2 80% live in rural areas Arable land base: ha/person Ave. farm size 1.43 ha in 1961, 0.87 ha in 1994 Head count index 51% High vulnerability to floods & typhoons

Vulnerability due to environmental risk Cartographic Model Flooding hazard Depth of inundation Unclassified High risk Low risk Moderate Very low risk Data source: BARC, IRRI Dhaka BANGLADESH LEGEND

Problem Definition What we think might be the problem/use Better understanding of the nature of poverty for determining appropriate interventions Agricultural R&D&E priorities Accompanying interventions to increase effectiveness of agricultural development programs What national decision makers think they could have use of To be determined through consultation Who defines the problem … the stakeholder or the researcher?

Approach i Create spatially coherent database - basic layers - single variable indicators Map weighted indices of poverty/need/vulnerability Stakeholder ranking Ask stakeholders to rank indicators in their perceived order of importance Probit analysis Use regression coefficients from probit analysis of HIE survey data and corresponding “area” characteristics Identify where “priority areas” do not correspond & understand why they don’t Almanac of human well-being

Spatial distribution of human well-being Many facets of human well-being i Economic well-being Income & expenditure Household assets Non-economic well-being Health & nutritional level Literacy Resource endowment Land area & quality Ownership/tenancy relationship Technology & input level Agric productivity Accessibility Markets, social services Information/technology Alternative income sources Vulnerability Environmental constraints – growing period, drought, submergence Environmental hazards – flood, typhoon

Dimension of well- being AssetsPossible dataPossible indicators AEconomic well-beingFinancial Income and consumption Food ratio Access to credit Head count index (HCI), human poverty index (HPI), poverty gap index % of household budget spent on food Credit obtained BNon-economic well- being HumanAnthropometric measurements Mortality Literacy Education/schooling Height and weight for age Caloric intake Infant and child mortality Overall, male and female literacy Enrollment rate CResource availability and accessibility Physical (privately owned) Human Social Household assets Land ownership Land productivity Labor availability Community support Information access Quality of housing Ownership of transportation vehicles Availability of piped water Farm size Tenancy Yields and cropping intensity Input use Household size Working age population Wage rate Organizational membership & activity level Access to extension services

Dimension of well- being CapitalExamples of indicators Examples of variables DPhysical accessibilityPhysical/ commun al Access to markets Access to infrastructure Covering index Average travel cost Potential accessibility index Irrigation coverage EVulnerabilityNaturalEnvironmental constraints Environmental hazard Climate–quantum, seasonality Soil fertility Specific soil problems Agroecological zonation Flood extent, frequency, and duration Typhoon susceptibility

Stakeholder ranking Conduct survey of decision makers Ask them to rank different indicators of well-being Convert ranks to weights for m indicators W 1,…,W m Compute & map weighted scores S j =  i {W i  I ij } i = 1,…, m indicators j = 1,…, n areas

Probit analysis Probability that a household is poor (i.e. below poverty level z) P{y ij  z} where y ij is the income of the i th household residing in the j th community Do probit analysis of P{y ij  z} in terms of household and “area” characteristics P{y ij  z} = f(  1j …  mj ;  1j …  kj ) i = 1,…, m indicators j = 1,…,n community Use coefficients  1 …  k of variables  1 …  k to compute & map weighted index V j =  i {  i  A ij } j = 1,…,n

Comparison of scores & indices Create comparison map of S j and V ij Identify areas of low correspondence Characterize these areas to determine why correspondence is low

Who does what in Bangladesh BARC: agro-ecological zoning SRDI: soils data base LGED: infrastructure and administrative boundaries BBS: census (1993/94, 1999/2000); socio-economic surveys (HES, etc.) BIDS: socio-economic surveys, policy analysis DHS? The challenge is to bring these institutions together & process the data for computing measures of human well-being

Passing on to users Key agencies to be involved in the project Data sets & analytical tools remain with them Capacity to develop other applications Data sets approved for distribution to be disseminated using client-preferred digital & printed media

Thank you... … and we welcome constructive ideas to improve the suggested approach & methodology