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Poverty Mapping Glenn Hyman CIAT.

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Presentation on theme: "Poverty Mapping Glenn Hyman CIAT."— Presentation transcript:

1 Poverty Mapping Glenn Hyman CIAT

2 Are we successfully targeting the poor?
Source: CIMMYT

3 CGIAR/FAO/UNEP Poverty Mapping Project: Country Case Studies
Competitive grants and peer review Working with NARS and national governments Mapping, tracking and explaining poverty State of the art spatial analysis methods Impact assessment for NARS, national governments, the CGIAR and donors Support for resource allocation for international development community

4 7 Country Case Studies

5 METHODS Compare these poverty indicators: Poverty intensity according to the unsatisfied basic need method Human development index (literacy, life expectancy and per capita GDP) Household consumption from small area estimation

6 ECUADOR Grid file: timetopo_ll (Accessibility to Local Markets)
Attribute Field: Value Layer: Accesibilidad.lyr Metadata: timetopo.xml Access to markets, facilities and resources is an important consideration for food security and poverty. CIAT has been measuring travel time to markets and incorporating this information into their food security analyses.

7 Malnutrtion in Ecuador is clearly greater in the Andean Mountains, relative the Amazon and coastal regions.

8 ECUADOR File: consumo90_2001_dd.shp (Cambio Headcount ratio ) Attribute Field: D_F_0_H Layer: Cambio headcount ratio lyr CIAT has also been analyzing the change in the headcount ratio between 1990 and Parroquias in the coastal section of the country were worse off in Some evidence points to the severe effects of the 1998 El Nino on the coastal region.

9 This map show areas where over 50 % of the population falls below the food poverty line in Mexico – as established by the Mexican Committee on Food Security.

10 Traditionally food security has been more severe for indigenous communities. This map show communities where over half the population speaks an indigenous language.

11 File: food_poverty00_mun.shp (Food Poverty)
Layer: Food Poverty.lyr Indigenous.shp (Majority Indigenous Population) Layer: Indigenous.lyr There is a striking spatial overlap between the areas with the most severe food poverty and majority indigenous population.

12 IITA researchers have integrated information from two separate household surveys – one on livelihoods and another on nutrition. This shows that the two surveys did not cover exactly the same states, but that both surveys covered the major zones of Nigeria, from humid lowlands in the south to dry savannahs in the north.

13 Poverty in this case is based on income, re-calculated in dollars per day, extrapolated from original sample sites by universal kriging. This preliminary extrapolation shows that the areas of greatest poverty are in the south-west, north-centre and north-east, while the richest rural areas are in the south-centre, along the valleys of the Niger and Benue rivers.

14 Micronutrients in childrens’ blood as an indicator of nutrition
Shapefile is estimated_micronutrient.shp, legend nutrit.avl, fields as follows:- Chilq=iodine deficiency - blue Chznlq= zinc deficiency - green Chfelq = iron deficiency - yellow Chvitelq = vitamin E deficiency - orange Chvitalq = vitamin A deficiency – red Areas of greatest iodine deficiency are in the central areas of Nigeria, and near the coast, zinc deficiency is most acute in the centre and north-west, iron deficiency is prevalent in the Niger Delta and north-east, vitamin E deficiency is greatest in the Niger Delta, south-west and north-east, while vitamin A deficiency is seen only in the extreme south-west. There is no clear correlation between micronutrient deficiencies and poverty.

15 File: activegroups.shp (Active Groups – Social Capital)
Attribute Field: GROUPDENS Layer: Active Groups.lyr marketcenter.shp: Market Centers healthfacilities,shp: Health Facilities This series of maps shows mapping of the “5 Capitals” in the Kajiado distict of Kenya. These “capitals” refer to the livlihood assets that support the well-being of a community or household. There has been considerable interest in this “livelihoods methodology. ILRI researcher are examining the geographic dimensions of livelihoods. This map shows the number of farmer groups and NGO’s per 1000 persons in the unit – a measure of social capital Social capital: The social resources (networks, memberships of groups, relationships of trust, access to wider institutions of society) upon which people draw in pursuit of livelihoods

16 File: distance to towns.shp (Distance to Towns – Physical Capital)
Attribute Field: PTOWNKM Layer: Distance to Towns.lyr marketcenter.shp: Market Centers healthfacilities,shp: Health Facilities This map shows the average distance to towns within a sub-district unit. Physical capital: The basic infrastructure (transport, shelter, water, energy and communications) and the production equipment and means that enable people to pursue their livelihoods

17 File: livestockdensity.shp (Livestock Density – Financial Capital)
Attribute Field: FLIVDEN Layer: Livestock Density.lyr marketcenter.shp: Market Centers healthfacilities,shp: Health Facilities The number of livestock that a household or community owns is an indicator of financial capital Financial capital: The financial resources available to people (whether savings, supplies of credit or regular remittances or pensions), providing them with different livelihood options

18 File: accesibility to water
File: accesibility to water.shp (Accesibility to Water – Capital Natural) Attribute Field: NNATWAT2 Layer: Accesibility to Water.lyr marketcenter.shp: Market Centers healthfacilities,shp: Health Facilities Access to water is an indicator of natural capital Natural capital: The natural resource stock from which resource flows useful for livelihoods are derived (e.g., land, water, wildlife, bio-diversity, and environmental resources)

19 File: poorpeopledensity.shp (Poor People Density – Capital Human)
Attribute Field: FGT0 Layer: Poor People Density.lyr marketcenter.shp: Market Centers healthfacilities,shp: Health Facilities The poverty map for the district is an indicator of human capital. Human capital: The skills, knowledge, ability to labour and good health important to the ability to purse different livelihood strategies

20 File: p0_gwr_adap_ll.shp (Avg. Max education level in HHs_data)
Attribute Field: MAXED Layer: Avg_maxeducation.lyr Country Boundary: Administrative Boundaries Lakes: Our studies have been analyzing a wide range of variables related to food security and poverty. This map from the Malawi case study shows that people in the northern part of the country stay in school more years than other parts of the country.

21 File: p0_gwr_adap_ll.shp (Avg. Max education level in HHs_ts18)
Attribute Field: TVAL18 Layer: Country Boundary: Administrative Boundaries Lakes: But when the coefficient on educational attainment is mapped from a geographically weighted regression of poverty and explanatory variables, the relationship is counter-intuitive. This result suggests that poverty strategies to improve education in the northern part of the country may have less effect than in other parts of the country.

22 Layer file: hci_p_ll.shp (Head Count Index (%), by quartile)
Field: INCDENCE Layer: Head Count Index.lyr This map shows the percentage of people in each district that fall below the poverty line in Bangladesh. Higher poverty prevalence is seen in the north and the southeast.

23 File: sqpap_ll.shp (Squared Poverty Gap Index (%), by quartile)
Attribute Field: SEVERITY Layer: Squared Poverty Gap Index.lyr If we look at the severity of poverty – where the most extremely poor areas are – we see a similar pattern to the previous map.

24 Layer file: ginicoefficient_ll
Layer file: ginicoefficient_ll.shp (Gini coefficient (%) based on per capita income, by quartile) Attribute Field: GIN_PC_D Layer Field: Gini coefficient.lyr But if we look at income inequality, the pattern of the most unequal areas does not match as well to poverty incidence and severity shown in the previous 2 maps. When targeting policy interventions, we cannot leave out the poor in districts with high inequality, but low overall incidence and severity.

25 File: slprovince.shp (Poverty at provincial level)
Attribute field: PPOOHH Layer: Poverty at provincial level.lyr Metadata: Slprovince_Geo.xml In Sri Lanka, IWMI researchers have been analyzing how geographic targeting affects our understanding of poverty and how it could affect policy making. This map show the percentage of people below the poverty line at the provincial level.

26 File: sldistricts.shp (Poverty at district level)
Attribute field: PPOORHH Layer: Poverty at district level.lyr Metadata: Sldistricts_Geo.xml When we drill down to the district level, a new more detailed pattern emerges.

27 File: dspoverty.shp (Poverty at divisional level)
Attribute field: NEWPOVGRP Layer: Poverty at divisional level.lyr Metadata: Dspoverty_Geo.xml At the divisional level, greater detail is exposed……Geographic targeting of research and development efforts are much more likely to reach the population that need them most.

28 File: pval_pctpo.shp (Statistically significant level)
Attribute field: PVAL_PCTPO Layer: Statistically significant poverty.lyr Administrative Boundaries: sldistricts.shp Metadata: Pval_pctpo_Geo.xml This map shows the spatial clustering of poverty using a measure of statistical signficance. The red areas in the south and the light green areas in the north central part of Sri Lanka have both high incidence of poverty, and high spatial clustering of poverty. However, only the spatial clustering of poverty is signficant in the southern rural areas.

29 Published studies Web page with reports, maps and information on poverty mapping ( Digital geographic information from case studies available through global spatial data clearinghouses Poverty mapping conference held in August 2004 in conjunction with 2004 ESRI User Conference Special issue of the journal Food Policy and edited book volume forthcoming in 2005

30 Agrobiodiversity and poverty
Wild relatives and land races conservation as environmental services There could be a move to more fruits and vegetables: diet diversification Conservation to prevent famines due to pests and diseases; diversity as insurance

31 Poverty and Deforestation Period
Higher index = higher living standard

32 Regression of stunted children and food consumption: where staple crop consumption dominates, stunting Z scores go down (more stunting)

33 More drought, more (insurance) crops
9 8 9 6 5

34 Global inventory of sub-national poverty maps

35 Total paises 204
Países con datos Total shapefiles Total Unidades adm

36 Headcount ratios for the two dollar a day poverty line
Absolute number of people living below the $1.25 a day poverty line Absolute number of people living below the two dollar a day poverty line. Headcount ratios for the $1.25 a day poverty line

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