Presented at the Basin Focal Project Poverty Mapping Workshop, November 2007, Chiang Mai, Thailand

1. Analyzing Water Poverty, 2nd
Workshop
Chiang Mai Thailand; October 31-
Mai,
2 November 2007
Jorge Rubiano
Associated Professor, Colombian National University
A d f l b l
Environmental Engineering Faculty, Palmira
Jerubianome@unal.edu.co

2. ECUADOR-DATA
Farrow, A., Larrea, C., Hyman, G. G., and Lema, G. (2005).
Exploring the spatial variation of food poverty in Ecuador.
Food Policy 30 510

3. What is the question
• Question (Targeting interventions)
• Knowledge acquisition (panel of food security experts in
Ecuador)
• Data Acquisition/processing (1998 Living Standards
q p g( g
Measurement Study (LSMS) survey (INEC and World Bank, 1998)
and the 2001 Ecuadorian national population census (INEC, 2001)).
• A l i (Geographically weighted regression (GWR))
Analysis
• New knowledge/questions

5. Resource
R Access
A Use
U Capacity
C it Environment
E i t Other
Oth
-Mean No -Mean -% of area -%of -Mean -Food
of Access to with crops farmers with elevation Poverty
consecutive local Salary -Mean Slope Severity
dry months markets -%of
%of -Mean Food
Mean
-%of (minutes) farmers Consumptio
irrigated -Mean Time economicall n
units to y active
Provincial
ov c a -%of
%o
Capital indigenous
population
GINI
Table 1 Variables used in the Ecuadorian study case organised
accordingly to the WPI components.

6. What is the current situation

7. What is the current situation

8. Some Conclusions
• Poor accessibility to markets and services
and environmental constraints to agriculture
have negative impacts on wealth and food
security outcomes.
• Different problems in different locations
• Land tenure , off-farm income ,
productivity, remittances among other
variables.

9. Variables
• FID Internal code for identification
• PAR_CODIGO Parroquia code (Administrative code)
• PARROQUIA Parroquia Name
• INDNBI Basic Insatisfied Needs Index
• AVG_ACC_20 Mean Access to local markets (minutes)
• AVG_FGT2HP % County food poverty severity using the higher food poverty line
• AVG_MN_DRY Mean No of consecutive dry months
• AVG_MNAPHR Mean Time to Provincial Capital
• AVG_MN_ELE Mean elevation
• AVG_MN_SLP Mean Slope
• AVG_PR_RIE Proportion of p
p productive units with irrigation p county
g per y
• AVG_GINI GINI coefficient of land ownership per county
• AVG_PORASA % of farmers with Salary
• AVG_PORAGR % of area with crops
• AVG_PORIND
AVG PORIND % of indigenous population
• AVG_COASTA Dummy variable for counties that have a coastline (counties that
benefit from fishing and tourism)

10. Figure 1 Bayesian Network of poverty related variables in the Ecuadorian case study.

11. Figure 2 Bayesian Network of the Ecuadorian case study after setting up evidence on
the state 3 of Food Poverty Severity (encircled).

12. Figure 3 Bayesian Network of the Ecuadorian case study after setting up evidence on
the driest parroquias and in those with less irrigated number of units (encircled).

13. VOLTA-DATA
ANALYSIS OF WATER RELATED POVERTY IN THE
VOLTA BASIN OF GHANA
By
Felix A k
F li Ankomah A t
h Asante
Institute of Statistical, Social and Economic Research (ISSER)
University of Ghana
P. O. Box LG 74
Legon, Accra Ghana.

14. What is the question
• Question (Not explicitly defined, Poverty is a fact)
• Knowledge acquisition
• Data Acquisition/processing
• Analysis
A l i
• New knowledge/questions

15. SOURCES
• Core Welfare Indicators Questionnaire (CWIQ)
(2003) Survey Report.
• Ghana Census Based Poverty Map, District and
h d i i d
Sub District Levels. 2005
• GLSS 4 Ghana Living Standards Survey, 4th
Survey
round(1998/99).
• GSS Housing and population census, 2000 GSS
g p p ,
ISSER
• INSD La pauvreté au Burkina Faso (INSD 2003)

16. Study Area

17. 45
162
Adm.Bnds.

18. BF
1.Quintile: Poverty Distr.
2.Poverty: Poor-NonPoor 45
3.Water-source: (Main source of water)
5. Access-time to water in minutes:
7. Food-Security:
9. Landless (Cropped area in Ha):
11. Population Distribution in %:
13. tetes gros bétail possédées (Cattle)
15. catégorie petit betail (Minor Cattle)
162
BF-var.

19. CHILDNUT_U % underweight children
EDUC_ADULT % Adult Literacy
EDUC_YOUTH % Youth Literacy
UNEMPLOYED % Unemployed
UNDEREMPLO % Underemployed
LANDLESS % Landless
LL1_2HA % with less than 2 Ha
LL2_3HA % with less than 3 Ha
LL3_4HA % with less than 4 Ha
LL4_5HA % with less than 5 Ha
LL5_8HA % with less than 8 Ha
LL_8_HA % with more than 8 Ha
FOODNEEDS Foodneeds
CER_AMOY92 Cereal Area
CER_PMOY92 Cereal Production
POP Population
162
MAISAMOY92 Maize Area
MAISPMOY92 Maize Production
MAISYMOY92 Maize Yi ld
M i Yield
MAISWP9201 Water Productivity of Maize
GH-vav. NBDRY_MONT
months
Number of consecutive dry

20. 3O1
BF-
BF
Adm.Bnds

21. 149
V0LTA SET

22. 141
V0LTA SET-P0V.

25. OTHER VARS.
• Soils texture, drainage, depth, fertility
constraints.
• Roads
• Climate data

26. Figure 4 Relationship between the lowest poverty headcount level and three water related variables.

27. Figure 5 Volta bayesian network with an expert defined water-poverty node.

28. Figure 6 Volta Bayesian network with the new water-poverty node and the relationship with the
lowest water productivity of maize.

29. Figure 7 Water-Poverty when setting evidence in the lowest maize water productivity level
compared with standard measure of child nutrition (% underweight).