Poverty mapping involves using household survey data and census data to estimate welfare levels and poverty rates at small geographic areas. It relates household characteristics from the survey to welfare levels, then uses those relationships to predict welfare for all households in the census based on their characteristics. This allows for spatially disaggregated poverty estimates. The document describes applying this method to Malawi using a 1997 household survey and 1998 census, generating poverty estimates for districts and wards. It outlines the key steps of variable harmonization, model development, and applying the model to census data.

1. Poverty Mapping
An overview of methods,
based on a Malawi analysis
Todd Benson
International Food Policy Research Institute
June 2009 [t.benson@cgiar.org]

2. Poverty Mapping
 Small-area estimation poverty mapping involves:
1. Discovering relationships between household
characteristics and the welfare level of households.
 Through analysis of a detailed household survey.
2. Applying model of these relationships to data on the
same household characteristics contained in a
national census.
 Predict the welfare level of all households in census.
 Resulting estimates of aggregate welfare and poverty are
spatially disaggregated to a much higher degree than is
possible using survey.
2

3. Poverty headcount estimates from
survey analysis & poverty mapping
Individual poverty
headcount
by district
National poverty
headcount: 65.3%
Mzuzu
Poverty
analysis of
HH survey,
under 55% district esti-
55 to 75%
above 75%
mates (29)
Poverty
Lilongwe
mapping,
City
local gov’t
Zomba
Munic.
ward esti-
mates (848)
Blantyre
City
3

4. Malawi – Poverty monitoring
 Will use Malawi examples here.
 IFPRI provided technical support to Malawi Poverty
Monitoring System, 1999-2002. Key technical
products were:
 Poverty analysis of 1997/98 Malawi Integrated Household
Survey (IHS).
 Determinants of poverty analysis.
 Poverty map – based on IHS and 1998 Population and
Housing Census – focus here
 Detailed national spatial data sets developed.
 Malawi – An atlas of social statistics
4

5. Poverty mapping –
simple overview of analysis (1)
 Daily per capita household consumption &
expenditure for survey households is modeled on set
of household and local (cluster) characteristics.
 Per capita household consumption & expenditure is
our household welfare indicator and is used with a
basic-needs poverty line to determine whether
household is poor or non-poor.
 Developed through poverty analysis of household survey.
 Independent household variables chosen for model
are only those that also appear in national census.
 Also add cluster (spatial) characteristics – these need to be
available for entire country.
5

6. Poverty mapping –
simple overview of analysis (2)
 Having developed model(s) of household welfare
based on household characteristics from survey, use
those same characteristics from all households in
census to estimate welfare of all households in
population using the model(s).
 Analysis draws on:
 Rich data on welfare and household characteristics, but
coarse representativity (region or district-level, at best) of
household survey.
 Fine, comprehensive coverage, but limited HH character-
istics and no welfare information in national census.
 Survey and census should have been implemented
at same time, or within a few years of each other. 6

7. Small-area estimation
poverty mapping method
 Elbers, C.; J. Lanjouw; & P. Lanjouw. 2003. Micro-level estimation
of poverty & inequality. Econometrica. 71: 355-364.
 Greater detail found in Elbers, Lanjouw, & Lanjouw. 2002.
Micro-Level Estimation of Welfare. Research Working
Paper 2911. World Bank, Development Research Group.
 Since 2001, method has been applied in several dozen
countries around the globe.
 Malawi IHS-derived models applied to census data used the
Poverty and Inequality Mapper module (SAS-based program).
 This program since updated with PovMap 2.0. See
http://iresearch.worldbank.org/PovMap/PovMap2/PovMap2Main.asp.
 Other software tools needed are a statistical program (Stata was
used for Malawi work) and, ideally, GIS.
7

8. Malawi poverty map - data
 1997-98 Malawi Integrated Household Survey (IHS-1)
 Representative at level of district & urban center.
 6,586 survey households.
 IHS used for national poverty analysis and
production of a poverty profile of Malawi in 2000.
 Detailed information on consumption & expenditure,
as well as range of other HH characteristics.
 1998 Population & Housing Census.
 2.25 million households
 Limited range of variables.
 Basic demography, education, employment, housing.
8

9. Poverty mapping –
Summary steps in analysis
1. Data preparation.
 The same variables from the two data sets –
census and IHS – are subject to means
comparison tests.
 To make sure that they really are the same variable –
similar definition and measurement.
 Sometimes referred to as the ‘Stage 0’ step.
 Independent variables for the model are those
variables which are found in both the household
survey and the census.
 Possible that survey may not contain all census variables.
 For example, in case of Malawi, survey did not contain the
housing quality variables in the census. 9

10. Summary steps (cont.)
2. Initial models developed from IHS household survey data using
a backward stepwise regression procedure.
 Dependent variable: natural log (ln) of household welfare indicator
 household welfare indicator: Daily per capita consumption and
expenditure of IHS survey household expressed in spatially-
deflated April 1998 Malawi Kwacha.
 ln of welfare indicator has more normal distribution than that of real
indicator, so preferred for model development.
 Aim is to develop models with greatest predictive power.
• While do want model coefficients to make theoretical sense, this is
not fundamental to analysis.
• Hence, stepwise regression used.
 Candidate HH independent variables on next slide.
 Also did a close examination of potentially problematic cases in the
household survey data.
 Used a range of regression diagnostic statistics for this.
 Dropped 63 out of 6,586 cases in IHS dataset as a result.
10

11. Candidate household
variables
AGEHHH age of head of household HOUSRNT hh rents house in which it lives (0/1)
AGESQ squared age of hh head KIDBORN tot. kids ever born to fertile women in hh
BIKE hh owns a bicycle (0/1) KIDDEAD prop. kids born in hh now dead
BRTHRTE yrs. between births for women in hh M0_05 males aged 0 to 5
COOKEL hh cooks over electricity or gas (0/1) M15_29 males aged 15 to 29
COOKWD hh cooks over firewood (0/1) M30_49 males aged 30 to 49
EMPLYEE head of household employee (0/1) M50UP males aged 50 and up
EMPLYR head of household employer (0/1) M6_14 males aged 6 to 14
F0_05 females aged 0 to 5 MMAXCL maximum class attained by males
F15_29 females aged 15 to 29 NETENRL primary age children in primary school
F30_49 females aged 30 to 49 NOMARRY household head not married (0/1)
F50UP females aged 50 and up NONFAM members who are not of nuclear family
F6_14 females aged 6 to 14 OTHROCC hh mem. w/ other occupation
FAMBU hh has a family business (0/1) PRFNLIT hh gets lighting from paraffin (0/1)
FEMALE females in household PRIMAGE primary age child in hh (0/1)
FEMHHH female headed household (0/1) PROF hh mem. w/ prof, admin, clerical occup.
FINPRIM total members finished primary SALES hh members with sales occupation
FMAXCL maximum class attained by females SECIND hh members in secondary industry
FRTWMN woman in hh in fertile years (15-45) SERVICE hh members with services occupation
HHHED educational level of head of hh in years TAPH2O hh gets water from tap (0/1)
HHSIZE household size TERIND hh members in tertiary industry
HHSIZSQ squared household size
11

12. Summary steps (cont.)
3. Enumeration area (cluster) variables added to
each model to improve predictive power and to
deal with some econometric problems.
 Candidate enumeration area variables.
 Census variable means for EA.
 Some distance and agricultural production variables
developed using Geographic Information System (GIS).
 Stepwise regression is again used to select for
inclusion in model.
12

13. GIS distance variable
generation
 Straight-line
distance to
market center
 left – market
centers
 center –
distance to
market
centers
 right – EA
centerpoints
on distance
layer (data
extraction)
13

14. Candidate enumeration area
variables
AVGMXED Average maximum education level MARKET Straight-line distance (km) to
in households in EA nearest market centre
BOMA Straight-line distance (km) to MN_YLD Mean maize yield in EA over 20
nearest boma (district HQ) years
DIFF Difference maize yield in EA in '97 POPDENS Population density (persons/sq.
& '98 from long term mean km.)
EAIMPTL Proportion of households in EA ROAD Straight-line distance (km) to
with improved toilets nearest primary or secondary road
EANTENR Net enrollment rate in EA ROOMPC Avg. rooms per person in EA
EAPRMHS Proportion of hhs in EA with URBAN Straight-line distance (km) to
houses of permanent materials nearest urban centre
HELTHFA Straight-line distance (km) to
nearest health facility
14

15. Summary steps (cont.)
 23 separate models developed.
 One for each of 22 IHS analytical strata, plus stratum of
enumeration areas which, although rural, are urban in
character.
 Relatively common set of independent
variables seen across models
 Rural models: age-sex group variables, HH head education,
highest ed. attainment by female, bicycle ownership, female
headship, and more.
 Urban models: fewer age-sex group variables, HH head
education, cook on wood, EA variables on sanitation,
education, rooms per capita.
15

16. Summary steps (cont.)
4. Assessment made of resultant models made up of
both household and EA variables.
 Final models developed at this step.
5. The error in each base household model is then
modeled in order to control for heteroskedasticity.
6. IHS-derived models then applied to the census data.
 Using special program for poverty mapping.
 Bootstrap method used to generate:
 Point and variance estimates of welfare indicator for a range of
spatial groupings of households.
 Poverty measures, with standard errors.
 Inequality measures, with standard errors. 16

17. Performance of the poverty
mapping models
 23 models - Developing so many models is definitely NOT
‘best practice’.
 Adjusted-R2 for base models range from 0.248 to
0.594, with mean of 0.380.
 Urban models have higher R2s.
 Assessment:
 The 11 models in Mozambique poverty maps had R2s
ranging from 0.26 to 0.55.
 Other countries with sharp economic divides that are defined
on racial lines (or other household and individual
characteristics) tend to have higher R2s.
• South Africa (around 0.70), Ecuador (0.50).
17

18. Poverty map validation –
national & regional
 Poverty headcount results compared between IHS
poverty analysis & poverty mapping analysis.
IHS PovMap
National 65.3% 64.3%
Regional
Southern 68.1% 65.4%
Central 62.8% 63.9%
Northern 62.5% 61.1%
 Differences at national and regional levels are not
statistically significant.
18

19. Poverty map validation – district
19

20. Poverty map validation – district
(cont.)
 District level
 In comparing the IHS poverty analysis to the
poverty mapping results, the headcounts for seven
out of the 31 districts are significantly different.
 Blantyre rural & Lilongwe rural are the most important of
these.
 In the other five districts, lack of congruence may be due to
problems with the IHS data or the IHS sampling scheme.
 In spite of the problems in these seven districts,
results are reasonable, overall.
 Following slides show results mapped at the more
local scale of the sub-district Traditional Authority.
20

21. Depth & severity of poverty –
Traditional Authority
21

22. Local government ward
 Poverty mapping method used can provide
estimates of poverty for populations of 500 to 1000
households and above.
 Census enumeration areas (EA) are too small,
being 250 households on average.
 Only intermediate spatial unit between TA and EA is
the local government ward.
 Following map shows poverty headcount for the
almost 850 local government wards.
 Such small-area maps potentially of value for sub-
district planning.
22

23. Poverty
headcount –
Local
government
ward
23

24. Error in poverty maps
 Poverty estimates for
populations in each
aggregated EA.
 Poverty measures are
estimated with standard
errors.
 Mean s.e. for poverty
head-count for rural
aggregated EAs of 8.2
percent.
 In examining results of
analysis, the consider-
able imprecision in
underlying estimates
should be kept in
mind.
24

25. Additional analyses based on
poverty map
 Assessment of how well
programs target the poor
 Determine poverty
characteristics of
population in program
areas and compare to
wider poverty prevalence.
 Ex-post analysis of
poverty targeting.
 Malawi Social Action
Fund public works
locations & WFP Food
for Assets & Develop-
ment project sites.
 See IFPRI’s FCND
discussion paper
no. 205
25

26. Geographic scale and poverty
mapping – even more local
 Constructed a ‘new’ geography for Malawi based
on amalgamations of two or three EAs.
 Subdivided the country into about 3,400 spatial units
with populations above 500 households in order to
calculate local welfare and poverty measures for
population in each.
 Primarily for analytical purposes, rather than for
planning.
 Used in the investigation of the spatial determinants
of poverty prevalence in rural Malawi.
26

27. Creating the Malawi poverty map
– Institutional considerations
 Poverty map creation was an activity of the Poverty
Monitoring System of the government of Malawi.
 Three principal institutions:
 National Statistical Office (NSO)
 National Economic Council (NEC - now Ministry of Economic
Planning & Development)
 Centre for Social Research (CSR) of the University of Malawi
 IFPRI provided technical assistance on poverty
analysis to all three.
 Poverty map was logical extension of the poverty
analysis work that NSO and NEC jointly led.
 However, NEC did not engage in actual analysis.
27

28. Creating the Malawi poverty map
– Institutional considerations (cont.)
 Poverty mapping work done by two statisticians from NSO, led by
IFPRI researcher.
 In retrospect, this was not ideal.
 Poverty mapping, to do confidently, requires relatively sophisticated
econometric understanding & abilities.
 Such abilities not best placed within a national statistical office.
 Their principal activities do not include such high-level analyses.
 Rather, should have involved university-based Malawian
econometrician(s), as well.
 Technical abilities required, retention & refinement of poverty mapping
skills, and analytical leadership for future poverty mapping efforts
nationally, best served by universities.
 So, Malawi case was a missed opportunity for sustainable capacity
strengthening in technical analysis for poverty mapping.
28

29. Creating the Malawi poverty map
– Data considerations
 Had contemporaneous IHS and census data sets.
 However, this temporal correspondence not
absolutely critical.
 Key concern is that nature of correlation between
household welfare and independent variables used in
poverty mapping model for each stratum will not have
changed significantly between the two time periods.
 Judgment call that takes into account the dynamism and
character of economic transformation across country.
29

30. Creating the Malawi poverty map
– Data considerations (cont.)
 Had two separate GIS shapefiles of enumeration areas. For both:
 IHS, sample design of which was based on EAs for 1987 Malawi census, and
 1998 Census.
 Facilitated generation of GIS-based cluster-level variables for inclusion in
stratum models and then for use in applying models to census data.
 Also permitted extensive spatial analysis of poverty mapping results.
 However, having EA digital maps not absolutely critical.
 Can use smaller-scale, higher geographical units, such as districts, for
generating cluster variables.
 GIS-derived, census-aggregates, or developed from other geographically-
comprehensive ancillary data.
 Loss of information since any intra-district variation in the data is lost.
 Econometrically also not ideal, as EA-level cluster variables in models assist in
dealing issues arising due to expected correlation of characteristics of survey
households in same EA.
 Results in some loss in explanatory power and performance of models, but an
acceptable solution.
30