Poverty Mapping: An overview of methods, based on a Malawi
Upcoming SlideShare
Loading in...5
×
 

Poverty Mapping: An overview of methods, based on a Malawi

on

  • 2,538 views

Central Statistics Agency (CSA), Addis Ababa, June 19, 2009

Central Statistics Agency (CSA), Addis Ababa, June 19, 2009

Statistics

Views

Total Views
2,538
Views on SlideShare
2,534
Embed Views
4

Actions

Likes
0
Downloads
54
Comments
0

1 Embed 4

http://www.slideshare.net 4

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

Poverty Mapping: An overview of methods, based on a Malawi Poverty Mapping: An overview of methods, based on a Malawi Presentation Transcript

  • Poverty Mapping An overview of methods, based on a Malawi analysis Todd Benson International Food Policy Research Institute June 2009 [t.benson@cgiar.org]
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • Poverty map validation – district 19
  • 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
  • Depth & severity of poverty – Traditional Authority 21
  • 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
  • Poverty headcount – Local government ward 23
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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