Census-Based Welfare Estimates for Small Populations
Poverty and Disability in Uganda
HD weekHans Hoogeveen
Poverty profiles are limited
Poverty profiles are almost exclusively based on information available in LSMS-type surveys
Education, age, housing characteristics, family size, spatial
Information for small target populations is absent
Statistical invisibility of poverty amongst vulnerable groups
People with disabilities Child headed households Ethnic minorities
Poverty profiles are limited
Illustration: regional poverty in Uganda in 1992, according to IHS
Rural UrbanP(0) Std.e P(0) Std.e
Central 54.1 2.2 21.0 3.1East 60.6 2.3 39.8 4.0North 74.3 2.9 49.4 5.4West 54.4 2.5 32.8 3.5
Why not combine surveys with other data sets? E.g. combine with census data to get spatial detail
Disaggregating spatially
Uganda poverty map
Poverty estimates at LC3 level
Small standard errors
Disaggregating by disability
Some censuses also provide information on disability Uganda (1991, 2002); Tanzania (2000) Aruba (1991); Bahamas (1990, 2000); Bahrain (1991, 2001);
Bangladesh (2001); Belize (1991, 2000); Bermuda (1991); Botswana (1991)
Census manual defines disability as any condition which prevents a person from living a normal social and working live.
Head of household is considered disabled if this prevents him/her from being actively engaged in labor activities during the past week
Combining census and survey data
Elbers, Lanjouw & Lanjouw, econometrica 2003
Estimate with IHS :
Predict with census:
Calculate welfare stat:
chcTchch Xy ~~~~ln
chcTchch Xy ln
],~,|[~ dymWE dd
Data
1991 Population and Housing census Long form with info on disability Administered in urban areas only 22,165 households with disabled head (5% of total) 425,333 households with non-disabled head
1992 IHS Consumption aggregate Information on disability is absent 4 urban strata
Key statistics on welfare from censusUrban areas only Head
disabledHead not disabled
Age 37.6 34.7
Female headed 45% 32%
Household size 4.7 3.9
Years of education 6.2 7.6
Education deficit at age 12
1.1 0.9
Use wood as fuel 54% 35%
House w. mud walls 57% 47%
House w. mud floors 60% 48%
Self employed 63% 33%
Employee 21% 45%
Number of hh’s 22,165 425,333
Do census estimates replicate the survey?
IHS Census based
PovertyIncidenc
e
Std. Error
Poverty incidenc
e
Std. Error
Central 21.0 3.0 19.2 1.5
East 39.8 4.0 38.3 1.1
North 49.4 5.4 49.6 2.0
West 32.8 3.5 32.0 1.6
Census-based poverty for (non)-disabled households
Disabled head of hh
Non-disabled head of hh
PovertyIncidence
Std. Error
PovertyIncidence
Std. Error
Central 26.4 2.2 18.8 1.5
East 50.4 1.5 36.9 1.2
North 56.6 2.0 48.4 2.0
West 45.7 2.7 31.0 1.5
Census-based poverty for (non)-disabled households
Fraction disabled
Relative difference in
poverty incidence
Central 2.9% 40.4%
East 8.7% 36.5%
North 11.8% 16.8%
West 5.4% 47.4%
Is poverty under-estimated? Reconsider the model estimated in survey
Survey comprises no information on disability Strictly speaking not correct, we also include census means and
their interactions with household characteristics
Only correlates of disability are captured Education, age, household size, female headed, marital stat. Housing conditions, toilet, access to safe water Location means capturing employment etc.
’s are the same for disabled and non-disabled E.g. return to education could be different
chcTchch Xy ln
We estimate
The model we would like to estimate is:
If ’s would be negative, ch is negative for disabled hh’s predicted consumption is too high, poverty is under-
estimated
If ’s would be positive, ch is positive for disabled hh’s predicted consumption is too low, poverty is over-estimated
Is poverty under-estimated?
chcT
chTchch DXXy )(ln
chcTchch Xy ln
Conclusion Combining census and survey data gives new insights
Spatial poverty profile Poverty amongst small target populations
Poverty amongst households with disabled head is 38% higher
Method can be used for other vulnerable groups Child headed households Elderly Ethnic minorities People in hazardous occupation
Caveat: estimates are an lower or upper bound