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