1. Census-Based Welfare Estimates for Small Populations Poverty and Disability in Uganda HD week Hans Hoogeveen

2. 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

3. Poverty profiles are limited Illustration: regional poverty in Uganda in 1992, according to IHS RuralUrban P(0)Std.eP(0)Std.e Central 54.12.2 21.03.1 East60.62.339.84.0 North74.32.949.45.4 West54.42.532.83.5 Why not combine surveys with other data sets? E.g. combine with census data to get spatial detail

4. Disaggregating spatially Uganda poverty map Poverty estimates at LC3 level Small standard errors

5. 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

6. Combining census and survey data Elbers, Lanjouw & Lanjouw, econometrica 2003 Estimate with IHS : Predict with census: Calculate welfare stat:

7. 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

8. Key statistics on welfare from census Urban areas onlyHead disabledHead not disabled Age37.634.7 Female headed45%32% Household size4.73.9 Years of education6.27.6 Education deficit at age 121.10.9 Use wood as fuel54%35% House w. mud walls57%47% House w. mud floors60%48% Self employed63%33% Employee21%45% Number of hh ’ s22,165425,333

9. Do census estimates replicate the survey? IHSCensus based Poverty Incidence Std. ErrorPoverty incidence Std. Error Central21.03.019.21.5 East39.84.038.31.1 North49.45.449.62.0 West32.83.532.01.6

10. Census-based poverty for (non)-disabled households Disabled head of hhNon-disabled head of hh Poverty Incidence Std. ErrorPoverty Incidence Std. Error Central26.42.218.81.5 East50.41.536.91.2 North56.62.048.42.0 West45.72.731.01.5

11. Census-based poverty for (non)-disabled households Fraction disabledRelative difference in poverty incidence Central2.9%40.4% East8.7%36.5% North11.8%16.8% West5.4%47.4%

12. 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

13. 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?

14. 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