Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Small area estimates of poverty and inequality statistics, through survey-to-census imputation that lets consumption be estimated for each and every household in a census, are useful for at least three reasons. First, they can help improve the effectiveness of public spending, by targeting to prevent the leakage of benefits to the non-poor (and prevent the under-coverage of the poor). If poor people are concentrated in certain areas, spatial targeting by directing extra development projects and public services to those areas, may be more feasible than trying to individually target the poor. Geographic targeting is highly relevant in countries like Timor Leste, where mountainous topography contributes to high levels of heterogeneity. In similar environments, such as Papua New Guinea, the enclave nature of some modern economic development has created high levels of spatial inequality. The basic details are that household survey data are used to estimate a model of consumption, with explanatory variables restricted to those that have overlapping distributions from a census. The coefficients from this model are then combined with the variables from the census, and consumption is predicted for each household in the census. With these predictions available for all households, inequality and poverty statistics can be estimated for small geographic areas (Elbers et al, 2003).2 In the results below, the poverty statistics that are calculated by using the predicted consumption data for each census household are reported at the suco level (n=442). For the headcount poverty rate, the standard errors at the suco level (relative to the poverty index) average one-quarter and so this is a comparable degree of precision to what the survey offered at the municipality level (n=13) for a variable like the poverty severity index.

Demographic and Health Survey --- Poverty Line

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Health Survey --- Health Surveys --- Health surveys --- Infants --- Mothers --- Pregnancy Outcome --- Prenatal Care --- statistics. --- Statistical methods. --- Health and hygiene --- Statistics. --- National Maternal and Infant Health Survey (U.S.).

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Hospitals --- Health surveys --- Admission and discharge --- Data processing. --- National Health Survey (U.S.) --- United States.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This report is structured as follows: an in-depth explanation of the FHSAE method is presented in section two. Section three reviews the sub-district level data used in this study, which includes imprecise TL-SLS and DHS direct estimates, as well as satellite imagery data used in this study. The variable selection method used for the FHSAE model in this model is explained in section four. Section five provides the results of the FHSAE exercise on poverty estimates, average real per capita consumption and welfare index, presenting them in the graphical maps. Section six concludes.

Demographic and Health Survey --- Living Standards --- Poverty Assessment --- Poverty Lines --- Poverty Monitoring and Analysis --- Poverty Reduction

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Pregnancy --- Childbirth --- Birth certificates --- Evaluation. --- National Maternal and Infant Health Survey (U.S.) --- United States.