Choose an application

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

The small-area estimation technique developed for producing poverty maps has been applied in a large number of developing countries. Opportunities to formally test the validity of this approach remain rare due to lack of appropriately detailed data. This paper compares a set of predicted welfare estimates based on this methodology against their true values, in a setting where these true values are known. A recent study draws on Monte Carlo evidence to warn that the small-area estimation methodology could significantly over-state the precision of local-level estimates of poverty, if underlying assumptions of spatial homogeneity do not hold. Despite these concerns, the findings in this paper for the state of Minas Gerais, Brazil, indicate that the small-area estimation approach is able to produce estimates of welfare that line up quite closely to their true values. Although the setting considered here would seem, a priori, unlikely to meet the homogeneity conditions that have been argued to be essential for the method, confidence intervals for the poverty estimates also appear to be appropriate. However, this latter conclusion holds only after carefully controlling for community-level factors that are correlated with household level welfare.

Confidence intervals --- Descriptive statistics --- Education --- Enumeration --- Geographical Information Systems --- Precision --- Predictions --- Reliability --- Sample design --- Sample surveys --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Small Area Estimation Poverty Mapping --- Standard errors --- Statistical and Mathematical Sciences --- Validity

Choose an application

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

The collection of survey data from war zones or other unstable security situations is vulnerable to error because conflict often limits the implementation options. Although there are elevated risks throughout the process, this paper focuses specifically on challenges to frame construction and sample selection. The paper uses simulations based on data from the Mogadishu High Frequency Survey Pilot to examine the implications of the choice of second-stage selection methodology on bias and variance. Among the other findings, the simulations show the bias introduced by a random walk design leads to the underestimation of the poverty headcount by more than 10 percent. The paper also discusses the experience of the authors in the time required and technical complexity of the associated back-office preparation work and weight calculations for each method. Finally, as the simulations assume perfect implementation of the design, the paper also discusses practicality, including the ease of implementation and options for remote verification, and outlines areas for future research and pilot testing.

Administrative Records --- Age --- Algorithms --- Back Office --- Best Practice --- Business --- Calculation --- Case --- Cell Phones --- Classification --- Clustering --- Computer --- Confidence Intervals --- Counting --- Data --- Data Collection --- Description --- Document --- Effects --- Enumeration --- Equipment --- Errors --- Estimates --- Estimating --- Gps --- Human Error --- Image --- Implementation Plans --- Implementations --- Information --- Interviews --- Measurement --- Measures --- Methodology --- Methods --- Missing Values --- Modeling --- Monitoring --- Navigation --- Network --- Object --- Open Access --- Performance --- Phones --- Pilot Testing --- Precision --- Prediction --- Probability --- Probability Samples --- Protocol --- Random Sampling --- Random Walk --- Research --- Research Working Papers --- Researchers --- Result --- Risk --- Routing --- Sample Design --- Sample Size --- Samples --- Sampling --- Sampling Designs --- Satellite --- Scenarios --- Search --- Security --- Simulation --- Size --- Smart Phones --- Software --- Space --- Standard --- Standard Deviation --- Statistics --- Supervision --- Survey Data --- Survey Methodology --- Surveys --- Target --- Technical Training --- Techniques --- Technology --- Testing --- Theory --- Time --- URL --- Uses --- Variables --- Verification --- Web --- Weight --- Weighting --- WWW