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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

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This paper presents the first critical review of literature on poverty published in Russia between 1992 and 2006. Using a dataset of about 250 publications in Russian scientific journals, the authors assess whether the poverty research in Russia satisfies the general criteria of a scientific publication and if such studies could provide reliable guidance to the Russian government as it maps out its anti-poverty policies. The findings indicate that only a small proportion of papers on poverty published in Russia in 1992-2006 follow the universally-recognized principles of the scientific method. The utility of policy advice based on such research is questionable. The authors also suggest steps that could, in their view, improve the quality of poverty research in Russia.

Education --- Information Security and Privacy --- Literature --- Papers --- Poverty Monitoring and Analysis --- Poverty Reduction --- Research findings --- Researchers --- Science and Technology Development --- Science Education --- Scientific journals --- Scientific knowledge --- Scientific papers --- Scientific research --- Scientific Research and Science Parks --- Scientists --- Social science --- Tertiary Education

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Knowledge about development effectiveness is constrained by two factors. First, the project staff in governments and international agencies who decide how much to invest in research on specific interventions are often not well informed about the returns to rigorous evaluation and (even when they are) cannot be expected to take full account of the external benefits to others from new knowledge. This leads to under-investment in evaluative research. Second, while standard methods of impact evaluation are useful, they often leave many questions about development effectiveness unanswered. The paper proposes ten steps for making evaluations more relevant to the needs of practitioners. It is argued that more attention needs to be given to identifying policy-relevant questions (including the case for intervention); that a broader approach should be taken to the problems of internal validity; and that the problems of external validity (including scaling up) merit more attention.

Beneficiaries --- Counterfactual --- Economic Theory and Research --- Education --- Impact assessment --- Impact evaluation --- Infrastructure projects --- Intervention --- Learning --- Macroeconomics and Economic Growth --- Poverty Monitoring and Analysis --- Poverty outcomes --- Poverty Reduction --- Programs --- Science and Technology Development --- Science Education --- Scientific Research and Science Parks --- Targeting --- Tertiary Education