Poverty Impact Analysis: Approaches and Methods - Chapter 5

Identifying Poverty Predictors Using Household Living Standards Surveys in Viet Nam - Giới thiệu mô hình dự đoán đói nghèo (PPM) dựa trên một phân tích hồi quy loại thu nhập hộ gia đình, chi và các biến khác (dự đoán) từ các cuộc điều tra hộ gia đình tiêu chuẩn sống, đã nhận được sự chú ý nhiều hơn từ các nhà nghiên cứu và các học viên. Quan tâm này xuất phát từ thực tế là PPM cung cấp một cách dễ dàng và chi phí thấp để thu thập cơ bản và theo dõi các biện. | CHAPTER 5 Identifying Poverty Predictors Using Household Living Standards Surveys in Viet Nam Linh Nguyen Introduction Poverty predictor modeling PPM based on a regression-type analysis of household income and expenditure and other variables predictors from household surveys of living standards has been receiving more attention from researchers and practitioners. This interest comes from the fact that PPM provides an easy and low-cost way to collect baseline and follow-up poverty measures for monitoring progress and evaluating the poverty impact of development projects and policies. But while PPM is popular the reliability of this methodology has yet to be checked. In Viet Nam there have been a number of efforts to develop and use poverty predictor models for poverty mapping Minot 1998 Minot and Baulch 2002 and 2003 MOLISA 2005 . These studies were mostly intended for use in poverty targeting and budget transfers. There has been no effort however to apply the approach to ex-ante poverty estimates of participatory assessments of various policies. Moreover there has been no attempt to use data sets of the subsequent comparable household surveys to assess how good the predictors really are. The approach presented in this study is an attempt to develop a practical alternative to the time-consuming and expensive collection of income and expenditure data for assessing poverty at local levels. In Phase 1 of the study data from 2002 living standards surveys of Viet Nam s General Statistical Office were used to examine the relationship between poverty and a household s characteristics using a multiple regression modeling technique. This technique detects variables or predictors that have correlated effects on a household s living standards and consequently its poverty status. In Phase 2 significant predictors were tested using a 1997 98 living standards survey to check the consistency and stability of the models across time. In Phase 3 another regression modeling procedure