tailieunhanh - Poverty Impact Analysis

To be able to use the functionality of PRISM in full, users have to register in the system by entering their user identifi cation and password (which are not case sensitive) and clicking the REGISTER NOW menu. The registration is needed to enable the users to receive a confi rmation e-mail message when their simulations are done so that they can view the results. | PART ONE Application of Tools to Identify the Poor CHAPTER 1 Predicting Household Poverty Status in Indonesia Sudarno Sumarto Daniel Suryadarma and Asep Suryahadi Introduction Indonesia is the fourth most populous country in the world and it has a large poor population. Official poverty estimates indicate that in 2004 the poor numbered about 36 million or 17 percent of the total population with about two-thirds of the poor living in rural areas. The most widely used data for measuring poverty is household total consumption expenditure expressed in monetary terms. The use of expenditure data is particularly common in developing countries where expenditure data is less difficult to collect and more accurate than household income data. Collecting household consumption expenditure data however requires plenty of time and effort. Respondents must be willing and patient enough to document their own expenditure over a period of time. For instance in Indonesia the recording of food expenditure is done over one week and the enumerators have to ensure that the respondents are correctly noting down their actual expenditure. In addition some questions on nonfood items require respondents to remember expenditure incurred as far back as one year. In this case reliability and accuracy of data become an important issue to settle. Amid such empirical problems a number of studies in developing countries have been focusing on proxy variables that measure expenditure and poverty. A proxy is calculated using several widely recognized methodologies employing household characteristics data that are auxiliary to poverty and are easier to collect. Examples of proxy variables are asset ownership and education level which can be used to rank households similar to the rank based on per capita consumption expenditure. One of the more widely cited studies is that of Filmer and Pritchett 1998a which used long-term household wealth to predict school enrolment in India. The authors employed .