چکیده:
In this paper I develop a Proxy Means Tests (PMT) model and examine several targeting lines based on 2008 household survey data to identify beneficiaries for a targeting subsidy scheme in Iran. Based on the findings of this study, setting a cut-off percentile of 40% is expected to provide compensation for almost 70 percent of the poorest households. This will result in the highest accuracy mainly in rural areas where poverty is much more severe than elsewhere in the country. Substituting the current scheme which covers almost all households in Iran with a targeting scheme based on the results of the PMT model will allow for either transferring larger amount of money to the extreme poor at the current budget, or reducing the government expenditure in the form of repayment after removing subsidies on fuel and energy.
خلاصه ماشینی:
"com Abstract In this paper I develop a Proxy Means Tests (PMT) model andexamine several targeting lines based on 2008 household survey data to identify beneficiaries for a targeting subsidy scheme in Iran.
Substituting the current scheme which covers almost all households in Iran with a targeting scheme based on the results of the PMT model will allow for either transferring larger amount of money to the extreme poor at the current budget, or reducing the government expenditure in the form of repayment after removing subsidies on fuel and energy.
The PMT are amongst the poverty targeting methods that also include verified means tests, simple means tests and community-based targeting (see Houssou 2010 for a detailed review; Zuhr 2009 for a summary of PMT; Dutrey 2007 and AusAID 2011 for strengths and weaknesses of PMT and the robustness of their implementation; Grosh 1994 for an assessment of the mechanisms of eligibility for social welfare assistance; and Coady and Skoufias 2004 for a comparison of the targeting indicators.
This paper aims to present a household targeting system for Iran to identify the extreme poor and to determine eligibility for repayments based on a PMT model which is applied to 2008 household survey data.
actual poverty and the accuracy measures of PMT model at 40% cut-off line in rural and urban Iran Predicted poverty status Non-poor Poor Total Rural areas Urban areas The results confirm the findings of Sharif (2009) who showed that the under-coverage rate in urban areas is considerably higher than that in rural areas, whereas, the gap between rural and urban leakage rates is much smaller."