Targeting the protecting programs such as subsidies requires identifying the poor in the society. In order to recognize beneficiary households for cash payment after subsidy reform in Iran, PROXY MEANS TEST was applied in this study to households' survey data of 2009. Based on the findings, it is possible to identify the poor not based on their claimed incomes but according to their total scores calculated by their traits, asset ownership and places of living. Those who own car, motorcycle, PC, have their own orchard and invested on fixed assets and/or live in houses larger than 100 m2, for instance, are found not to be beneficiaries. Moreover, the government can protect reasonable people by choosing targeting line of 40% in the society.

Subsidies have remarkably grown from 4888 billion Rials in 1374 to 23035 billion Rials in 1384. Yet, the poor have not gained much. For example, in energy subsidies, 20 percent of the richest people have gained about 15 times more than the 20 percent of the poorest people. This is because the payments of subsidies are unified and not targeted. Increasing subsidy, budget deficit and low incidence of the poor are the main reason why Iranian government made a decision for targeting subsidies. In fact, targeting subsidies is necessary to distinguish poor families from non-poor families. The survey of targeting methods in 30 Latin American countries indicates PROXY MEANS TEST in developing countries are the best method for targeting subsidies. Therefore, this study employs this method to identify the poor and the non poor. The results show this method could distinguish 79 percent of the poor families, and 21 percent of poor families are identified as non poor families. Moreover, this method identifies 83 percent of non poor families and 17 percent of non poor families are identified as poor families.

This article points to the identification, an important stage in subsidy targeting. Identification of vulnerable groups or households is important from efficiency aspects of governmental provisions and transfer payments. We define targeting and clarify various procedures of targeting systems with emphasis on identification position in them. Then main methods of individual identification especially "MEANS TEST" and "PROXY MEANS TEST" reviewed and we will declare some imperfections of these two methods. To get rid of the weaknesses, we will introduce Two-Stage Identification (TSI) TEST with Endogenous Household Clustering (EHC) method and will try an experiment with Iranian raw household data in urban areas. The results of using TSI method show five different household groups with derivation of varied characteristics of household such as Head (Gender, Marriage, Employment), Members (Age Composition, Number of Employee, Size, Education), Home (Occupation, Number of Room, Width, Framework) and Expenditures (Total, Food and Durables). Also with this verification, policy makers acquire a rich database for employing one or various targeting systems.

In this paper, the dispersion of vulnerable households is determined in different geographical areas. For this purpose, we used the PROXY MEANS TEST strategy, based on an assessment of individual households in the population. Using the Household Expenditure and Income Survey data, first, a regression model is designed with independent variables, which correlated with households’ income. Then the estimation of expected income is obtained for each household in the sample. This model is used for estimating the expected income for all settled households hand groups of households whose information was gathered in 2011 Population and Housing Census database. The results that are used for classifying all rural and urban areas across the country show that the Sistan and Baluchestan and Tehran provinces have the highest and lowest percentages of households in the first two deciles of expected income respectively. Also, Tehran and Bashagard cities respectively have the highest and lowest percentages of the households that belong to the first two deciles of expected income.

Using a sample including 2642 observations of 2003-2016 annual data of firms listed in Tehran Security Exchange, this study investigates the implications of correlation between non-discretionary accruals and partitioning variables when TESTing the positive accounting theories hypotheses. In earnings management detection TESTs, it is common for researchers to use variables which partition their sample into two groups, for which differences in motivation for income manipulation are predicted. Since earnings management stimuli are assumed to be correlated with variables like firm performance, leverage or size, the use of these proxies are popular in empirical TESTs of positive accounting theories hypotheses. The correlation between non-discretionary accruals and the partitioning variable implies that part of the variation of non-discretionary accruals is generated by the partitioning variable. If accruals model does not control for the correlation, this part will be added to the discretionary accruals. In this study, we provide evidence that, when correlation between non-discretionary accruals and the above-mentioned partitioning variables remains uncontrolled, accrual-based models tend to generate measurement error in the estimate of discretionary accruals that significantly affects the sign and the magnitude of correlation between discretionary accruals and the partitioning variables. The findings of a Monte Carlo simulation also indicate that the Jones model relatively generates less type I error when it is adjusted to control for the relation between non-discretionary accruals and firm performance.