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Introduction:
The law on targeted subsidies was presented as a bill by the ninth government of the Islamic Republic of Iran in 2008 and eventually with amendments was passed by the Islamic Consultative Assembly.
 The law that was supposed to be the revenues to increase productivity and economic growth, became a serious problem for the government due to false implementation. In the second phase of implementing this law, the removal of the wealthy people who receive direct subsidy led to a lot of discussions that, at the end of the parliament's review of the budget bill for the year 2016, the removal of 24 million people ,who receive subsidies, from the government's list was approved. . For a proper implementation of this directive, high-income households should be identified correctly. Therefore, the four criteria of "climatic conditions", "household size", "area of living" and "level of income" were proposed to identify these households. If these variables are significant in mathematical and statistical models, the impact of these variables on high-income households can be determined.
Method:
In applied studies, regression models are usually used to analyze  data that are related to a set of explanatory variables. The fitting of these models is done by assuming the normality of the response variable or its transformation, along with the constant of the variance of the response variable and the incompatibility of the error components.. In some cases, the response variable may be limited in the interval (1 and 0) and, by fitting the regression model, predictions are obtained outside the defined interval.
  In these cases the use of normal regression models are  not appropriate and beta regression model is suggested. This model is based on the assumption that response variable has  Beta distribution, and the mean of the response variable is linked by a linear predictor with unknown coefficients and a link function to a set of explanatory variables.  If the response variable gets the values of zero and one, an augmented Beta model that is mixture of a Beta distribution with two degenerated distributions at 0 and 1 has been suggested. Since in this study the variable of the response, that is the share of household income from the subsidy, can take values in the closed interval [1.0], to examine the effective variables on that additional beta regression model has been used. The data used in this study were collected from Iran's household income and household expenditure survey (HIES) which has been implemented by the Statistical Center of Iran.. The main objective of the household income and expenditure survey is to measure the average of food-expenditure, non-food expenditure, and total expenditure of the urban and rural households in Iran. Information provided by HIES has been applied to calculate poverty line,  study the impurity in household income and facilities. Statistical population in this survey is all of the households which are settled in urban and rural areas. The presented regression model to understand the effective covariates on response rate is applied by the data set of Tehran city. Deviance Information Criterion (DIC) is used for model evaluation.
 
Finding:
 To distinguish high-income households, this paper presented modeling of the share   of household income from subsidies in Tehran city using the results of Household Income and Expenditure survey conducted by the Statistical Center of Iran in 1390. After checking different covariates, deciles of household income and household size were  entered into the augmented beta regression model with negative and positive coefficients respectively. Spatial map of Tehran city shows heterogeneity among different districts of Tehran municipality, which is significant in the spatial augmented beta regression model with a positive coefficient. Hence, decile of income, household size, and area of  living were identified as significant variables on the proportion of household income from subsidies.
Discussion:
. The findings of this study confirm the criteria proposed by economic experts to identify high-income households. Since information about area of living is more accessible in comparison to other covariates, level of income and household size, it is suggested to imply an area of living geographically for grouping households in order to make a decision about getting the direct subsidies.
To access the information about the area of household living, it is suggested to use  Instruments and Landed Property Registration Organization data base. Since decile of income is a significant variable on share  of household income from subsidies, information about status of ownership and area of housing unit which are available in Instruments and Landed Property Registration Organization data base can be used as an indicator to identify wealthy people. Moreover,, Numbers and prices of automobiles, which  belong to a household and having a business license can also be used for this purpose.
Another effective variable on share  of household income from subsidies is household size, which is updated through population and housing census. In order to plan for the allocation of subsidies to households it is necessary to consider household size, decile on income and area of household living simultaneously, for a decision to be taken  in regard to per capita household income from subsidies criterion. Identification of wealthy households is being possible by linking  these data bases.
 

Keywords: augmented Beta regression, direct subsidies, urban household income and expenditure survey

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