روشنک علی اکبری صبا

  According to the importance of presenting change estimation of labor force survey indicators along with their variance, in this paper, the use of Jackknife method in estimating variance of changes has been investigated. Then, the effect of nonresponse primary sampling units on estimating the variance of changes has been studied by use of Jackknife method via intensive simulation study. Further that, the effect of nonresponse on estimating the variance of key estimations changes has been investigated based on labor force survey data of 2009 and 2010. The results of this study show that if a pseudo-district is nonresponse from a specific point of time in consecutive survey periods, this will affect the estimation of variance of changes of key estimations, however this effect may be small.

Aim and Introduction :

The poverty is not affected only by income level. Some variables such as lack of access to welfare and health facilities, deprivation of education, physical weakness and vulnerability to diseases can be influential factors in poverty. Accordingly, indexes such as the poverty line, the percentage of poor people, and the intensity of poverty, which are used to study of the poverty in the society, do not fully represent the situation of poverty dimensions. Therefore, for measuring poverty in different dimensions, Oxford Poverty and Human Development Initiative in collaboration with United Nations Development Programme introduced the Multidimensional Poverty Index in 2010. This index includes deprivations such as low levels of health, lack of education, inadequate living standards, disability, low quality work, threats of violence, and living in areas with hazardous environments that poor people face in their daily lives. Several researches have been conducted on multidimensional poverty at the national and international levels, which have estimated the multidimensional poverty index using the Alkire-Foster methodological framework. At the same time, the multidimensional poverty of children has been neglected at the national level. This paper aims to study of multidimensional poverty among Iranian children based on the Alkire-Foster methodological framework. The multidimensional poverty of under five children using Multiple Indicator Demographic and Health Survey data in four dimensions of "living standards", "health", "support" and "development" has been measured.

The Alkire-Foster methodology has been used for measuring the multidimensional poverty index. For measuring the children multidimensional poverty index (MPI) in Iran, data of the Multiple Indicator Demographic and Health Survey has been used. This survey covers the latest available data on various sociodemographic and health subjects of children, women and men. A comprehensive and new framework for measuring child poverty in Iran has been provided using the indicators of child mortality, cooking fuel, drinking water, sanitation discharge and housing from the global framework. The other indicators such as vaccination index and insufficient care has been extracted from other researches.

The results show that MPI of children in Iran is equal to 0.002. The incidence of multidimensional poverty is 0.5% and the intensity of child poverty is 38.6%. In general, the results show that girls compared to boys experience more multidimensional poverty and as mothers' education levels increase, children experience less multidimensional poverty. Children who live in large households (5 people or more) experience more multidimensional poverty compared to children who live in small households. Children in developed provinces experience multidimensional poverty less than other children and finally, children under five in the first economic quintile (the lowest economic level of the society) have the highest multidimensional poverty among all households in Iran.Among children under five whose mothers have primary education, "health" dimension plays the most important role in measuring the multidimensional poverty index. In less developed provinces, the most important factor in mesearunig multidimensional poverty among children under five is "child development" dimension. Also, in households that have a low level of economic and social status (based on economic quintile variable), “child development " and "living standards” are important respectively.

The purpose of presenting the Child multidimensional poverty index is to provide understandable and important statistics for clarify level and form of multidimensional poverty. The findings of this study, which was conducted for the first time in Iran, show that although value of the child multidimensional poverty index based on "living standards”, "health", "support" and "child development" is not a large number, however children in social and demographic groups have had encounters with different levels of multidimensional poverty. Considering that the growth and development of the children in the appropriate context is the basis for formation of a healthy and stable family and developed society, it is necessary to make policies in order to reduce the multidimensional poverty of children.

Household Income and Expenditure Survey (HEIS) is one of the most important surveys of the Statistical Center of Iran, the main parameters of which are spatially correlated. When there is a spatial correlation between the units of population, the classical way of selecting independent sampling units is challenging due to the lack of basic condition for the independence. Using spatial sampling is a solution to encounter this problem. Implementation of spatial sampling has received less attention in official statistics due to the lack of access to a suitable framework. In this paper we review a design-based model assisted method for optimal spatial stratification of the target population.  At present, spatial information of  population units are not available in the framework of HEIS, but access to spatial information of some sample units has been achieved by the Statistical Center of Iran for this study. The production of spatial data is one of the main components in the modernization of the statistical system which is considered by  Statistical Center of Iran.  In this paper, the sampling frame is simulated based on the HEIS data and then application of optimal spatial stratification based on a generalized distance is performed. The results demonstrate an increase in the efficiency of the mentioned sampling method compared to simple random sampling at the level of geographical areas. Also, simulation of grids with different sizes  and  correlations  reflects the better performance of this method compared to the current method of HEIS.

Identification of Coronavirus in the winter of 2019 and, consequently, outbreak of Covid-19 disease, led to beginning of a crisis in the country that affects various aspects of population in some areas such as health, education and training. In such situation, the vulnerability of households was not the same at different economic levels. In order to investigate the impacts of Covid-19 disease epidemic on the life dimensions of households at different economic levels of country, in this paper, the "socio-economic, demographic and mental health impacts of Covid-19" survey data have been used. This national survey was conducted in 2020 by Statistics Research and Training Center (SRTC) of Iran as a sample survey. In this paper, linking the survey data with household expenditure and income survey data in 2019 and 2018, and calculating estimates for the sub-populations of deciles using sampling design, significant results were obtained by economic level of households. The results show that the effects of prevalence of Covid 19 disease and its constraints in the population have often affected households in the lower economic deciles more than other households. Although the prevalence of the disease in economic deciles, especially in urban areas, has been increasing, but the needs and adverse effects of the disease in households of lower economic deciles have been more than other households.

The ranked set sampling method uses the ranking information of the units to provide a more representative sample of the population to the survey designers. The sampling distribution is closer to the actual distribution of the population. In this article, to ensure the effectiveness of ranked set sampling in extensive surveys conducted to prepare official statistics, we intend to use this sampling method to improve the efficiency of key estimates of household expenditure and income survey of the Statistics Center of Iran. The results show that using ranked set sampling to design household expenditure and income surveys can improve the efficiency of key estimates of the study, provided that the ranking variable used has a high correlation with the main variables of the study. Obviously, in the absence of a suitable and available variable for ranking the units, the information of the sampling frame can be used to construct a ranking variable correlated with the key variables of the survey.

 This Article introduce method of ranked set sampling with replacement (RSSWR) in finite population and express how to computing samples of inclusion probability for this method. The Horvitz-Thompson and Hansen-Hurwtz estimators using auxiliary variables introduce for this design and use 2011-12 Urban Households Income and Income and Expenditure survey data, gathered for Tehran by statistical center of Iran to show some estimators introduced for RSS design are more efficient than corresponding estimators in SRSWOR design.

One of the challenges at National Statistical Organizations is conducting a survey with a long questionnaire and a large amount of items. Filling out a lengthly questionnaire is time consuming and leads to problems such as increased unresponsiveness rate. Especially during critical situations such as pandemic Covid-19, this would be more challenging. A solution to encounter this difficulty is using a split questionnaire design that is more flexible and taking less time for collecting data. In this paper, we propose a split questionnaire design for Household Expenditure and Income Survey(HEIS) which is one of the most important surveys conducted annually by Statistical Center of Iran and its questionnaire is really long and time consuming to complete.

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.

Ranked set sampling is a well-known method, that its superiority in comparison with simple random sampling method is recognized by statisticians. By using some changes in the structure of ranked set sampling method, we can increase its accuracy and efficiency in estimating the central parameters of the population. In order to do so, in recent years, researchers introduced many different types of these methods. Using these methods increases the efficiency in estimating of central parameters of population. In this paper, in order to find more efficient method in estimating the cost of housing for rural households of Mazandaran province, the efficiency of Robust Extreme Ranked Set Sampling and Double Robust Extreme Ranked Set Sampling methods in comparison with Simple Random Sampling is recognized.

Annual estimation of average household incomes is one of the main goals of the household income and expenditure survey in Iran. So، regarding importance of accuracy of gathered data and reasons that lead to error in measuring household income، in this paper، model-based methods are used for estimating income measurement error and adjusting sample households declared income for 2011 household income and expenditure survey.

In this paper، for the Stratified Median Ranked Set Sampling (SMRSS)، proposed by Ibrahim et al. (2010)، we examine the proportional and optimum sample allocations that are two well-known methods for sample allocation in stratified sampling. We show that the variances of the mean estimators of a symmetric population in SMRSS using optimum and proportional allocations to strata are smaller than the corresponding variances in Stratified Random Sampling (STRS). It is also shown that for a fixed value of sampling cost in strata، the variance of mean estimator with optimum allocation is less than or equal to the variance of mean estimator with proportional allocation in SMRSS. In addition، we develop the results obtained by Ibrahim et al. (2010) for proportional allocation in SMRSS for some symmetric and non-symmetric distributions when the parameters of distributions are varying.