Designing stratified sampling in economic and business surveys

Abstract

In most economic and business surveys, the target variables (e.g. turnover of enterprises, income of households, etc.) commonly resemble skewed distributions with many small and few large units. In such surveys, if a stratified sampling technique is used as a method of sampling and estimation, the convenient way of stratification such as the use of demographical variables (e.g. gender, socioeconomic class, geographical region, religion, ethnicity, etc.) or other natural criteria, which is widely practiced in economic surveys, may fail to form homogeneous strata and is not much useful in order to increase the precision of the estimates of variables of interest. In this paper, a stratified sampling design for economic surveys based on auxiliary information has been developed, which can be used for constructing optimum stratification and determining optimum sample allocation to maximize the precision in estimate.

stratifyR: An R Package for optimal stratification and sample allocation for univariate populations

“…org/web/packages/stratifyR/index.html. For the numerical illustrations and application of the package, some of the real datasets such as sugarcane of Khan et al (2015), anaemia of Reddy et al (2018), hies and math data are provided with the stratifyR package. The quakes and Boston data provided in the datasets package in R statistical software may also be used for illustration purposes.…”

“…Other advantages of the method are that it leads to substantial gains in the precision of the estimates over other available methods. Results from Khan et al (2015); Reddy et al (2018) and Reddy & Khan (2019) reveal that the variances get smaller with increasing number of strata (L), and they get much smaller at a much faster rate than other available methods. Once the OSB have been determined, the OSS can be easily calculated for each stratum using Neyman (1934) allocation.…”

“…A good review of this method can be found in Khan, Nand & Ahmad (2008). The DP procedure is applied in some of the following articles by Khan, Khan & Ahsan (2002); Khan, Reddy & Rao (2015); Reddy, Khan & Khan (2018) and Reddy & Khan (2019) for determining OSB for many different distributions. With the known frequency function of the study variable, they considered the problem of finding OSB as an equivalent problem of determining optimum strata width (OSW), which is formulated as a mathematical programming problem (MPP) and solved by using DP techniques.…”

Revisiting sample allocation methods: a simulation-based comparison

“…In stratified sampling, the optimal allocation of sample units is an ubiquitous problem, especially in business surveys when the survey frame changes continuously due to highly frequent firm inclusions and exclusions, or when new economic sectors are inserted in the official classification (Hidiroglou & Srinath 1993, Khan et al 2015. Stratification is often based on predetermined rules and constraints according to some fixed geographical administrative strata and economic classifications (e.g.…”

Obtaining Strata Boundaries under Proportional Allocation with Varying Cost of Every Unit

“…Kosak and Verma (2006) proved that optimization approach used for obtaining OSB is better than classical approach. Khan et al (2015) developed a technique for determining OSB when the population is skewed and compare the given results with the results obtained by using classical technique with the conclusion of superiority of mathematical programming approach over classical approach.…”