A framework for cut-off sampling in business survey design

Bee, Marco and Benedetti, Roberto and Espa, Giuseppe (2007) A framework for cut-off sampling in business survey design. UNSPECIFIED.

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    In sampling theory the large concentration of the population with respect to most surveyed variables constitutes a problem which is difficult to tackle by means of classical tools. One possible solution is given by cut-off sampling, which explicitly prescribes to discard part of the population; in particular, if the population is composed by firms or establishments, the method results in the exclusion of the “smallest” firms. Whereas this sampling scheme is common among practitioners, its theoretical foundations tend to be considered weak, because the inclusion probability of some units is equal to zero. In this paper we propose a framework to justify cut-off sampling and to determine the census and cut-off thresholds. We use an estimation model which assumes as known the weight of the discarded units with respect to each variable; we compute the variance of the estimator and its bias, which is caused by violations of the aforementioned hypothesis. We develop an algorithm which minimizes the MSE as a function of multivariate auxiliary information at the population level. Considering the combinatorial optimization nature of the model, we resort to the theory of stochastic relaxation: in particular, we use the simulated annealing algorithm.

    Item Type: Departmental Technical Report
    Department or Research center: UNSPECIFIED
    Subjects: H Social Sciences > HB Economic Theory > HB131 Methodology
    Uncontrolled Keywords: Cut-off sampling, skewed populations, model-based estimation, optimal stratification, simulated annealing
    Additional Information: Pubblicazione registrata presso il Tribunale di Trento.
    Report Number: 9
    Repository staff approval on: 21 Nov 2007

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