A framework for cut-off sampling in business survey design

Marco Bee, Roberto Benedetti () and Giuseppe Espa ()

No 709, Department of Economics Working Papers from Department of Economics, University of Trento, Italia

Abstract: 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.

Keywords: Cut-off sampling; skewed populations; model-based estimation; optimal stratification; simulated annealing (search for similar items in EconPapers)
JEL-codes: C21 D92 L60 O18 R12 (search for similar items in EconPapers)
Date: 2007
New Economics Papers: this item is included in nep-cmp and nep-ecm
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Citations: View citations in EconPapers (3)

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