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Divide, Allocate et Impera: Comparing Allocation Strategies via Simulation


  • Paola CHIODINI


  • Giancarlo MANZI


  • Bianca Maria MARTELLI





In stratified sampling, the problem of optimally allocating the sample size is of primary importance, especially when reliable estimates are required both for the overall population and for subdomains. To this purpose, in this paper we compare multiple standard allocation mechanisms. In particular, standard allocation methods are compared with an allocation method that has been recently adopted by the Italian National Statistical Institute: the Robust Optimal Allocation with Uniform Stratum Threshold (ROAUST) method. Standard allocation methods considered in this comparison are: (i) the optimal Neyman allocation, (ii) the multivariate Neyman allocation, (iii) the Costa allocation, (iv) the Bankier allocation, and (v) the Interior Point Non Linear Programming (IPNLP) allocation. Results show that the optimal Neyman allocation method outperforms the ROAUST method at the overall sample level, whereas the latter method performs better at the stratum level. Some results on the Nonlinear Programming method are particularly interesting.

Suggested Citation

  • Paola CHIODINI & Giancarlo MANZI & Bianca Maria MARTELLI & Flavio VERRECCHIA, 2017. "Divide, Allocate et Impera: Comparing Allocation Strategies via Simulation," Departmental Working Papers 2017-09, Department of Economics, Management and Quantitative Methods at Universit√† degli Studi di Milano.
  • Handle: RePEc:mil:wpdepa:2017-09

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    More about this item


    Firm; Stratified Sampling; Permanent Random Numbers; Monte Carlo Simulation; Compromise Allocation; Interior Point Non Linear Programming;

    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access


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