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Sampling variability: some observations from a labour supply equation

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  • Daniel Gordon
  • Lars Osberg
  • Shelley Phipps

Abstract

In economics, the number of observations available for empirical work is often predetermined. Researchers assume some large sample distribution and carry through with measurement and testing applied to data sets of varying sizes. The consequences of sampling variability are generally ignored. It is shown in a re-sampling experiment, using data sets of different sizes and estimating log-linear male labour supply equations, that a wide range of what appears to be statistically supported estimates of the wage elasticity of labour supply are generated. Testing based on bootstrapped estimates shows that 4000 observations are required to reduce sampling variability to statistically acceptable levels.

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  • Daniel Gordon & Lars Osberg & Shelley Phipps, 2005. "Sampling variability: some observations from a labour supply equation," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2167-2175.
    • Handle: RePEc:taf:applec:v:37:y:2005:i:18:p:2167-2175
    DOI: 10.1080/00036840500215428
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    References listed on IDEAS

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    1. Gyanendra Singh Sisodia & Isabel Soares & Paula Ferreira, 2016. "The effect of sample size on European Union’s renewable energy investment drivers," Applied Economics, Taylor & Francis Journals, vol. 48(53), pages 5129-5137, November.

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