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Estimation of Scale Economies Underlying Growth and Productivity: The Empirical Implications of Data Aggregation


  • Catherine J. Morrison Paul
  • Donald Siegel


Estimation of scale economies underlying growth and productivity patterns is typically based on aggregated data, raising questions about the potential for aggregation biases. This paper provides empirical evidence on the existence and patterns of such biases. We use a cost-based model to estimate short/long-run and internal/external scale effects for U.S. manufacturing data at different aggregation levels. Our results suggest that aggregation biases in such a model are not substantive. Also, internal scale economies seem more appropriately represented by the aggregate data, whereas more disaggregated data appears preferable for estimation of external or spillover effects that occur between industries or sectors.

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  • Catherine J. Morrison Paul & Donald Siegel, 1999. "Estimation of Scale Economies Underlying Growth and Productivity: The Empirical Implications of Data Aggregation," Southern Economic Journal, Southern Economic Association, vol. 65(4), pages 739-756, April.
  • Handle: RePEc:sej:ancoec:v:65:4:y:1999:p:739-756

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    References listed on IDEAS

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    Cited by:

    1. Bertrand Koebel & Fran├žois Laisney, 2016. "Aggregation with Cournot Competition: An Empirical Investigation," Annals of Economics and Statistics, GENES, issue 121-122, pages 91-119.
    2. Lila Truett & Dale Truett, 2006. "Production and costs in the South African motor vehicle industry," Applied Economics, Taylor & Francis Journals, vol. 38(20), pages 2381-2392.

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