Estimation of scale economies underlying growth and productivity: The empirical implications of data aggregation

Catherine J. Morrison Paul, Donald Siegel

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)739-756
Number of pages18
JournalSouthern Economic Journal
Volume65
Issue number4
StatePublished - Apr 1 1999

Fingerprint

Aggregation bias
Data aggregation
Economies of scale
Productivity
Spillover effects
External effects
Costs
Scale economies
Industry
Aggregate data
Empirical evidence
Scale effect
Manufacturing

ASJC Scopus subject areas

  • Economics and Econometrics

Cite this

Estimation of scale economies underlying growth and productivity : The empirical implications of data aggregation. / Morrison Paul, Catherine J.; Siegel, Donald.

In: Southern Economic Journal, Vol. 65, No. 4, 01.04.1999, p. 739-756.

Research output: Contribution to journalArticle

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