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Estimation of Scale Economies Underlying Growth and Productivity: The Empirical Implications of Data Aggregation
Author(s) -
Paul Catherine J. Morrison,
Siegel Donald
Publication year - 1999
Publication title -
southern economic journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.762
H-Index - 58
eISSN - 2325-8012
pISSN - 0038-4038
DOI - 10.1002/j.2325-8012.1999.tb00197.x
Subject(s) - spillover effect , economics , estimation , aggregate (composite) , econometrics , productivity , economies of scale , data aggregator , scale (ratio) , aggregate data , empirical evidence , economies of agglomeration , macroeconomics , microeconomics , computer science , statistics , mathematics , geography , computer network , philosophy , materials science , wireless sensor network , management , cartography , epistemology , composite material
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.