Technology-specific Production Functions

Michele Battisti, Filippo Belloc () and Massimo Del Gatto

Working Paper series from Rimini Centre for Economic Analysis

Abstract: We rely on mixture models to estimate technology-specific production functions avoiding any type of ex-ante assumption on the degree of technological sharing across firms and leaving the number of available technologies unconstrained. Internationally comparable firm-level data are used, to potentially capture all possible technologies available worldwide. Differently from conventional TFP estimates, where the terms "TFP", "productivity" and "technology" are often used interchangeably, our approach enables us to isolate the contribution to labour productivity stemming from technology (i.e. between-technology TFP) from the contribution associated to idiosyncratic productivity shocks not related to technology (i.e. within-technology TFP). While we find the former to be much larger than the latter in most sectors, the relative role of these two dimensions varies considerably across firms, being often reversed. We also find that the firm-level gaps are non-linearly correlated with the international flows of technology, as measured by the OECD country-sector technology payments and receipts. In particular, we show higher incoming (outcoming) flows of technology to be associated to higher (lower) average and dispersion of the between-technology TFP gaps. This stresses the growing importance of the availability of internationally comparable data in dealing with the technological dimension of firm-level productivity.

Keywords: TFP; technology adoption; production function estimation; mixture models (search for similar items in EconPapers)
JEL-codes: C29 D24 O33 (search for similar items in EconPapers)
Date: 2017-10
New Economics Papers: this item is included in nep-eff and nep-tid
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http://www.rcea.org/RePEc/pdf/wp17-26.pdf

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Working Paper: Technology-specific Production Functions (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:rim:rimwps:17-26

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