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    Analysing Firm-Level Labour Productivity Using Survey Data

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    Analysing Firm-Level Labour Productivity Using Survey Data (392.9Kb)

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    Author
    ROGERS, MARK; TSENG, YI-PING
    Date
    2000-06
    University of Melbourne Author/s
    ROGERS, MARK; Tseng, Yi-Ping
    Affiliation
    Economics and Commerce: Melbourne Institute of Applied Economic and Social Research
    Metadata
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    Document Type
    Preprint
    Citations
    Rogers, Mark and Tseng, Yi-Ping (2000) Analysing Firm-Level Labour Productivity Using Survey Data.
    Access Status
    Open Access
    URI
    http://hdl.handle.net/11343/33633
    Description

    This paper is the result of work being undertaken as part of a collaborative research program entitled 'The Performance of Australian Enterprises: Innovation, Productivity and Profitability'. The project is generously supported by the Australia Research Council and the following collaborative partners: Australian Tax Office, Commonwealth Office of Small Business, IBIS Business Information Pty Ltd, Productivity Commission, and Victorian Department of State Development. The views expressed in this paper represent those of the authors and not necessarily the views of the collaborative partne

    Abstract
    This paper investigates the determinants of firm-level labour productivity in the manufacturing sector using GAPS data. These data are from a stratified survey, where the strata are based on industry and firm size. The paper focuses on whether weights should be applied in the regression analysis. Augmented Cobb-Douglas production functions are estimated, where a set of dummies are used as proxies for firm-level knowledge stocks. The regression results show that there are significant differences between the parameters estimated by weighted least squares (WLS) and OLS, particularly for the variables union density and training expenditure. These differences can be caused by parameter heterogeneity (across strata); in theoretical terms this means that applying the same production function across all firms is not appropriate. Given this parameter heterogeneity, both the OLS and WLS methods do not estimate parameters of interest. Instead, there is a requirement to estimate sub-sample regressions. These are presented in the second part of the empirical results.
    Keywords
    labour productivity; weights; survey.

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