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Measuring Total Factor Productivity and Variable Factor Utilization

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  • Ludmila Fadejeva
  • Aleksejs Melihovs

Abstract

This research constructs estimates of total factor productivity (TFP) growth for six sectors of the Latvian economy for the period 2000-2008, using a sectoral quarterly data set. Estimates are obtained by controlling for qualitative changes in production factors and assuming a mechanism for capturing changes in the utilization of labor and capital. The study provides two main results. First, the use of indicators for labor and capital utilization intensity allows for minimization of fluctuations in the TFP measure and makes it less dependent on output growth compared with the Solow residual approach. Second, the comparison of both methods shows that the estimate of TFP growth obtained by the Solow residual approach might be undervalued for manufacturing, electricity, gas and water supply, wholesale and retail trade, as well as hotels and restaurants, while overvalued for growth in the transport, storage, and communication sector of the Latvian economy.

Suggested Citation

  • Ludmila Fadejeva & Aleksejs Melihovs, 2010. "Measuring Total Factor Productivity and Variable Factor Utilization," Eastern European Economics, Taylor & Francis Journals, vol. 48(5), pages 63-101, September.
    • Handle: RePEc:mes:eaeuec:v:48:y:2010:i:5:p:63-101
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    Cited by:

    1. Tomáš Volek & Martina Novotná, 2015. "Gross Value Added and Total Factor Productivity In Czech Sectors," Contemporary Economics, University of Economics and Human Sciences in Warsaw., vol. 9(1), March.
    2. Konstantins Benkovskis & Ludmila Fadejeva & Robert Stehrer & Julia Woerz, 2012. "How Important is Total Factor Productivity for Growth in Central, Eastern and Southeastern European Countries?," Working Papers 2012/05, Latvijas Banka.
    3. Konstantins Benkovskis & Ludmila Fadejeva & Julia Wörz, 2013. "How Important Is Total Factor Productivity for Growth in Central, Eastern and Southeastern European Countries?," Focus on European Economic Integration, Oesterreichische Nationalbank (Austrian Central Bank), issue 1, pages 8-27.

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    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity

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