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Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries

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Abstract

Based on data of 99 nations during 1991–2003, the Malmquist index and its composition of technical change and efficiency change are estimated. In particular, the hypothesis of neutral technology is released to divide technology into the magnitude of the shift in the world production frontier and input-biased technology, and to show that in order to gain more benefit or not to lose so much benefit from technology change, it is important for countries to coordinate their choice of input mix with the directions of technology bias if their technical changes are biased. The results indicate that both OECD and non-OECD countries tend to show capital-using/labor-saving, capital-using/energy-saving and energy-using/labor-saving technical change bias over the entire period. The production pattern of a majority of countries is shown to have been able to take advantage of their technological innovations.

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Notes

  1. It is noted that various approaches have been developed for the measurement of TFP growth other than the growth accounting method and DEA-based Malmquist productivity index. We can refer to Diewert (1981), which classified the various approaches into nonparametric methods using linear programming, parametric estimation of production/cost/distance functions, nonparametric indices, and exact index numbers (Kumbhakar and Sun 2011).

  2. According to Färe et al. (2001), the decomposition components of technical change consist of output bias, input bias and magnitude index. However, the only output specified in this study leads to output bias=1, thus we omit the term output bias in this statement.

  3. As Wang (2007) indicated, many theoretical papers and much empirical research in the literature have concluded that it is appropriate to employ energy as an input in production analysis. For example, Murillo-Zamorano (2005) did statistical tests and concluded that energy consumption is a relevant productive input.

  4. There are numerous papers discussing other types of technology bias for a single country. For example, Acemoglu (1998, 2002) addressed the impacts of skill-biased technical change on the U.S. labor markets. Moro (2011) aimed to quantify the impact of intermediates-biased technical change for measured TFP growth in Italy. Greenwood et al. (1997) found that investment-specific technical change, which contributed 58%, was a major driving source of postwar growth in the U.S.

  5. Refer to more detailed information on multiple inputs and multiple outputs biased technology changes in Färe and Grosskopf (1996) and Barros and Weber (2009).

  6. For the details of output biased technical change, please refer to Färe et al. (2001) and Barros and Weber (2009).

  7. This point of view does not hold under the situation in which the ratio of input used is unchanged for the adjacent period, since as Färe et al. (2001) pointed out, if \(x_{2}^{t + 1} /x_{1}^{t + 1} = x_{2}^{t} / x_{1}^{t} \), then IBTC=1; therefore, one is unable to draw any conclusion about the type of technical change. In that case, we cannot tell how the frontier moves and cannot regard MTC as a measure which evaluates the degree of neutral movements of the best-practice frontier.

  8. PWT 6.2 is the same as PWT 6.1 (data provided up to 2000) in that it does not provide the data of GDP and employment directly. Therefore, there is an extended database referred to as EPWT 2.1 which provides some of the data such as GDP and employment not obtained directly in the PWT 6.1. We follow the procedure by which EPWT 2.1 computed GDP and employment to calculate our GDP and employment. Put more concretely, by using the variable names specified by PWT 6.2, our computation of GDP and employment can be expressed as: GDP = real GDP per capita (= rgdpch ) ∗ population (= pop), employment=GDP/real GDP per worker (= rgdpwok).

  9. The website address is: http://homepage.newschool.edu/~foleyd/epwt/.

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Authors and Affiliations

  1. Department of International Business, Chung Hua University, Hsinchu, Taiwan

    Po-Chi Chen

  2. Department of Transportation Science, National Taiwan Ocean University, Keelung, Taiwan

    Ming-Miin Yu

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  1. Po-Chi Chen
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Correspondence to Ming-Miin Yu.

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Chen, PC., Yu, MM. Total factor productivity growth and directions of technical change bias: evidence from 99 OECD and non-OECD countries. Ann Oper Res 214, 143–165 (2014). https://doi.org/10.1007/s10479-012-1087-4

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