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Is newer always better? A reinvestigation of productivity dynamics using updated PWT data

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Abstract

Understanding the drivers of productivity remains one of the most sought after phenomena in economics. The ability to create produce more from less resources is undoubtedly appealing. Using recently updated Penn World Table data, we investigate to what degree previous results using a popular productivity decomposition are maintained. We find that, contrary to conclusions from earlier work, technical efficiency (catching up) played a more pronounced role in the global increase in productivity over the 1965–1990 period. We also find a larger effect for technical change than earlier work and a far lesser role for capital deepening. This suite of results augurs the coming information age that placed less weight on physical capital to create and sustain wealth. Taken together our findings here suggest that as data collection, its quality and evaluation methods evolve, so too will our understanding of productivity dynamics.

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Notes

  1. Badunenko & Romero-Ávila (2013) further extended this approach by accounting for financial development in the decomposition. For a detailed review of this method, other variations and empirical applications, see Badunenko et al. (2017).

  2. We note at the outset here that this analysis requires us to drop four countries Honduras, Panama, Sierra Leone, and Yugoslavia that did not have full data availability.

  3. Aggregate output, physical capital and labor inputs were measured in thousands in PWT5.6. We divide them by one thousand to make it comparable with PWT10 (measured in millions).

  4. Due to the fact that output is measured in different dollar equivalents we cannot plot both sets of frontiers on the same curve: HR is in 1985 international prices, while PWT10 are in 2017 international prices.

  5. Taken as capital per efficiency unit of worker measure with 1985 international dollars.

  6. More details can be found in Section 3.1 of HR.

  7. Specifically, the lines are OLS fitted lines with robust standard errors.

  8. Detailed results are attached in supplementary material B.

  9. Detailed results are attached in supplementary material C.

  10. Detailed results are attached in supplementary material D.

  11. A caveat of the FDH estimator is the relatively slow statistical rate of convergence, which often results in a low discriminatory power in the sense of assigning 100% efficiency to many observations, and high upward bias. Indeed, for this particular data, FDH assigned 100%-efficiency to almost all countries in 1965 and to a majority in 1990. Larger data sets, however, might prove to be more fruitful for this interesting approach. Also, see a related discussion about the convexity vs. non-convexity in Jin et al. (2020).

  12. See, for example, Parmeter & Zelenyuk (2019) and Kumbhakar et al. (2020) (Kumbhakar et al., 2020, b).

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Acknowledgements

The comments from four anonymous reviewers and the Editor greatly improved the paper. Comments and feedback from colleagues and audiences where earlier versions of this paper were presented also helped to improve the paper. We thank Arhan Boyd, Zichao Wang and Evelyn Smart for specific comments and help on the draft. Dr. Yan Meng is an associate at Analysis Group, Ltd. Research for this article was undertaken when she was working at the University of Melbourne. Valentin Zelenyuk acknowledges the support from the Australian Research Council (FT170100401) and The University of Queensland. All remaining errors are ours alone.

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Authors and Affiliations

  1. University of Melbourne, Melbourne, VIC, Australia

    Yan Meng

  2. Miami Herbert Business School, University of Miami, Coral Gables, FL, USA

    Christopher F. Parmeter

  3. University of Queensland, Brisbane, QLD, Australia

    Valentin Zelenyuk

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  1. Yan Meng
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  2. Christopher F. Parmeter
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  3. Valentin Zelenyuk
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Correspondence to Christopher F. Parmeter.

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Meng, Y., Parmeter, C.F. & Zelenyuk, V. Is newer always better? A reinvestigation of productivity dynamics using updated PWT data. J Prod Anal 59, 1–13 (2023). https://doi.org/10.1007/s11123-022-00649-w

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