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Scientific output: labor or capital intensive? An analysis for selected countries

Author

Listed:
  • Elham Erfanian

    (Regional Research Institute, West Virginia University)

  • Amir B. Ferreira Neto

    (Regional Research Institute, West Virginia University)

Abstract

Scientific research contributes to sustainable economic growth environments. Hence, policy-makers should understand how the different inputs – namely labor and capital – are related to a country’s scientific output. This paper addresses this issue by estimating output elasticities for labor and capital using a panel of 31 countries in nine years. Due to the nature of scientific output, we also use spatial econometric models to take into account the spillover effects from knowledge produced as well as labor and capital. The results show that capital elasticity is closer to the labor elasticity. The results suggest a decreasing return to scale production of scientific output. The spatial model points to negative spillovers from capital expenditure and no spillovers from labor or the scientific output.

Suggested Citation

  • Elham Erfanian & Amir B. Ferreira Neto, 2017. "Scientific output: labor or capital intensive? An analysis for selected countries," Working Papers Research Paper 2016-05, Regional Research Institute, West Virginia University.
    • Handle: RePEc:rri:wpaper:2016rp05
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    File URL: https://researchrepository.wvu.edu/rri_pubs/30/
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    Cited by:

    1. Copiello, Sergio, 2019. "Peer and neighborhood effects: Citation analysis using a spatial autoregressive model and pseudo-spatial data," Journal of Informetrics, Elsevier, vol. 13(1), pages 238-254.
    2. Xu, Xu & Reed, Markum, 2021. "The impact of internet access on research output - a cross-country study," Information Economics and Policy, Elsevier, vol. 56(C).
    3. Mehmet Pinar, 2023. "Do research performances of universities and disciplines in England converge or diverge? An assessment of the progress between research excellence frameworks in 2014 and 2021," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5731-5766, October.
    4. Bárbara S. Lancho-Barrantes & Francisco J. Cantú-Ortiz, 2019. "Science in Mexico: a bibliometric analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(2), pages 499-517, February.
    5. Xionghe Qin & Debin Du & Mei-Po Kwan, 2019. "Spatial spillovers and value chain spillovers: evaluating regional R&D efficiency and its spillover effects in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 721-747, May.
    6. Khosrowjerdi, Mahmood & Bornmann, Lutz, 2021. "Is culture related to strong science? An empirical investigation," Journal of Informetrics, Elsevier, vol. 15(4).
    7. Qin, Xionghe & Wang, Xueli & Kwan, Mei-Po, 2023. "The contrasting effects of interregional networks and local agglomeration on R&D productivity in Chinese provinces: Insights from an empirical spatial Durbin model," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    8. Mahmood Khosrowjerdi & Lutz Bornmann, 2019. "Is culture related to strong science? An empirical investigation," Papers 1909.04521, arXiv.org, revised Apr 2021.
    9. Henry Laverde-Rojas & Juan C. Correa, 2019. "Can scientific productivity impact the economic complexity of countries?," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 267-282, July.

    More about this item

    Keywords

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    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • F01 - International Economics - - General - - - Global Outlook
    • O15 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Economic Development: Human Resources; Human Development; Income Distribution; Migration

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