IDEAS home Printed from https://ideas.repec.org/p/iza/izadps/dp15708.html
   My bibliography  Save this paper

The Identification of Time-Invariant Variables in Panel Data Model: Exploring the Role of Science in Firms’ Productivity

Author

Listed:
  • Amoroso, Sara

    (DIW Berlin)

  • Bruno, Randolph Luca

    (University College London)

  • Magazzini, Laura

    (Sant'Anna School of Advanced Studies)

Abstract

Recent literature has raised the attention on the estimation of time-invariant variables both in a static and a dynmamic framework. In this context, Hausman-Taylor type estimators have been applied, relying crucially on the distinction between exogenous and endogenous variables (in terms of correlation with the time-invariant error component). We show that this provision can be relaxed, and identification can be achieved by relying on the milder assumption that the correlation between the individual effect and the time-varying regressors is homogenous over time. The methodology is applied to identify the role of inputs from "Science" (firm-level publications' stock) on firms' labour productivity, showing that the effect is larger for those firms with higher level of R&D investments. The results further support the dual – direct and indirect – role of R&D.

Suggested Citation

  • Amoroso, Sara & Bruno, Randolph Luca & Magazzini, Laura, 2022. "The Identification of Time-Invariant Variables in Panel Data Model: Exploring the Role of Science in Firms’ Productivity," IZA Discussion Papers 15708, Institute of Labor Economics (IZA).
    • Handle: RePEc:iza:izadps:dp15708
    as

    Download full text from publisher

    File URL: https://docs.iza.org/dp15708.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hansen, Lars Peter, 1982. "Large Sample Properties of Generalized Method of Moments Estimators," Econometrica, Econometric Society, vol. 50(4), pages 1029-1054, July.
    2. Hall, Bronwyn H. & Mairesse, Jacques, 1995. "Exploring the relationship between R&D and productivity in French manufacturing firms," Journal of Econometrics, Elsevier, vol. 65(1), pages 263-293, January.
    3. So Im, Kyung & Ahn, Seung C. & Schmidt, Peter & Wooldridge, Jeffrey M., 1999. "Efficient estimation of panel data models with strictly exogenous explanatory variables," Journal of Econometrics, Elsevier, vol. 93(1), pages 177-201, November.
    4. Badi H. Baltagi & Georges Bresson, 2012. "A Robust Hausman–Taylor Estimator," Advances in Econometrics, in: Essays in Honor of Jerry Hausman, pages 175-214, Emerald Group Publishing Limited.
    5. Sebastian Kripfganz, 2019. "Generalized method of moments estimation of linear dynamic panel-data models," London Stata Conference 2019 17, Stata Users Group.
    6. Mansfield, Edwin, 1998. "Academic research and industrial innovation: An update of empirical findings1," Research Policy, Elsevier, vol. 26(7-8), pages 773-776, April.
    7. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
    8. Amemiya, Takeshi & MaCurdy, Thomas E, 1986. "Instrumental-Variable Estimation of an Error-Components Model," Econometrica, Econometric Society, vol. 54(4), pages 869-880, July.
    9. Badi H. Baltagi, 2021. "Econometric Analysis of Panel Data," Springer Texts in Business and Economics, Springer, edition 6, number 978-3-030-53953-5, June.
    10. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    11. Breusch, Trevor S & Mizon, Grayham E & Schmidt, Peter, 1989. "Efficient Estimation Using Panel Data," Econometrica, Econometric Society, vol. 57(3), pages 695-700, May.
    12. Chen, Jing & Yue, Rongxian & Wu, Jianhong, 2020. "Testing for individual and time effects in the two-way error component model with time-invariant regressors," Economic Modelling, Elsevier, vol. 92(C), pages 216-229.
    13. Hausman, Jerry A & Taylor, William E, 1981. "Panel Data and Unobservable Individual Effects," Econometrica, Econometric Society, vol. 49(6), pages 1377-1398, November.
    14. Mansfield, Edwin, 1991. "Academic research and industrial innovation," Research Policy, Elsevier, vol. 20(1), pages 1-12, February.
    15. Greene, William, 2011. "Fixed Effects Vector Decomposition: A Magical Solution to the Problem of Time-Invariant Variables in Fixed Effects Models?," Political Analysis, Cambridge University Press, vol. 19(2), pages 135-146, April.
    16. Ahn, Seung C. & Schmidt, Peter, 1995. "Efficient estimation of models for dynamic panel data," Journal of Econometrics, Elsevier, vol. 68(1), pages 5-27, July.
    17. Plümper, Thomas & Troeger, Vera E., 2007. "Efficient Estimation of Time-Invariant and Rarely Changing Variables in Finite Sample Panel Analyses with Unit Fixed Effects," Political Analysis, Cambridge University Press, vol. 15(2), pages 124-139, April.
    18. Cohen, Wesley M & Levinthal, Daniel A, 1989. "Innovation and Learning: The Two Faces of R&D," Economic Journal, Royal Economic Society, vol. 99(397), pages 569-596, September.
    19. Nathan ROSENBERG, 2009. "Why do firms do basic research (with their own money)?," World Scientific Book Chapters, in: Nathan Rosenberg (ed.), Studies On Science And The Innovation Process Selected Works of Nathan Rosenberg, chapter 11, pages 225-234, World Scientific Publishing Co. Pte. Ltd..
    20. Peter N. Gal, 2013. "Measuring Total Factor Productivity at the Firm Level using OECD-ORBIS," OECD Economics Department Working Papers 1049, OECD Publishing.
    21. Newey, Whitney K., 1984. "A method of moments interpretation of sequential estimators," Economics Letters, Elsevier, vol. 14(2-3), pages 201-206.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sebastian Kripfganz & Claudia Schwarz, 2019. "Estimation of linear dynamic panel data models with time‐invariant regressors," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(4), pages 526-546, June.
    2. Ahn, Seung C. & Lee, Young H. & Schmidt, Peter, 2013. "Panel data models with multiple time-varying individual effects," Journal of Econometrics, Elsevier, vol. 174(1), pages 1-14.
    3. Andrews, Donald W. K. & Lu, Biao, 2001. "Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models," Journal of Econometrics, Elsevier, vol. 101(1), pages 123-164, March.
    4. Donald W.K. Andrews & Biao Lu, 1999. "Consistent Model and Moment Selection Criteria for GMM Estimation with Applications to Dynamic Panel Data Models," Cowles Foundation Discussion Papers 1233, Cowles Foundation for Research in Economics, Yale University.
    5. Chatelain, Jean-Bernard & Ralf, Kirsten, 2021. "Inference on time-invariant variables using panel data: A pretest estimator," Economic Modelling, Elsevier, vol. 97(C), pages 157-166.
    6. Sakari Lähdemäki & Eero Lehto & Eero Mäkynen, 2018. "The Role of Natural Resources and Geography for Productivity in Developed Countries," Working Papers 320, Työn ja talouden tutkimus LABORE, The Labour Institute for Economic Research LABORE.
    7. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July.
    8. M. Hashem Pesaran & Qiankun Zhou, 2018. "Estimation of time-invariant effects in static panel data models," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1137-1171, November.
    9. Lekha Chakraborty & Pinaki Chakraborty, 2018. "Federalism, fiscal asymmetries and economic convergence: evidence from Indian States," Asia-Pacific Journal of Regional Science, Springer, vol. 2(1), pages 83-113, April.
    10. Doug J. Chung & Byungyeon Kim & Byoung G. Park, 2019. "How Do Sales Efforts Pay Off? Dynamic Panel Data Analysis in the Nerlove–Arrow Framework," Management Science, INFORMS, vol. 65(11), pages 5197-5218, November.
    11. Yang, Yimin & Schmidt, Peter, 2021. "An econometric approach to the estimation of multi-level models," Journal of Econometrics, Elsevier, vol. 220(2), pages 532-543.
    12. Chen, Jing & Yue, Rongxian & Wu, Jianhong, 2020. "Testing for individual and time effects in the two-way error component model with time-invariant regressors," Economic Modelling, Elsevier, vol. 92(C), pages 216-229.
    13. Hak Yeung & Jürgen Huber, 2022. "Further Evidence on China’s B&R Impact on Host Countries’ Quality of Institutions," Sustainability, MDPI, vol. 14(9), pages 1-17, May.
    14. Eduardo Fé Rodríguez, 2009. "Adaptive Instrumental Variable Estimation of Heteroskedastic Error Component Models," Economics Discussion Paper Series 0921, Economics, The University of Manchester.
    15. Benzaim, Samia & Ftiti, Zied & Khedhaouria, Anis & Djermane, Rebai, 2023. "US foreign investments: Technology transfer, relative backwardness, and the productivity growth of host countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 275-295.
    16. Bruno, Randolph Luca & Magazzini, Laura & Stampini, Marco, 2020. "Exploiting information from singletons in panel data analysis: A GMM approach," Economics Letters, Elsevier, vol. 186(C).
    17. Czarnitzki, Dirk & Thorwarth, Susanne, 2012. "Productivity effects of basic research in low-tech and high-tech industries," Research Policy, Elsevier, vol. 41(9), pages 1555-1564.
    18. Garcia, Serge & Moreaux, Michel & Reynaud, Arnaud, 2007. "Measuring economies of vertical integration in network industries: An application to the water sector," International Journal of Industrial Organization, Elsevier, vol. 25(4), pages 791-820, August.
    19. Bigerna, Simona & D'Errico, Maria Chiara & Polinori, Paolo, 2022. "Environmental variables and power firms' productivity: micro panel estimation with time-Invariant variables," MPRA Paper 114157, University Library of Munich, Germany.
    20. Arnab Bhattacharjee & Sean Holly, 2013. "Understanding Interactions in Social Networks and Committees," Spatial Economic Analysis, Taylor & Francis Journals, vol. 8(1), pages 23-53, March.

    More about this item

    Keywords

    panel data; time-invariant variables; science; productivity; R&D;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iza:izadps:dp15708. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Holger Hinte (email available below). General contact details of provider: https://edirc.repec.org/data/izaaade.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.