IDEAS home Printed from https://ideas.repec.org/a/sgh/gosnar/y2020i2p71-94.html
   My bibliography  Save this article

Sektorowe funkcje produkcji - wnioski z modeli panelowych dla Polski

Author

Listed:
  • Magdalena Ulrichs
  • Emilia Gosińska

Abstract

Celem prezentowanego badania jest ocena wpływu poszczególnych form kapitału na kształtowanie się wartości dodanej brutto w poszczególnych sekcjach działalności gospodarczej w Polsce. W badaniu oszacowano parametry funkcji produkcji opisującej wpływ zmiennych reprezentujących kapitał rzeczowy oraz pracę na wartość dodaną brutto w Polsce. Jako narzędzie analizy przyjęto funkcję produkcji typu Cobba-Douglasa. Do estymacji wykorzystano dane panelowe dla poszczególnych województw obejmujące lata 2003–2015. Ze względu na skorelowanie zmiennych objaśniających ze składnikiem losowym zastosowano w pełni zmodyfikowaną metodę najmniejszych kwadratów. Wnioski z przeprowadzonego badania potwierdzają istnienie różnic pomiędzy wpływem poszczególnych czynników produkcji na wartość dodaną brutto w uwzględnionych sekcjach działalności gospodarczej. W większości sekcji elastyczności wartości dodanej brutto względem nakładów pracy są większe niż elastyczności względem środków trwałych oraz występuje istotny statystycznie postęp techniczno-organizacyjny. Dla wszystkich sekcji potwierdzono również statystyczną istotność występowania nieobserwowalnych, stałych efektów indywidualnych dla poszczególnych województw.

Suggested Citation

  • Magdalena Ulrichs & Emilia Gosińska, 2020. "Sektorowe funkcje produkcji - wnioski z modeli panelowych dla Polski," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 2, pages 71-94.
    • Handle: RePEc:sgh:gosnar:y:2020:i:2:p:71-94
    as

    Download full text from publisher

    File URL: http://www.journalssystem.com/gna/pdf-116617-52868
    File Function: Full text
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zvi Griliches & Jacques Mairesse, 1995. "Production Functions: The Search for Identification," NBER Working Papers 5067, National Bureau of Economic Research, Inc.
    2. Pedroni, Peter, 2004. "Panel Cointegration: Asymptotic And Finite Sample Properties Of Pooled Time Series Tests With An Application To The Ppp Hypothesis," Econometric Theory, Cambridge University Press, vol. 20(3), pages 597-625, June.
    3. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 70(1), pages 65-94.
    4. Griliches, Zvi & Hausman, Jerry A., 1986. "Errors in variables in panel data," Journal of Econometrics, Elsevier, vol. 31(1), pages 93-118, February.
    5. T. W. Swan, 1956. "ECONOMIC GROWTH and CAPITAL ACCUMULATION," The Economic Record, The Economic Society of Australia, vol. 32(2), pages 334-361, November.
    6. Kao, Chihwa, 1999. "Spurious regression and residual-based tests for cointegration in panel data," Journal of Econometrics, Elsevier, vol. 90(1), pages 1-44, May.
    7. Peter Pedroni, 2000. "Fully Modified OLS for Heterogeneous Cointegrated Panels," Department of Economics Working Papers 2000-03, Department of Economics, Williams College.
    8. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    9. Baltagi, Badi H. & Bresson, Georges & Pirotte, Alain, 2003. "Fixed effects, random effects or Hausman-Taylor?: A pretest estimator," Economics Letters, Elsevier, vol. 79(3), pages 361-369, June.
    10. Peter C. B. Phillips & Hyungsik R. Moon, 1999. "Linear Regression Limit Theory for Nonstationary Panel Data," Econometrica, Econometric Society, vol. 67(5), pages 1057-1112, September.
    11. Daniel A. Ackerberg & Kevin Caves & Garth Frazer, 2015. "Identification Properties of Recent Production Function Estimators," Econometrica, Econometric Society, vol. 83, pages 2411-2451, November.
    12. Peter Pedroni, 2007. "Social capital, barriers to production and capital shares: implications for the importance of parameter heterogeneity from a nonstationary panel approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 429-451.
    13. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    14. Peter C. B. Phillips & Bruce E. Hansen, 1990. "Statistical Inference in Instrumental Variables Regression with I(1) Processes," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 57(1), pages 99-125.
    15. Malgorzata Sulimierska, 2014. "Total factor productivity estimation for Polish manufacturing industry: A comparison of alternative methods," Working Paper Series 6714, Department of Economics, University of Sussex Business School.
    16. Aleksander Welfe & Piotr Karp, 2017. "Makroekonometryczny miesięczny model gospodarki Polski WM-1," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 4, pages 5-38.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dariusz Kotlewski, 2022. "Przesłanki za wykorzystaniem rachunkowości wzrostu gospodarczego w badaniu specjalizacji regionalnych," Ekonomista, Polskie Towarzystwo Ekonomiczne, issue 2, pages 235-258.
    2. Dańska-Borsiak Barbara, 2022. "GDP and TFP in Poviats of the Łódzkie Voivodeship. Estimation and Analysis of Differentiation," Econometrics. Advances in Applied Data Analysis, Sciendo, vol. 26(1), pages 14-30, March.
    3. Mariusz & Mirosław Błażej, 2020. "A control function approach to measuring the total factor productivity of enterprises in Poland," Bank i Kredyt, Narodowy Bank Polski, vol. 51(3), pages 293-316.

    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. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, February.
    2. Eberhardt, Markus & Teal, Francis, 2008. "Modeling technology and technological change in manufacturing: how do countries differ?," MPRA Paper 10690, University Library of Munich, Germany.
    3. Dierk Herzer & Holger Strulik & Sebastian Vollmer, 2012. "The long-run determinants of fertility: one century of demographic change 1900–1999," Journal of Economic Growth, Springer, vol. 17(4), pages 357-385, December.
    4. Wagner, Martin, 2008. "The carbon Kuznets curve: A cloudy picture emitted by bad econometrics?," Resource and Energy Economics, Elsevier, vol. 30(3), pages 388-408, August.
    5. Kahia, Montassar & Ben Aïssa, Mohamed Safouane & Charfeddine, Lanouar, 2016. "Impact of renewable and non-renewable energy consumption on economic growth: New evidence from the MENA Net Oil Exporting Countries (NOECs)," Energy, Elsevier, vol. 116(P1), pages 102-115.
    6. Markus Eberhardt & Francis Teal, 2010. "Aggregation versus Heterogeneity in Cross-Country Growth Empirics," CSAE Working Paper Series 2010-32, Centre for the Study of African Economies, University of Oxford.
    7. Markus Eberhardt & Francis Teal, 2011. "Econometrics For Grumblers: A New Look At The Literature On Cross‐Country Growth Empirics," Journal of Economic Surveys, Wiley Blackwell, vol. 25(1), pages 109-155, 02.

    More about this item

    Keywords

    modele panelowe; sektorowe funkcje produkcji; FMOLS;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E23 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Production

    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:sgh:gosnar:y:2020:i:2:p:71-94. 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: Grzegorz Konat (email available below). General contact details of provider: https://edirc.repec.org/data/sgwawpl.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.