IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/114157.html
   My bibliography  Save this paper

Environmental variables and power firms' productivity: micro panel estimation with time-Invariant variables

Author

Listed:
  • Bigerna, Simona
  • D'Errico, Maria Chiara
  • Polinori, Paolo

Abstract

Internal and external institutions play a crucial role in the firms’ decision-making process and their productivity. Along with internal institutional features, such as the corporate ownership structure, external institutions, such as the stringency of market and environmental regulations, shape the framework in which firms operate. This research explores the role of these determinants and their interactions in affecting the productivity changes of the power generating firms in 15 European countries between 2010 and 2016. In a first step, using the firm-level ORBIS dataset, we first the productivity changes over time of power generating companies (NACE Code Rev.2.3511) using the global Malmquist index. Then, in a second step, dynamic panel linear model is applied to investigate how the internal and external institutional variables affect the dynamic of the global Malmquist index. In a preliminary analysis a wide range of tests are performed to detect the presence of outliers, the returns to scale, the correlation among inputs, out- puts and the productivity indexes, the independence between the distribution of the productivity indexes and the second-stage institutional variables. The institutional variables are almost time-invariant, the procedure proposed by Kripfganz and Schwarz (2019) is applied to consistently identify the effects of time invariant variables. This new method provides valuable robustness against wrong assumptions on the exogeneity on the instruments. To capture the interplay among external 54 and internal institutional variables, interaction variables are used. Results highlight the need to fine-tune the environmental regulation with the firm-specific internal features, to avoid hindering firm-level productivity in the power generation sector.

Suggested Citation

  • 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.
  • Handle: RePEc:pra:mprapa:114157
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/114157/1/MPRA_paper_114157.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Léopold Simar & Paul Wilson, 2000. "Statistical Inference in Nonparametric Frontier Models: The State of the Art," Journal of Productivity Analysis, Springer, vol. 13(1), pages 49-78, January.
    2. Stefan Ambec & Mark A. Cohen & Stewart Elgie & Paul Lanoie, 2013. "The Porter Hypothesis at 20: Can Environmental Regulation Enhance Innovation and Competitiveness?," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 7(1), pages 2-22, January.
    3. Yin-Fang Zhang & David Parker & Colin Kirkpatrick, 2008. "Electricity sector reform in developing countries: an econometric assessment of the effects of privatization, competition and regulation," Journal of Regulatory Economics, Springer, vol. 33(2), pages 159-178, April.
    4. 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.
    5. Montgomery, W. David, 1972. "Markets in licenses and efficient pollution control programs," Journal of Economic Theory, Elsevier, vol. 5(3), pages 395-418, December.
    6. Alois Kneip & Léopold Simar & Paul W. Wilson, 2016. "Testing Hypotheses in Nonparametric Models of Production," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(3), pages 435-456, July.
    7. Cinzia Daraio & Léopold Simar & Paul W. Wilson, 2018. "Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non‐parametric, two‐stage models of production," Econometrics Journal, Royal Economic Society, vol. 21(2), pages 170-191, June.
    8. Peyrache, Antonio & Coelli, Tim, 2009. "Testing procedures for detection of linear dependencies in efficiency models," European Journal of Operational Research, Elsevier, vol. 198(2), pages 647-654, October.
    9. Leonard F. S. Wang & Ya-chin Wang & Lihong Zhao, 2009. "Privatization and the Environment in a Mixed Duopoly with Pollution Abatement," Economics Bulletin, AccessEcon, vol. 29(4), pages 3112-3119.
    10. Daraio, Cinzia & Simar, Leopold & Wilson, Paul, 2018. "Central limit theorems for conditional efficiency measures and tests of the ‘separability’ condition in non-parametric, two-stage models of production," LIDAM Reprints ISBA 2018023, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    11. Herman R. J. Vollebergh & Edwin van der Werf, 2014. "The Role of Standards in Eco-innovation: Lessons for Policymakers," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 8(2), pages 230-248.
    12. Song, Malin & Zhu, Shuai & Wang, Jianlin & Zhao, Jiajia, 2020. "Share green growth: Regional evaluation of green output performance in China," International Journal of Production Economics, Elsevier, vol. 219(C), pages 152-163.
    13. repec:clg:wpaper:2008-02 is not listed on IDEAS
    14. 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.
    15. Stijn Claessens & Simeon Djankov & Joseph P. H. Fan & Larry H. P. Lang, 2002. "Disentangling the Incentive and Entrenchment Effects of Large Shareholdings," Journal of Finance, American Finance Association, vol. 57(6), pages 2741-2771, December.
    16. Paul L. Joskow, 2008. "Lessons Learned From Electricity Market Liberalization," The Energy Journal, , vol. 29(2_suppl), pages 9-42, December.
    17. Ajayi, Victor & Weyman-Jones, Thomas & Glass, Anthony, 2017. "Cost efficiency and electricity market structure: A case study of OECD countries," Energy Economics, Elsevier, vol. 65(C), pages 283-291.
    18. Atkinson, Scott E. & Primont, Daniel, 2002. "Stochastic estimation of firm technology, inefficiency, and productivity growth using shadow cost and distance functions," Journal of Econometrics, Elsevier, vol. 108(2), pages 203-225, June.
    19. Nakano, Makiko & Managi, Shunsuke, 2008. "Regulatory reforms and productivity: An empirical analysis of the Japanese electricity industry," Energy Policy, Elsevier, vol. 36(1), pages 201-209, January.
    20. Simar, Leopold & Wilson, Paul W., 2002. "Non-parametric tests of returns to scale," European Journal of Operational Research, Elsevier, vol. 139(1), pages 115-132, May.
    21. Bennedsen, Morten & Nielsen, Kasper Meisner, 2010. "Incentive and entrenchment effects in European ownership," Journal of Banking & Finance, Elsevier, vol. 34(9), pages 2212-2229, September.
    22. Léopold Simar & Paul W. Wilson, 2020. "Hypothesis testing in nonparametric models of production using multiple sample splits," Journal of Productivity Analysis, Springer, vol. 53(3), pages 287-303, June.
    23. Sebastian Kripfganz, 2019. "Generalized method of moments estimation of linear dynamic panel-data models," London Stata Conference 2019 17, Stata Users Group.
    24. Johnstone, Nick & Managi, Shunsuke & Rodríguez, Miguel Cárdenas & Haščič, Ivan & Fujii, Hidemichi & Souchier, Martin, 2017. "Environmental policy design, innovation and efficiency gains in electricity generation," Energy Economics, Elsevier, vol. 63(C), pages 106-115.
    25. Requate, Till & Unold, Wolfram, 2003. "Environmental policy incentives to adopt advanced abatement technology:: Will the true ranking please stand up?," European Economic Review, Elsevier, vol. 47(1), pages 125-146, February.
    26. Robin Sickles & David Good & Lullit Getachew, 2002. "Specification of Distance Functions Using Semi- and Nonparametric Methods with an Application to the Dynamic Performance of Eastern and Western European Air Carriers," Journal of Productivity Analysis, Springer, vol. 17(1), pages 133-155, January.
    27. David J. Mayston, 2017. "Data envelopment analysis, endogeneity and the quality frontier for public services," Annals of Operations Research, Springer, vol. 250(1), pages 185-203, March.
    28. Rajiv D. Banker & Ram Natarajan, 2011. "Statistical Tests Based on DEA Efficiency Scores," International Series in Operations Research & Management Science, in: William W. Cooper & Lawrence M. Seiford & Joe Zhu (ed.), Handbook on Data Envelopment Analysis, chapter 0, pages 273-295, Springer.
    29. Earnhart, Dietrich & Lizal, Lubomir, 2006. "Effects of ownership and financial performance on corporate environmental performance," Journal of Comparative Economics, Elsevier, vol. 34(1), pages 111-129, March.
    30. Arocena, Pablo & Waddams Price, Catherine, 2002. "Generating efficiency: economic and environmental regulation of public and private electricity generators in Spain," International Journal of Industrial Organization, Elsevier, vol. 20(1), pages 41-69, January.
    31. Arik Levinson & M. Scott Taylor, 2008. "Unmasking The Pollution Haven Effect," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 49(1), pages 223-254, February.
    32. Pastor, Jesus T. & Lovell, C.A. Knox, 2005. "A global Malmquist productivity index," Economics Letters, Elsevier, vol. 88(2), pages 266-271, August.
    33. Wang, Ke & Wei, Yi-Ming & Huang, Zhimin, 2018. "Environmental efficiency and abatement efficiency measurements of China's thermal power industry: A data envelopment analysis based materials balance approach," European Journal of Operational Research, Elsevier, vol. 269(1), pages 35-50.
    34. 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.
    35. Bahçe, Serdal & Taymaz, Erol, 2008. "The impact of electricity market liberalization in Turkey: "Free consumer" and distributional monopoly cases," Energy Economics, Elsevier, vol. 30(4), pages 1603-1624, July.
    36. Triebs, Thomas P. & Pollitt, Michael G., 2019. "Objectives and incentives: Evidence from the privatization of Great Britain’s power plants," International Journal of Industrial Organization, Elsevier, vol. 65(C), pages 1-29.
    37. Baumol,William J. & Oates,Wallace E., 1988. "The Theory of Environmental Policy," Cambridge Books, Cambridge University Press, number 9780521322249, September.
    38. Beladi, Hamid & Chao, Chi-Chur, 2006. "Does privatization improve the environment?," Economics Letters, Elsevier, vol. 93(3), pages 343-347, December.
    39. Kun Wang & Greg Shailer, 2015. "Ownership Concentration And Firm Performance In Emerging Markets: A Meta-Analysis," Journal of Economic Surveys, Wiley Blackwell, vol. 29(2), pages 199-229, April.
    40. Hsiao, Cheng & Hashem Pesaran, M. & Kamil Tahmiscioglu, A., 2002. "Maximum likelihood estimation of fixed effects dynamic panel data models covering short time periods," Journal of Econometrics, Elsevier, vol. 109(1), pages 107-150, July.
    41. Wilson, Paul W, 1993. "Detecting Outliers in Deterministic Nonparametric Frontier Models with Multiple Outputs," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 319-323, July.
    42. Bifulco, Robert & Bretschneider, Stuart, 2001. "Estimating school efficiency: A comparison of methods using simulated data," Economics of Education Review, Elsevier, vol. 20(5), pages 417-429, October.
    43. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-1426, November.
    44. W. J. Henisz, 2000. "The Institutional Environment for Economic Growth," Economics and Politics, Wiley Blackwell, vol. 12(1), pages 1-31, March.
    45. Wooldridge, Jeffrey M., 2009. "On estimating firm-level production functions using proxy variables to control for unobservables," Economics Letters, Elsevier, vol. 104(3), pages 112-114, September.
    46. Zhang, Fan, 2013. "How fit are feed-in tariff policies ? evidence from the European wind market," Policy Research Working Paper Series 6376, The World Bank.
    47. Manuel Arellano & Stephen Bond, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 58(2), pages 277-297.
    48. Meredith Fowlie, 2010. "Emissions Trading, Electricity Restructuring, and Investment in Pollution Abatement," American Economic Review, American Economic Association, vol. 100(3), pages 837-869, June.
    49. Windmeijer, Frank, 2005. "A finite sample correction for the variance of linear efficient two-step GMM estimators," Journal of Econometrics, Elsevier, vol. 126(1), pages 25-51, May.
    50. Knittel, Christopher R. & Metaxoglou, Konstantinos & Trindade, André, 2019. "Environmental implications of market structure: Shale gas and electricity markets," International Journal of Industrial Organization, Elsevier, vol. 63(C), pages 511-550.
    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. Simona Bigerna & Maria Chiara D’Errico & Paolo Polinori, 2022. "Sustainable Power Generation in Europe: A Panel Data Analysis of the Effects of Market and Environmental Regulations," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 83(2), pages 445-479, October.
    2. Bigerna, Simona & D'Errico, Maria Chiara & Polinori, Paolo, 2020. "Heterogeneous impacts of regulatory policy stringency on the EU electricity Industry:A Bayesian shrinkage dynamic analysis," Energy Policy, Elsevier, vol. 142(C).
    3. Kripfganz, Sebastian, 2014. "Unconditional Transformed Likelihood Estimation of Time-Space Dynamic Panel Data Models," VfS Annual Conference 2014 (Hamburg): Evidence-based Economic Policy 100604, Verein für Socialpolitik / German Economic Association.
    4. 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.
    5. Vadim Kufenko & Klaus Prettner, 2021. "Do you know your biases? A Monte Carlo analysis of dynamic panel data estimators," Department of Economics Working Papers wuwp316, Vienna University of Economics and Business, Department of Economics.
    6. Ofori, Isaac K. & Gbolonyo, Emmanuel Y. & Ojong, Nathanael, 2022. "Foreign Direct Investment and Inclusive Green Growth in Africa: Energy Efficiency Contingencies and Thresholds," MPRA Paper 115379, University Library of Munich, Germany, revised 09 Nov 2022.
    7. Kruiniger, Hugo, 2013. "Quasi ML estimation of the panel AR(1) model with arbitrary initial conditions," Journal of Econometrics, Elsevier, vol. 173(2), pages 175-188.
    8. Ofori, Isaac K. & Gbolonyo, Emmanuel Y. & Ojong, Nathanael, 2023. "Foreign direct investment and inclusive green growth in Africa: Energy efficiency contingencies and thresholds," Energy Economics, Elsevier, vol. 117(C).
    9. Youssef, Ahmed & Abonazel, Mohamed R., 2015. "Alternative GMM Estimators for First-order Autoregressive Panel Model: An Improving Efficiency Approach," MPRA Paper 68674, University Library of Munich, Germany.
    10. Breitung, Jörg & Kripfganz, Sebastian & Hayakawa, Kazuhiko, 2022. "Bias-corrected method of moments estimators for dynamic panel data models," Econometrics and Statistics, Elsevier, vol. 24(C), pages 116-132.
    11. Jan F. Kiviet & Milan Pleus & Rutger Poldermans, 2014. "Accuracy and efficiency of various GMM inference techniques in dynamic micro panel data models," UvA-Econometrics Working Papers 14-09, Universiteit van Amsterdam, Dept. of Econometrics.
    12. Cave, Joshua & Chaudhuri, Kausik & Kumbhakar, Subal C., 2023. "Dynamic firm performance and estimator choice: A comparison of dynamic panel data estimators," European Journal of Operational Research, Elsevier, vol. 307(1), pages 447-467.
    13. Jan Kiviet & Milan Pleus & Rutger Poldermans, 2017. "Accuracy and Efficiency of Various GMM Inference Techniques in Dynamic Micro Panel Data Models," Econometrics, MDPI, vol. 5(1), pages 1-54, March.
    14. S. C. West & A. W. Mugera & R. S. Kingwell, 2022. "The choice of efficiency benchmarking metric in evaluating firm productivity and viability," Journal of Productivity Analysis, Springer, vol. 57(2), pages 193-211, April.
    15. Alvarez, Javier & Arellano, Manuel, 2022. "Robust likelihood estimation of dynamic panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 21-61.
    16. Maurice J.G. Bun & Sarafidis, V., 2013. "Dynamic Panel Data Models," UvA-Econometrics Working Papers 13-01, Universiteit van Amsterdam, Dept. of Econometrics.
    17. Narjess Boubakri & Jean-Claude Cosset & Nassima Debab & Pascale Valéry, 2011. "Privatization and Globalization: an Empirical Analysis," Cahiers de recherche 1130, CIRPEE.
    18. 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.
    19. Medina-Durango, Carlos Alberto & Posso Suárez, Christian Manuel & Tamayo, Jorge A. & Monsalve, Emma, 2012. "Dinámica de la demanda laboral en la industria manufacturera colombiana 1993-2009 : una estimación panel VAR," Chapters, in: Arango-Thomas, Luis Eduardo & Hamann-Salcedo, Franz Alonso (ed.), El mercado de trabajo en Colombia : hechos, tendencias e instituciones, chapter 7, pages 289-330, Banco de la Republica de Colombia.
    20. Kufenko, Vadmin & Prettner, Klaus, 2017. "You can't always get what you want? A Monte Carlo analysis of the bias and the efficiency of dynamic panel data estimators," ECON WPS - Working Papers in Economic Theory and Policy 07/2017, TU Wien, Institute of Statistics and Mathematical Methods in Economics, Economics Research Unit.

    More about this item

    Keywords

    Environmental and Market regulation; Time-Invariant Variables; Global Malmquist Index; Electricity Sector;
    All these keywords.

    JEL classification:

    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • L5 - Industrial Organization - - Regulation and Industrial Policy
    • L9 - Industrial Organization - - Industry Studies: Transportation and Utilities
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    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:pra:mprapa:114157. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.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.