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Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach

Author

Listed:
  • Jorge Antunes

    (COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal, Lemme, 355, 21949-900 Rio de Janeiro, Brazil)

  • Goodness C. Aye

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Rangan Gupta

    (Department of Economics, University of Pretoria, Pretoria, 0002, South Africa)

  • Peter Wanke

    (COPPEAD Graduate Business School, Federal University of Rio de Janeiro, Rua Paschoal, Lemme, 355, 21949-900 Rio de Janeiro, Brazil)

  • Yong Tan

    (School of Management, University of Bradford, Bradford, West Yorkshire, UK, BD7 1DP, UK)

Abstract

Long-term productivity performance at the country level has been a research object under different theoretical lenses, scrutinized by different modelling approaches. This paper revisits the dataset used in Bergeaud et al. (2016) and investigates the endogenous sources of distinct productivity performance in different advanced countries. Country-level data series with more than one hundred year time-span were collected for each one of the following attributes: Labor productivity (LP), Total Factor Productivity (TFP), Capital Intensity (KI), Gross Domestic Product per capita (GDP pc), Average age of equipment capital stock (Age K), and Human Capital intensity (Human K).Differently from previous studies, a Two-Dimensional Fuzzy-Monte Carlo Analysis (2DFMC) approach is proposed here to decompose the sources of long-term productivity performance. In the first dimension, a novel multi-attribute decision-making (MADM) model based on Type-2 Fuzzy Sets (T2FS) is developed to compute and rank long-term productivity performance of each county using Unbiased-Power functions for Ideal Solutions (UP-IS). Next, in the second dimension, a Stochastic Structural Relationship Programming (SSRP) Model based on neural networks is proposed to evaluate the endogenous feedbacks among the aforementioned productivity attributes and overall productivity performance. Results suggest that the UP-IS presented higher cross-performance scores relative to the TOPSIS base-case. Norway is the best performing country with a positive performance score of 0.854 while Portugal is the worst with a score of 0.347. In terms of ordinal ranking of long-term productivity performance, UP-IS ranked first, followed by TPF and next LP and GDPpc. Further, Human K and Age K have positive and negative impact respectively on long-term productivity performance in advanced countries. On the other hand, productivity performance has positive impact on KI and TFP but negative impact on LP and GDPpc.

Suggested Citation

  • Jorge Antunes & Goodness C. Aye & Rangan Gupta & Peter Wanke & Yong Tan, 2020. "Endogenous Long-Term Productivity Performance in Advanced Countries: A Novel Two-Dimensional Fuzzy-Monte Carlo Approach," Working Papers 2020111, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:2020111
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    1. Gathon, H. -J. & Pestieau, P., 1995. "Decomposing efficiency into its managerial and its regulatory components: The case of European railways," European Journal of Operational Research, Elsevier, vol. 80(3), pages 500-507, February.
    2. Padilla-Pérez, Ramón & Villarreal, Francisco G., 2017. "Structural change and productivity growth in Mexico, 1990–2014," Structural Change and Economic Dynamics, Elsevier, vol. 41(C), pages 53-63.
    3. Maroto-Sánchez, Andrés & Cuadrado-Roura, Juan R., 2009. "Is growth of services an obstacle to productivity growth? A comparative analysis," Structural Change and Economic Dynamics, Elsevier, vol. 20(4), pages 254-265, December.
    4. Yu, Ming-Miin, 2008. "Assessing the technical efficiency, service effectiveness, and technical effectiveness of the world's railways through NDEA analysis," Transportation Research Part A: Policy and Practice, Elsevier, vol. 42(10), pages 1283-1294, December.
    5. Bergeaud, A. & Cette, G. & Lecat, R., 2015. "Productivity trends from 1890 to 2012 in advanced countries," Rue de la Banque, Banque de France, issue 07, June..
    6. Yu, Ming-Miin & Lin, Erwin T.J., 2008. "Efficiency and effectiveness in railway performance using a multi-activity network DEA model," Omega, Elsevier, vol. 36(6), pages 1005-1017, December.
    7. Wang, Shaw-Er & Liao, Chun-Hsiung, 2006. "Cost structure and productivity growth of the Taiwan Railway," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 42(4), pages 317-339, July.
    8. Compagnucci, Fabiano & Gentili, Andrea & Valentini, Enzo & Gallegati, Mauro, 2021. "Have jobs and wages stopped rising? Productivity and structural change in advanced countries," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 412-430.
    9. Marchetti, Dalmo & Wanke, Peter F., 2019. "Efficiency in rail transport: Evaluation of the main drivers through meta-analysis with resampling," Transportation Research Part A: Policy and Practice, Elsevier, vol. 120(C), pages 83-100.
    10. P. Brockett & B. Golany & Shan Li, 1999. "Analysis of Intertemporal Efficiency Trends Using Rank Statistics With an Application Evaluating the Macro Economic Performance of OECD Nations," Journal of Productivity Analysis, Springer, vol. 11(2), pages 169-182, April.
    11. David M. Byrne & John G. Fernald & Marshall B. Reinsdorf, 2016. "Does the United States Have a Productivity Slowdown or a Measurement Problem?," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 47(1 (Spring), pages 109-182.
    12. Loizides, John & Tsionas, Efthymios G., 2002. "Productivity growth in European railways: a new approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 36(7), pages 633-644, August.
    13. Cantos, Pedro & Maudos, Joaqui­n, 2001. "Regulation and efficiency: the case of European railways," Transportation Research Part A: Policy and Practice, Elsevier, vol. 35(5), pages 459-472, June.
    14. Friesenbichler, Klaus S. & Glocker, Christian, 2019. "Tradability and productivity growth differentials across EU Member States," Structural Change and Economic Dynamics, Elsevier, vol. 50(C), pages 1-13.
    15. Chou, Jui-Sheng & Kim, Changwan & Kuo, Yao-Chen & Ou, Nai-Chi, 2011. "Deploying effective service strategy in the operations stage of high-speed rail," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 47(4), pages 507-519, July.
    16. Alexander Murray, 2017. "What Explains the Post-2004 U.S.Productivity Slowdown?," CSLS Research Reports 2017-05, Centre for the Study of Living Standards.
    17. Gilbert Cette & Christian Clerc & Lea Bresson, 2015. "Contribution of ICT Diffusion to Labour Productivity Growth: The United States, Canada, the Eurozone, and the United Kingdom, 1970-2013," International Productivity Monitor, Centre for the Study of Living Standards, vol. 28, pages 81-88, Spring.
    18. Azenui, Ngwinui Belinda & Rada, Codrina, 2021. "Labor productivity growth in sub-Sahara African LDCs: sectoral contributions and macroeconomic factors," Structural Change and Economic Dynamics, Elsevier, vol. 56(C), pages 10-26.
    19. Huang, Wencheng & Zhang, Yue & Shuai, Bin & Xu, Minhao & Xiao, Wei & Zhang, Rui & Xu, Yifei, 2019. "China railway industry reform evolution approach: Based on the Vertical Separation Model," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 546-556.
    20. Fare, Rolf & Shawna Grosskopf & Mary Norris & Zhongyang Zhang, 1994. "Productivity Growth, Technical Progress, and Efficiency Change in Industrialized Countries," American Economic Review, American Economic Association, vol. 84(1), pages 66-83, March.
    21. Amendola, Mario & Gaffard, Jean Luc & Saraceno, Francesco, 2005. "Technical progress, accumulation and financial constraints: is the productivity paradox really a paradox?," Structural Change and Economic Dynamics, Elsevier, vol. 16(2), pages 243-261, June.
    22. David M. Byrne & Stephen D. Oliner & Daniel E. Sichel, 2018. "How Fast are Semiconductor Prices Falling?," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 64(3), pages 679-702, September.
    23. Decker, Ryan A. & Haltiwanger, John & Jarmin, Ron S. & Miranda, Javier, 2016. "Where has all the skewness gone? The decline in high-growth (young) firms in the U.S," European Economic Review, Elsevier, vol. 86(C), pages 4-23.
    24. Azomahou, Théophile T. & Diene, Bity & Diene, Mbaye, 2013. "Nonlinearities in productivity growth: A semi-parametric panel analysis," Structural Change and Economic Dynamics, Elsevier, vol. 24(C), pages 45-75.
    25. Broadberry, Stephen & Ghosal, Sayantan, 2005. "Technology, organisation and productivity performance in services: lessons from Britain and the United States since 1870," Structural Change and Economic Dynamics, Elsevier, vol. 16(4), pages 437-466, December.
    26. Emrah Onder & Nihat TaÅŸ & Ali HepÅŸen, 2015. "Economic Performance Evaluation of Fragile 5 Countries After the Great Recession of 2008-2009 Using Analytic Network Process and Topsis Methods," Journal of Applied Finance & Banking, SCIENPRESS Ltd, vol. 5(1), pages 1-1.
    27. Robert J. Gordon, 2012. "Is U.S. Economic Growth Over? Faltering Innovation Confronts the Six Headwinds," NBER Working Papers 18315, National Bureau of Economic Research, Inc.
    28. Houssein Abdo & Jean-Marie Flaus, 2016. "Uncertainty quantification in dynamic system risk assessment: a new approach with randomness and fuzzy theory," International Journal of Production Research, Taylor & Francis Journals, vol. 54(19), pages 5862-5885, October.
    29. Bity Diene & Mbaye Diene & Théophile Azomahou, 2013. "Nonlinearities in Productivity Growth: A Semi-parametric Panel Analysis," Post-Print halshs-00914557, HAL.
    30. Antonin Bergeaud & Gilbert Cette & Rémy Lecat, 2016. "Productivity Trends in Advanced Countries between 1890 and 2012," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 62(3), pages 420-444, September.
    31. Naastepad, C. W. M. & Kleinknecht, Alfred, 2004. "The Dutch productivity slowdown: the culprit at last?," Structural Change and Economic Dynamics, Elsevier, vol. 15(2), pages 137-163, June.
    32. Po-Chin Wu & Tzu-Hsien Huang & Sheng-Chieh Pan, 2014. "Country Performance Evaluation: The DEA Model Approach," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 118(2), pages 835-849, September.
    33. Palazuelos, Enrique & Fernández, Rafael, 2009. "Demand, employment, and labour productivity in the European economies," Structural Change and Economic Dynamics, Elsevier, vol. 20(1), pages 1-15, March.
    34. Tang, Jianmin & Wang, Weimin, 2020. "Technological frontier, technical efficiency and the post-2000 productivity slowdown in Canada," Structural Change and Economic Dynamics, Elsevier, vol. 55(C), pages 12-25.
    35. Millemaci, Emanuele & Ofria, Ferdinando, 2016. "Supply and demand-side determinants of productivity growth in Italian regions," Structural Change and Economic Dynamics, Elsevier, vol. 37(C), pages 138-146.
    36. Marchetti, Dalmo & Wanke, Peter, 2020. "Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    37. Moussir, Charaf-Eddine & Chatri, Abdellatif, 2020. "Structural change and labour productivity growth in Morocco," Structural Change and Economic Dynamics, Elsevier, vol. 53(C), pages 353-358.
    38. Nishat Tasnim & Munshi Naser Ibne Afzal, 2018. "An empirical investigation of country level efficiency and national systems of entrepreneurship using Data Envelopment Analysis (DEA) and the TOBIT model," Journal of Global Entrepreneurship Research, Springer;UNESCO Chair in Entrepreneurship, vol. 8(1), pages 1-17, December.
    39. Banos-Pino, Jose & Fernandez-Blanco, Victor & Rodriguez-Alvarez, Ana, 2002. "The allocative efficiency measure by means of a distance function: The case of Spanish public railways," European Journal of Operational Research, Elsevier, vol. 137(1), pages 191-205, February.
    40. Fraszczyk, Anna & Lamb, Thomas & Marinov, Marin, 2016. "Are railways really that bad? An evaluation of rail systems performance in Europe with a focus on passenger rail," Transportation Research Part A: Policy and Practice, Elsevier, vol. 94(C), pages 573-591.
    41. Roets, Bart & Verschelde, Marijn & Christiaens, Johan, 2018. "Multi-output efficiency and operational safety: An analysis of railway traffic control centre performance," European Journal of Operational Research, Elsevier, vol. 271(1), pages 224-237.
    42. Bo Hsiao & LihChyun Shu & Ming-Miin Yu & Li-Kang Shen & Ding-Jiun Wang, 2017. "Performance evaluation of the Taiwan railway administration," Annals of Operations Research, Springer, vol. 259(1), pages 119-156, December.
    43. Ball, E. & Fare, R. & Grosskopf, S. & Zaim, O., 2005. "Accounting for externalities in the measurement of productivity growth: the Malmquist cost productivity measure," Structural Change and Economic Dynamics, Elsevier, vol. 16(3), pages 374-394, September.
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    Keywords

    endogeneity; type-2 fuzzy sets; 2DFMC; stochastic performance; long-term productivity; advanced countries;
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