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Public policy, innovation and total factor productivity : An application to Taiwan's manufacturing industry

Author

Listed:
  • Chia-Lin Chang

    (NCHU - National Chung Hsing University - NUST - National University System of Taiwan)

  • Stéphane Robin

    (BETA - Bureau d'Économie Théorique et Appliquée - INRA - Institut National de la Recherche Agronomique - UNISTRA - Université de Strasbourg - UL - Université de Lorraine - CNRS - Centre National de la Recherche Scientifique)

Abstract

This paper analyses the impact of innovation on productivity in Taiwan. Using a panel of 48,794 firms observed over the 1997-2003 period and distributed across 23 industries. we compute total factor productivity (TFP) by estimating Translog production functions with C, L, E, M inputs. We evaluate the impact of being an innovator on TFP using propensity score matching. The rationale is that, over the period, innovating firms are likely to have benefited from one of many innovation policy measures known as statute for upgrading industry (SUI) (until 1999) or "New SUI" (after 1999). Our results show a significantly negative effect of being an innovator on TFP in most industries, both before and after 1999. This suggests that firms having innovation expenditures either perform less well than the others because of unobserved factors. or are further away from the production frontier. Therefore, innovation in Taiwan seems to be associated with catching-up strategies.

Suggested Citation

  • Chia-Lin Chang & Stéphane Robin, 2008. "Public policy, innovation and total factor productivity : An application to Taiwan's manufacturing industry," Post-Print hal-03691878, HAL.
    • Handle: RePEc:hal:journl:hal-03691878
    DOI: 10.1016/j.matcom.2008.01.005
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    Cited by:

    1. Xueqing Wang & Yuan Chen & Bingsheng Liu & Yinghua Shen & Hui Sun, 2013. "A total factor productivity measure for the construction industry and analysis of its spatial difference: a case study in China," Construction Management and Economics, Taylor & Francis Journals, vol. 31(10), pages 1059-1071, October.
    2. repec:lic:licosd:32112 is not listed on IDEAS
    3. Wei Wei & Qiao Fan & Aijun Guo, 2022. "China’s Industrial TFPs at the Prefectural Level and the Law of Their Spatial–Temporal Evolution," Sustainability, MDPI, vol. 15(1), pages 1-21, December.
    4. Kancs, d’Artis & Siliverstovs, Boriss, 2016. "R&D and non-linear productivity growth," Research Policy, Elsevier, vol. 45(3), pages 634-646.
    5. Dimitrios Giokas & Nicolaos Eriotis & Ioannis Dokas, 2015. "Efficiency and productivity of the food and beverage listed firms in the pre-recession and recessionary periods in Greece," Applied Economics, Taylor & Francis Journals, vol. 47(19), pages 1927-1941, April.
    6. Xiao Xiang & Qiao Fan, 2024. "Advancing Green TFP Calculation: A Novel Spatiotemporal Econometric Solow Residual Method and Its Application to China’s Urban Industrial Sectors," Mathematics, MDPI, vol. 12(9), pages 1-33, April.
    7. Mohamad Yunus, Norhanishah & Said, Rusmawati & Law, Siong Hook, 2014. "Do Cost of Training, Education Level and R&D Investment Matter towards Influencing Labour Productivity?," Jurnal Ekonomi Malaysia, Faculty of Economics and Business, Universiti Kebangsaan Malaysia, vol. 48(1), pages 133-142.

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