TFP shocks and endogenous innovation ability in manufacturing industry: from the perspective of structural stickiness
Abstract
This paper identifies the systemic shocks of total factor productivity (TFP) at the macro level and industry level, and then evaluates the structural stickiness of TFP shocks by using information entropy and industry correlation degree through counterfactual structural simulation based on China’s manufacturing companies. We find that: in the face of TFP systemic shocks, the industries with less structural stickiness include computer communication and other electronic equipment manufacturing, special equipment manufacturing and general equipment manufacturing, indicating that these industries have a strong internal innovation power. The TFP distribution of electrical machinery and equipment manufacturing industry and ferrous metal smelting and rolling industry showed structural differentiation, and the lower tail enterprises are not sensitive to TFP shocks. The industries with strong structural stickiness are non-ferrous metal processing industry and non-metallic mineral products industry, etc., which have weak internal innovation power and need exogenous innovation incentives. In addition, there is a significant positive correlation between industry correlation and information entropy, which emphasizes the radiation effect role of industries with high industry correlation degree. The research provides a new method to evaluate the innovation ability of the industry and a basis for the differentiation of innovation incentive policies in the industry.
First published online 15 November 2024
Keyword : total factor productivity shocks, manufacturing industry, structural viscosity, endogenous innovation
How to Cite
Zhao, D., Zhao, T., & Du, R. (2024). TFP shocks and endogenous innovation ability in manufacturing industry: from the perspective of structural stickiness. Technological and Economic Development of Economy, 1-33. https://doi.org/10.3846/tede.2024.22020
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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León-Ledesma, M. A., McAdam, P., & Willman, A. (2015). Production technology estimates and balanced growth. Oxford Bulletin of Economics and Statistics, 77(1), 40–65. https://doi.org/10.1111/obes.12049
Leontief, W. W. (1936). Quantitative input and output relations in the economic systems of the United States. The Review of Economics and Statistics, 18(3), 105–125. https://doi.org/10.2307/1927837
Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. Review of Economic Studies, 70(2), 317–341. https://doi.org/10.1111/1467-937X.00246
Li, G., Wang, X., Su, S., & Su, Y. (2019). How green technological innovation ability influences enterprise competitiveness. Technology in Society, 59, Article 101136. https://doi.org/10.1016/j.techsoc.2019.04.012
Li, J., Zou, Y., & Li, M. (2022). Dynamic evaluation of the technological innovation capability of patent-intensive industries in China. Managerial and Decision Economics, 43(7), 3198–3218. https://doi.org/10.1002/mde.3591
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Powell, D. (2022). Quantile regression with nonadditive fixed effects. Empirical Economics, 63(5), 2675–2691. https://doi.org/10.1007/s00181-022-02216-6
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Song, M., Peng, L., Shang, Y., & Zhao, X. (2022). Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation. Technological Forecasting and Social Change, 174, Article 121276. https://doi.org/10.1016/j.techfore.2021.121276
Syverson, C. (2011). What determines productivity? Journal of Economic Literature, 49(2), 326–365. https://doi.org/10.1257/jel.49.2.326
Tseng, C.-Y., & Wu, L.-Y. (2007). Innovation quality in the automobile industry: Measurement indicators and performance implications. International Journal of Technology Management, 37(1–2), 162–177. https://doi.org/10.1504/IJTM.2007.011809
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Antonelli, C., & Scellato, G. (2011). Out-of-equilibrium profit and innovation. Economics of Innovation and New Technology, 20(5), 405–421. https://doi.org/10.1080/10438599.2011.562350
Bai, J. (2009). Panel data models with interactive fixed effects. Econometrica, 77(4), 1229–1279. https://doi.org/10.3982/ECTA6135
Bai, J., & Wang, P. (2015). Identification and Bayesian estimation of dynamic factor models. Journal of Business & Economic Statistics, 33(2), 221–240. https://doi.org/10.1080/07350015.2014.941467
Baqaee, D. R., & Farhi, E. (2019). The macroeconomic impact of microeconomic shocks: Beyond Hulten’s theorem. Econometrica, 87(4), 1155–1203. https://doi.org/10.3982/ECTA15202
Bena, J., Ortiz-Molina, H., & Simintzi, E. (2022). Shielding firm value: Employment protection and process innovation. Journal of Financial Economics, 146(2), 637–664. https://doi.org/10.1016/j.jfineco.2021.10.005
Buccirossi, P., Ciari, L., Duso, T., Spagnolo, G., & Vitale, C. (2013). Competition policy and productivity growth: An empirical assessment. The Review of Economics and Statistics, 95(4), 1324–1336. https://doi.org/10.1162/REST_a_00304
Bussière, M., Fratzscher, M., & Müller, G. J. (2010). Productivity shocks, budget deficits and the current account. Journal of International Money and Finance, 29(8), 1562–1579. https://doi.org/10.1016/j.jimonfin.2010.05.012
Cameron, G., Proudman, J., & Redding, S. (2005). Technological convergence, R&D, trade and productivity growth. European Economic Review, 49(3), 775–807. https://doi.org/10.1016/S0014-2921(03)00070-9
Chen, X., Liu, X., & Zhu, Q. (2022). Comparative analysis of total factor productivity in China’s high-tech industries. Technological Forecasting and Social Change, 175, Article 121332. https://doi.org/10.1016/j.techfore.2021.121332
Cheng, Y., Zhou, X., & Li, Y. (2023). The effect of digital transformation on real economy enterprises’ total factor productivity. International Review of Economics & Finance, 85, 488–501. https://doi.org/10.1016/j.iref.2023.02.007
Crepon, B., & Duguet, E. (1997). Research and development, competition and innovation pseudo-maximum likelihood and simulated maximum likelihood methods applied to count data models with heterogeneity. Journal of Econometrics, 79(2), 355–378. https://doi.org/10.1016/S0304-4076(97)00027-4
Decressin, J., & Disyatat, P. (2008). Productivity shocks and the current account: An alternative perspective of capital market integration. Journal of International Money and Finance, 27(6), 897–914. https://doi.org/10.1016/j.jimonfin.2008.04.010
DeJong, D. N., Ingram, B. F., & Whiteman, C. H. (2000). Keynesian impulses versus Solow residuals: Identifying sources of business cycle fluctuations. Journal of Applied Econometrics, 15(3), 311–329. https://doi.org/10.1002/1099-1255(200005/06)15:3<311::AID-JAE557>3.0.CO;2-L
El-Shagi, M. (2023). Productivity shocks and capital flows. Economics Letters, 225, Article 111015. https://doi.org/10.1016/j.econlet.2023.111015
Feng, R., Shen, C., & Guo, Y. (2024). Digital finance and labor demand of manufacturing enterprises: Theoretical mechanism and heterogeneity analysis. International Review of Economics & Finance, 89, 17–32. https://doi.org/10.1016/j.iref.2023.07.065
Fisch, C., Sandner, P., & Regner, L. (2017). The value of Chinese patents: An empirical investigation of citation lags. China Economic Review, 45, 22–34. https://doi.org/10.1016/j.chieco.2017.05.011
Glick, R., & Rogoff, K. (1995). Global versus country-specific productivity shocks and the current account. Journal of Monetary Economics, 35(1), 159–192. https://doi.org/10.1016/0304-3932(94)01181-9
Griliches, Z. (1980). R & D and the productivity slowdown. The American Economic Review, 70(2), 343–348. http://www.jstor.org/stable/1815495
Griliches, Z., & Mairesse, J. (1981). Productivity and R and D at the firm level (Working Paper No. 826). National Bureau of Economic Research. https://doi.org/10.3386/w0826
Grindley, P., & Teece, D. (1997). Managing intellectual capital: Licensing and cross-licensing in semiconductors and electronics. California Management Review, 39(2), 41–48. https://doi.org/10.2307/41165885
Harhoff, D. (1998). R&D and productivity in German manufacturing firms. Economics of Innovation and New Technology, 6(1), 29–50. https://doi.org/10.1080/10438599800000012
Hu, J., Xue, H., & Yu, Z. (2023). Study on the effect of R&D investment on technical progress of manufacture in China. Journal of the Knowledge Economy, 15, 9899. https://doi.org/10.1007/s13132-023-01414-6 (Retraction published 12 February 2024, Journal of the Knowledge Economy, 15, 9899).
Kaplinsky, R., & Readman, J. (2005). Globalization and upgrading: What can (and cannot) be learnt from international trade statistics in the wood furniture sector? Industrial and Corporate Change, 14(4), 679–703. https://doi.org/10.1093/icc/dth065
Kim, J., & Lee, S. (2015). Patent databases for innovation studies: A comparative analysis of USPTO, EPO, JPO and KIPO. Technological Forecasting and Social Change, 92, 332–345. https://doi.org/10.1016/j.techfore.2015.01.009
King, R. G., Plosser, C. I., Stock, J. H., & Watson, M. W. (1987). Stochastic trends and economic fluctuations (Working Paper No. 2229). National Bureau of Economic Research. https://doi.org/10.3386/w2229
Klump, R., McAdam, P., & Willman, A. (2012). The normalized CES production function: Theory and empirics. Journal of Economic Surveys, 26(5), 769–799. https://doi.org/10.1111/j.1467-6419.2012.00730.x
König, M., Storesletten, K., Song, Z., & Zilibotti, F. (2022). From imitation to innovation: Where is all that Chinese R&D going? Econometrica, 90(4), 1615–1654. https://doi.org/10.3982/ECTA18586
León-Ledesma, M. A., McAdam, P., & Willman, A. (2010). Identifying the elasticity of substitution with biased technical change. The American Economic Review, 100(4), 1330–1357. https://doi.org/10.1257/aer.100.4.1330
León-Ledesma, M. A., McAdam, P., & Willman, A. (2015). Production technology estimates and balanced growth. Oxford Bulletin of Economics and Statistics, 77(1), 40–65. https://doi.org/10.1111/obes.12049
Leontief, W. W. (1936). Quantitative input and output relations in the economic systems of the United States. The Review of Economics and Statistics, 18(3), 105–125. https://doi.org/10.2307/1927837
Levinsohn, J., & Petrin, A. (2003). Estimating production functions using inputs to control for unobservables. Review of Economic Studies, 70(2), 317–341. https://doi.org/10.1111/1467-937X.00246
Li, G., Wang, X., Su, S., & Su, Y. (2019). How green technological innovation ability influences enterprise competitiveness. Technology in Society, 59, Article 101136. https://doi.org/10.1016/j.techsoc.2019.04.012
Li, J., Zou, Y., & Li, M. (2022). Dynamic evaluation of the technological innovation capability of patent-intensive industries in China. Managerial and Decision Economics, 43(7), 3198–3218. https://doi.org/10.1002/mde.3591
Li, L. (2013). The path to made-in-China: How this was done and future prospects. International Journal of Production Economics, 146(1), 4–13. https://doi.org/10.1016/j.ijpe.2013.05.022
Lin, T.-X., Wu, Z.-H., & Yang, J.-J. (2023). The evaluation of innovation efficiency of China’s high-tech manufacturing industry based on the analysis of the three-stage network DEA-Malmquist model. Production Planning & Control, 1–13. https://doi.org/10.1080/09537287.2023.2165189
Ma, Y., Ni, H., Yang, X., Kong, L., & Liu, C. (2023). Government subsidies and total factor productivity of enterprises: A life cycle perspective. Economia Politica, 40(1), 153–188. https://doi.org/10.1007/s40888-022-00292-6
Maćkowiak, B., & Wiederholt, M. (2015). Business cycle dynamics under rational inattention. The Review of Economic Studies, 82(4), 1502–1532. https://doi.org/10.1093/restud/rdv027
Malewicki, D., & Sivakumar, K. (2004). Patents and product development strategies: A model of antecedents and consequences of patent value. European Journal of Innovation Management, 7(1), 5–22. https://doi.org/10.1108/14601060410515600
Moser, P. (2012). Innovation without patents: Evidence from world’s fairs. The Journal of Law and Economics, 55(1), 43–74. https://doi.org/10.1086/663631
National Bureau of Statistics of China. (2022). China statistical yearbook. China Statistics Press. https://www.stats.gov.cn/sj/ndsj/2022/indexeh.htm
National Bureau of Statistics of the People’s Republic of China. (2020). National data. https://data.stats.gov.cn/ifnormal.htm?u=/files/html/quickSearch/trcc/trcc01.html&h=740&from=groupmessage&isappinstalled=0
Olley, G. S., & Pakes, A. (1996). The dynamics of productivity in the telecommunications equipment industry. Econometrica, 64(6), 1263–1297. https://doi.org/10.2307/2171831
Orlando, B., Ballestra, L. V., Magni, D., & Ciampi, F. (2020). Open innovation and patenting activity in health care. Journal of Intellectual Capital, 22(2), 384–402. https://doi.org/10.1108/JIC-03-2020-0076
Powell, D. (2020). Quantile treatment effects in the presence of covariates. The Review of Economics and Statistics, 102(5), 994–1005. https://doi.org/10.1162/rest_a_00858
Powell, D. (2022). Quantile regression with nonadditive fixed effects. Empirical Economics, 63(5), 2675–2691. https://doi.org/10.1007/s00181-022-02216-6
Salgado, S., Guvenen, F., & Bloom, N. (2019). Skewed business cycles (Working Paper No. 26565). National Bureau of Economic Research. https://doi.org/10.3386/w26565
Schulze, M.-S. (2007). Origins of catch-up failure: Comparative productivity growth in the Habsburg Empire, 1870–1910. European Review of Economic History, 11(2), 189–218. https://doi.org/10.1017/S1361491607001955
Song, M., Peng, L., Shang, Y., & Zhao, X. (2022). Green technology progress and total factor productivity of resource-based enterprises: A perspective of technical compensation of environmental regulation. Technological Forecasting and Social Change, 174, Article 121276. https://doi.org/10.1016/j.techfore.2021.121276
Syverson, C. (2011). What determines productivity? Journal of Economic Literature, 49(2), 326–365. https://doi.org/10.1257/jel.49.2.326
Tseng, C.-Y., & Wu, L.-Y. (2007). Innovation quality in the automobile industry: Measurement indicators and performance implications. International Journal of Technology Management, 37(1–2), 162–177. https://doi.org/10.1504/IJTM.2007.011809
Yang, J., & Yang, N. (2023a). Macroeconomic shocks, investment volatility and centrality in global manufacturing network. Empirical Economics, 65(3), 1433–1451. https://doi.org/10.1007/s00181-023-02372-3
Yang, J., & Yang, N. (2023b). Macroeconomic systematic shocks and industrial investment vulnerability: An international comparative perspective. Applied Economics, 56(43), 5190–5204. https://doi.org/10.1080/00036846.2023.2244245
Yu, L., Duan, Y., & Fan, T. (2020). Innovation performance of new products in China’s high-technology industry. International Journal of Production Economics, 219, 204–215. https://doi.org/10.1016/j.ijpe.2019.06.002
Yuan, R., & Wen, W. (2018). Managerial foreign experience and corporate innovation. Journal of Corporate Finance, 48, 752–770. https://doi.org/10.1016/j.jcorpfin.2017.12.015
Zhen, W., Xin-gang, Z., & Ying, Z. (2021). Biased technological progress and total factor productivity growth: From the perspective of China’s renewable energy industry. Renewable and Sustainable Energy Reviews, 146, Article 111136. https://doi.org/10.1016/j.rser.2021.111136