摘要 资源配置效率是影响经济增长的重要因素,但现有文献在研究资源配置效率时存在两方面不足:一是对金融危机以来中国企业层面配置效率缺少关注;二是研究方法上忽略了产业链上行业间的相互影响。本文将国内外投入产出联系引入Hsieh and Klenow(2009)增加值模型,构建基于总产出的模型,利用全国税收调查数据库的数据,考察中国制造业部门的配置效率。分析显示,2009—2015年间,中国制造业部门的配置效率呈现先波动后改善的趋势。这一发现对于不同设定都十分稳健,但随企业规模和地理位置存在异质性。不过,样本期资源配置效率改善,对制造业部门生产效率提高的贡献较弱。本文有助于理解“国内大循环为主体、国内国际双循环”新发展格局下产业链联系对经济效率的影响。
Summary:
Modern economic growth is driven mainly by improved production efficiency, which may be due to technical changes or improved resource allocation. The latter has attracted the attention of economists in recent years, resulting in a large volume of literature on resource misallocation. Such research echoes China's call for “high-quality development,” of which improved efficiency of resource allocation is a key component. However, the literature on China's allocative efficiency suffers from two problems, substantially reducing its value in terms of informing policy making in China. First, most studies do not cover periods after 2008 and thus do not address important recent changes in the Chinese economy. Second, the literature neglects the interplay between industries in the production network, which might bias estimates of allocative efficiency in China. This paper helps to fill both gaps by (1) developing a misallocation accounting framework that extends the value-added framework of Hsieh and Klenow (2009) by incorporating domestic and foreign input linkages, and (2) applying this framework to a new dataset covering the years 2009-2015, enabling an assessment of allocative efficiency in more recent years than was previously available. Our findings suggest that during 2009-2015, allocative efficiency initially fluctuated but began to improve in 2012. Over the longer term, these changes reversed the trend of worsening allocative efficiency that had begun in 2004. These findings are robust to various alternative specifications. The improvement in allocative efficiency since 2012 is probably attributable to the Chinese government's efforts to allow market forces to play a larger role in resource allocation, as well as wiser design of economic policies. When we decompose aggregate productivity growth into different components, however, we find that improved allocative efficiency plays a minor role in productivity growth, suggesting room for future improvement. Methodologically, our analysis shows that the estimation results differ between the value-added model, which does not consider intersectoral linkages, and the gross-output model, which allows for a full-fledged production network. As argued by Hang et al. (2020), ignoring the production network when measuring misallocation introduces biases. Our empirical findings thus caution against the future use of the value-added model. Fortunately, our analysis shows that with a few minor changes, the gross-output model is as easy to implement as the value-added model. We draw a few implications from our study. First, we find that the Chinese economy is still far from efficient. The potential output is less than 50% of the efficient level. Continued improvements in allocative efficiency will require substantial effort. On a more positive note, however, this finding demonstrates potential of the Chinese economy for fast economic growth in the near future. Second, the Chinese economy is experiencing important structural changes. Academic studies must be up to date to inform policy decisions. Therefore, Chinese economists have a responsibility to study new economic trends in a timely fashion. Third, our study emphasizes the importance of intersectoral and foreign linkages. This emphasis echoes the new developmental paradigm, which features dual circulation. Studies in this area could help build a foundation for current developmental policy making. Still, future studies can improve on our work in several ways. First, and most obviously, the data can be expanded to more recent years to further update the research. To obtain a full picture of the Chinese economy, future studies should also look beyond the manufacturing sector and examine other sectors, especially the services sector, which is becoming increasingly important. Second, our study mainly focuses on resource allocation within industries. While the production network still plays an important role in magnifying the effects of within-industry misallocation, between-industry misallocation is an important issue and should not be ignored. Studying between-industry misallocation would elucidate the role of the production network in magnifying within-industry effects, providing very valuable information for the new developmental paradigm featuring dual circulation. Third, due to data limitations, we cannot study the misallocation of individual intermediate inputs but must take all intermediate inputs as a whole. This approach introduces an upward bias in the estimates of allocative efficiency. That is, our estimates of allocative efficiency should be considered as an upper bound. Future studies could use better data and new methods to improve our estimates.
杭静, 申广军. 金融危机后中国制造业部门的配置效率——基于生产网络视角的研究[J]. 金融研究, 2024, 524(2): 57-75.
HANG Jing, SHEN Guangjun. Allocative Efficiency in China's Manufacturing Sector After the Global Financial Crisis: A Production Network Approach. Journal of Financial Research, 2024, 524(2): 57-75.
[1] 蔡昉、Albert Park和赵耀辉,2009,《改革中的中国劳动力市场》,载勃兰特和罗斯基主编《伟大的中国经济转型》,上海:世纪出版集团,第140~181页。 [2] 陈晓光,2013,《增值税有效税率差异与效率损失——兼议对“营改增”的启示》,《中国社会科学》第8期,第67～84页。 [3] 陈永伟和胡伟民,2011,《价格扭曲、要素错配和效率损失:理论和应用》,《经济学(季刊)》第4期,第1401～1422页。 [4] 龚关和胡关亮,2013,《中国制造业资源配置效率与全要素生产率》,《经济研究》第4期,第4～15页。 [5] 刘世锦、余斌和陈昌盛,2014,《金融危机后世界经济格局调整与变化趋势》,《中国发展观察》第2期,第18～19页。 [6] 马草原、孙思洋和张昭,2023,《中国地区间素市场分割的识别与影响因素分析》,《金融研究》第2期,第78～95页。 [7] 宁博、潘越和汤潮,2022,《地域商会有助于缓解企业融资约束吗？——来自A股民营上市企业的证据》,《金融研究》第2期,第153～170页。 [8] 邵宜航、步晓宁和张天华,2013,《资源配置扭曲与中国工业全要素生产率——基于工业企业数据库再测算》,《中国工业经济》第12期,第39～51页。 [9] 申广军、陈斌开和杨汝岱,2016,《减税能否提振中国经济？——基于中国增值税改革的实证研究》,《经济研究》第11期,第70～82页。 [10] 申广军、姚洋和钟宁桦,2020,《民营企业融资难与我国劳动力市场的结构性问题》,《管理世界》第2期,第41～58页。 [11] 杨汝岱,2015,《中国制造业企业全要素生产率研究》,《经济研究》第2期,第61～74页。 [12] 尹恒和李世刚,2019,《资源配置效率改善的空间有多大？——基于中国制造业的结构估计》,《管理世界》第12期,第28～44页。 [13] 邹静娴、申广军和刘超,2022,《减税政策对小微企业债务期限结构的影响》,《金融研究》第6期,第74～93页。 [14] Bigio, S., and J. La'O, 2020, “Distortions in Production Networks”, Quarterly Journal of Economics, 154(4): pp.2187～2253. [15] Bils, M., P. J. Klenow, and C. Ruane, 2021, “Misallocation or Mismeasurement？”, Journal of Monetary Economics, 124: pp.S39～S56. [16] Brandt, L., J. Van Biesebroeck, Y. Zhang, 2012, “Creative Accounting or Creative Destruction？ Firm-level Productivity Growth in Chinese Manufacturing”, Journal of Development Economics, 97: pp.339～351. [17] Brandt, L., T. Tombe, and X. Zhu, 2013, “Factor Market Distortions across Time, Space, and Sectors in China”, Review of Economic Dynamics, 16: pp.39～58. [18] Broda, C., and D. E. Weinstein, 2006, “Globalization and the Gains from Variety”, Quarterly Journal of Economics, 121(2): pp.541～585. [19] Caselli, F., 2005, “Accounting for Cross-country Income Differences”, in P. Aghion and S. Durlauf (ed.) Handbook of Economic Growth, Amsterdam: North-Holland, pp.679～741. [20] Chen, Z., Z. Liu, J. C. Suárez Serrato, and D. Y. Xu, 2021, “Notching R＆D Investment with Corporate Income Tax Cuts in China”, American Economic Review, 111(7): pp.2065～2100. [21] Hang, J., P. Krishna, and H. Tang, 2020, “Production Networks, Input Trade, and Misallocation”, NBER Working Paper, No. 27983. [22] Hsieh, C.-T., and P. J. Klenow, 2009, “Misallocation and Manufacturing TFP in China and India”, Quarterly Journal of Economics, 124 (4): pp.1403～1448. [23] Jones, C. I., 2011, “Intermediate Goods and Weak Links in the Theory of Economic Development”, American Economic Journal: Macroeconomics, 3(2): pp.1～28. [24] Jones, C. I., 2013, “Misallocation, Economic Growth, and Input-Output Economics”, in D. Acemoglu, M. Arellano, and E. Dekel (ed.) Advances in Economics and Econometrics. Cambridge University Press, pp.419～456. [25] Liu, E., 2019, “Industrial Policies in Production Networks”, Quarterly Journal of Economics, 134(4): pp.1883～1948. [26] Restuccia, D., and R. Rogerson, 2008, “Policy Distortions and Aggregate Productivity with Heterogenous Establishments”, Review of Economic Dynamics, 11: pp.707～720.
