Emerging capital market distortions, capital bias and total factor productivity in China

Faculty of Management and Economics, Kunming University of Science and Technology, No. 727, South Jingming Road, Chenggong District, Kunming City, Yunnan Province, P.R. China

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Received: 7 March 2024
Accepted: 16 June 2025

Abstract

Against the backdrop of global economic structural transformation and factor market reforms, the linkage mechanism between capital allocation efficiency and total factor productivity (TFP) has become a core topic in academic research. Based on the theory of emerging capital, this paper combines robust optimization methods and capital allocation distortion theory to empirically analyze the impact of market distortions in emerging capital and traditional capital on TFP at the micro-enterprise level. The research classifies enterprise capital into emerging capital (Ec), centered on technological, information, and knowledge capital, and traditional capital (Tc), comprising monetary, physical, and human capital. It constructs a production function model incorporating capital distortion coefficients, uses the classical framework of Hsieh and Klenow to characterize factor allocation biases, and measures TFP through five methods: OLS, FE, SFA, OP, and LP. Theoretically, it draws on Uğurlu’s [Math. Stat. J. (2024) 1743–1761] robust optimization framework to treat capital distortions as resource allocation variables under uncertainty, demonstrating their dynamic impact on corporate production decisions. It also introduces Uğurlu and Brzeczek’s [Cent. Eur. J. Oper. Res. 31 (2023) 1043–1060] theory of probability distortion operators to analyze how capital bias regulates factor input efficiency through price mechanisms. Empirical results show that capital allocation distortions have a significantly positive impact on TFP: a 1% increase in emerging capital distortion raises TFP by 3.89%, while a 1% increase in traditional capital distortion boosts TFP by 9.51%. Heterogeneity analysis reveals more pronounced effects of capital distortions in western and central regions, the secondary industry, and state-owned enterprises, reflecting the differentiated role of government-led capital bias in regional and industrial development. Counterfactual experiments further indicate that correcting capital factor market distortions could increase enterprise TFP by 10%–20%, validating the promoting effect of benign capital distortions on production efficiency. This study challenges the traditional neoclassical assumption of the “entirely negative effects” of factor market distortions and introduces the concept of “capital bias”, revealing how factor price deviations under government intervention (such as undervaluation of emerging capital) offset the negative impacts of distortions by reducing enterprise factor acquisition costs and increasing capital intensity. Robust optimization analysis shows that in uncertain environments, enterprises can achieve stable TFP growth by adjusting the allocation ratio between emerging and traditional capital, providing a decision-making basis for coping with market fluctuations. The innovation of this paper lies in integrating capital allocation distortion theory with robust optimization methods to construct an analytical framework of “factor distortion–capital bias–TFP”, offering a new perspective for understanding China’s unique factor market reforms. The conclusions suggest that moderate capital bias policies (e.g., targeted subsidies, technological capital prioritization) can enhance resource allocation efficiency through benign distortions, but excessive intervention that undermines market mechanisms should be avoided.