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Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency

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Abstract

The excessive debt ratio of Chinese firms has raised concerns over its impact on productive efficiency. We employ a firm-level dataset over 1998–2007 to investigate the role of debt in the firm’s production frontier and technical efficiency. The impact of debt on frontier is decomposed into a stand-alone neutral effect and indirect non-neutral effects, which alter the output elasticity of production inputs. We estimate the effects through a semiparametric smooth coefficient stochastic frontier model. We allow a nonzero probability for the firms to be fully efficient and model it as a function of debt and technical progress represented by time. We observe that an increase in debt significantly shifts firms’ frontier downward across different ownerships, regions, and industries. Foreign and private firms are more efficient, with their full efficiency probability increased by debt and technical progress. By contrast, state-owned enterprises and collective firms are much less efficient and their probability of being fully efficient does not increase with more debt. Furthermore, lower efficiency levels are concentrated in the central and western regions and in the mining and public utility industries.

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Notes

  1. We note that the measure of firm’s performance varies in the literature. The measures commonly include ROA, ROE, profitability, TFP growth, and technical efficiency. In this paper, we extend the effect of debt to both firm’s frontier (measured by net output values) and technical efficiency (measured by the mean of technical efficiency conditioning on a composite error).

  2. See Park et al. (2015b) for a review of recent development in smooth coefficient regression.

  3. See Zhang et al. (2018) for more discussion on the limitation of modeling the determinants of inefficiency through only its conditional mean function.

  4. See Parmeter and Kumbhakar (2014) and Kumbhakar et al. (2015) for an comprehensive review of recent development in SF models with empirical applications.

  5. We did not explore the identification of p and \(\sigma _u^2\) detailed in simulation studies in Rho and Schmidt (2015), because we observe in our empirical investigation that \(\lambda = \frac{\sigma _u}{\sigma _v}\) estimates are generally larger than one and \({\hat{p}}(\cdot )\) is not close to zero.

  6. We thank a referee to bring our attention to this point.

  7. See Fu et al. (2008). In addition, using firms’ output to measure of \( Y_{it} \) typically requires production inputs to have capital, labor, and raw materials. In this setup, the multicollinearity issue appears and affects the convergence of MLE estimation in our second step and the parametric SF models, making the empirical results unreliable. A similar issue is also pointed out by Movshuk (2004).

  8. Legal persons are defined as various domestic institutions such as banks and research institutions. Because the goal of this ownership is to profit, it is treated as private ownership.

  9. One can alternatively employ Moran’s I test to check for spatial dependence. Implementation of such test requires data on firms’ location and their distance, both of which are unfortunately unavailable in our dataset. Since we reject the null of the weak CSD, we proceed to model the strong CSD through a multifactor approach.

  10. Other issues have been well discussed in explaining SOEs’ low production efficiency. For example, the deviation of bureaucrats’ political interests (i.e., focusing on annual production target by the central government) from shareholder’s interests (i.e., profit maximization) reduces the efficiency of SOEs (He et al. 2015). Additionally, the political pressure on SOEs induces SOEs managers to pursue not probability but capital accumulation with a cheap price and secured employment. As a result, the input can be seriously misallocated, causing the efficiency of SOEs to decrease significantly (Berkowitz et al. 2017; Hsieh and Klenow 2009).

  11. Zhang et al. (2012) find that the eastern region in China has a highly intensive R&D inventory compared to central and western region.

  12. We note that a direct comparison between our results and Zhou et al. (2011) is inappropriate. They measure the technical inefficiency as the difference between firms’ estimated output level and the highest outputs estimated in the sample, whereas we measure the inefficiency through its density conditioning on composite error. Their frontier model is deterministic and thus does not separate inefficiency term from the random noisy. We model the probability of inefficiency explicitly in a semiparametric frontier model with the composite error structure.

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Authors and Affiliations

  1. International School of Economics and Management, Capital University of Economics and Business, Beijing, 100070, China

    Taining Wang

  2. The Institute for Advanced Economic Research (IAER), Dongbei University of Finance and Economics, Dalian, 116025, Liaoning, China

    Jinjing Tian

  3. Department of Economics, West Virginia University, Morgantown, WV, 26505, USA

    Feng Yao

  4. China Center for Special Economic Zone Research, Shenzhen University, Guangdong, China

    Feng Yao

  5. School of Economics and Trade, Guangdong University of Foreign Studies, Guangdong, China

    Feng Yao

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Wang, T., Tian, J. & Yao, F. Does high debt ratio influence Chinese firms’ performance? A semiparametric stochastic frontier approach with zero inefficiency. Empir Econ 61, 587–636 (2021). https://doi.org/10.1007/s00181-020-01889-1

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