僵尸企业的识别与预警——来自中国上市公司的证据

The Recognition and Warning of Zombie Enterprises:Evidence from Chinese Listed Companies

僵尸企业是影响经济可持续发展、破坏金融系统稳定性和阻碍我国经济转型升级的顽疾,但目前相关研究仍处于起步阶段。文章尝试从改进计算方法、加入政府补贴依赖程度指标等方面改进了已有模型,并利用我国2010-2016年上市公司数据进行了识别分析。研究发现,2011年和2012年我国僵尸企业比例约为3.3%,2013年后这一比例升至5%,2016年有所回落;2008年"4万亿"经济刺激计划造成固定资产投资过快增长,可能是2013年后僵尸企业比例上升的原因;我国东、中、西部地区的僵尸企业比例依次递增,黑色金属冶炼、造纸等传统产业的僵尸企业比例最高,国有企业僵尸比例是非国有企业的4倍;常青贷款是我国僵尸企业的最主要吸血方式,其次为利息补贴和政府补贴。文章利用面板logit模型构建了一个僵尸企业预警体系,样本内和样本外的预测准确率分别高达88.57%和96.58%。文章的研究对于僵尸企业的预判、识别和治理具有一定的现实意义。

In recent years,China has been plagued by overcapacity problem and zombie enterprises,which are seriously harmful to sustainable economic development and the stability of financial system. More importantly,it slows down the process of economic restructuring. But in fact,the problem of zombie enterprises is not a unique phenomenon to China and is also not a new word. The Japanese economy had experienced rapid growth of zombie firms in the last decade of the 1990s. This circumstance aroused widespread interest of scholars,leading to a growing number of researches. Caballero,Hoshi and Kashyap（2008）proposed＂CHK＂ model by calculating a hypothetical ＂risk free interest payment＂. Later,Fukuda and Nakamura（2011）developed a more accurate ＂FN-CHK＂ model by adding two indicators,namely ＂profitability criterion＂ and＂evergreen lending criterion＂. However,＂FN-CHK＂ model has two defects if it is directly applied to China.On the one hand,it totally overlooks the significant role of government subsidies,which is especially serious in China. On the other hand,＂FN-CHK＂ model regards the interest rate of convertible bonds as the unique criterion when calculating the hypothetical ＂risk free interest payment＂. As a consequence,this model has a tendency to underestimate the number of zombie firms. This paper attempts to improve ＂FN-CHK＂ model in several aspects,for instance,improving calculation method of the ＂risk free interest payment＂,and taking into account the dependence of government subsidies,and then identify zombie enterprises by using the data of Chinese listed companies from 2010 to 2016. This paper shows that,the rate of zombie companies was around 3.3% in 2011 and 2012,while it rose to5% after 2013 and slowed down in 2016. This tendency could be related with different features of the early and late periods of the prolonged recessions. In the earlier stage（between 2011 and 2013）,economy experienced an unexpected downturn and fewer companies could adjust their management tactics. As a result,the number of zombie enterprises was growing. In the later period（between 2013 and 2016）,however,the economic decline was much more modest and more firms had adjusted their management strategies. Consequently,the number of zombie enterprises declined. Compared with our improved recognition model,＂FNCHK＂ model misses 16.59% of actual zombie firms. Using unique indicator when calculating the ＂risk free interest payment＂ and ignoring government subsidies have equal weighting in missing samples. It demonstrates that,our proposed model can improve the accuracy of recognition effectively,while directly using ＂FN-CHK＂model might lead to error. We also find that,the proportion of zombie companies in western region is the highest,followed by central and eastern regions. The rates of zombie companies in traditional industries,like iron industry and papermaking industry,are the highest. It is remarkable that the rates of zombie companies in these traditional industries decreased the most in 2016. It might be attributed to a big rise in commodity prices and the success of dealing with zombie enterprise problem in iron industry in 2016. Previous studies show that the increasingly serious zombie problem might be blamed on the overcapacity caused by the ＂4 trillion yuan stimulus plan＂. Using statistical analysis and ＂DID＂ regression method,this paper confirms that,the over-rapid growth of investment in fixed assets caused by ＂4 trillion yuan stimulus plan＂might be responsible to the increasing rate of zombie enterprises after 2013. Besides,we also find that,the proportion of zombie state-owned companies is much higher than others. Moreover,this proportion is much higher in the areas where the state seriously intervenes the market. The most frequently used method of receiving financial assistance is evergreen lending,which is followed by interest discount and government subsidies. Because of the harmfulness of zombie firms,precise prediction is extremely important in prevention. We attempt to predict zombie enterprises by using Panel Logit model,the correct forecast rates in sample and out of sample are 88.57% and 96.58%. These results have significance to preventing and controlling zombie enterprises.