Zombie firms in China's coal mining sector: Identification, transition determinants and policy implications

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Zombie firms in China's coal mining sector: Identification, transition determinants and policy implications ⁎,2

Xiaoyong Dai1, Xiaole Qiao

, Lin Song3

School of Economics and Finance, Xi’an Jiaotong University, 74 Yanta west road, Xi’an, Shaanxi province, China

ARTICLE INFO

ABSTRACT

JEL classifications: L71 Q38 D01 O12

This paper uses a modified identification method to identify zombie firms from a large sample of coal mining companies in China. It analyzes the prevalence of zombie firms over time and their distribution across different regions and ownership types. It also investigates the causes of the emergence of zombies and evaluates the effectiveness of various restructuring measures for resolving zombie firms. The results indicate that government interventions and subsidized bank credit are important causes of zombie firms, and continuous financial support from the government or banks does not contribute to the recovery of zombie firms. While reductions in labor costs, ownership reforms, and deleveraging can effectively resolve zombie firms, the injection or sale of assets has an ambiguous impact on the recovery of zombie firms. This study contributes to an understanding of the causes of the emergence of zombie firms and provides some policy implications for tackling zombie firms in China's coal sector.

Keywords: Zombie firms China's coal mining sector Government interventions Subsidized bank credit Restructuring measures

1. Introduction The term “zombie firms” generally refers to firms that are insolvent but remain in operation with financial support from the government or banks. Since the economic stagnation of the “lost decade” in Japan during the 1990s and the early 2000s, the problem of zombie firms has gained worldwide attention from the public, economists, and politicians. After the financial crisis of 2008, the global economic recession retriggered extensive discussions of the threats zombie firms pose to economic growth. In China, the economic growth rate dropped from 14.2% in 2007 to 9.7% in 2008; it fell even further to 6.7% in 2016. With this substantial economic slowdown, there are growing concerns about the emergence of zombie companies and their impacts on the Chinese economy. For this reason, the word “zombie” has begun to appear frequently in Chinese media as well as government reports in recent years. The Chinese government has also launched a series of documents and has explicitly set targets to deal with zombie firms.4 To address these concerns and to support the development of appropriate

policies, it is necessary to identify zombie firms, analyze their distribution, understand the causes, and evaluate the effectiveness of various measures for resolving them. This paper identifies zombie firms in China's coal mining sector using a modified identification approach to a large sample of Chinese coal mining companies. We chose to focus on the coal mining sector because this sector has been suffering from persistent losses and overcapacity (Wang et al., 2018). Although coal consumption still accounts for over 60% of total energy consumption in China, coal plants have a severe overcapacity problem due to decreasing demands, which are fueled by the economic slowdown and environmental pressures. The National Development and Reform Commission of China has set targets to reduce coal capacity and shut down coal power plants.5 China's reduced coal capacity was over 290 million tons in 2016 and 150 million tons in 2017. Therefore, it is believed that zombie firms are prevalent in China's coal sector. Resolving zombie firms is an important step towards reducing overcapacity. This is why the current study focuses on the coal sector.

Corresponding author. E-mail addresses: [email protected] (X. Dai), [email protected] (X. Qiao), [email protected] (L. Song). 1 Xiaoyong Dai's research lies in the areas of development economics with a particular focus on innovation and productivity. 2 Xiaole Qiao's researches in the areas of industrial economics. 3 Lin Song's research interest focus on industrial organization and corporate finance. 4 For example, in 2015, the National Development and Reform commission issued a report titled Speeding up the process of eliminating zombie firms and effectively reducing excess capacity. 5 For example, according to the Notice concerning properly undertaking work for the dissolution of excess capacity in key sectors in 2018, coal capacity must be reduced by approximately 150 million tons, and coal power plants with a capacity lower than 300,000 kW must be shut down. ⁎

https://doi.org/10.1016/j.resourpol.2018.11.016 Received 14 June 2018; Received in revised form 26 November 2018; Accepted 26 November 2018 0301-4207/ © 2018 Elsevier Ltd. All rights reserved.

Please cite this article as: Dai, X., Resources Policy, https://doi.org/10.1016/j.resourpol.2018.11.016

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After identifying zombie firms in China's coal sector, this study analyzes the distribution of zombie firms, investigates the causes of their emergence, and evaluates the effectiveness of various restructuring measures for resolving them. Specifically, we investigate the prevalence of zombie firms over time and their distribution in different regions as well as across different ownership types. In addition to internal factors such as operating characteristics, government interventions and subsided bank credits are identified as key external causes of the emergence of zombie firms. This study also evaluates the effectiveness of various restructuring options, including reductions in labor costs, state ownership reform, deleveraging, asset injection, and asset sale. These analyses help identify the distribution and causes of the emergence of zombie firms in China's coal mining sector and also provide insights for the design of more appropriate policy measures to deal with zombie firms. This study is closely related to previous studies on conceptualizing and identifying zombie firms. The term “zombie” was first introduced in economics research by Kane (1987), who used it to describe insolvent savings-and-loan institutions during the crisis. According to Hoshi (2006), zombie firms are defined as insolvent firms with little hope of recovery, but avoid bankruptcy due to support from their banks. In the seminal work of Caballero et al. (2008), they proposed an approach to identify zombie firms by comparing firms’ actual interest payments to their risk-free interest payments. The idea behind this method is that zombie firms rely on abnormal interest relief in order to survive. To avoid misclassifications, Fukuda and Nakamura (2011) further modified the approach by considering profitability and evergreen lending criteria. Taking the “lost decade” in Japan as a laboratory, existing studies provide extensive evidence that the prevalence of zombie firms is a major cause of economic stagnation (Giannetti and Simonov, 2013; Imai, 2016; Kwon et al., 2015; Nakamura and Fukuda, 2013). Although these studies provide numerous insights for the present study, the empirical methods and frameworks designed for research in Japan cannot be directly applied to Chinese firms because of the different institutional backgrounds of these two countries. For example, along with subsided credits, government interventions can be another important cause of the emergence of zombie firms. Therefore, in this study, the role of government interventions was considered, and the identification method and empirical design were modified for application to the Chinese coal mining sector. Only a handful of studies investigated the presence of zombie firms in China (He and Zhu, 2016; Jiang et al., 2017; Nie et al., 2016). Closely related to our study, Lam et al. (2017) identify zombie firms using firmlevel industrial survey data; they suggest that China could significantly improve productivity by resolving zombie firms. Using the same dataset, Tan et al. (2016) find that government investments boost the performance of zombie firms and also crowd out the growth of private firms. Moreover, the prevalence of zombie firms is highly associated with overcapacity in the Chinese manufacturing industry (Shen and Chen, 2017). In contrast with previous studies, which have examined Chinese manufacturing sectors or listed companies, the current study focuses on the coal mining sector. This industry was chosen because it suffers more than other sectors from overcapacity and losses (Shen et al., 2012; Wang et al., 2018). Therefore, we expected zombie firms to be more prevalent in the coal sector than in other industries. This paper contributes to the existing literature in several ways. First, this paper presents a method for identifying zombie firms that has been modified based on data availability and on characteristics of the Chinese coal sector. Second, it provides a comprehensive analysis of the distribution and characteristics of the identified zombie firms in China's coal mining sector and analyzes the internal and external causes of their “zombification.” Third, it evaluates the effectiveness of various restructuring measures and provides some implications for policies designed to tackle zombie firms. Therefore, this study may help clarify the distribution and causes of zombie firms in China's coal mining sector and provide insights for policy design.

The remainder of the paper is structured in the following manner. Section 2 introduces the data and methodology used to identify zombie firms. Section 3 analyzes the prevalence and distribution of the identified zombie firms in China's coal mining sector. Section 4 empirically investigates the causes of the emergence of zombie firms and evaluates the effectiveness of various restructuring measures designed to resolve them. Section 5 concludes the paper and discusses the policy implications of the findings. 2. Data and methodology used to identify zombie firms 2.1. Data The dataset used in this study was taken from China's Annual Surveys of Industrial Firms (ASIF). The survey was conducted by China's National Bureau of Statistics. It covers all state-owned enterprises (SOE) and non-SOEs with sales revenues over 5 million RMB from 1998 to 2009 and over 20 million RMB from 2011 to 2013. It is the most comprehensive and representative firm-level data in China, accounting for approximately 90% of the total industrial value added of the entire economy.6 The dataset has been proven to be reasonably accurate and has been widely used in economic research in recent years (Dai and Cheng, 2016; Lu and Yu, 2015; Yu, 2015). The coal mining and washing sector includes three sub-industries: bituminous coal and anthracite, lignite coal, and other coal. The two-digit GB/T codes for these industries are 61, 62, and 69, respectively. To identify zombie firms, it is necessary to track firms’ operating characteristics over time. To do this, we reconstructed the panel data in Brandt et al. (2012). Most firms can be uniquely identified by their identity (ID) numbers, but IDs may appear in and disappear from the dataset due to mergers and acquisitions, as well as for statistical reasons. In this study, along with IDs, firm names, firm phone numbers, region and industry codes, and the names of the legal representatives were used to track firms over time. To avoid noise and misleading information, we excluded some observations that were either seriously misreported or missing entirely.7 Restricting our sample to firms in the coal mining sector resulted in unbalanced panel data of 78,101 observations for 10,477 individual firms for 1998–2009 and 2011–2013. Table 1 summarizes the statistical data. It shows the number of firms that experienced a slight decrease in revenue: 3202 in 1998 and 2603 in 2001. This number then increased rapidly to 8998 in 2008 before dropping to 6385 in 2012. There was a dramatic increase in total sales revenue and fixed capital during the sample period; labor increases were more moderate, as the number of employees in the sector grew from 3.69 to 5.55 million workers. This suggests that the sector has become more capital-intensive since 1998. These figures also reveal the dramatic expansion in the Chinese coal mining sector during this period, and this expansion brings with it growing concerns about overcapacity and the prevalence of zombie firms. As Fig. 1 shows, SOE reforms have begun a substantial restructuring of the coal mining sector. The proportion of SOEs decreased dramatically during the sample period, from 50.59% in 1998 to 9.37% in 2008; it then increased slightly to 13.15% in 2013. However, despite this dramatic decrease, SOEs still account for a large share of this sector: 85.64% of employees in the sector worked for SOEs in 1998, a percentage that dropped to 55.21% in 2013. The asymmetrical decrease in 6 The dataset covers three broad sectors: mining, manufacturing, and public utilities. In the National Industries Classification System (GB/T 4754–2002), the two-digit industry codes range from 06 to 46; the mining sector codes are 06–12. 7 For example, a firm's output, which should be numeric and positive, is a string or negative. Observations that lack key variables for identifying zombies are also excluded. Excluding these observations does not affect the representativeness of the sample.

2

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Table 1 Summery statistics of the firm-level data in China's coal mining sector. Year

Number of firms

Sales revenue

Employment

Fixed capital

Leverage ratio

Profit margin

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2011 2012 2013

3202 2795 2666 2603 2812 3140 5249 5789 6765 7488 8998 6430 6904 6385 6875

125.06 119.42 126.46 152.26 194.37 234.07 401.07 562.90 702.69 886.07 1420.77 1293.65 2754.33 2867.49 2878.41

4.30 4.27 3.99 3.75 3.80 3.69 4.20 4.36 4.55 4.51 4.97 4.16 4.32 4.51 5.55

199.49 218.50 218.74 241.24 271.38 286.74 362.60 433.61 543.25 654.84 792.17 798.14 1375.51 1540.92 1780.48

62.94 63.96 65.33 63.42 56.95 55.60 60.60 61.33 60.80 61.36 59.00 59.24 59.46 60.78 64.61

− 0.34 − 1.52 0.04 2.75 4.36 5.59 8.72 9.96 9.78 11.47 16.37 13.75 16.18 12.69 9.27

Note: Sale revenue and fixed capital are denoted in billion RMB; Employment is denoted in millions of workers; Leverage ratio and profit margin are denoted in percentages.

dropping to 9.27% in 2013. However, the leverage ratio evolved in the adverse direction, with a significant decrease before 2003, then followed by an increase and stable fluctuation during the period 2004–2008 and increased to 64.61% in 2013. This suggests that the number of zombie firms may have first decreased and then increased during the sample period. The next section will illustrate more rigorous methods for identifying zombie firms and analyzes dynamic trends and distributions. Since this study focuses on identifying zombie firms and analyzing the factors that cause them, the development of China's coal sector will not be discussed in detail here. More information about the development of China's coal sector as well as related policies and reforms is provided in other studies (Peng, 2011; Rui, 2005; Shen et al., 2012). This background information is useful to understanding and interpreting the results of this study as presented in the following sections.

Fig. 1. Employment share (left scale) and percent of SOEs (right).

employment share and the total percentage of SOEs is consistent with the SOE reforms that sought to “grasp the large and let go of the small.” That is, small SOEs were privatized or closed, while large SOEs were corporatized and merged into industrial conglomerates under the control of the government. To take a first glance at the prevalence of zombie firms, Fig. 2 shows the leverage ratios and profit margins of the Chinese coal mining sector over time. Since zombie firms usually have high levels of debt and low profits (Hoshi, 2006; Peek and Rosengren, 2000), we expect that the prevalence of zombie firms should increase with higher leverage ratios and decrease with higher profit margins. Firms with high leverage ratios and low profit margins are more likely to be zombies (Fukuda and Nakamura, 2011). Fig. 2 shows that the sector's profit margin was negative before 2000. It increased to a peak of 16.37% in 2008 before

2.2. The approach for identifying zombie firms The identification of zombie firms depends, first, on how zombies are defined. There are several definitions, but zombie firms certainly share a number of common characteristics, such as low profitability, low productivity, a high debt ratio, insolvency, subsidized bank credit, and abnormal government bailouts. Based on the work of Caballero et al. (2008), hereafter referred to CHK, and Fukuda and Nakamura (2011), we modified the CHK approach (modified CHK) to fit the available data and some unique features of the Chinese coal mining sector. We also used the actual profit criterion (AP) and the evergreen lending criterion (EL) for robustness checks. Caballero et al. (2008) defined zombie firms as firms whose actual interest payments are lower than the hypothetical risk-free interest bound. The basic idea behind this approach is that zombie firms have to rely on abnormal interest relief in order to survive, and, therefore, they enjoy lower interest rates than the most favorable rates that banks can provide to quality clients. The advantage of the CHK approach is that it classifies zombie firms based only on whether they receive subsided credits, rather than on operating characteristics of profitability or other dimensions of performance. This exogenous classification facilitates the evaluation of the effects of zombies on the performance of the zombie firms themselves as well as on the entire economy. The key step of the CHK approach is calculating hypothetical risk-free interest payments. Due to data availability, we modified this calculation and used the following equation8: 8 Caballero et Ri*, t = rst 1 BSi,t 1 +

Fig. 2. The dynamics of leverage ratio (left scale) and profit margin (right). 3

(

1 5

al.

(2008) BLi, t

5 rl j =1 t j

)

1

use the following equation: + rcb5years, t Bondsi, t 1, where Bondsi,t

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Ri*, t = rst

1

BSi, t

1

+

1 5

Step 1: Zombie firms were identified using the standard CHK approach. First, the hypothetical risk-free interest payments Ri*, t were calculated using Eq. (1). Then, firms’ actual interest payments Ri, t were compared to the estimated payments Ri*, t . The interest gap was calculated as: Xi, t = (Ri, t Ri*, t )/ Bi, t . If the interest gap was negative ( Xi, t < 0 ), the firm was classified as a zombie firm during period t; otherwise, it was classified as a non-zombie firm. Step 2: Over-identification of zombie firms was then addressed. Using the profitability criterion, firms with positive EBITs were excluded from classification as zombies. The EBIT is the firm's operating profit plus interest payments. Since firms that are capable of repaying their debts should not be classified as zombies, this exclusion of healthy firms addresses the type II error. Step 3: Zombie firms misclassified as non-zombies were then identified. The firms classified as non-zombies in the first step were examined again. They were identified as zombie firms in period t if they met the following three conditions: the ratio of total debt to total assets was over 50% during period t-1; AP (operating profit excluding government subsidies) was negative in period t; and total debt in period t exceeded that of period t-1. This step uses the EL criterion to address the type I error. Two simple alternative approaches were then used to check robustness: the AP and EL approaches. With the AP approach, a firm is defined as a zombie during period t if its actual profits were negative in both period t and period t-1. Actual profits were calculated by subtracting subsidies and other non-operating income from the book value of the firm's profits. With the EL approach, a firm is defined as a zombie if its actual profits are negative, its leverage ratio is over 50%, and its borrowing increased at the end of period t. The AP and EL approaches are simpler but share some aspects with the modified CHK approach. Therefore, in this paper, the modified CHK approach was used as the benchmark method for identifying zombie firms.

5

rlt j=1

j

BLi, t 1,

(1)

where BSi, t and BLi, t represent short-term debts and long-term debts, respectively, of firm i at the end of year t, and rst and rlt are the shortterm and long-term prime rates, respectively, in year t. The indicators of short-term and long-term debts are available in the ASIF dataset, and the prime rates were calculated using data from the WIND database.9 While the CHK approach is attractive for its simplicity and exogenous classification, it also may suffer from two types of errors: misclassifying zombie firms as non-zombies (type I error) or misclassifying healthy firms as zombies (type II error). A type I error may occur because banks have strong incentives to provide EL to troubled clients in order to conceal non-performing loans (NPLs). In these cases, unhealthy firms pay interest at normal rates without any concessions, leading to the incorrect identification of these unhealthy firms as non-zombie firms. A type II error arises when banks provide loans at interest rates lower than risk-free lending rates to clients with good reputations and low default risks (Fukuda and Nakamura, 2011). To avoid these errors, Fukuda and Nakamura (2011) propose the use of two additional criteria: the profitability criterion and the EL criterion. The profitability criterion requires zombie firms’ earnings before interest and taxes (EBIT) to be less than the hypothetical riskfree interest payments. This avoids a type II error, the identification of profitable firms as zombie firms. The EL criterion classifies firms as zombies whose EBIT was less than the hypothetical risk-free interest payments, whose total external debts were over half of their total assets, and whose borrowings increased during the sample period. Since these kinds of firms are unlikely to obtain new loans, the EL criterion avoids type I errors, the misclassification of unhealthy firms as non-zombies. In China, a unique factor that must be taken into consideration is that the government extensively influences the market through direct subsidies, tax allowances, and low-interest loans. In particular, state ownership still plays an important role in China's coal mining sector; SOEs generally have political connections and obtain substantial fiscal support from the government (Dai and Cheng, 2015). Therefore, the book value of a firm's profits cannot reflect its actual profitability, because a positive book value may be due to fiscal subsidies, tax rebates, or extraordinary gains and losses. Therefore, we calculated firms’ actual profits instead of using their book values. Our modified CHK method accounts for the misclassification errors discussed by Fukuda and Nakamura (2011) and proceeds with three steps10:

3. Identification results of zombie firms in China's coal mining sector 3.1. The prevalence of zombie firms over time We used the modified CHK approach to identify zombies among Chinese coal mining firms, as described in Section 2. Fig. 3 shows the number and proportion of identified zombies over time. Since the number of zombie firms, shown on the left scale, can be significantly affected by the substantial changes in the size of the sample, the right scale shows the proportion of zombie firms to the entire sample for each year. Fig. 3 shows that the proportion of zombie firms decreased significantly from 1999 to 2004, stabilized from 2005 to 2008, and began increasing thereafter.11 These different patterns can be used to divide

(footnote continued) is total bonds outstanding (including convertible bonds and warrant- attached bonds) and rcb5years, t is the minimum observed coupon rate on any convertible corporate bond issued in the last five years before t. We did not include bonds outstanding in the calculation because they are not available in the ASIF dataset. Since bond markets are still underdeveloped in China, this omission does not lead to substantial bias because almost all firms are non-listed companies and only a few of them have bonds. 9 The prime interest rates are calculated based on the benchmark interest rates stated by the central bank. Since the benchmark interest rates can change within a single year, we calculated the duration-weighted average of benchmark interest rates. That is, the short-term prime rate is calculated as rst = n wnt brnt , where brnt is the nth benchmark interest rate during period t and wnt is the corresponding weight. For example, wnt = 1/2 if the nth benchmark rate lasted for half a year in period t. To determine the long-term prime rate, we first calculated the weighted average of benchmark interest rates of loans lasting for 1–3 years, 3–5 years, and more than 5 years, respectively. Then, the long-term prime rate is defined as the mean of the three weighted averages of benchmark interest rates. 10 We appreciate the comments from one anonymous reviewer, suggesting us to consider the flow of production factors in the identification of zombie firms. We agree that barriers to the flow of production factors can be some fundamental causes of the emergence of zombie firms. However, as stated in the beginning of the paper, the term “zombie firms” generally refers to firms that are

(footnote continued) insolvent but remain in operation with financial support from the government or banks. Under this definition, profitability, losses, and financial support from the government or banks are regarded as the proximate causes of zombie firms. Therefore, we can use the proximate causes to identify zombie firms, while the fundamental causes are not necessary to be taken into account. In Section 4.1, the fundamental causes are analyzed after the identification. The provincial level marketization index can partially reflect the flexibility of production factor flow, as regions with lower degree of marketization is expected to be associated with more barriers to production factor flow. Moreover, the concept of production factor flow is not firm-specific but reflects factor mobility across firms within specific industry or region. For this reason, the industry or regionspecific measures of production factor mobility cannot be used to identify zombie firms. 11 The reason that we do not report the results in 2010 and 2011 is due to data unavailability. The data for 2010 are missing from our dataset, which makes it impossible to identify zombie firms in that year and also in 2011, since we need 4

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Fig. 4. The proportion of identified zombies based on different approaches.

Fig. 3. The number (left scale) and proportion (left scale) of zombie firms identified using the modified CHK approach.

firms were also identified using the AP and EL approaches (Fig. 4). The numbers of identified zombie firms follow similar patterns with all three identification methods. That is, with all three methods, the proportion of zombie firms during the sample period decreased significantly, remained stable for a time, and then increased significantly. More zombie firms were identified with the modified CHK approach than the EL approach, which suggests that the EL approach may underestimate the prevalence of zombies. As discussed in Section 2, the modified CHK criterion is a more comprehensive approach than the AP and EL approaches. Therefore, in this study, the results of the modified CHK method are used as the benchmark. Nevertheless, the results suggest that the main findings of the evolution pattern shown in Fig. 3 are robust and consistent across different identification methods. Table 2 shows the ratios of zombie firms under different weights and identification approaches over time. The pattern of the evolution of zombies is similar to that shown in Fig. 4. However, Table 2 shows that zombie firms often account for a higher proportion of assets, employment, and sales revenue in the industry than warranted by the percentage of zombie firms. For example, according to the CHK method, 25.41% of coal mining firms were zombies in 1999. These zombies, however, accounted for 46.07% of assets, 46.31% of employees, and 35.28% of sales revenue in the sector. Moreover, this discrepancy is even greater when the ratios are weighted by asset and number of employees rather than by sales revenue. This suggests that zombie firms represent a large portion of capital and labor inputs but produce less than healthy firms. Zombie firms not only operate less efficiently themselves, but may also crowd out investments in non-zombie firms, decreasing the efficiency of the entire industry. Resolving zombie firms can release resources towards more efficient firms. These findings are also robust across different identification methods, as shown in Table 2.

the sample period into three intervals. During the first period, 1999–2004, the proportion of identified zombie firms decreased from 25.41% to 5.05%. The total number of zombie firms decreased from 709 to 265 during this period. During the second period, 2005–2008, the proportion of zombie firms ranged from 3.89% to 6.97%, and the total number of zombie firms ranged from 265 to 463. However, during the third period, 2009–2013, the proportion rebounded to 10.16%, and the total number of zombie firms increased to 669 at the end of 2013. The prevalence of zombie firms for the entire sample period corresponds to the changes in the leverage ratio (Fig. 2). That is, a high proportion of zombie firms is associated with a high level of leverage ratios throughout the industry, which makes sense, since zombie firms are indebted firms that perform poorly. The evolution of zombie firms is also consistent with the structural changes and reforms in China's coal mining sector during the sample period (Shen et al., 2012; Tang and Peng, 2017; Xu and Nakajima, 2016). China's coal sector was one of the most problematic industries overall during the sample period. To ease the pressure of energy shortages, the number of small coal mines grew rapidly from 1979 to 1992. Although state-owned coal mines were not profitable, the central government took measures to help them survive. Coal mines continued to lose money until 2001 (Shen et al., 2012). Since the late 1990s, the sector has experienced significant restructuring. These measures include liberalizing coal prices, controlling macro-level capacity, reducing the burden of coal mining companies, closing down of small-scale coal mines, bankrupting of loss-making state-owned coal companies, and building large mining groups (Shen et al., 2012). These measures significantly strengthened competition in the coal market and enabled coal companies to become profitable, which, in turn, led to a substantial decrease in the number of zombie firms. The increase in the prevalence of zombie firms after 2008 may be due mainly to the financial crisis and the decrease in the demand for coal. In response to the global recession that followed the financial crisis of 2008, the Chinese government launched a massive stimulus package to boost economic growth. This led to over-investment and excessive expansion in a few industries, including the coal mining sector. Moreover, the demand for coal has decreased substantially in recent years due to growing concerns over environmental issues, which has automatically led to overcapacity and declines in prices. The worsening fundamentals of coal companies have led to the development of more zombie firms. To confront these challenges, supply-side structural reforms have been implemented in the coal sector. These reforms aim to control coal production, eliminate outdated capacity, and improve industry profitability. Resolving zombie firms is an important factor affecting these goals since zombies are the major loss-makers in the sector. To check robustness and compare results, the proportion of zombie

3.2. Distribution of zombie firms Fig. 5 shows the regional distribution of the number and proportion of zombie firms.12 There is substantial regional variation. Generally, zombie firms are more prevalent in less-developed central and western areas such as the Shanxi, Guangxi, Guizhou, Ningxia, Yunnan, and Inner Mongolia provinces. In contrast, there are significantly fewer zombie firms in developed coastal areas such as the Jiangsu, Shanghai, Zhejiang, and Fujian provinces. For example, the largest numbers of zombie firms are found in the Shanxi (515), Guizhou (203), Yunnan (130), and Inner Mongolia (112) provinces. The highest proportions of zombie firms were found in the Guangxi (47.1%), Ningxia (22.8%), Shanxi (18%), and Qinghai (17.9%) provinces. The prevalence of zombie firms is found to be highly correlated to the regional distribution of coal production.13 While central and western regions rely more 12 The figure uses the number of zombie firms identified in 2009, 2012, and 2013. The numbers and proportions were calculated as the average values for the three periods. 13 We collect provincial data of raw coal production during 1999–2013 from the China Energy Statistical Yearbook. We then calculate the correlation coefficients between the number of zombie firms and raw coal production. Overall,

(footnote continued) information on firms’ previous operating characteristics to identify zombie firms. 5

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Table 2 The ratios of zombie firms under different weights. Year

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2012 2013

Weighted by asset

Weighted by employee

Weighted by revenue

CHK

AP

EL

CHK

AP

EL

CHK

AP

EL

46.07 41.44 31.19 14.91 14.55 10.79 10.71 12.40 11.12 4.79 7.18 13.47 21.07

57.30 60.19 39.62 36.65 28.57 17.34 13.61 11.89 10.41 4.16 4.07 6.48 15.68

37.39 37.94 24.34 15.74 11.16 9.17 9.28 10.03 10.28 4.48 7.31 12.21 19.13

46.31 44.52 33.84 18.94 17.57 12.23 12.70 13.00 13.07 6.27 8.76 13.38 16.66

58.47 61.89 45.47 38.62 30.75 17.76 14.39 13.57 11.80 6.04 5.65 7.19 14.18

36.84 38.02 25.51 17.28 13.61 9.53 11.10 10.34 11.13 5.54 8.75 11.22 14.69

35.28 32.22 25.07 11.48 11.65 8.25 7.57 8.20 7.48 3.30 4.33 8.75 12.28

46.00 47.83 32.07 28.38 20.98 11.52 8.82 7.85 6.80 2.90 2.48 4.00 10.33

29.90 29.43 20.09 12.76 8.78 7.35 6.97 6.54 6.51 2.96 4.08 7.80 11.39

Note: all the reported values are presented in percentages.

Fig. 5. The number and proportion of zombie firms in different regions of China.

heavily on energy and natural resources, their industrial structures are more vulnerable to economic recessions and exogenous shocks. Fig. 6 compares SOEs and non-SOEs.14 A much higher proportion of zombies are owned by the state than not, which suggests that SOEs are more likely to become zombies. However, after 2004, the number of non-state-owned zombies exceeded the number of state-owned zombie firms; this may be attributed to large-scale SOE reforms. The number of zombies in non-SOEs then increased rapidly, and their proportion increased to 7.55% by the end of the sample period. The number of stateowned zombie firms decreased significantly from 2005 to 2008 and increased slightly from 2009 to 2013. Despite this decrease, the proportion of state-owned zombie firms still remained as high as 19.19%. These results are consistent with the intuitive idea that SOEs are less efficient but have easy excess to external financing, which increases the likelihood that they will become zombies.

Fig. 6. The number and proportion of zombie firms under different type of ownership.

firms. That is, it examines why healthy firms become zombies and what factors can help resolve zombie firms. This section will attempt to identify the key determinants, analyze why firms become zombies, and identify the effective measures for tackling zombie firms.

4. Transition determinants of zombie firms This section investigates the transition determinants of zombie (footnote continued) the estimated correlation coefficient is 0.6515, ranging from 0.4294 to 0.8171 during the sample period. More detailed results are reported in Table A4 in the Appendix. 14 The sample is divided into three periods: 1999–2004, 2005–2009, and 2012–2013. To simplify the comparison, the number and proportion of zombie firms in each period are reported as annual averages.

4.1. Determinants of becoming zombie firms By definition, zombie firms are insolvent firms that continue to operate with the help of support such as government subsidies and continuous bank loans. This section investigates the role of both firms’ operating characteristics and external factors in determining the 6

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presence of zombie companies. A logistic model was estimated using the following equation:

Pr(zombieit = 1) =

1 Bank it

+ ui +

it ,

+

2 Govit

+

3 Marketit

+ Xit + µt +

Table 3 Regression results on the determinants of becoming zombie firms. Variables

(1)

Bank

1.164*** (18.575)

(2)

(3)

(4)

(5) Marginal effect

− 0.060*** (−3.887) 0.506*** (10.021) − 0.269*** (−52.201) 2.011*** (25.052) 0.058*** (4.840) 0.011*** (6.866) 38,075 0.599

1.208*** (18.742) 0.156*** (18.218) − 0.127*** (−7.794) 0.197*** (3.658) − 0.275*** (−51.015) 2.116*** (25.090) 0.086*** (6.759) 0.002 (1.272) 38,075 0.632

0.093*** (19.080) 0.012*** (15.050) − 0.010*** (−6.870) 0.015*** (3.660) − 0.021*** (−30.610) 0.163*** (23.800) 0.007*** (7.940) 0.000 (1.270) 38,075 0.632

r

(2)

where the dependent variable (zombieit ) is a dummy indicating whether firm i is identified as a zombie firm during period t. µt , i , and ui are fixed effects with respect to time, region, and firm-specific factors, respectively; and it represents the error term. The explanatory variables in Eq. (2) comprise two categories of indicators that may affect the probability that a firm will become a zombie. The first category is external factors, such as government interventions and subsided bank credits. It includes three indicators: Bankit , Govit , and Marketit . Bankit is a dummy variable which equals one if the firm receives subsidized credits and zero otherwise. Subsidized credits are identified by comparing hypothetical risk-free interest payments to a firm's actual interest payments. Govit is the ratio of government subsidies to sales revenue. In the dataset used for this paper, firms report government subsidies, so this indicator can be calculated directly. Since both government subsidies and subsided credits are used to identify zombie firms, it is reasonable to expect that these two indicators should positively correlate with a firm's probability of becoming a zombie. To examine the exogenous impact of government interventions, a province-level marketization index (Market it ) was used to indicate the level of government interventions. This index was developed by Fang et al. (2017) and has been widely used in economics research. A higher value for the marketization index reflects a lower level of government intervention. The second category of variables are internal factors that may affect a firm's probability of becoming a zombie. This category is represented by a collection of firm characteristics ( Xit ) in Eq. (2). Specifically, it includes (1) leverage, measured by the debt-to-asset ratio—zombie firms usually have higher leverage ratios; (2) firm size, measured by the natural log of the firm's sales revenue; (3) firm age, measured as the number of years since it was founded; (4) return on asset (ROA), which captures a firm's profitability relative to total assets—more profitable firms tend to be healthy; and (5) SOE, a dummy variable which is set to one for SOEs and zero for non-SOEs. Due to their commonly poor performance and political connections, we expected SOEs to be more likely to become zombies. Summary statistics of the employed variables for the entire sample is reported in panel A of Table A1 in the appendix. Table 3 shows the results of the logistic model in Eq. (2). In columns (1)-(3) of Table 3, the indicators for subsided bank credits, government subsidies, and the marketization index are introduced into the regression model separately. In column (4), all three indicators are included simultaneously, and column (5) shows the marginal effects. As for the external factors, the results indicate that government interventions and subsided bank credits are important factors determining whether a firm become a zombie. In columns (1) and (2), the estimated coefficients for Bank and Gov are positive and significant at the 1% level, which suggests that firms with government subsidies or subsided bank credits are more likely to become zombie firms. In column (3), the estimated coefficients are significantly negative, which suggests that higher levels of marketization are negatively associated with a high prevalence of zombie firms at the province level. In other words, government interventions increase the prevalence of zombie firms. In column (4), when all three indicators are included in the regression, the results turn out to be fairly robust. The signs and the significances for the three independent variables remain unchanged. Column (5) of Table 3 shows the marginal effects. In contrast to the estimated coefficients in columns (1)-(4), the marginal effect measures the predicted probability change if one specific independent variable is changed and all other explanatory variables remain constant. According to column (5), receiving subsided bank credits increases a firm's probability of becoming a zombie by 9.3%, while a 1% increase in

Gov Market SOE ROA Leverage Size Age N Pseudo R2

0.389*** (7.527) − 0.281*** (−52.197) 2.073*** (25.138) 0.096*** (7.819) 0.007*** (4.295) 38,076 0.614

0.158*** (18.815) 0.370*** (7.095) − 0.265*** (−51.648) 2.077*** (25.376) 0.048*** (3.928) 0.004*** (2.665) 38,076 0.614

Note: . *** indicate significance at the 1% level; Fixed effects for time, province and individual are included in all regressions.

government subsidies is associated with a 1.2% increase in the probability of its becoming a zombie firm. At the provincial level, a 1% increase in the marketization index reduces the probability of becoming zombie firms by 1%. These results confirm the role of government interventions and bank support in the prevalence of zombie firms in China. As for the internal determinants, the estimated results are all as expected. For example, the estimated coefficient for SOE is significantly positive, which suggests that SOEs are more likely to become zombie firms. ROA is negatively associated with the probability of becoming a zombie, and firms with a higher leverage ratio are very likely to become zombies. The estimated coefficients on firm size and age are significantly positive, which suggests that larger and older firms are more likely to become zombies. Due to space constraints, the fixed effects for time, province, and firm-specific factors were included in the regressions but are not shown in Table 3. The results confirm that firms become zombies due to both internal and external factors. The internal factors are firms’ operating characteristics that determine profitability prospect. When firms become inefficient and unprofitable, continuous support from the government or banks can keep them operating and cause them to become zombie firms. Internal or external factors are necessary but not sufficient conditions for a firm to become a zombie. 4.2. Determinants of resolving zombie firms Along with the factors that cause firms to become zombies, this study sought to identify the factors that can help zombie firms recover. This information may provide insights for better policy design and management. To investigate the factors determining zombie firm resolution, restructuring options were considered and a logistic model was estimated using the following specification:

Pr(zombieit = 0 | zombieit 1 = 1) = 1 LaborReductionit + 2 SOEReformit + 3 Deleverageit + 4 AssetInjectionit + 5 AssetSaleit + Xit + µt + r + ui +

it ,

(3)

where the dependent variable, Pr(zombieit = 0 | zombieit 1 = 1) , denotes the probability that firm i, which is a zombie in period t-1, will become healthy in period t. Therefore, the sample for period t is restricted to firms that were zombies during period t-1. The dependent variable is 7

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one for firms that became non-zombies and zero otherwise. The independent variables include a number of restructuring options for resolving zombie firms. These include (1) reductions in labor costs, measured as a dummy variable which takes on the value of one if a firm's wage payment relative to sales revenue decreased from period t1 to t and zero otherwise; labor costs can be reduced by reducing redundancy or cutting wages and employment benefits, particularly for senior executives; (2) SOE reform, measured as a dummy variable which equals one if an SOE becomes privately owned and zero otherwise; since the late 1990s, many SOEs have been privatized or corporatized under the reform “grasping the large and letting go of the small”; (3) deleveraging, measured as a dummy variable which equals one if a firm's debt-to-asset ratio was reduced by more than five percentage points from period t-1 to t and zero otherwise; deleveraging is expected to reduce firms’ debt burdens and promote operating performance; (4) asset injection, measured as a dummy variable that equals one if a firm's fixed assets increased by ten or more percentage points in the last year and zero otherwise; and (5) asset sale, a dummy variable which equals one if a firm's assets decreased by more than ten percentage points in the last year and zero otherwise. As suggested by Lam et al. (2017), the rejection or sales of assets are important restructuring options; they involve the sale of noncore assets or the injection of assets by parent companies. Since data on asset injections and sales were not available from our dataset, fixed asset growth was used as a proxy variable. Eq. (3) also employs a vector of control variables ( X ). These control variables include Gov, Bank, ROA, Size, and Age. While government subsidies and subsided bank credits have been identified as important factors fostering zombie firms, this model investigates whether continuous financial support from the government and banks can also help resolve zombie firms. All these control variables were measured in the same manner as in Eq. (2). This model also controlled for time-specific, region-specific, and firm-specific fixed effects. A summary statistics of the variables for the employed sub-sample is reported in panel B of Table A1. It is worth noting the different restructuring options adopted by the identified zombie firms. In this sample, 44% of zombie firms used deleveraging, 41% reduced labor costs, and 19.5% of stated-owned zombie firms were restructured to non-SOEs. While 55.3% of zombie firms experienced asset injection, 41% sold assets. The substantial proportions of each type of zombie firm allows an investigation of the effects of the different restructuring

options. Table 4 presents the estimated results of Eq. (3). In columns (1)–(4), we separately estimate the effects of different restructuring options on resolving zombie firms. In column (5), all restructuring options are simultaneously included in the estimation. Column (6) shows the marginal effects of the logistic model. The results indicate that reductions in labor costs, SOE reforms, deleveraging, and the sale of assets contribute to the recovery of zombie firms, while asset injection has a negative or insignificant impact on their recovery. Columns (1)-(3) show the estimated coefficients for the restructuring options; all are significantly positive, which suggests that zombie firms are more likely to recover after restructuring by deleveraging, implementing owner reforms, and reducing labor costs. However, in column (4), the estimated coefficient on asset sales is significantly positive; it is negative on the explanatory variable of asset injection. In column (5), which combines all the restructuring options simultaneously as explanatory variables, the results are fairly robust. While the coefficient on asset injection in column (4) is negative and significant at the 1% level, its estimated coefficient in column (5) becomes insignificant, which suggests that asset injection has no significant impact on the recovery of zombie firms. Notably, the results also demonstrate that continuous subsidies from the government or banks negatively contribute to the recovery of zombie firms. In all regressions in Table 4, the estimated coefficients on Gov and Bank are negative and significant at the 1% level. Furthermore, government interventions and subsided bank loans have been identified as important external factors affecting the presence of zombie firms, as shown in Table 3. Therefore, Table 4 provides further evidence that continuous support from the government or banks for insolvent firms has a negative effect on the recovery of zombie firms. Government subsidies or subsided credits may prevent zombie firms from restructuring to improve performance and encourage them to continue operating inefficiently. Column (6) of Table 4 shows the marginal effects. This provides a more accurate picture of the change in probability of the recovery of zombie firms in response to different restructuring measures. For example, reducing labor costs alone can increase the recovery probability of zombie firms by 4.8% on average. SOE reforms can lead to an increase of 5.9% in recovery probability for state-owned zombies. Furthermore, deleveraging can lead to an increase of 15.5% in the probability of recovery, and the marginal effect of the sale of assets is 6%.

Table 4 Regression results on the determinants of resolving zombie firms. Variables

(1)

Labor-Reduction

0.717*** (9.190)

SOE-Reform Deleverage

(2)

0.848*** (7.846)

Asset-Injection

(3)

1.456*** (16.980)

Asset-Sale Gov Bank ROA Size Age N Pseudo R2

− 0.071*** (−7.366) − 0.846*** (−6.403) 0.343*** (27.308) − 0.050** (−2.080) − 0.004 (−1.441) 9079 0.699

− 0.070*** (−7.440) − 0.847*** (−6.364) 0.353*** (27.929) − 0.021 (−0.889) − 0.001 (−0.401) 9079 0.696

− 0.072*** (−7.529) − 0.999*** (−6.837) 0.322*** (25.520) − 0.024 (−0.943) − 0.007** (−2.384) 9079 0.73

(4)

(5)

(6) Marginal Effect

− 0.334* (−1.709) 0.595*** (2.951) − 0.069*** (−7.293) − 0.797*** (−6.004) 0.354*** (27.975) 0.073*** (2.825) − 0.003 (−1.182) 9079 0.703

0.448*** (5.108) 0.552*** (4.547) 1.441*** (16.173) − 0.219 (−1.019) 0.557** (2.533) − 0.065*** (−6.572) − 0.885*** (−5.912) 0.305*** (24.305) 0.048* (1.645) 0.000 (−0.057) 9079 0.751

0.048*** (5.160) 0.059*** (4.560) 0.155*** (17.900) − 0.024 (−1.030) 0.060** (2.480) − 0.007*** (−6.640) − 0.095*** (−6.220) 0.033*** (32.440) 0.005 (1.610) 0.000 (−0.060) 9079 0.751

Note: *, **, *** indicate significance at the 10%, 5%, and 1% level, respectively; Fixed effects for time, province and individual are included in all regressions. 8

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However, asset injection has no significant effect on the recovery of zombie firms. These results provide evidence for the effectiveness of resolving zombie firms through deleveraging, selling assets, reducing labor costs, and implementing ownership reforms.

This study provides contributes to an understanding of the distribution and causes of the emergence of zombie firms in China's coal mining sector. It also provides insights for resolving zombie firms. The policy implications of the paper are summarized as follows:

4.3. Robustness checks

(1) A more competitive coal market requires fewer government interventions to avoid the development of zombie firms. Without government intervention or support, insolvent firms should go bankrupt after several years of losses and release their resources to more efficient uses. The incentive for government support for zombie firms is essentially rooted in China's fiscal decentralization and taxsharing system. To stimulate the development of local economy, government officials have strong incentives to pursue short-term economic performance by supporting government investment in SOEs or establishing preferential policies to encourage private investment. These short-term objectives may result in overcapacity and investment in underperforming investment and overcapacity. To avoid unemployment and tax revenue losses, local governments usually choose to provide financial support to lossmaking companies, which leads to the emergence of zombie firms. Therefore, market-oriented reforms that include reducing government interventions are one key to resolving zombie firms in the coal mining sector as well as other sectors. (2) A more effective mechanism for preventing zombie lending must be adopted. Banks are motivated to provide subsided credits or EL to zombie firms in order to cover their non-performing loans. Consequently, the more zombie firms lose, the more they can borrow from their banks. To address this dilemma, the central bank should urge commercial banks to declare their non-performing assets and avoid further losses. To reduce banks’ incentives for concealing NPLs, regulators can also adopt schemes that subsidize the foreclosure or modification of bad loans by the banks (Bruche and Llobet, 2013). The direct purchase of the collateral on the market has been found to be more effective than capital injection into banks for solving the problem of zombie lending (Jaskowski, 2015). (3) Various restructuring measures should be implemented to resolve zombie firms, depending on firm heterogeneity and circumstances. In fact, the restructuring options examined in this paper are only a few of the possibilities. Therefore, restructuring measures should be selected based on each firm's unique characteristics and circumstances. For example, insolvent zombie firms that do not meet environmental and technological standards should declare bankruptcy. However, zombie firms that still have valuable assets can be restructured or merged into conglomerates. The government has recently launched a new restructuring plan known as debt-forequity swaps; this plan enables commercial banks to swap the debts that they hold in underperforming firms for stock holdings. Although the measure may greatly reduce the leverage of the coal mining sector, it should only be implemented for zombies with valuable assets and a high probability of recovery. Otherwise, it might temporarily relieve repayment pressure but leave long-term risks for banks unchanged. (4) The resolution of zombie firms can go hand-in-hand with the transition of the coal mining sector. Although China is still the world's largest coal producer and consumer, coal's share in total energy consumption has been decreasing in response to environmental pressures and to support sustainable growth. China can take use the resolution of zombie firms as an opportunity to close down small, inefficient mines and build large mine groups, which will increase the global competitiveness of China's coal mining sector and also foster innovation in this industry. As a result, the sector will be able to use coal resources more cleanly and efficiently, and coal mining companies will be able to transform themselves into clean energy suppliers. (5) Supportive measures are required to deal with the unemployment associated with the resolution of zombie firms. Resolving zombie

In Tables 3, 4, zombie firms are identified on the basis of the modified CHK approach. To check the robustness of the identification method, we then used the AP and EL criteria to identify zombies. Eqs. (2 and 3) were then replicated using those results. The replicated results are shown in Tables A2 and A3 in the appendix. The results turn out to be fairly robust. The results shown in Table A2 are similar to those in Table 3. This confirms the external and internal causes of the emergence of zombie firms. That is, zombie firms are inefficient and loss-makers themselves, and financial support from banks and the government keeps them in the market. The results shown in Table A3 also suggest that government subsidies and subsided bank credits negatively affect the recovery of zombie firms. Instead, the restructuring measures of deleveraging, reducing labor costs, and reforming ownership structures are again proven to be effective methods for tackling zombie firms. The estimated coefficients for the injection and sale of assets are insignificant, suggesting that the effects of these measures are ambiguous. Due to data unavailability, this empirical study is limited to the period from 1998 to 2013. A major limitation of this paper is that we cannot track the prevalence of zombie firms more recently than 2013. Despite this limitation, this study can still provide many insights on the causes of the emergence of zombie firms and the effectiveness of various restructuring measures on their recovery. This is because the causes and the effectiveness of restructuring measures should not change substantially over time. Nevertheless, further investigations should be conducted using Chinese listed firms and macro-level data. Listed companies’ reports provide more detailed information about firm characteristics. This data could be used to obtain more comprehensive evidence for the determinants of the development of zombie firms. However, a limitation of conducting such a study using only listed firms is that the sample size is much smaller, which reduces the reliability of the identification results. Macro-level data are more readily available and up-to-date. However, the aggregated macro-level data ignore firm heterogeneity and can only provide information on the overall performance of the coal industry. Different datasets all have their own advantages and disadvantages, so complementary studies using different datasets are needed. 5. Conclusions and policy implications In this paper, we used a modified identification method to identify zombie firms from a large sample of Chinese coal mining companies. We then analyzed the prevalence and distribution of zombie firms, investigated the causes of the emergence of zombie firms, and evaluated the effectiveness of various restructuring measures for resolving zombie firms. We found that the proportion of zombie firms in the Chinese coal mining sector decreased significantly from 1999 to 2004, remained fairly constant from 2005 to 2008, and began to increase thereafter. The distribution of zombie firms varies substantially in different regions of China, and SOEs are more likely to be zombie firms than non-SOEs. The emergence of zombie firms are caused by both internal and external factors. In addition to internal factors, government interventions and subsidized bank credits were identified as important external factors that contribute to the emergence of zombie firms. Furthermore, continuous financial support from the government or banks negatively affects the recovery of zombies. We also found that reductions in labor costs, SOE reform, and deleveraging support the resolution of zombie firms, while the injection or sale of assets have a ambiguous impact on their recovery. 9

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firms is likely to be a painful and costly process, particularly because it could lead to large-scale unemployment. To deal with this, the Chinese government has established a fund of 100 billion RMB to assist unemployed workers. These efforts are worthwhile because the costs of subsiding zombie firms can exceed the costs of compensating layoffs. In addition to unemployment compensation, the government could implement measures such as the resettlement of redundant employees, providing jobs in public welfare departments, supporting entrepreneurship, and reemployment training and guidance.

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Acknowledgments We thank Professor Gary Campbell and Yalin Lei (the editors), and two anonymous reviewers for the constructive comments. The financial support from the National Natural Science Foundation of China (No. 71804140), the Humanities and Social Science Foundation of the Ministry of Education of China [No. 18YJC790021], the China Postdoctoral Science Foundation [No. 2017M610626, 2018T111026], the Social Science Foundation of Shaanxi Province (No. 2017D021), and the Fundamental Research Funds of Xi’an Jiaotong University (No. SK2018020) are gratefully acknowledged. Appendix A. Supplementary material Supplementary data associated with this article can be found in the online version at doi:10.1016/j.resourpol.2018.11.016. References Brandt, L., Van Biesebroeck, J., Zhang, Y., 2012. Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing. J. Dev. Econ. 97, 339–351. Bruche, M., Llobet, G., 2013. Preventing zombie lending. Rev. Financ. Stud. 27, 923–956. Caballero, R.J., Hoshi, T., Kashyap, A.K., 2008. Zombie lending and depressed restructuring in Japan. Am. Econ. Rev. 98, 1943–1977. Dai, X., Cheng, L., 2015. Public selection and research and development effort of manufacturing enterprises in China: state owned enterprises versus non-state owned enterprises. Innovation 17, 182–195. Dai, X., Cheng, L., 2016. Market distortions and aggregate productivity: evidence from

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