An Empirical Test of the Financial Accelerator in China: Evidence from the Chinese Industrial Enterprises Database

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1 Front. Econ. China 2015, 10(3): DOI /s RESEARCH ARTICLE Jie Chen, Zhe Li An Empirical Test of the Financial Accelerator in China: Evidence from the Chinese Industrial Enterprises Database Abstract The purpose of this paper is to test the applicability of the financial accelerator mechanism to China. Using the Chinese Industrial Enterprises Database, we find strong evidence suggesting that the employment and investment of leveraged firms are less responsive to aggregate fluctuations. This finding goes against the implications of the financial accelerator. To make sure our empirical result is reliable, we have done several robustness checks using different estimation methods and subsamples. Keywords Financial Accelerator; Leverage; Chinese Industrial Enterprises Database JEL Classification G30 E32 D22 1 Introduction Over the past few years, many developed countries have experienced the most severe financial crisis of the post-war period. The huge losses undergone by the economy in the ensuing recession indicate that the financial system can have a significant im- Received March 16, 2015 Jie Chen ( ) School of Economics, Shanghai University of Finance and Economics, Shanghai , China; China (Shanghai) Pilot Free Trade Zone Subbranch, Shanghai Branch, Bank of Communications, Shanghai , China Zhe Li School of Economics and Key Laboratory of Mathematical Economics, Shanghai University of Finance and Economics. Shanghai , China

2 510 Jie Chen, Zhe Li pact on the real economy. In light of that, many Chinese scholars find it necessary to take the financial sector more seriously and to begin to incorporate the financial accelerator mechanism into their research (Yuan, Chen and Liu, 2011; Wang, Zhang and Liu (2012)). Briefly speaking, the financial accelerator mechanism works as follows: In an efficient financial system, a highly leveraged firm will be seen as more risky and hence find it harder to obtain external financial support during economic downturns. A lack of financial support will force the firm to layoff employees and reduce investment. In other words, leverage amplifies the impact of aggregate fluctuations. However, there is no direct evidence that this idea of the financial accelerator applies to China. A number of empirical researches have shown that the financial accelerator can help explain the interaction between the financial system and the real economy in the U.S. (Cantor(1990), Sharpe (1994), Calomiris, Orphanides and Sharpe (1997)). However, since China s financial system is quite different from that of the U.S., whether or not the financial accelerator also applies to China is uncertain. In China, financial markets are underdeveloped and banks play a dominant role in allocating resources (Naughton (2007) and Allen, Qian and Qian (2008)). 1 In addition to that, most banks in China are owned by the government and hence, are inefficient in allocating resources (Yu and Pan (2008), Li and Su (2011) and Dong, Liu, Shen and Sun (2014)). 2 The purpose of this paper is to test the applicability of the financial accelerator mechanism to China, based on data from the Chinese Industrial Enterprises Database (CIED hereafter) from 1998 to To make our results comparable to those of the U.S., the empirical test is specified to follow the work of Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997). Using data from CIED, we find strong evidence suggesting that the employment and investment of highly leveraged firms are less responsive to aggregate fluctuations. This finding goes against the implications of the financial accelerator that higher leveraged firm is more inclined to layoff employees and reduce investment during economic downturns. One difficulty in testing the financial accelerator mechanism is the potential endogeneity of leverage. To deal with that, we have done three modifications to Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997). First, we use lags of leverage as instrumental variables. Second, the rich information in CIED makes it possible to 1 According to the China Monetary Policy Report issued by the People s Bank of China, direct finance accounted for only 11.7% of all-system financing aggregates in All-system financing aggregates refer to the total volume of funds in the financial system that is available to the real economy during a certain period of time. 2 In fact, Yu and Pan (2008) and Li and Su (2011) find that a good relationship with banks is vital for a firm to raise external funds.

3 An Empirical Test of the Financial Accelerator In China 511 include more control variables into the empirical test in order to reduce the omitted variable bias. Finally, we construct an unbalanced panel data out of CIED and use the fixed effect estimator to control for firm heterogeneity. We further examine the robustness of our results by looking at different subsamples. In the first robustness check, we exclude state-owned enterprises (SOEs) from CIED, because Chinese SOEs are less efficient than non-state-owned enterprises (non-soes) in allocating resources (Wang, Wong and Xia, 2008; Li, 2010). Empirical evidence shows that the financial accelerator mechanism still does not hold in China. In the second robustness check, we use data of listed industrial enterprises only. We find evidence that even for listed enterprises, the financial accelerator mechanism does not hold. Our conclusion that the mechanism of the financial accelerator does not apply to China is robust with respect to the sample selection. This paper is organized as follows. Section 2 reviews the existing literature on the financial accelerator mechanism. In section 3 we describe our data. In section 4 we present the specifications of our empirical model. Section 5 gives a detailed account of our empirical results. Section 6 concludes. 2 Literature Review There is a long debate on whether or not a firm s financial situation, especially its leverage, will affect its employment and investment decisions. On one side of this issue, the Modigliani-Miller (1958) theorem argues that financial structure is both indeterminate and irrelevant to real economic outcomes. On the other side, many scholars find financial structure to be of great importance in determining a firm s choice of employment and investment. Based on ideas by Fisher and Keynes, Bernanke and Gertler (1989) and Bernanke, Gertler and Gilchrist (1996, 1999) proposed the concept of the financial accelerator to help explain the close interaction between the financial system and the real economy. These papers show that a small negative shock in the real side of the economy can be amplified by financial friction and cause a large loss. 3 3 The financial accelerator mechanism works as follows: A fall in asset prices caused by a small negative shock in the real economy can deteriorate the balance sheets of the firms and their net worth. The resulting deterioration of their ability to borrow, due to asymmetric information, has a negative impact on their investment and employment growth. The reduction of investment and employment growth causes a further decline in economic activity and further cuts in asset prices, leading to a feedback cycle of falling asset prices, deteriorating balance sheets, tightening financing conditions and declining economic activity.

4 512 Jie Chen, Zhe Li Many empirical works have examined whether or not financial markets and the real economy do interact in the U.S. in the way described by the financial accelerator mechanism. Cantor (1990) finds annual employment and capital expenditures to be more volatile at more highly leveraged firms. Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997) find that employment growth and investment level at more highly leveraged firms are more sensitive to aggregate fluctuations. The findings of these papers show that the idea of a financial accelerator does hold in the U.S. economy. In the aftermath of the U.S. subprime crisis, some Chinese scholars followed in the footsteps of Bernanke, Gertler and Gilchrist (1999) and attempted to quantify the impact of the financial system on China s real economy. Yuan, Chen and Liu (2011) constructed a small open economy model in order to analyze the different possible impacts of a financial accelerator under different exchange rate systems. Wang, Zhang and Liu (2012) built a DSGE model to study the idea of a financial accelerator mechanism in China under the conditions of sticky prices and sticky information. By following the framework of Bernanke, Gertler and Gilchrist (1999), these papers assume that a financial accelerator mechanism applies to China. However, there is no direct evidence that such a mechanism applies to China. To test this, one should check whether or not financial leverage can amplify the impact of aggregate fluctuations on a firm s employment growth and investment level. While there is no empirical research studying the connection between leverage and employment growth, there are some papers that discuss the relation between leverage and investment level. For example, Wang and Mao (2010) and Ma and Wang (2012) use Chinese listed manufacturers data, and find that investment level and leverage are negatively related. However, these papers do not investigate how leverage affects the responsiveness of investment to aggregate fluctuations. In this paper, we try to systemically test the applicability of the financial accelerator mechanism to China. The specifications of our empirical model follow the work of Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997), with modifications to deal with the potential endogeneity of leverage. We also use subsamples of CIED in empirical analysis to check the robustness of our findings. We find strong evidence that the idea of a financial accelerator does not apply to China. 3 Data Macroeconomic series to be used to proxy for the business cycle is the growth rate of

5 An Empirical Test of the Financial Accelerator In China 513 total industrial production (ΔIP t ). Table 1 gives descriptive statistics of ΔIP t from 1998 to As we can see from table 1, ΔIP t varies from a minimum of 8.5% to a maximum of 14.9%. This variation is not small considering the average growth rate is 10.7%. Table 1 Summary Statistics of ΔIP t Variable Mean Std. Min Max ΔIP t Source: National Bureau of Statistics. Firm level data are obtained from Chinese Industrial Enterprises Database. CIED is collected and released annually by the Chinese government s National Bureau of Statistics (NBS). CIED is a census that collects observations on all non-soes with more than 5 million yuan in revenue (about $800,000) plus all SOEs. The raw data consists of over 160,000 enterprises in 1998 and grows to over 300,000 enterprises in According to the sixth nationwide population census, the sales of enterprises in CIED accounts for 89.5% of total sales of industrial enterprises in The data in CIED includes basic information about the firm as well as its financial statements. 4 The richness of information, the large number of firms covered by the database, and the long duration makes CIED one of the best longitudinal micro-level sources of data for Chinese companies. However, since some enterprises are not familiar with accounting standards, a small portion of enterprises may fail to correctly report their financial statements. To alleviate this problem, we have eliminated outliers from CIED. Since all listed industrial enterprises have a revenue of more than 5 million yuan, they are all included in CIED. In 2013, the number of listed industrial enterprises was Most enterprises in CIED are non-listed and have little access to the financial market. In one robustness check, we only use data of listed industrial enterprises in our regressions. However, to do this robustness check, we have to use a separate database (Wind) to get information on listed industrial enterprises, since CIED does not include a variable to show whether an enterprise is listed or not. The key firm-level variables of interest include growth rate of employment, change in investment-capital ratio, leverage, firm size, cash flow, duration of operations, sale- 4 Basic information about the firm includes name, representative of artificial person, address, phone number, , business license number, number of employees, ownership, affiliation, year of incorporation, etc.

6 514 Jie Chen, Zhe Li output ratio, export-sale ratio, and interest rate. Leverage is computed as the book value of total debt over the book value of total assets: Leverage i,t = liability i,t / asset i,t. Since most enterprises in CIED are non-listed, we can only calculate leverage using book values. For the robustness check with listed enterprises, it is possible to calculate leverage using market values. For a consistent presentation, we will report the result when we calculate leverage using book values. Our conclusion remains unchanged if we calculate leverage using market values. Firm size is measured as the log of its total inflation-adjusted total assets. Interest rate is calculated as average interest rate: InterestP aid/liability. We can also calculate interest rate as marginal interest rate: (InterestPaid LagInterestP aid)/(liability LagLiability). Using a different definition does not alter our conclusion. For the robustness check with listed enterprises, we do not have data on sale-output ratio, export-sale ratio and interest rate. Instead, dividend ratio and price to earning ratios (PE) are included as control variables. Descriptive statistics of firm level data is reported in Table 2 for the whole sample, SOEs, non-soes and listed enterprises, respectively. As we can see from the table, SOEs and non-soes differ in many aspects. For example, SOEs, on average, are more highly leveraged relative to non-soes. This and the fact that interest rates for SOEs are, on average, lower than for non-soes is consistent with the conventional wisdom that SOEs have better access to banks. Table 2 Summary Statistics Variable Description Whole Sample SOEs Non- SOEs Listed ΔE Growth Rate of Employment (0.326) (0.275) (0.332) (0.191) Δ(I/K) Change of Investment Capital Ratio (0.830) (0.628) (0.865) (0.511) Leverage Liability Divided by Asset (0.250) (0.241) (0.251) (0.192) Size Log of Price Adjusted Total Asset (1.272) (1.597) (1.202) (1.123) Cash (Operation Income F ixedasset +Depreciation) Divided (0.599) (0.400) (0.614) (2.967) by Fixed Asset Age Duration of Operation (9.421) (15.597) (6.373) (5.139) (To be continued)

7 An Empirical Test of the Financial Accelerator In China 515 Variable Description Whole Non- SOEs Sample SOEs Sale Sale Divided Output by Output (0.061) (0.070) (0.059) Export Export Value Divided Sale by Sale (0.350) (0.140) (0.365) InterestRate Interest Paid divided by Liability (3.283) (2.650) (3.363) Dividend Rate Dividend divided by Stock Price PE Price to Earnings Ratio (Continued) Listed (1.497) (26.217) Observation Note: Standard deviations are listed in parentheses. 4 Model Specification Based on the work of Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997), we specify the empirical model as follows. ΔE i,t =Σ 3 s=1(a s + b s Leverage i,t 1 )Z t s + gleverage i,t 1 + γx i,t 1 + e i + u i,t. (1) Δ(I/K) i,t =Σ 3 s=1(a s + b s Leverage i,t 1 )Z t s + gleverage i,t 1 + γx i,t 1 + e i + u i,t. (2) As in Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997), the growth rate of employment ΔE i,t and change of investment-capital ratio Δ(I/K) i,t are used to stand for a firm s choice of employment and investment. Z t stands for aggregate demand, and the detrended growth rate of total industrial production (ΔIP t ) is used as a proxy for it. Leverage i,t records the firm s leverage, X i,t contains other information for the firm as control variables and e i is included to control for fixed effect. We include Z t 1,Z t 2,Z t 3 so that we can consider both the impact multiplier and the long-run multiplier of aggregate demand. Since including further lags of Z t will not change our conclusion, for the convenience of presentation we only report the result with Z t 1,Z t 2,Z t 3.

8 516 Jie Chen, Zhe Li As these two equations show, the impact of aggregate demand Z t s on employment growth (investment) comes from two parts: a direct part a s and the part affected by leverage b s Leverage i,t 1. The key is the sign of b s, since it determines whether a higher leverage amplifies or alleviates the impact of aggregate fluctuations. In the case of impact multiplier, b 1 > 0 means leverage will amplify the impact of aggregate fluctuations, while b 1 < 0 means leverage can alleviate the impact of aggregate fluctuations. In the case of long-run multiplier, Σ 3 s=1 b s > 0 indicates the existence of a financial accelerator mechanism while Σ 3 s=1b s < 0 indicates that the financial accelerator mechanism does not hold. The first part of the setting Σ 3 s=1 (a s + b s Leverage i,t 1 )Z t s + gleverage i,t 1 follows Sharpe (1994) and Calomiris, Orphanides and Sharpe (1997) to make our result comparable to that of the U.S. The second part of the setting γx i,t 1 + e i is used to alleviate the endogeneity problem. Control variables X i,t are included to alleviate the endogeneity caused by omitting explanatory variables, while e i is included to control for fixed effect. To deal with endogeneity, we also use lags of leverage and their interaction terms with aggregate demand Z t s as instrumental variables in our empirical analysis. As for control variables X i,t, we follow the work of Tian (2005) and Shao, Bao and Ye (2013) to include the following characteristics of firms: firm size, cash flow, duration of operations, sale-output ratio, export-sale ratio, and interest rate. For listed firms, we do not have data on sale-output ratio, export-sale ratio and interest rate, instead, we use dividend ratio and price to earnings ratio. 5 Empirical Results This section reports the main results of our empirical analysis. In the first subsection, we report the estimation result of equation (1). The empirical result shows that a highly leveraged firm will have more stable employment growth during economic fluctuations. In the second subsection, we report the estimation result of equation (2). The empirical result shows that a highly leveraged firm will adjust its investment level less intensively in response to the fluctuations of aggregate demand. 5.1 Leverage and Firm s Employment Growth Table 3 reports the estimation result of equation (1). The first column reports OLS estimation of equation (1). Although we have included some control variables in equation

9 Table 3 Leverage and Firms Labor Demand Dependent Variable OLS FE Independent Variable: Growth Rate of Employment ΔE i,t (SOEs) (non-soes) ΔIP t *** *** *** *** *** (0.0652) (0.0724) (0.123) (Listed) (0.208) (0.143) (0.569) Leverage i,t 1 * ΔIP t *** *** *** *** *** (0.107) (0.120) (0.215) (0.359) (0.252) (1.120) ΔIP t *** *** *** *** *** (0.0550) (0.0634) (0.102) (0.169) (0.120) (0.409) Leverage i,t 1 * ΔIP t *** *** *** *** *** (0.0905) (0.106) (0.177) (0.290) (0.210) (0.831) ΔIP t *** *** *** *** *** (0.0548) (0.0646) (0.0983) (0.185) (0.112) (0.555) Leverage i,t 1 * ΔIP t *** *** *** (0.0908) (0.109) (0.172) (0.320) (0.198) (1.114) (To be continued)

10 Dependent Variables OLS FE Independent Variable: Growth Rate of Employment ΔE i,t (SOEs) (non-soes) (Listed) Leverage i,t *** * *** * *** ** (Continued) ( ) ( ) (0.0230) (0.0232) (0.0315) (0.0473) Size i,t *** *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) (0.0108) Cash_FixedAsset i,t *** *** *** ** ( ) ( ) ( ) ( ) ( ) ( ) Age i,t *** *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Sale_Output i,t *** *** ( ) ( ) ( ) (0.0169) (0.0101) Export_Sale i,t *** *** ( ) ( ) ( ) (0.0163) ( ) (To be continued)

11 Dependent Variables OLS FE InterestRate i,t ( ) Independent Variable: Growth Rate of Employment ΔE i,t (SOEs) (non-soes) ( ) ( ) ( ) ( ) (Listed) (Continued) PE i,t ( ) Dividend i,t ** ( ) Constant *** ( ) ( ) (0.0206) (0.0464) (0.0250) (0.167) Observation R Note: (1) * significant at 10%, ** significant at 5%, *** significant at 1%. (2) Standard errors in parentheses.

12 520 Jie Chen, Zhe Li (1), leverage and its interaction term with aggregate demand may still be endogenous. Since we can construct an unbalanced panel data out of CIED, we are able to use fixed effect to estimate equation (1). The result is reported in the second column. Furthermore, we can use lag of leverage and its interaction term with aggregate variable as instrumental variables to further alleviate endogeneity. The estimation result is reported in the third column. In columns four to six, we report the estimation result of SOEs, non-soes and listed enterprises, respectively. From Table 3, we can see how aggregate demand ΔIP t s affects a firm s employment decision ΔE i,t and whether this effect will be amplified or alleviated by the firm s leverage. The impact multiplier of aggregate demand ΔIP t 1 is Leverage i,t 1 in column one, which means that a one percent increase in growth rate of industrial production ΔIP t 1 will lead to Leverage i,t 1 percent increase in a firm s growth rate of employment. Since the coefficient before the interaction term of leverage and aggregate demand ( 0.686) is negative and statistically significant at the 1-percent level, one can conclude that the impact of industrial production on a firm s employment growth decreases with leverage. When the firm s leverage increases, its magnitude of adjustment of employment growth in response to economic fluctuations decreases. That is, a more highly leveraged firm has a more stable employment growth. This result is robust when we use other estimation methods (see columns two and three). The long-run multiplier of aggregate demand should be the sum of coefficients before ΔIP t 1, ΔIP t 2, ΔIP t 3. As shown in the first column, this effect is ( Leverage i,t 1 )+ ( Leverage i,t 1 )+( Leverage i,t 1 ), which decreases with Leverage i,t 1. A test of joint significance of the leverage interaction terms rejects the null hypothesis at the 1-percent level. We can conclude that the long-run multiplier of aggregate demand also decreases with a firm s leverage. This finding is also robust when we use different estimation methods. From the fifth column, we can see that both the impact multiplier and the longrun multiplier of aggregate demand on a firm s employment growth will decrease as leverage increases, even after we have excluded SOEs from CIED. From the sixth column, one can see that this effect of leverage also holds for listed enterprises. 5.2 Leverage and Firm s Investment Table 4 reports estimation results of equation (2). As in the case of employment, we use different methods to estimate equation (2) in order to deal with endogeneity and

13 Table 4 Leverage and Firms Investment Decision Dependent Variables OLS FE Independent Variable: Change of Investment-Capital Ratio Δ(I/K)i,t (SOEs) (non-soes) ΔIP t *** *** ** ** (Listed) (0.255) (0.321) (0.498) (0.629) (0.654) (1.558) Leverage i,t 1 * ΔIP t *** *** *** *** *** (0.426) (0.542) (0.880) (1.087) (1.157) (3.069) ΔIP t *** *** (0.196) (0.248) (0.415) (0.505) (0.540) (1.116) Leverage i,t 1 * ΔIP t *** *** (0.324) (0.418) (0.725) (0.868) (0.953) (2.267) ΔIP t *** *** ** (0.179) (0.234) (0.371) (0.542) (0.468) (1.521) Leverage i,t 1 * ΔIP t *** * (0.296) (0.396) (0.653) (0.936) (0.827) (3.054) (To be continued)

14 Dependent Variables OLS FE Independent Variable: Change of Investment-Capital Ratio Δ(I/K) i,t (SOEs) (non-soes) (Listed) Leverage i,t *** *** *** *** *** ( ) (0.0114) (0.116) (0.0767) (0.196) (0.130) (Continued) Size i,t *** *** *** *** *** ** ( ) ( ) (0.0130) (0.0265) (0.0156) (0.0297) Cash_FixedAsset i,t *** *** *** *** *** *** ( ) ( ) ( ) (0.0124) ( ) (0.0170) Age i,t *** *** *** *** ( ) ( ) ( ) ( ) ( ) ( ) Sale_Output i,t *** ** (0.0170) (0.0271) (0.0332) (0.0504) (0.0410) Export_Sale i,t *** *** *** ( ) ( ) (0.0120) (0.0493) (0.0133) (To be continued)

15 Dependent Variables OLS FE Independent Variable: Change of Investment-Capital Ratio Δ(I/K) i,t (SOEs) (non-soes) InterstRate i,t ( ) ( ) ( ) ( ) ( ) (Listed) (Continued) PE i,t ( ) Dividend i,t ( ) Constant *** *** *** *** *** ** (0.0174) (0.0341) (0.0891) (0.149) (0.131) (0.460) Observation R Note: (1) * significant at 10%, ** significant at 5%, *** significant at 1%. (2) Standard errors in parentheses.

16 524 Jie Chen, Zhe Li get a robust result. From Table 4, we can see how aggregate demand ΔIP t s affects a firm s investment plan Δ(I/K) i,t and whether this effect will be amplified or alleviated by the firm s leverage. The impact multiplier of aggregate demand ΔIP t 1 is Leverage i,t 1 in column one, which means that a one percent increase in growth rate of industrial production ΔIP t 1 will lead to Leverage i,t 1 percent increase in a firm s investment level. Since the coefficient before interaction term of leverage and aggregate demand (-1.821) is negative and statistically significant at the 1-percent level, one can conclude that the impact of industrial production on a firm s investment decreases with leverage. When the firm s leverage increases, its magnitude of adjustment of the investment level in response to economic fluctuations decreases. That is, a more highly leveraged firm has a more stable investment level. This result is robust when we use other estimation methods (see columns two and three). The long-run multiplier of aggregate demand should be the sum of coefficients before ΔIP t 1, ΔIP t 2, ΔIP t 3. As shown in the first column, this effect is ( Leverage i,t 1 )+ ( Leverage i,t 1 )+( Leverage i,t 1 ). As the sum of coefficients before Leverage i,t 1 is negative and the test of joint significance of the leverage interaction terms rejects the null hypothesis at the 1-percent level, we can conclude that the long-run multiplier of aggregate demand also decreases with the firm s leverage. This finding is also robust when we use different estimation methods. From the fifth column, we can see that both the impact multiplier and the longrun multiplier of aggregate demand on a firm s investment will decrease as leverage increases, even if we only use non-soes in our regression. From the sixth column, one can see that this effect of leverage also holds for listed enterprises. 6 Conclusion In this paper, we use data from the Chinese Industrial Enterprises Database from the period to study the implications of firms leverage on their employment growth and investment level. We find that leverage can alleviate the impact of aggregate demand on a firms employment growth and investment level. This goes against the implication of a financial accelerator mechanism acting on the Chinese economy. This finding is robust when we use different estimation methods and subsamples in our empirical analysis.

17 An Empirical Test of the Financial Accelerator In China 525 Acknowledgements Li acknowledges financial support from the National Science Foundation of China (No ). References Allen F, Qian J, Qian M (2008). China s financial system: Past, present, and future. In: Brandt L, Rawski T G (eds.), China s Great Economic Transformation: Origins, Mechanism, and Consequences (Chinese version), Shanghai, China: Gezhi chubanshe (Wisdom Press) Bernanke B S, Gertler M (1989). Agency costs, net worth, and business fluctuations. American Economic Review, 79: Bernanke B S, Gertler M, Gilchrist S (1996). The financial accelerator and the flight to quality. Review of Economics and Statistics, 78: 1 15 Bernanke B S, Gertler M, Gilchrist S (1999). The financial accelerator in a quantitative business cycle framework. In: Taylor J, Wooford M (ed.), Handbook of Macroeconomics, Volume 1, Amsterdam, Netherland: Elsevier Science Publishers Calomiris C, Orphanides A, Sharpe S A (1997). Leverage as a state variable for employment, inventory accumulation, and fixed investment. In: Capie F, Wood G E (eds.), Asset Prices and the Real Economy, London, UK: Macmillan Press Cantor R (1990). Effects of leverage on corporate investment and hiring decisions. Federal Reserve Bank of New York Quarterly Review, 15: Dong Y, Liu Z, Shen Z, Sun Q (2014). Political patronage and capital structure in China. Emerging Markets Finance and Trade, 50: Li L, Su Q ( ) (2011). Relationship banking and credit availability of small and medium-sized enterprises : An empirical study of China s small and medium-sized enterprises ( ). Finance Forum ( ), (4): Li Nan ( ) (2010). Chinese ownership dispersion and employment: Research based on industrial enterprises dynamic panel data ( ). Chinese Journal of Population Science ( ), (supplement): 1 11 Ma R, Wang Y ( ). Leverage, debt maturity and firm investments Evidences from China s listed company ( ). Research on Economics and Management ( ), (8): Modigliani F, Miller M H (1958). The cost of capital, corporation finance and the theory of investment. American Economic Review, 48: Naughton B (2007). The Chinese economy: Transitions and growth. Cambridge, MA: Massachusetts Institute of Technology

18 526 Jie Chen, Zhe Li Shao M, Bao Q, Ye N ( ) (2013). Credit constraints and employee income: Empirical evidence for firms in China ( ). China Economic Quarterly ( ), 12(3): Sharpe S (1994). Financial market imperfections, firm leverage, and the cyclicality of employment. American Economic Review, 84: TianL( ) (2005). State ownership, soft budget constraint and the leverage management of China s listed enterprises ( ). Management World ( ), (7): Wang L, Zhang L, Liu W ( ) (2012). Empirical researches on financial accelerator effects under different stickiness conditions ( ). Economic Research Journal ( ), (10): Wang L, Mao W ( ) (2010). Financial leverage, growth opportunities and corporate investment: Based on panel data of listed Chinese manufacturers ( Panel Data ). Management Review ( ), (11): Wang Q, Wong T J, Xia L J (2008). State ownership, the institutional environment, and auditor choice: Evidence from China. Journal of Accounting and Economics, 46: Yu M, Pan H ( ) (2008). The relationship between politics, institutional environments and private enterprises access to bank loans ( ). Management World (), (8): 9 39 Yuan S, Chen P, Liu L ( ). Exchange rate system, financial accelerator and economical undulation ( ). Economic Research Journal ( ), (1): 57 70

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