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Chinese State-Owned Companies, Misallocation and the Reform Policy

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Abstract

This paper attempts to quantify the resource misallocation arising from firm ownership and to evaluate the SOE reform policy in China. State-owned enterprises (SOEs) in China typically enjoy preferred policies such as better access to financial system, reflecting the connections between these firms and various levels of government. This, however, leads to potential important distortions, with implications for resource allocation, productivity, and thus long-term growth. The SOE reform policy that seeks to “Grasp the Large, Let Go of the Small” could further exacerbate the distortions. By investigating a large firm-level data set with the methodology of Foster et al. (Am Econ Rev 98(1): 394–425, 2008) and Olley and Pakes (Econometrica 64(6):1263–1297, 1996), we show that SOEs are significantly less likely to have efficient resource allocation than firms not controlled by the various levels of China’s government. Industries with dominant state-owned firms likewise have less efficiency, suggesting the impact China’s SOE policy that has especially favored the largest of the state-owned firms. The results have important policy implications in suggesting that reforms that allow for greater market forces among firms would lead to potential economic growth in China.

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Fig. 1

Source: Author’s mapping using CASMF

Fig. 2

Source: Author’s mapping using CASMF

Fig. 3

Source: Author’s mapping using CASMF

Fig. 4

Source: Author’s mapping using CASMF

Fig. 5

Source: Author’s mapping using CASMF

Fig. 6

Source: Author’s mapping using CASMF

Fig. 7

Source: Author’s mapping using CASMF

Fig. 8

Sources: The data on China is in 2007 from author’s calculation using CASMF. Indian data is in 1994 from Hsieh and Klenow (2009). The rest countries are from Bartelsman et al. (2013) averaged between 1993 and 2001

Fig. 9

Source: Author’s mapping using CASMF

Fig. 10

Source: Author’s mapping using CASMF

Fig. 11

Source: Author’s mapping using CASMF

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Notes

  1. Banerjee and Duflo (2005) summarized extensively the reasons for the difference in productivity across countries. They point out that credit constraints seem pervasive in developing countries but did not further discuss the various institutional reasons for this constraints across countries. Therefore, we focus specifically on the credit constraints because of ownership which leads to different access to credit market.

  2. For more detailed information on the SOEs before 1978, please refer to the National Statistical Bureau website.

  3. The loose definition for “The Big” SOEs are those on seven key areas and industries related to national security and economic lifelines, including military industry, electricity and the electrical grid, oil and the petrochemical industry, telecommunications, coal, civil aviation and shipping.

  4. Privatization process of SOEs in China in some places early 2000s is very similar to the shock therapy in former Soviet Union countries. A most significant approach is management buyout (MBO) which transfer state assets to individual managers at usually much lower price than book value. Whereas this approach promoted trigger better performance for those companies, it also raise fierce debate as whether it is subject to social justice. In many cases, this strategy is under criticism as effectively transferring public wealth to a minority elite who collude with governments. One famous case is Lang–Gu dispute where academic scholar Larry H. P. Lang, also a critic of the MBO reforms, accused Gu Chujun, a private entrepreneur, of illegally seizing state assets, which eventually lead to the conviction of Gu who was later imprisoned in January 2008.

  5. An important note here is that local SASAC are administered by local government rather than higher level SASAC. The fundamental thing is that those local SOEs are legally the assets of local governments instead of central government.

  6. For further information on Fortunate 500 list, please refer to Fortune 500, 2014.

  7. The complete list and information is here: http://www.cec1979.org.cn/c500/chinese/.

  8. The exchange rate used here is US$ 1 = RMB 8.0. The Chinese currency was basically pegged the dollar during our observation period, 1998–2007.

  9. Following existing literature (see, e.g., Foster et al. 2008; Melitz and Polanec 2012; Banerjee and Moll 2010; Bartelsman et al. 2013), we define two-digit industry as base industry. Moreover, due to the inconsistency and various change in the definition of industry code in our data, it is more safe for us to use two-digit industry code.

  10. Following the definition of Foster et al. (2008) and Bartelsman et al. (2013), we obtain TFP as a regression residual of firm-level production function regression: \(\ln \;{\text{TFP}} = \ln Y_{i,t} - \hat{\alpha }\ln K_{i,t} - \hat{\beta }\ln L_{i,t} ,\) where Yi,t, Ki,t and Li,t is the real value added, real capital input, and employment of firm i in period t, and \(\hat{\alpha }\) and \(\hat{\beta }\) denote the estimates of regression coefficients for capital and labor inputs. As always, we include two-digit industry dummies and year dummies in our production function regressions. Though Petrin and Levinsohn (2012) pointed out the advantage of value-added production function over gross output production function as the former method yields a much more direct welfare interpretation, we still examine gross output in the production function to calculate TFP and find the pattern robust to this alternative measure for different firm ownership. Detailed results are available upon request.

  11. We define firm labor productivity as log profit per employee at the firm level for ai,t. For market share measurement, we follow Bartelsman et al. (2013)’s way by defining labor share in the industry for Si,t. However, we have also used log value added per employee at the firm level for ai,t and the sales for Si,t, respectively. The results are generally robust and available upon request.

  12. We define coastal areas as Hebei, Liaoning, Beijing, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan, all of which have benefited from their location and policy priorities in the process of reform and opening-up. This definition is not consistent with the distribution shown in Figs. 1, 2, 3 and 4, but is also based on the index marketization index proposed by Wang et al. (2007). They find that coastal areas have higher level of marketization above 6.0 while other provinces are usually lower than 6.0. There is an exception, though, Sichuan whose index is close to some coastal provinces. This is also evident in our figures that Sichuan’s dispersion is more like coastal regions than their Middle or Western provinces.

  13. We also have information for the rest of years between 1998 to 2007. However, we only picked 1999, 2002, 2005 and 2007 for the purpose of being more readable meanwhile not to compromise the main time trend.

  14. The information on United States, France and Netherlands are the averaged value over 1993–2003 from Bartelsman et al. (2013). The data on India is in 1994 which is most recent one in the table by Hsieh and Klenow (2009). We have also compared our calculation with Hsieh and Klenow (2009) since we are using the same data. We find our results almost the same as theirs.

  15. We also conducted various robustness check. However, due to limited space, we don’t include them though they are available upon request.

  16. This is the Herfhindal–Hirshmann index of concentration, adapted to this purpose. The mathematical form is expressed as HHI = M Si, where M Si is the market share, measured here by firm sales, of ith firm in an industry comprising n firms. Higher values indicates that a fewer firms account for a large share of the market power, measured by sales, in its industry. For robustness purpose, we also experimented the calculation of HHI index by total assets and find the pattern quite robust. Detailed results are available upon request.

  17. To obtain consistent estimates of specification (2), we use Arellano and Bond (1991a)’s first-difference Generalized Method of Moments (GMM) estimation for the dynamic specification. However, for difference GMM estimator to be valid, consistent and efficient, two critical assumptions must be satisfied. First, regressors must be predetermined by at least one period, which is hard to test with existing econometric tools. Second, the error terms should not be serially correlated (Arellano and Bond 1991b; Forbes 2000). For the first assumption, we simply use the lagged value as the respective instruments of those potential endogenous variables. To test the second assumption, we firstly make Sagan/Hansen test, which is used for overidentifying restrictions. Under the null hypothesis of instrument validity, the asymptotically distribution can serve as Chi square test where the degree of freedom is the number of instruments less the number of parameters. The alternative method is to make first-order and second-order serial correlation test. We report the results of these two tests in the regression tables. Following Gilchrist and Himmelberg (1995) and Forbes (2000), we use two period lagged value as instruments for endogenous regressors. One main reason for us to choose t − 2 as instruments is that it passed the Sagan/Hansen J test for volatility and Arellano–Bond test for autocorrelation test while not risking losing too many observations. Therefore, our first-difference GMM estimator use the moment condition as follows.

  18. Here we do not include the table that exclude ownership variable. However, the coefficient and signs are almost the same as what reported in Table 3.

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Authors and Affiliations

  1. School of International Relations and Public Affairs, Fudan University, Handan Road 220, Yangpu, Shanghai, 200433, China

    Jianzhi Zhao

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Correspondence to Jianzhi Zhao.

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I am grateful to Peter Reuter, Takeo Hoshi, Albert “Pete” Kyle, Anton Korinek, Vojislav Maksimovic, Susan Schwab and participants of the University of Maryland Tuesday Policy Forum and The US Congressional Budget Office (CBO) for their helpful comments. All errors are ours.

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Zhao, J. Chinese State-Owned Companies, Misallocation and the Reform Policy. Chin. Polit. Sci. Rev. 4, 28–51 (2019). https://doi.org/10.1007/s41111-018-0112-4

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