Government's Budget Constraint, Competition

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Journal of Comparative Economics 31 (2003) 486–502 www.elsevier.com/locate/jce

Government’s budget constraint, competition, and privatization: evidence from China’s rural industry

Hongbin Li

The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China Received 28 March 2002; revised 21 April 2003

Li, Hongbin—Government’s budget constraint, competition, and privatization: evidence from China’s rural industry This paper examines the determinants of privatization in Chinese rural industry by using data that we collected from Southern China in 1998. By employing several econometric specifications, we find that the probability of a firm being privatized increases with the degree of product market competition and the hardness of the government’s budget constraint, but that it does not vary with government policies. Our findings highlight the decentralized nature of China’s reforms. Journal of Comparative Economics 31 (3) (2003) 486–502. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China.  2003 Association for Comparative Economic Studies. Published by Elsevier Inc. All rights reserved. JEL classification: G32; G34; L32; L33; P20; P31

1. Introduction

Township and Village Enterprises (TVEs) have made an extraordinary contribution to China’s rapid economic growth over the past 20 years. Their contribution to national gross industrial output rose from about 10% in 1979 to nearly 40% in 1996 and increased at an average annual rate of nearly 20% during the 1980s (Chen and Rozelle, 1999). However, during the late 1990s, China’s TVEs were privatized quietly but in large numbers. According to our recent survey in China’s Lower Yangtse Delta region, more than half of all enterprises owned by township governments in 1994 had been partially or completely privatized by the end of 1997. This privatization has not been universal, either across

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regions or by pace. The government in southern Zhejiang, in particular the region around Wenzhou and Ningbo, started privatizing TVEs as early as 1994 and finished the process for the most part by 1996. On the other hand, southern Jiangsu, including townships in Wuxi that had been upheld previously as a model for China’s TVEs, began privatization only in 1998. The swiftness of privatization and the sharp differences in the rates of privatization over time and across regions raise questions about the determining factors. Moreover, researchers differ sharply on how the process should be interpreted. Qin (1998) and Yang (1999) assert that privatization is the result of central government policy. Others argue that privatization is the result of changes in the institutional environment, including the comparative advantage of management shifting from government officials to managers, increases in product market competition, and hardening of the government’s budget constraint (Cao et al., 1999; Li and Rozelle, in press; Lin et al., 1998). However, these hypotheses have not been tested empirically. This paper examines the determinants of privatization and tests the above hypotheses using a comprehensive set of primary data that we collected from Southern China in 1998. By employing probit and hazard models, we find that the probability of a firm being privatized increases with the degree of product market competition and the hardening of the government’s budget constraint, but that it does not vary with government policies. To control for the potential endogeneity of the hardness of the government’s budget constraint, we employ a two stage least squares (2SLS) model and derive results that are consistent with those from other models. Our findings support the decentralized nature of China’s reforms, including the privatization process (Cao et al., 1999; Li, 2003; Qian and Weingast, 1997). Existing empirical work on privatization focuses on studying the effect of privatization on performance (Anderson et al., 2000; Barberis et al., 1996; Frydman et al., 1999; Li and Rozelle, 2003).1 Megginson and Netter (2001) and Sonobe et al. (2001) examine the determinants of privatization, but they focus on how firm efficiency affects privatization.2 There are two exceptions. Li et al. (2000) use aggregate data to study the effect of competition on privatization and find that privatization increases with product market competition. However, the aggregate nature of their data makes it difficult to make any inference about individual firms. Brandt et al. (2003) study how bank objectives and bank liquidity interact with government incentives and ultimately determine privatization. Our paper uses firm-level data to test whether competition and hardening of the government’s budget constraint foster privatization. Several theories explain the sudden rise of TVEs. These can be categorized into two schools. One school uses the failure of markets to explain the success of TVEs. Both Chang and Wang (1994) and Chen and Rozelle (1999) argue that the success of TVEs is the result of the government’s monopolistic control over input, output, and credit markets. Li (1996) argues that government ownership of township enterprises is the least costly way

1 Djankov and Murrell (2002), Havrylyshyn and McGettigan (1999), and Megginson and Netter (2001) survey this literature. 2 Park and Shen (in press) argue that one of the reasons for privatization is the collapse of the group lending schemes that supported the growth of TVEs. 中国科技论文在线 http://www.paper.edu.cn

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to secure the state’s help when firms face difficulties in the market. According to the market failure school, the improvement of market conditions, such as increased competitiveness in the input and output markets, and the hardening of the government’s budget constraint in the lending market should decrease the value of government ownership and lead to privatization. The second school argues that local government ownership is a response to state failure. According to this school, when the state is unwilling to make a commitment not to act predatorily against private firms, local government ownership protects the local firms’ property rights (Qian and Weingast, 1997; Che and Qian, 1998a, 1998b). However, the role of local government in protecting property rights may have been weakened or reduced in the past decade because China enacted several important policy reforms to strengthen private property rights. In the Fourteenth Congress of the Chinese Communist Party in 1992, the Communist Party recognized the important role played by the private sector in China’s social-economic development for the first time. Moreover, it stated that the government should create fair market conditions under which firms characterized by different types of ownership structures can compete fairly with each other. The legal status of private firms was improved further in 1999 when a constitutional amendment redefined the private sector from supplementary in nature to an important integral part of the economy (Kung and Lin, 2002). Recently, China’s leaders have encouraged private entrepreneurs to join the Communist Party. Hence, improvements in the legal protection of private property make the role of local government ownership less important.3 The structure of the paper is as follows. Section 2 introduces the survey and the data followed by a brief description of the privatization process. Section 3 presents several hypotheses concerning the determinants of privatization. Section 4 tests empirically the resulting hypotheses from regressions. Section 5 concludes with policy implications.

2. The data and the privatization process

The data were collected from field surveys conducted by me and my colleagues in the summer of 1998.4 Most of the information comes from face-to-face interviews with firm managers and government officials at the township level. Historical data were copied from accounting books. The survey focuses on Township Enterprises (TEs); our data cover the period from 1994 to 1997.5 We took a random sample of 59 townships in 15 counties in Jiangsu and Zhejiang; these are two of China’s most developed coastal provinces, one north

3 Representing another school of thought, Weitzman and Xu (1994) assert that TVEs are vaguely deﬁned cooperatives. According to this argument, in a highly cooperative society like China, vaguely deﬁned property rights could even dominate certain explicit written contracts. However, this view can not explain the dramatic privatization of TVEs in the past decade. 4 Li (2001) and Li and Rozelle (in press) provide a more detailed description of the survey. 5 The township or town is the lowest level of government in China’s administrative hierarchy. Township governments established many enterprises in the 1980s, referred to as Township Enterprises (TEs). This paper will use TEs and locally government-owned enterprises interchangeably. Che and Qian (1998a, 1998b), Chang and Wang (1994), Chen and Rozelle (1999), Li (1996), Putterman (1997), and Weitzman and Xu (1994) have also studied township enterprises. 中国科技论文在线 http://www.paper.edu.cn

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and one south of Shanghai.6 We choose Zhejiang and Jiangsu because they represent the heartland of China’s rural enterprises and contain both well-developed and underdeveloped regions. We chose 3 firms in each township randomly from the pool of all of the TEs and private firms doing businesses in 1998, for a total of 168 firms.7 Thirty-three out of the 168 firms were established as private firms, but these are not considered in this paper. In 1994, the government owned 135 of these firms, denoted government-owned firms. Enumerators collected information for the period between 1994 and 1997. Since most privatization has occurred since the mid-1990s, 1994 was chosen as the starting year. We conducted surveys with both government officials and firm managers. The firm survey had two main parts. First, enumerators conducted a sit-down survey with the firm manager or owner to elicit detailed information on the change of the firm’s ownership during the survey period and the privatization process, information about the firm’s production and marketing activities and about the manager’s human capital attributes. Second, the firm’s accountant filled in a set of tables from the firm’s financial and cost accounting records. The survey of government officials focused on questions concerning privatization policies, the government’s role in acquiring loans from banks for TEs, and the government official’s human capital attributes. Depending on the exact nature of the definition that we use for changes in ownership, firms are classified as township or privatized. By a share-shifting definition, privatized enterprises are those in which any of the firm’s shares shift to private individuals. By controlling-interest shifting definition, a privatized enterprise is a firm in which the township has shifted more than 50% of the shares to private individuals.8 The most restrictive definition, complete privatization, implies that a privatized firm is one in which all the shares have been shifted to private individuals. Rural China experienced rapid privatization beginning in the mid-1990s. All of the townships in our sample undertook some privatization during the time period; and some townships sold all of their firms to private owners by 1998. Privatization decisions are decentralized in China with most made at the township level. Of the 59 townships, only 34% reported facing any county-level policies regarding privatization. The remaining 66% determined privatization policies themselves or had no overarching guiding policy. Of the 88 privatized firms, 81 reported that the township government made the privatization decision. Table 1 shows the surprisingly high proportion of firms undergoing privatization. Using the share-shifting definition, which is the broadest measure of privatization, 65% of the sample firms were privatized. Although the proportion of privatized firms grows progressively less as the definition of privatization becomes more restrictive, even using the most restrictive definition that the firm becomes 100% private, the table indicates

6 We sampled 60 townships initially, but we could not conduct interviews in one township due to factors beyond our control. 7 Because seven townships have fewer than three firms available for interviews, we have fewer than 177 firms. 8 La Porta et al. (1999) define a controlling share as 20% of the ownership rights. In our sample, most of the shares are either held by the township government or private individuals so that only 51% of the shares guarantee control of a firm. 中国科技论文在线 http://www.paper.edu.cn

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Table 1 Ownership structure after privatization under alternative deﬁnitions of privatization in Jiangsu and Zhejiang provinces, 1994 to 1997 (N = 134) Proportion of ﬁrms in each type of privatization category Firm type Share-shifting Controlling-interest shifting Complete (1) (2) (3) Township enterprises 35 41 53 Privatized 65 59 47

Table 2 Distribution of the pace of privatization at the township level among townships in China, 1994 to 1997 Percentage of township Number of townships Percentage enterprises privatized 0–20 10 18 21–40 9 16 41–60 17 30 61–80 14 24 81–100 7 12 Total 57 100

that officials privatized 47% of the firms. Following La Porta et al. (1999), who define ownership status in terms of the ultimate controlling shareholder of a firm, we use the controlling-interest shifting definition for the rest of the paper. The empirical results are similar if either of the other two definitions of privatization is used. One of the most remarkable features of the privatization trends in rural China after 1993 is the considerable heterogeneity across townships regarding the pace of privatization as Table 2 indicates.9 From data on the percentage of privatized firms in each township, 10 townships had only started privatization in 1997 while seven townships had almost or completely finished privatization by that year. The differing pace of privatization across townships is consistent with privatization decisions being taken mainly at the township level. In summary, our data suggest that the rural areas of Southern China undertook considerable privatization of TEs in the mid-1990s. The shift in share and firm ownership forms shows that privatization is fundamental and widespread. Although all townships are involved in the move to privatization, they differ in the degree and pace of privatization.

3. The determinants of privatization

The rapid privatization and the heterogeneity across townships raise a fundamental question about the determinants of privatization. Some researchers have argued that privatization is the result of central government policy (Qin, 1998; Yang, 1999). However,

9 The pace of privatization is missing for two townships in our survey. 中国科技论文在线 http://www.paper.edu.cn

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the differences in the pace and scope of privatization over time and across space are not consistent with a centrally directed movement. Others have argued that privatization is just another means of rent seeking and asset stripping (Lu, 1998; PBC, 1998; Sun, 1998). If the opportunities for asset stripping were random, the observed pattern of privatization might be consistent with this view. However, to the extent that opportunities for asset stripping are idiosyncratic, there should be no correlation between the pace of privatization and structural factors if corruption is the only reason for privatization. In this and the following sections, we attempt to show that privatization arises as township enterprises adapt to the newly emerging economic and political environments of the mid- to late-1990s. The rise of TVEs in the 1980s and early 1990s took place in an institutional environment in which the product, input, and credit markets are imperfect and property rights are not well protected. Although government ownership involves some costs, e.g., the agency cost caused by the separation of ownership and control and incentives for government officials different from profit maximization, the form of ownership could be optimal. In such an environment, government officials could even be better managers (Che and Qian, 1998a; Chen and Rozelle, 1999). However, the change in the institutional environment over the past decade alters the benefits and costs of government ownership. Property reforms in the past decade make government ownership less important in protecting property rights. Moreover, the comparative advantage of shifting control of firm operations from officials to managers, an increase in market competitiveness, and the hardening of the government’s budget constraint could make privatization a natural choice for local officials. In the rest of the section, we examine the role that each of these factors may have played in the emergence of China’s rapid privatization movement. Privatization, or the shift of operations and ownership out of the hands of the state, may occur when a manager has a comparative advantage in running the firm relative to an official. In fact, such a shift in income and control rights has been underway since the mid-1980s (Chen and Rozelle, 1999). Initially, when the market did not function well, local officials were more effective in allocating resources or distributing products, which is defined as external management activities, because they drew on their connections resulting from their state leadership positions. Individuals, lacking such connections, were unable to do business profitably. Typically, the officials hired a manager to do routine internal management activities, such as operating the product line and managing workers (Che and Qian, 1998a). However, with the development of markets, the relative advantage of officials in mobilizing resources and engaging in firm management has disappeared (Naughton, 1995; Jin and Qian, 1998). Hence, even in the early stages of the shift, officials began to offer incentive contracts to managers that would induce them to exert more effort in managing government-owned firms. At the same time, managers began to accumulate human capital that enabled them to execute both external and internal management functions. To the extent that individuals have better incentives to do business than officials, the continued development of markets in rural China during the 1990s has led to the next stage of enterprise reform (Nyberg and Rozelle, 1999). When managers have acquired better human capital than officials, we expect that firms should be more profitable because managers’ skills are more specialized. Using observations on the human capital 中国科技论文在线 http://www.paper.edu.cn

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attributes of officials and managers, i.e., their education and age, we test how cross- sectional differences affect the rate of privatization. Thus, the first hypothesis is that the probability of privatization increases with the manager’s human capital, but decreases with the official’s human capital. Related to human capital theory, we should also expect to see officials being more likely to privatize their firms when the market becomes competitive. This prediction is supported by a number of theories, including those developed to explain the success of TVEs. In an early paper, Chang and Wang (1994) argue that the ownership structure of TVEs is a result of the government’s monopolistic power in the market. They predict that, when the Chinese economy moves toward a more market-oriented system so that the government does not monopolize key economic resources, TVEs will be privatized. The effect of competition on ownership changes can also be generated by the theory of ambiguous property rights developed by Li (1996), who argues that township government ownership of firms, or ambiguous property rights, is a response to the imperfections of the market or government regulations. When firms face difficulties in market transactions, they turn to the government for help. However, due to information difficulties, firms can get help from the government at a low cost only when they include the government as an ambiguous owner. In this situation, agency cost arises due to the separation of ownership and control because entrepreneurs run firms that they do not own. Moreover, the government may have incentives other than profitability, such as employment and local public welfare. According to this theory, the role of government ownership should be reduced and more privatization should take place in more competitive markets in which the number of government regulations is less significant. From a different perspective, Li et al. (2000) develop another theory that supports the same prediction. In their model, local governments care about their share of the revenues generated by local firms. When the market is monopolistic, government ownership could be revenue maximizing even with significant agency costs because, although total revenue available is small, the government takes it all. When inter-regional competition becomes intense, profit margins become smaller and total available revenue becomes so small that the government must provide the manager with incentives to increase its size. Hence, privatization is a result of competition. All three theories predict that privatization is more likely when the product market is more competitive; this is the second hypothesis that we test. To test this hypothesis, we employ the indexes developed by Yang (1998) to measure market competitiveness. Using an econometric model and a data set from 1988 to 1993, Yang creates an index based on 40 industries in China. He regresses the entry rate for each industry on a number of factors including economies of scale, product differentiation, the degree of firm concentration, past industry profit rates, market size, and expected growth of markets. The author constructs a barriers-to-entry index by weighting the value of each entry barrier factor by its regression coefficient. He finds that the least competitive group is concentrated mainly in natural resources industries and the most competitive group includes many of the light industries. Our sample contains nine industries, but clothing, machinery, electronics, chemistry, and textiles are the major ones making up 60% of the firms. We create a market competitiveness measure by subtracting the barriers- to-entry index from one. The market competitiveness measures of the major industries 中国科技论文在线 http://www.paper.edu.cn

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in our sample, from the most competitive to the least competitive, are clothing at 0.85, machinery at 0.72, electronics at 0.72, chemistry at 0.59, and textiles at 0.15. We test whether privatization is more likely when the product market becomes competitive using these industry-specific market competitiveness measures.10 Privatization decisions may depend on factors outside of the rural industrial sector. In particular, the changing role of the rural financial system may influence the decisions of many officials to undertake privatization. The bank can discipline local governments by hardening their budget constraints (Bai and Wang, 1998; Cao et al., 1999). If the government’s budget is soft, banks bail out government-owned firms in financial distress continually (Kornai, 1993). As a consequence, local governments have no incentive to improve the efficiency of their firms. Although TVEs face much harder budget constraints than SOEs, local governments in the 1980s and early 1990s could still use their political power to influence local banks’ lending decisions (Che and Qian, 1998a). The expansion of TVEs took place during this period of time, especially in the early 1990s, when banks provided local governments with relatively soft budgets. Projects initiated by local governments were often supported directly by banks in rural areas, especially by the township branches of the Agricultural Bank of China (ABC) and the Rural Credit Cooperatives (RCCs) (Park and Shen, in press). Even if many of these projects were unsuccessful from the beginning and even if the investment funds were diverted, local governments still managed to get further financing and expand inefficient firms (Oi, 1999). Bank reform in 1994 changed fundamentally the way banks deal with local governments (Cao et al., 1999; Brandt and Li, in press). Before 1994, local branches made 70% of the loans in China. Bank reform centralized the lending authorities to upper-level banks, which reduced the local government’s influence on the bank’s lending decisions. In a recent study of 110 township branches of the ABC and the RCCs, Park and Shen (2002) find that more than half of the lending decisions was taken away from the township level. By the end of 1997, these authors conclude that 66% of the township branches cannot make lending decisions at all, 30% have limited lending authority, and only 4% can lend with full authority to local firms. Because it is more difficult for township governments to influence banks at an upper or county level, local governments face a much harder budget constraint after the banking reform. Hence, township governments are responsible financially for bad projects and firm losses. As a result, inefficient firms are less valuable to these governments. Because township governments no longer have any advantage in the credit market, transferring ownership to managers is rational. Our survey on the relationship between local governments and banks allows us to measure the hardness of the government’s budget constraint and to test its effect on privatization. The hardness of the government’s budget constraint is a qualitative variable based on township level interviews with government officials who provided an answer

10 Theoretically, we could use a firm-level competitiveness measure by obtaining the exact structure of the market in which each firm sells its products (Liu and Woo, 2001). Empirically, this is very difficult to do. We tried to ask questions about the structure of the market in which each individual firm sells its products in the pretest, but it turned out that most firms could not provide an answer. As a compromise, we use the industry- level competitiveness measure by assuming that firms in the same industry face the same competition. Using the industry-level measure has the benefit of avoiding the endogeneity issue of a firm-level measure. 中国科技论文在线 http://www.paper.edu.cn

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to the following question: ‘How difficult is it to ask for an extension when a loan is overdue?’ We consider the budget constraint to be hard if the local government official cannot persuade the banks to give extensions of overdue loans to township enterprises before liquidating them, and soft otherwise.11 Hence, we are able to test the hypothesis that the probability of privatization increases with the hardness of the government’s budget constraint. Although heterogeneity across townships indicates that privatization may not be driven by government policies, we can test this assertion directly by using two measures from the survey, namely, the presence of an upper-government policy and its intensity.12 Out of 59 townships, only 34% reported having county-level policies regarding privatization. The remainder made privatization policies themselves with no guiding policy. The intensity of upper-government policy pressure is measured by the number of months that the township government is given to finish privatization even though many townships did not fulfill the deadline. Hence, we test the hypothesis that the probability of privatization increases with the existence of upper-government policy and its intensity.13 Other factors could also affect the probability of privatization. For example, a firm’s quality may influence the form of ownership chosen by officials, but the choice is unclear. Some argue that officials tend to privatize good firms because they are easier to sell (Megginson and Netter, 2001). Others argue that bad firms are privatized because officials derive political capital from control over good firms (Oi, 1999). To measure the quality of a firm, we use the profit of a firm and an export indicator, assuming that exporting firms are of higher quality. In addition to quality, the size of the firm may affect the probability of privatization. Small firms may be more likely to be privatized because officials derive more political capital from larger firms and cash-constrained buyers are more likely to buy smaller firms.

4. Empirical tests

In this section, we first use a simple conditional probability analysis to examine the correlation between the likelihood of privatization and other independent variables. We then employ probit, hazard, and 2SLS models to examine the determinants of privatization. The dependent variable is an indicator variable that equals 1 if a firm is privatized, using the controlling-interest shifting definition, from 1994 to 1997, and 0 otherwise. For all independent variables, we use 1994 values.

11 The official was given five choices, namely, no problem, usually no problem, sometimes possible, occasionally possible, and impossible. However, our survey results show that all township officials chose one of the last two options. Thus, the indicator is binary. This measure of hardness is used in Li and Rozelle (in press) and Brandt et al. (2003). 12 County government is the level above township government so that upper government refers to county-level government. 13 Since fewer months means higher intensity, we hypothesize that the coefficient on the measure of policy intensity to be negative. 中国科技论文在线 http://www.paper.edu.cn

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Table 3 Conditional probability of privatization after controlling for township, firm and bank characteristics (N = 134) Probability of privatization Independent variables Less than median More than median Official’s education 0.63 0.58 Official’s age 0.67 0.52 Manager’s education 0.67 0.53 Manager’s age 0.61 0.58 Hardness of government’s budget constraint 0.51 0.71 Competitiveness of product market 0.52 0.67 Upper-government policy 0.70 0.56 Policy intensity 0.53 0.68 Employment 0.72 0.48 Profit 0.69 0.55 Export 0.65 0.59 The numbers in the table are the probabilities of a firm being privatized conditional on a firm belonging to the group.

Table 3 presents the probabilities of privatization as a function of its potential determinants. We divide the firms into two groups depending on the median of each independent variable. The human capital variables provided mixed results. On one hand, privatization is more likely when the officials have less education (0.63 vs. 0.58) and are younger (0.67 vs. 0.52); on the other hand, privatization is more likely when the managers have less education (0.67 vs. 0.53) and are younger (0.61 vs. 0.58). Our hypothesis predicted the result concerning officials but not the one regarding managers. The likelihood of privatization increases with the hardness of the government’s budget constraint; governments facing a soft budget constraint are less likely to privatize firms, with a probability of 0.51, compared to governments with a hard budget constraint, with a probability of 0.71. Similarly, market competitiveness raises the probability of privatization from 0.52 to 0.67. Table 3 also indicates that government policy may not be the driving force for privatization. The likelihood of privatization for firms in townships with an upper-government privatization policy is 0.56 compared to 0.70 for firms in townships without such a policy. Furthermore, the probability of privatization for a firm in townships facing a tighter policy, i.e., a deadline of fewer months to privatize all firms, is 0.53 compared to 0.68 if the local governments were facing a looser policy. Multiple regressions support the conclusions based on simple conditional probabilities. In a probit model, we estimate the probability of a firm being privatized from 1994 to 1997, conditional on information about the independent variables in 1994. The dependent variable is equal to 1 if a firm is privatized, and 0 otherwise. The independent variables are the competitiveness of the product market, the hardness of the government’s budget constraint, the official’s education and age, the manger’s education and age, the policy indicator and policy tightness, the firm’s size measured by employment, an export indicator, profit, and four area indicators. 中国科技论文在线 http://www.paper.edu.cn

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Table 4 Probit and hazard models examining the determinants of privatization of township enterprises, 1994 to 1997 Independent variables in 1994 levels Probit model Hazard model (1) (2) (3) (4) Competitiveness of product market 1.010*** 1.264*** 2.967** 3.354* (4.37)(4.01)(2.17)(1.71) Hardness of government’s budget constraint 0.189* 0.276** 1.750*** 1.889** (1.68)(1.96)(2.61)(2.39) Official’s education −0.026 −0.028 0.935 0.890 (−0.61)(−0.56)(−0.90)(−1.61) Official’s age −0.017 −0.032 0.961 0.926** (−1.06)(−1.64)(−1.49)(−2.42) Manager’s education 0.030 0.051* 1.074 1.058 (1.23)(1.69)(1.46)(1.00) Manager’s age 0.010* 0.018** 1.012 1.019 (1.66)(2.30)(1.19)(1.44) Upper-government policy −0.176 −0.155 0.820 0.987 (−1.51)(−1.21)(−0.97)(−0.05) ∗ Policy intensity −0.0004 −0.0003 1.004 1.012 (−0.73)(−0.08)(0.64)(1.92) Employment −0.0003* −0.0002 0.999* 0.999 (−1.67)(−1.20)(−1.91)(−1.40) Profit −0.001** 0.999 (2.52)(−1.44) Export −0.098 −0.146 1.315 1.277 (−0.64)(−0.87)(0.98)(0.78) Area indicators Yes Yes Yes Yes Observations 125 94 125 94 Pseudo-R2 0.25 0.30 0.25 0.30 The t-ratios are reported in parentheses. *,**,*** Significance levels of 10, 5, and 1%, respectively.

The first two columns of Table 4 report estimated coefficients indicating the marginal change of probability when the independent variable increases.14 The coefficients pertaining to the competitiveness of the product market and the hardness of the government’s budget constraint are positive and significant at at least the 10% level in all specifications. When market competitiveness increases by one standard deviation, which is 22%, the probability of privatization increases by 22 to 28%. Similarly, firms in townships where the governments face a hard budget constraint have an 19 to 28% higher probability of being privatized than firms in townships with a soft budget constraint. The probit analysis indicates that the human capital attributes of the official and upper level government policy pressure are not significant in explaining privatization. All the coefficients measuring the official’s human capital attributes and policy pressure are not significant. The coefficients of the manager’s education and age are significant, and the

14 The number of observations in Table 4 is either 125 or 94 due to missing values of some independent variables. 中国科技论文在线 http://www.paper.edu.cn

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positive signs of these variables support our hypothesis that firms with more educated and experienced managers are more likely to be privatized. The probit analysis also shows that smaller and less profitable firms are more likely to be privatized. The coefficient for the employment variable is negative in both specifications and significant at the 10% level in one specification. The coefficient for the profit variable is also negative and significant. An increase in a firm’s employment by one standard deviation (410) leads to a 12% lower probability of privatization. An increase of profit by one standard deviation (1.37 million yuan) raises the probability of privatization by 13.7%. The export indicator is negative, but not significant in both regressions. Probit analysis indicates that privatization is more likely when governments face harder budget constraints, when the product market is competitive, and when the managers are more educated and experienced. These findings are consistent with our hypotheses. However, other findings are not consistent with our hypotheses. For example, we find that privatization does not vary significantly with the human capital attributes of officials or with government policy measures. Although the probit model is simple and intuitive, it has several drawbacks that might bias the results. First, it does not capture differences in the timing of privatization. Empirically, we observe that some firms were privatized earlier than others. The results from the probit estimation might be sensitive to the ending date we use. Second, the probit model does not deal with the censoring of the data so that no implications can be drawn about firms that were privatized after 1997. On the other hand, the hazard model captures the timing of the privatization decision and deals with the censoring of the data. We estimate the Cox Proportional Hazard Rate function given by h(t) = h0(t) exp(X ∗B),whereh0(t) is the baseline hazard, X is a vector of independent variables influencing privatization, and B is the vector of coefficients to be estimated. The effect of a variable x in X on the relative hazard rate is given by exp(b), where b is the coefficient of x. We report exp(b) below. If exp(b) is greater (less) than 1, we say that the variable x increases (decreases) the hazard of privatization. The hazard model is estimated using maximum likelihood. The results of the hazard model provide further confirmation of our hypotheses that privatization is more likely when the product market is more competitive and when the government faces a hard budget constraint (Table 4, columns 3 and 4). All coefficients for the hardness of the government’s budget constraint and the competitiveness of the product market are positive and significant. The results are also consistent with previous findings that upper government policies may not have played an important role in local government’s privatization decisions, since most policy variables are not significant and none have the expected signs. One problem with the above tests is that the variable measuring the hardness of the budget might be endogenous. There are two sources of endogeneity. Local governments may privatize firms in order to harden the budget constraint (Megginson and Netter, 2001) leading to a simultaneity problem. However, both the hardness of the budget in a township and privatization may be caused by a third unobserved variable. For example, localities in which institutional environments and entrepreneurship are strong, governments may be more likely to face a hard budget constraint and firms may be more likely to be privatized. 中国科技论文在线 http://www.paper.edu.cn

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Hence, we would have omitted variable bias. In either case, one can argue that privatization causes the hardening of the budget constraint. In order to test for and control both the simultaneity and the omitted variable bias, we employ a 2SLS approach. The key to using a 2SLS model is to find appropriate instrumental variables (IVs) to identify the hardness of the government’s budget constraint. Good IVs should be able to explain the hardness of the government’s budget constraint, but not have any independent explanatory power on the firm’s likelihood of privatization. To this end, we use the hardness of budget constraints of neighboring townships in the same county and county indicators as IVs. We argue that these measures satisfy both conditions. First, since all township bank branches in the same county have the same county headquarter, which formulates lending policies and designs incentive contracts for the township branches, some common factor must explain the hardness of the government’s budget among townships in the same county. Second, hardness of the government’s budget constraint in neighboring townships and county bank policies should have no direct influence on a township’s decision on privatization except through the township branches that lend to local township enterprises directly. To examine statistically the validity of our IVs, we conduct a Hausman overidentification restriction test on regressions with more than one IV (Hausman, 1983). The test results show that our IVs can be excluded from the second stage regressions.15 We also report an OLS, or linear probability model, regression as a comparison with the 2SLS regressions. The reasons for employing the linear regression are that standard tests exist for comparing the coefficients of OLS regressions to those of 2SLS regressions and also that OLS and probit regressions generate similar results. If the results of the OLS and 2SLS regressions are not significantly different from each other, endogeneity should not be a serious problem in the probit and hazard regressions. The results of the 2SLS regressions provide further confirmation of those of the probit and hazard analyses. In Table 5, columns 1 to 4, we report results of 2SLS using different combinations of the IVs, i.e., the hardness of the government’ budget constraint of the neighboring townships alone, county indicators alone, and both, and also an OLS regression for comparison. All coefficients of the hardness of the government’s budget constraint in 2SLS regressions are positive and significant. Another set of F-tests show that the coefficient on the hardness of the government’s budget constraint for the OLS regression of column 1 is not significantly different from that of the three 2SLS regressions. These tests indicate that even if endogeneity is a possibility, it is not a serious problem for our results. As a final robustness check, we re-estimate the OLS and 2SLS models with firms privatized in 1994 excluded from the estimation. In this way, we estimate how initial conditions in 1994 affected the ownership changes that occurred between 1995 and 1997.

15 The chi-square distributed test statistics with k − 1 degrees of freedom is N times R-squared, where k is the number of instruments, N is the number of observations, and R-squared is the measure of goodness of ﬁt of the regression of the residuals from the privatization equations on the variables, which are exogenous to the system. The test statistics for regressions (2) to (4) in Table 5 are 0, 19.7 and 24.8, which are smaller than their respective critical values. These test statistics indicate that the null hypothesis that there is no correlation between the exogenous instruments and the disturbance term from privatization equation cannot be rejected. 中国科技论文在线 http://www.paper.edu.cn

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Table 5 OLS and 2SLS regressions examining the determinants of privatization of township enterprises, 1994 to 1997 Sample Dependent variables: 1 = privatize Whole sample Excluding firms privatized in 1994 OLS 2SLS (IV: 2SLS (IV: 2SLS (IV: OLS 2SLS (IV: hardness of county hardness of hardness of budget in indicators) budget in budget in neighboring neighboring neighboring townships) townships townships and county and county indicators) indicators) (1) (2) (3) (4) (5) (6) Competitiveness of product 0.690*** 0.570** 0.635*** 0.684*** 0.620*** 0.642*** market (4.10)(2.25)(3.11)(3.62)(3.51)(3.15) Hardness of government’s 0.136 0.936** 0.553*** 0.267*** 0.127 0.248** budget constraint (1.41)(2.29)(3.28)(2.90)(1.11)(2.36) Official’s education −0.021 0.001 −0.009 −0.016 −0.009 −0.014 (−0.58)(0.02)(−0.27)(−0.52)(−0.19)(−0.38) Official’s age −0.013 −0.026 −0.021* −0.018 −0.015 −0.019 (−0.96)(−1.63)(−1.67)(−1.52)(−0.94)(−1.41) Manager’s education 0.016 0.039 0.025 0.015 0.015 0.014 (0.80)(1.29)(1.11)(0.72)(0.59)(0.57) Manager’s age 0.006 0.006 0.005 0.005 0.007 0.006 (1.14)(0.83)(0.92)(0.93)(1.13)(0.91) Upper-government policy −0.146* −0.066 −0.112 −0.147 −0.154 −0.178 (−1.67)(−0.51)(−1.12)(−1.59)(−1.45)(−1.63) Policy intensity −0.001 −0.0004 0.0001 0.0004 −0.001 −0.0004 (−0.42)(−0.14)(0.03)(0.19)(−0.38)(0.16) Employment −0.0001 −0.0003** −0.0003** −0.0002** −0.0002 −0.0002 (−1.26)(−2.23)(−2.36)(−2.15)(−1.11)(−1.62) Export −0.035 −0.099 −0.041 0.002 −0.022 −0.006 (−0.27)(−0.59)(−0.31)(0.02)(−0.13)(−0.04) Observations 125 125 126 125 104 104 The t-ratios are reported in parentheses. *,**,*** Significance levels of 10, 5, and 1%, respectively.

Excluding firms privatized in 1994 makes the estimation less likely to be subject to the endogeneity problem. Regression results using this smaller sample in columns 5 and 6 are very similar to those using the whole sample in columns 1 to 4. In summary, the descriptive and multiple regressions evident reveal that heterogeneity in the pace of privatization across time and space is related systematically to a number of factors. The highly variable nature of privatization means that central policy makers are not totally in control of China’s privatization movement. The systematic influences of a number of factors suggest that rent-seeking or corruption may not be the only reason that induces an official to privatize because the opportunities for corruption are more likely to be idiosyncratic. In contrast, our results show consistently a correlation between the variables that measure the hardness of the government’s budget constraint and the competitiveness of the product market with privatization. 中国科技论文在线 http://www.paper.edu.cn

500 H. Li / Journal of Comparative Economics 31 (2003) 486Ð502

5. Conclusions

In this paper, we show that the privatization of township enterprises during the mid- 1990s is widespread, regardless of the definition we used. By employing data collected in 1998, we test several hypotheses concerning the determinants of privatization. Our analysis shows that the incentives of local governments to privatize are affected by the competitiveness of the product markets, the hardness of their own budget constraints, and the quality of the firms. This paper demonstrates how changes in institutional environments induce other institutional changes, i.e., privatization. The rise of TVEs was a response to the imperfect conditions of the market and state; the privatization of TVEs is a response to the improving conditions of the market and state. The finding that several factors are correlated systematically with privatization shows that privatization may not be policy-driven; rather, local governments make privatization decisions based on their own calculations of the benefits and costs of different ownership forms. Our findings highlight the decentralized nature of China’s reforms. Although our study considers a subset of firms from Jiangsu and Zhejiang provinces, the empirical evidence suggests that rural industries in China will continue to contribute to the nation’s economic growth. Our analysis shows the ownership of rural firms is evolving in a way that indicates they will play a positive role in the future. While further research is needed, we characterize a rural industrial sector that is continuing to respond to the institutional changes in its environment.

Acknowledgments

The survey and research were funded by the Ford Foundation in Beijing, the William Davidson Institute, and the Social Science and Education Panel at the Chinese University of Hong Kong. The author gratefully acknowledges comments from John P. Bonin, two anonymous referees, and helpful discussions with Loren Brandt, Lawrence Lau, John McMillan, Yingyi Qian, Albert Park, Scott Rozelle, Minggao Shen, and Li-an Zhou.

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