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Firm dynamics and industrialization in the chinese economy in transition: Implications for small business policy
ELSEVIER
FIRM D Y N A M I C S A N D I N D U S T R I A L I Z A T I O N IN THE CHINESE E C O N O M Y IN TRANSITION: IMPLICATIONS F O R SMALL BUSINESS POLICY CLEMENT CHOW KONG WING Lingnan College, Hong Kong
M I C H A E L F U N G KA Y I U Chinese University o f Hong Kong
This article first presents" a review of the development of different types
EXECUTIVE SUMMARY
of domestic enterprises: state-owned enterprise (SOEs), collective-
owned enterprises (COEs), and other enterprises (OEs), which include mainly private enterprises. Compared with other transition economies, COEs are actually an important source of China's rapid economic growth. They are "vaguely defined cooperative~ without a well-defined ownership structure. There is" no equivalent organizational structure similar to COEs in Western economies. The purpose of this article is to study the empirical relationship between the size of domestic enterprises including state-owned enterprises, collective-owned enterprises, and other enterprises and their firm dynamics (growth) by investigating whether they grow according to Gibrat's Law. The majority of COEs and OEs were formed after the economic reform. The rapid growth of these non-state enterprises, especially C O Es, is actually the driving force behind the rapid development of the industrial sector. Our empirical results suggest that Gibrat's Law does not hold for all types of enterprises (the rejection is especially strong for COEs). Moreover, small enterprises are growing faster than their larger counterparts in terms of employment and output. Address correspondence to Michael Fung, Department of Decision Sciences and Managerial Economics, Faculty of Business Administration, Chinese University of Hong Kong, Shatin, N.T., Hong Kong. The authors thank an anonymous referee and the editor of this journal for their helpful comments on an earlier version of this article. All remaining errors are of course ours. We would also like to acknowledge the support from the Chinese Management Research Group, Faculty of Business Administration, Chinese University of Hong Kong. Journal of Business Venturing 11, 489-505 © 1996 Elsevier Science Inc. 655 Avenue of the Americas, New York. NY 10010
0883-9026/96/$15.00 PII S0883-9026(96)00019-5
490
C. CHOW AND M. FUNG
This conclusion leads to (at least) three implications: (1) There has been little research into the dynamics o f SOEs and COEs in an economy in transition. Our results not only help stimuhlte fi~rther research hi this area but also provide infornmtion for designing policies to speed up the economic reforms. (2) Until now, tile Chinese government has not had a comprehensive small business policy. Given our empirical results, there # till implication for goverllment policy prescription. To achieve ~ i n a ' s development targets o f rapid industrialization and lower u,employnlent or lmderemployment, the Chinese economv can reap more clynamic benefits by breaking ltp a giant and inefficient enterprise (especially i,e[ficient SOE) into a number gfsmaller enterprises. In addition, more small enterprises also improve competition ill the marketplace told force other inefficient SOEs to intprove their performance. Further research should he undertaken to examine the viabig O' o f this polio,. (3) Because COEs are the majori O, of small malutfacturing enterprises, the rapid growth o f COEs (~(f_tertheir formation) that are simply the opposite o f the O'pe o f private organizations, the center of" the Central and East European reforms, is rather surprising to academics. Although some hypotheses like "cooperative culturd and "moral Jkameu'ork [br righL¢ are put forward to explain their superior performance, the validi O' o f such hypotheses has not been empirically tested yet. This is" a fruitful area .for fltrther research. © 1996 Elsevier Science Inc.
INTRODUCTION
Since the implementation of economic reform in 1978 in the People's Republic of China. entrepreneurship--once under severe suppression before economic reform-has been slowly receiving more and more attention from the nation. The spirit of economic reform was to introduce regional and sectoral decentralization of economic decision-making to each individual economic unit under the banner of state ownership of resources. Since then the non-state sector where entrepreneurship and small businesses have a key role to play has been booming, making a significant contribution to the national economy. Although the non-state sector in China has been developing rapidly, academic research in this area started fairly recently. The impressive performance of collectiveowned enterprises (COEs) has been confirmed in various studies (Jefferson, Rawski, and Zheng 1992: McGuckin and Nguyen 1993: Prime 1992). Entrepreneurial development in China has attracted the attention of researchers who focus on particular aspects of the development process (Chang and Macmillan 1991: Reeder 1984: Tsang 1994). Rapid growth and expansion of these non-state enterprises are the driving forces behind the rapid development of the Chinese industrial sector. However, little is known about their dynamic behavior. The study of dynamic behavior is important in understanding the development of firms. One dynamic pattern that attracts many researchers is the relationship between firm size and firm growth. It is well known that the size distributions of companies are highly skewed: log-normal distribution is commonly used to approximate the size distribution of firms (Gibrat 1931: Ijiri and Simon 1977). Schmalensee (1989, p. 994) terms this regularity as a stylized fact. Do firms with different sizes face different growth opportunities? Early studies of firm's growth rates in the United States, e.g., Hymer and Pashigian (1962), Ijiri and Simon (1977), and Mansfield (1962), generally supported Gibrat's Law of Proportionate Growth. This law predicts that each firm faces the same distribution of growth possibilities, and each firm's actual growth is determined by random sampling from that distribution (Scherer and Ross 1990, p. 141; Wagner 1992). In other words, firm growth is independent of firm size. However, the empirical literature in this area produces inconclusive evidence. For example, on the
FIRM SIZE A N D D Y N A M I C S IN C H I N A
491
one hand, recent studies by Cardozo et al. (1992), Chesher (1979), Evans (1987a~ 1987b), Kumar (1985), Ranger-Moore et al. (1995), Storey et al. (1987), and Tschoegl and Yu (1990) found that Gibrat's Law is rejected at population level for the small firm sector because growth and size are negatively correlated, even allowing for the exits of slow growth firms. On the other, Singh and Whittington (1975) showed that growth rates are increasing in firm size. On the average, more researchers in this line of study support the presence of a negative relationship between firm growth and its size and conclude that small manufacturing firms have contributed disproportionately to economic growth by generating greater employment growth and technological advancement than their large and established counterparts (Birch 1987: Box, Watts, and Hisrich 1994; Cooper, Woo~ and Dunkelberg 1989). Why is Gibrat's Law important? First, firm size is an important factor in theoretical and empirical research in organizations (Baum 1995: Scott 1992). The validity of Gibrat's Law, which implies a relationship between firm size and firm growth, provides important information in understanding the organizational dynamics of enterprises. For example, in organizational ecology, size distribution is an important element of the competitive process in any market economy, e.g., organizational survival and diversity, which affect the opportunities of employment (Hannan and Freeman 1989). The available evidence suggests that there is a negative relationship between firm size and its death rate (Aldrich and Auster 1986: Birch 1979: Cooper et al. 1989). Moreover~ Ranger-Moore et al. (1995) suggest that there are internal and external effects of firm size on firm growth. The internal effect (within an organization) is mainly the structural inertia proposed by Hannan and Freeman (1984) to explain how the internal organizational structure interacts with the environment. This theory suggests that older and larger firms are slower to change because these changes can undermine their accountability and reliability. The external effect is the competition among organizations of different sizes. Organizations in a population are not equal competitors. For example, organizations similar in size engage in fiercer competition, resulting in increased failure rates (Baum and Mezias 1992: Hannah and Freeman 1977; Wholey et al. 1992). Moreover, the validity of Gibrat's Law has implications for industrial policies. The empirical literature of organizational ecology uses only the data in developed countries, and little is known about how the theories apply to developing countries. Unfortunately, for transition economies, there is no published work in this area at all. Nevertheless, for any transition economy like China, which focuses on economic development~ balanced economic growth and rapid industrialization are always top priority. If Gibrat's Law holds, i.e., firm growth is independent of firm size, firm size is not a concern in designing policy programs to promote business development. However. if Gibrat's Law does not hold, the government can possibly use policy to exploit the situation so as to achieve its development targets. For example, suppose all empirical results, including the impact of size on organization survival and localized competition, unambiguously point to a faster growth rate of smaller firms, the government should provide incentives to encourage the birth and growth of small firms. In most developing countries, unemployment caused by rapid urbanization and surplus labor in the rural sector is the most difficult problem faced by their governments. Encouraging the birth and development of small firms can help in solving the unemployment problem gradually if small firms grow faster and create more jobs. Obviously, at the present primitive stage of research in organizational ecology in China, there is still a lot of work yet to be done before reaching any conclusions for policy prescriptions.
492
c. C H O W A N D M. F U N G
The purpose of this article is to study the firm dynamics (growth) of domestic enterprises (including both state and non-state) in the manufacturing sector of Shanghai by investigating whether Gibrat's Law can accurately describe their dynamic behavior. Although Gibrat's Law has been tested previously (mainly by economists), our test is one of the only tests that is conducted in a complete population that includes a wide variety of different organization sizes in a transition economy like China. Our focus is on the effect of size on firm growth (Chesher 1979: Evans 1987a, 1987b: Hall 1987: Kumar 1985: Mansfield 1962: Ranger-Moore et al. 1995) and not on size-localized competition (Baum and Mezias 1992; Hannan and Freeman 1977: Ranger-Moore et al. 1995: Wholey et al. 1992). This study not only provides relevant implications for small business policy but also improves our understanding of the firm dynamics (growth) of state and non-state enterprises in China, which have been neglected in the literature. Therefore, our empirical results can provide some first-hand information of the relationship between firm growth and firm size in a transition economy. Moreover, unlike in Central and Eastern Europe where small businesses were developed only recently, the Chinese economy has been populated by a relatively high proportion of small businesses for the past 15 years of economic reform. The Chinese experience in small business development is actually valuable for Central and East European governments in designing their own policies.
E N T E R P R I S E S IN T H E C H I N E S E E C O N O M Y IN T R A N S I T I O N In 1978, just before reform began, the Chinese state-owned sector accounted for 78% of industrial output. Since then, the state share of industrial production had been declining steadily and at the end of 1990, it accounted for roughly 50% of industrial output. If agriculture and services were included, the state share of China's output was less than 25% (The Economist 1992). Substantial expansion of the non-state sector is probably one of the prominent features of the Chinese economic reform besides spectacular economic growth: average G D P was growing at 9.0% per year during the period of 1978-1992 (Solimano 1993). State-owned enterprises (SOEs) in the state sector were strictly controlled by the central government before economic reforms. They functioned as passive agents of the state economic bureaucracy (Jefferson and Rawski 1994). Managers of SOEs in China were rewarded primarily for their success in meeting physical output targets set by the government (Gordon and Li 1991). Facing so much competition from non-state enterprises, the Chinese government introduced measures to improve the productivity of state enterprises. The reform measures include the following changes: (1) A higher degree of autonomy is given to an enterprise's manager in input, output, and pricing decisions; (2) manager's rewards are linked to the enterprise's performance; (3) bonus out of profits made are given to workers: and (4) more contract workers are employed to replace permanent workers so as to add more flexibility to the labor force in a firm (Groves et al. 1994). After introducing these new measures, the total factor productivity (TFP, a measure of technical efficiency) growth rate in the state-owned enterprises was 3.4% from 1980 to 1984 (Chen et al. 1988). In addition, the TFP of the state-owned sector continued to grow at about 3% from 1984 to 1988 (Jefferson et al. 1992) and 2.5% from 1988 to 1992 (Jefferson and Rawski 1994). As part of the public sector, SOEs can access the supplies of raw materials and inputs through the official distribution systems. On the other hand, they are also facing soft budget constraints, which
FIRM SIZE AND DYNAMICS IN CHINA 493 correspond to loans from state-owned banks at a below-market interest rate (Gordon and Li 1991). The Chinese government develops a non-state sector by introducing different types of ownership into the economy, such as collective-owned, private, and foreigninvested enterprises. Since the economic reform, the non-state sector is dominated by COEs, especially in the industrial sector. These COEs are relatively small and have developed originally from cooperatives before the economic reform. In contrast with SOEs that can easily obtain input materials, energy, and loans from the central government (these inputs are under the control of the central government), these COEs have to rely on the market to obtain these input factors. In addition, products are sold in some form of marketplace rather than being allocated by state planning. Finally, these COEs are ultimately theoretically responsible for their own profits and losses. Therefore, their budget constraints are far from soft (Lockett 1988: The Economist 1992). As a result, these COEs are very much "marketizea except that their ownership structure is different from the western concept of a private firm. On the one hand, the formation of these collective enterprises and their subsequent entry into the market contribute substantially to improving the competitiveness of the economy: on the other, they also increase the flexibility and adaptability of the whole economy because of their small size. Although these COEs are typically controlled by local governments, their performance is much better than SOEs. According to G o r d o n and Li (1991), the allocation decisions of COEs are relatively more efficient, because the local governments effectively owned and controlled these firms and operated in competition with many other local jurisdictions. Their impressive performance is confirmed by their improvement in TFP: The annual average TFP growth rates of the collective sector were about 5% percent from 1980 to 1988 and 4.7% from 1988 to 1992 (Jefferson and Rawski 1994). These TFP growth rates were higher than the TFP growth rate of state enterprises (Jefferson et al. 1992). In a similar study on the collective sector in Jiangsu province from 1981 to 1988, the TFP growth rate of collective enterprises was 6.2%, which was also higher than the national level (Prime 1992). In addition, by using Industrial Census Data, McGuckin and Nguyen (1993) show that the TFP growth rate of collective enterprises was six times higher than that of state enterprises from 1981 to 1984. According to Weitzman (1993), these collective enterprises are actually the driving force behind the rapid economic growth of China. They are now the second biggest sector of the national economy (after the state sector), constituting over 40% of total industrial output, whereas they accounted for only 20% of industrial output before economic reform. Compared with collective and state enterprises, private enterprises in China are enjoying more flexibility in the process of decision-making. These enterprises are behaving like most private companies in other market economies, because they have to recruit resources in the markets, such as employing workers by paying market wages, and compete with all other enterprises in the marketplace. Unlike collective and state enterprises, these companies do not enjoy any privileges or special assistance from the central or local governments. Moreover, in the presence of severe financial repression, these enterprises have difficulties obtaining funding from the government-owned banking system (McKinnon 1994). Enterprises with different ownership structures imply different flexibility in decision-making and different levels of assistance from either the central or local governments. These differences may imply that enterprises of different ownership structure
494
C. C H O W A N D M. FUNG
are facing different growth opportunities. Do we expect different relationships between size and growth of enterprises of different ownership structures? This question will be investigated in this study.
DATA The data set supplied by the Shanghai Economic Commission covers 2083 domestic enterprises, which comprise state-, collective-owned, and other enterprises (mainly private enterprises) in Shanghai and includes the input-output information of these enterprises. This data set contains enterprises" net industrial output values (value-added, measured at current prices in Chinese yuan), gross industrial output values (also measured at current prices in Chinese yuan), original values of fixed assets (book value, in Chinese yuan), net values of fixed assets (book value, in Chinese yuan), and types of ownership. Shanghai, a metropolitan city of 14 million people, was chosen because it has been one of the important commercial and industrial cities before and after 1949. According to different issues of Shanghai's Statistical Yearbooks, the city's share in the national industrial output is very high: It had an average of 15% from 1949 to 1979 and about 7% in the post-reform period, although Shanghai accounts for only 1% of the national population. Despite these superior conditions, the central government gave little autonomy to Shanghai's government in managing its political and economic affairs. One reason for the central government's reluctance to give Shanghai more economic freedom in the earlier part of reform is that Shanghai has a sophisticated and relatively efficient taxation bureau and the highest industrial formation and per capita national income--all of which implies stable and substantial tax revenues for the central government. Although Shanghai only accounted for 1% of the national population, the central government relied on the city for 15% of its revenues (The Economist 1995). This situation changed dramatically when Jiang Zemin (ex-party chief and mayor of Shanghai) was elected as the chairman of the Chinese Communist Party in 1989 after the Tiananmen Square incident. Since 1990, Shanghai has been given a lot of autonomy in making economic and administrative decisions. The Shanghai government can now plan and invest at its own pace. As a result, we chose our sample to begin in 1989 because of the policy changes in Shanghai. Among all Shanghai's manufacturing industries, two labor or less skill-intensive industries (textile, clothing) and two capital or skill-intensive industries (machinery and equipment, electronics and telecommunication equipment) are considered in our study. These four industries were chosen because they are the largest four manufacturing industries in Shanghai. This panel data set covers 629 firms (SOEs:269, COEs:216, OEs:144) in the textile industry, 327 firms (SOEs:33, COEs:192, OEs:102) in the clothing industry, 965 firms (SOEs:421, COEs:435, OEs:109) in the machinery and equipment industry, and 162 firms (SOEs:50, COEs:97, OEs:15) in the electronics and telecommunication equipment industry. This data set tracks these 2083 firms from 1989 to 1992 and therefore is rectangular (i.e., complete panel data set). In addition, this data set allows a very high level of size variability (e.g., the average firm size (from 1989 to 1992) ranges from 13.5 workers to 9886.5 workers). This high variability of size can provide more information in understanding the relationship between firm size and firm growth.
FIRM SIZE AND DYNAMICS IN CHINA 495
METHODOLOGY In studying the relationship between organization size and growth rates, Gibrat (1931) found that organization size distributions were usually log-normal. Therefore, in studying firm growth problems, stochastic growth models are commonly adopted in the empirical literature (e.g., Baum and Mezias 1992; Barnett and Carroll 1987: Evans 1987a, 1987b: Hannan and Freeman 1989: Ranger-Moore et al. 1995; Tschoegl and Yu 1990). To derive empirical tests of firm growth, a simple stochastic growth model in which the growth rate of any firm (say, firm i), ~(i,t), in period t is drawn from a common normal distribution, N(c~,cr2), with mean c~ and variance cr-~,is considered. As a result, the size of firm i in period t follows the following process: S(i,t) = S~(i,t -
1)exp[p~(i,t)]
(1)
where S(i,t) is the size of firm i in period t: [3 is a growth parameter. Since p~(i,t) follows normal distribution, N(e~,cr-~), we can rewrite ~(i,t) as ~ ( i , t ) = e~ + e(i,t) where E[e(i,t)]
= 0
where ~(i,t) has mean zero and variance ¢r-~. This set-up implies that organization size follows log-normal distribution. Following the logic of Gibrat's Law, annual growth rate, g(i,t), is defined as the ratio of current size to previous size, g(i,t) = S(i,t)/S(i,t 1). This definition allows us to visualize growth in the following two ways: If size is measured in absolute terms, growth is multiplicative. However, it is equally valid to imagine additive growth in a situation where growth is measured using a logarithmic scale. Thus~ we can analyze the natural logarithm of growth: logg(i,t)
= log(o, S(i't) ~Jtt,t -
1)
) = logS(i,t) - logS(i,t
1 )
This definition is compatible with the previous empirical research that studies the problems of firm growth (Chesher 1979: Evans 1987a, 1987b: Ranger-Moore et al. 1995: Tschoegl and Yu 1990: Wagner 1992). Taking a logarithm of equation 1, we have the following cross-sectional relationship: logS(i,t)
= [31ogS(i,t -
1) + e~ + ~(i,t)
(2)
If we subtract log S(i,t - 1) from both sides of equation 2, we have logS(i,t)
- logS(i,t -
1) = l o g g ( i , t ) = ([3 - 1) l o g S ( i , t - 1 ) + e~ + e(i,t)
Obviously, if [3 is not equal to 1, the growth rate, g(i,t), is not independent of firm size, S(i,t - 1). Firms that started with a larger size can always grow faster (slower) than firms that started with a smaller size as long as [3 > ( < ) 1. In other words, if [3 is less than 1 (a negative relationship between growth and size), a firm that starts with a smaller initial size can grow faster and larger over time on average. This result is an important outcome derived from the rejection of Gibrat's Law. The acceptance of Gibrat% Law is equivalent to accepting the following three null hypotheses (Tschoegl and Yu 1990): H I : ~ = 1. H 2 (absence of serial correlation): Cov[e(i,t),e(i.t H 3 (homoscedasticity): E[e-~(i,t)] - ~z(t)
1)] - 0.
496
C. C H O W A N D M. F U N G
When firms accept these three null hypotheses simultaneously, these firms are growing according to Gibrat's Law. To avoid bias due to serial correlations in the presence of lagged dependent variables, parameter instability caused by multicollinearity, and to capture the effects caused by different industries, we adopt the following regression model, which can be derived from equation 2, 4
logS(i,t)
= b31ogS(i,t
1) + b 2 [ l o g S ( i , t - 2) - l o g S ( i , t -
1)] + ~ a ( j ) D ( i , j ) I
+ e(i,t)
(3)
I
where b3 = [3 + r - [3r (r is serial correlation coefficient, r = cov(e(i,t),e(i,t - 1))/~r2) and b, = - [3r. Dummy variable D(i,j) represents firm i belonging to industry j and j = 1 . . . . . 4. D0,j) = 1 if firm i belongs to industry j; otherwise, D(i,j) = 0. G is deleted in estimation so as to avoid its perfect collinearity with the four dummy variables. These dummy variables are used to capture the individual industry effect on firm growth as firms in different industries may have different patterns of firm growth. The details of recovering parameter [3 and r and the steps of deriving equation 3 from equation 2 by removing serial correlations, multicollinearity are shown in the Appendix. Equation 3 is our model for testing Gibrat's Law and estimating the parameter [3 and r. In H1 ([3 = 1), restriction b3 = 1 is imposed on equation 3: in H2 (r = 0), restriction b2 = 0 is imposed on equation 3. Mentioned in the previous section, the three types of domestic enterprises (stateowned, collective-owned, and other-owned) differ in terms of government involvement, management style, and access to important factor inputs like credit and materials. As a result, it is relevant to find out how the ownership effect affects the dynamic growth of enterprises. Therefore, equation 3 is applied to each type of ownership in order to test whether Gibrat's Law is accepted or rejected by each type. By conducting this empirical study, one can get a clearer picture of the dynamic performance of these three types of enterprises. Firm size is commonly measured by its employment, e.g., the U.S. Small Business Administration classifies firms that employ fewer than 500 workers as small firms, and by output measured by its value-added, which is net industrial output values of that enterprise. The net industrial output values of each firm are deflated by its product price index (1980 as base year) so as to remove any impact of inflation, which is common in the Chinese economy. Therefore, the output values obtained are measured in real terms. In testing Gibrat's Law, we adopt both definitions of firm size. In order to test hypothesis H3 (presence of heteroscedasticity), Breusch and Pagan (1979) and Godfrey (1978) test for heteroscedasticity is used. The estimation and testing are done by S H A Z A M 6.2.
EMPIRICAL RESULTS Before we discuss the empirical tests of Gibrat's Law, let us consider some descriptive statistics of those three types of domestic enterprises (SOEs, COEs, and OEs). Table 1 reports the means and standard deviations of employment, output (in millions of yuan), and growth rates measured by employment and output of these three types of enterprises. When firm size is measured in terms of either employment or output, state-owned enterprises are the largest, followed by other enterprises, and collectiveowned enterprises are the smallest. For example, in terms of average employment out-
FIRM SIZE AND DYNAMICS IN CHINA 497 TABLE 1
Means and Standard Deviations of Employment, Output, and Firm Growth Rates (in percentage) Measured by Employment and Output State
Year Employment: 1989 1990 1991 1992 Output (millions of yuan): 1989 1990 1991 1992 Growth rates (in %) measured by employment: 89-90 90-91 91-92 Growth rates (in %) measured by output: 89-90 90-91 91-92
Collective
Other
Mean
SD
Mean
SD
Mean
SD
1069.6 1072.9 1067.2 1056.0
1338.8 1343.1 1330.8 1333.5
296.0 295.5 295.8 288.2
242.7 241.6 244.5 240.1
521.8 544.7 554.8 558.2
972.5 1076.7 1091.1 1126.6
7.530 7.092 6.843 7.649
14.40 11.05 10.73 11.48
1.552 1.282 1.526 1.789
2.231 1.501 1.981 2.444
3.043 3.119 3.254 3.588
9.575 11.38 11.46 11.19
0.005 0.685 0.417
9.514 11.92 18.57
2.266 1.454 0.384
28.63 24.77 39.38
7.065 2.196 2.145
22.7 l 19.87 18.15
7.823 25.96 24.44
68.99 305.1 88.02
2.432 32.21 28.04
83.00 124.1 116.9
5.162 13.56 23.07
66.99 67.89 79.07
put, state-owned enterprises are three to four times larger than collective enterprises. This result confirms the observation that most collective enterprises are small. Similarly, the same ranking arises when we measure the standard deviations of firm size in terms of employment or output. When firm growth is measured in terms of employment, state-owned enterprises have the lowest growth rate but other enterprises have the highest growth rate. Since other enterprises include mainly private enterprises, their flexibility in employment decisions give them a higher capacity in creating jobs. Moreover, the growth rates actually declined from 1989 to 1992 for all three types of enterprises. On the other hand, in terms of variability of growth rates, collective enterprises have the highest variances. When growth rate is measured in terms of output, state and collective enterprises now have higher output growth rates relative to those of the other enterprises. Moreover, the variances of their output growth rates are also higher than those for the other enterprises as well. In addition, the output growth rates actually increased from 1989 to 1992 for all types of ownership. Combined with the declining employment growth rates, these enterprises are actually more cost-conscious and work hard to slow the increases in their wage bills while maintaining relatively high output growth. The results of empirical tests are reported in Tables 2 and 3. Since the acceptance of Gibrat's Law requires that HI, H2, and H3 hold simultaneously, all three types of enterprises cannot fully accept all three hypotheses. When firm size is measured by employment, Table 2A reports that almost all enterprises reject HI: 13 = 1. All SOEs strongly reject H1 and the estimated [3~s are all less than 1. This result also indicates that small SOEs are growing faster in terms of employment. For COEs, a similar result is also obtained. On the other hand, OEs have less rejections of Gibrat's Law in comparison with the SOEs and C O E s - - f o r example,
" Joint test of [3 - 1 and r
Firm no.
(33.91"*) 0.926** (37.36) 0.343":: (4(I.64) 0.831
(33.54**) 0.937** (40.34) 0.266** (36.01) 0.830
(8.628**) 0.959** (8.746) 0.175'::* (8.996) 0.839
(4.879*) 0.987** (7.445) 0.089 (2.963) 0.983
1992
(33.74**) 0.953** (15.091 0.244':* (42.26) I).722
(5.180") 0.975** (9.2811 0.086 (0.892) 0.925
1991-92
94[1
(26.50**) 0.931 ** (22.53) 0.262"::: (27.70) 0.739
(2.249) 0.979 (4.778) 0.096 (0.771) 0.938
1991
Collective
110.11"*) 0.975 (1.8611 0.254"::: (15.29) 0.709
(3.748) (I.971 * (6.0311 - 0.080 (0.31/)) 0.912
1992
0. The values tn parentheses are lht: F statistics of the corresponding restrictions. : p <~ .I)5 and " " p "--- .01.
773
(5.188") 0.989** (9.858) 0.1136 (0.173) 0.992
1991
(9.209**) 0.987** (17.92) //.024 ((I.161) 0.988
1991-92
State
R e s u l t s of T e s t i n g G i b r a t ' s L a w ([3 = 1 a n d r = 1)) o n D i f f e r c n t T y p e s o f O w n e r s h i p
A. Size measured by e m p l o y m e n t 13 and r ~ 13 (13 - 1) r (r - 0) Re B. Size measured by output 13 and r ~ 13 ( [ 3 - 11 r (r = 0) Re
TABLE 2
(3.809) 0.954* (5.693) 0.086 (2.852) (I.776
(2.790) 0.984* (5.1541 - 0.061 (0.667) 0.9(~0
1991 92
340
(3.694) 0.948* (4.443) -0.120 (2.962) (/.807
(0.288) 0.999 (0.003) 0.059 (0.572) 0.959
1991
Other
(1.104) (I.961 (1.9181 0.063 (0.623) 0.752
(4.925") 0.977* (6.219) 0.174 (2.329) 0.963
1992 :Z C~
> Z
Z ©
4~
FIRM SIZE AND DYNAMICS IN CHINA 499 TABLE 3 Results of Breusch-Pagan-Godfrey Test for H3 T y p e s of O w n e r s h i p Collective
State Period 1991-92 1991 1992
Other
Employment"
Outpuf'
Employment
Output
Employment
Output
8.333 7.838 4.215
53.37** 26.67** 24.91"*
62.99** 21.44"* 44.25**
30.52** 15.38"* 23.59**
23.46** 6.89 18.32"*
18.52"* 9.80 13.73"*
"Reported values are the Breusch-Pagan-Godfrey test statistics (X-~statistics) of the case in which firm size is measured by its employment. ~'Reported values are the Breusch-Pagan-Godfrey test statistics (?(2 statistics) of the case in which firm size is measured by its output. *p < .05: and **p < .01.
OEs accepted H1, H2, and H3 in 1991 (but rejected H1 in 1992 and the whole sample in 1991 to 1992). Since all estimated [3's are statistically significantly less than 1, this suggests that small enterprises grow faster and create more jobs. Nevertheless, H2 (absence of serial correlation) is accepted by these three types of enterprises. This suggests that the employment growth of firms in the current period is on average independent of the employment growth in the last period. When firm size is measured by outpuk not a single type of enterprise can accept both H1 and H2 for all periods (Table 2B). Both SOEs and COEs strongly reject both H1 and H2. Together with the rejection of H3, they reject all three hypotheses. However, OEs have much less rejection of H1 and H2 and reject only H1 mainly in 1991. After considering H3, OEs still cannot accept all three hypotheses, because they reject at least one hypothesis in any year. This result indicates that output growth of SOEs, COEs, and OEs does not follow Gibrat's Law as well. Since all estimated [3"s are also statistically significantly less than 1, this suggests that the output of all smaller enterprises is growing faster than that of larger enterprises. In addition, the rejection of H2 suggests that the output growth of enterprises in the current period is in general slightly negatively correlated with the output growth in the last period. Moreover, in Table 2A, the estimated [3 of collective enterprises (over the whole sample period) is the smallest among all three types of ownership, suggesting that the negative relationship (measured by [3 - 1 in equation 2) between the growth rates and the size (measured in terms of employment) of collective enterprises is the strongest (in absolute terms). In other words, if collective enterprises start with a smaller initial size measured in terms of employment, they can grow faster and create more jobs than the other types of ownership can. In addition, Table 1 shows that collective enterprises also have the smallest firm size (measured in terms of employment or output). This result gives support to the argument that small firms on average can grow faster and create more jobs than their larger counterparts. On the other hand, when firm size is measured in terms of output, the estimated [3 of state enterprises is the smallest over the whole sample period, similarly suggesting that the output of smaller state enterprises is going to grow faster than other types of ownership (Table 2B). Since accepting Gibrat's Law requires that H1, H2, and H3 are to be accepted simultaneously, our empirical results reject Gibrat's Law when growth is measured in both employment and output. In both cases, size and growth are negatively correlated: the larger the organization, the lower the growth rate. As growth rates are decreasing, this reinforces the view that the accrual of inertial forces reduces organizational performance over time (Hannan and Freeman 1984).
500
C. C H O W A N D M. F U N G
Although the Chinese economy is a transition economy, the growth patterns of manufacturing enterprises in Shanghai are similar to their counterparts in developed countries. Similar to what is obtained by Cardozo et al. (1992), Chesher (1979), Evans (1987a, 1987b), Kumar (1985), Ranger-Moore et al. (1995), Storey et al. (1987), and Tschoegl and Yu (1990), we also find that Gibrat's Law is rejected and smaller firms can grow faster than larger firms. The results here can be regarded as a first report on the firm dynamics of the Chinese enterprises. According to our empirical results, the Chinese economy may have more dynamic gains by forming 10 small enterprises each employing 100 workers than by creating a large enterprise employing 1000 workers. Since small non-state firms are more marketized, it is not very surprising that they resemble their counterparts in developed economies and grow faster. Although enterprises of different ownership structures differ in terms of flexibility and levels of assistance from either central or local governments, their dynamic patterns of the relationship between size and growth rate are very similar. This is implied from the sign and magnitude of [3's of different types of enterprises. The result that even small state-owned firms grow faster has important implications for the economic development policy of China. Despite the inefficiency of SOEs, breaking up a giant and inefficient SOE into several smaller SOEs may have some positive contribution to the economy over time, according to our results. Moreover, such a break-up may also improve the competition among enterprises in the marketplace. Definitely, our present empirical results cannot provide any conclusive evidence to support this policy, but they do indicate the possible benefits. Whether this policy really leads to any unambiguous improvement in the national welfare requires further research and more studies in this area. For example, one should construct models that incorporate the dynamic behavior of industries to measure the changes in social welfare in response to the implementation of this policy. One major problem that has concerned the Chinese government is how to stop the heavy losses made by giant state-owned enterprises. Such heavy losses are commonly the reason for the persistent budget deficits of the central government. To finance these deficits, the government has to resort to printing money, which creates inflation. One solution to ending this nightmare is to close these loss-making SOEs. However, letting these loss-making SOEs go bankrupt will create mass unemployment, which may cause instability in society. Given the possibility that small firms can create more jobs, more studies should be conducted to further examine the job creation ability of small firms in China so as to have a better understanding and measurement of this ability. If more solid evidence is available to confirm the job creation ability of small firms, small firms may help in restructuring the economy by re-allocating workers from the loss-making SOEs to these small firms. As a whole, the Chinese government may need a long-term and well-planned small business policy to promote the birth and development of small firms in order to meet their long-term development targets.
DISCUSSION AND CONCLUSION This article studies the empirical relationship between the size of SOEs, COEs, and OEs and their growth over time by testing whether Gibrat's Law can adequately describe the dynamic behavior of these domestic manufacturing enterprises. The panel data set used in this empirical study includes four industries in Shanghai from 1989 to 1992. Our empirical results obtained are as follows: (1) Small firms are better at creat-
FIRM SIZE A N D D Y N A M I C S IN C H I N A
501
ing jobs than their large counterparts. (2) In terms of output growth, small firms are also growing faster than large firms. Given the result that size and growth are negatively correlated, organizational inertia plays a part here in reducing the growth of larger firm. Based on this result, the Chinese economy may reap more dynamic benefits by creating more small firms than large firms, as small enterprises can create more jobs over time and help solve their unemployment problems. However, further research should be undertaken to understand the sources of faster growth of these smaller manufacturing enterprises. For example, the linkage between product markets and firm growth may provide a reason for faster growth of younger and smaller firms. Cardozo et al. (1993) have studied the relationship between product market strategies and firm growth and find that growth is greater for younger firms that change their product offerings. For the past 15 years of economic reform, the formation and rapid growth of nonstate enterprises, especially COEs, are actually an important source of growth of the industrial sector. Our empirical results do confirm that these small firms (including state-owned small firms) grow faster and can make significant contributions to the industrialization process. Because small state-owned firms also grow faster, the policy of breaking up a giant and inefficient SOE into a number of smaller firms may provide some benefits to the economy. More research should be conducted in this area to determine whether this policy is beneficial to the economy. In spite of the empirical evidence of the superiority of small firms (especially collective enterprises), economists have no comprehensive theory to explain the better performance of small firms (Aiginger and Tichy 1991). Similarly, although Storey (1990) argues that small firms are not scaled down versions of larger firms, no fully accepted theories are provided to explain their behavior. Therefore, Aiginger and Tichy (1991) suggest six hypotheses to account for the superiority of small firms. In examining these six hypotheses, not all of them can be applied to the situation in the Shanghai manufacturing sector. Because of the special organizational features of small manufacturing firms in China (e.g., the presence of COEs), our discussion concentrates on their hypothesis four, the absence of agency costs in small firms, because this hypothesis can be applied in the present context. Agency costs exist not only in the management of private enterprises, in which the principals are owners or shareholders and the agents are managers, but also in the management of SOEs and COEs. According to Groves et al. ( 1994), the communication and coordination between principals (state agencies) and managers can be very costly in SOEs. Such costs are even higher for large SOEs, which are subject to tighter control by government. China's large state enterprises in the early 1990s still found themselves halfway between a bureaucratic command system and a market system (Perkins 1994). In addition, given the large size of SOEs, agency costs are in general quite large. For COEs, managers who are not the owners of enterprises have to be employed to run their daily operation. As a result, agency costs may still be present, but they may be lower in smaller collective enterprises as the coordination costs among a smaller number of managers and local government are lower. However, based on the presence of these special collective enterprises which are more or less absent in western countries, there is no widely accepted theory to explain how their complicated organizational structure affects their operation and profitability. Recent studies do offer some ideas to explain why these small collective enterprises can have better performance: information channels linking principals (local government) and agents (managers) are
502
C. C H O W A N D M. F U N G
shorter and simpler compared with their larger counterparts (Groves et al. 1994). Therefore, the smaller the firms, the less the agency costs. Despite the absence of welldefined property rights, factors like the demographic stability of China's communities promote the emergence of "invisible institutions" to provide a "moral framework for rights" or "cooperative culture" that serves to reduce problems of shirking and monitoring found in most public enterprises (Byrd and Lin 1990: Yusuf 1993a, 1993b: Weitzman and Xu 1993). Obviously, if these collective enterprises were smaller, they would find it easier and less costly to establish the "cooperative culture" so as to keep their productivity high. These results help explain why these small and vaguely defined cooperatives could have performed surprisingly well in the past. Nevertheless, the validity of the hypotheses of cooperative culture and moral framework for rights has not been examined yet. This offers a direction in which to further extend this area of research. When these collective enterprises were still small in size in the early 1980s, the coordination and monitoring costs were still relatively low; therefore, managers could still make quick and accurate decisions in allocating resources. As they have grown larger and exhausted their growth potential for a given size of enterprises, they run into problems of finding sources of growth so as to sustain their rapid development. Moreover, agency costs may start to increase, and the channels linking the principals (local governments) and agents (managers) may be prolonged when they get larger. As a result, coordination and monitoring are now more costly and difficult when the enterprises are larger. More importantly, large firm size may affect the maintenance of cooperative culture within these enterprises. All these factors indicate that collective enterprises rely on their small size for rapid growth. Once they have grown larger, they have to rely on other factors to keep up with their performance. In addition, keener competition drives up the costs of labor and materials and lowers their output prices. These factors suggest that the collective-owned manufacturing sector may have significantly matured in its process of development.
A P P E N D I X
In this Appendix, the derivation of equation 3 from equation 2 is presented. Equation 2 is rearranged to take into account the possibility of first order serial correlation in growth rate (H3). This is done to avoid bias due to serial correlations in the presence of lagged dependent variables (Chesher 1979). The following model is obtained: logS(i,t)
= c~ + f J l o g S ( i , t
1) +
-
ru(i,t
-
1) +
(A1)
e(i,t)
where r is the serial correlation coefficient; u(i,t) = ru(i,t - 1) + ¢(i,t); and ¢(i,t) is the standard stochastic error term. Rewriting equation A1 in terms of lagged dependent variable, we have logS(i,t)
= b,, + b ~ l o g S ( i , t
-
1) +
b~_logS(i,t
2) +
-
(A2)
¢(i,t)
where b,~ = (1 - r)~x, bl = [3+r, b2 = -[3r. The inclusion of log S(i,t - 1) and log S(i,t - 2) may cause the possibility of high multicollinearity. To reduce the numerical problems caused by multicollinearity, we add and subtract [3r log S(i,t - 1) from the righthand side of equation A2. Rearranging terms results in the following equation: logS(i,t)
= bo + b 3 1 0 g S ( i , t
-
1) +
b~_[logS(i,t
-
2) -
logS(i,t
-
1)] +
e(i,t)
(A3)
FIRM SIZE AND DYNAMICS IN CHINA
503
N o w b3 = [3+r - [3r (b~ is easily r e c o v e r e d by b3 - b2 = bl), and b2 still equals -[3r. A l t h o u g h , mathematically, e q u a t i o n A 3 is identical to equation A2, equation A3 has a m u c h lower correlation b e t w e e n its first two explanatory variables. W e pool all four industries t o g e t h e r in the estimation and testing. Therefore, an additional set of d u m m y variables D(ij), j = 1..... 4, is a d d e d to e q u a t i o n A3 in order to capture the effects caused by different industries. Finally our equation 3 is obtained from this step. In H1 ([3 = 1), restriction b3 = 1 is imposed: in H2 (r = 0), restriction b2 = 0 is imposed. [3 and r are r e c o v e r e d by solving the following equations: [3 = [bl + x/b~ + 4be]~2, r : [b, - x/Cb~ + 4b,1/2
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Schmalensee, R. 1989. Inter-industry studies of structure and performance. In R. Schmalensee and R.D. Willig, eds., Handbook of Industrial Organization. New York: Elsevier. pp. 951-1009. Scott, W.R. 1992. Organizations: Rational, Natural, and Open Systems, 3rd ed. New York: Prentice Hall. Singh, A., and Whittington, G. 1975. The size and growth of organizations. Review of Economic Studies 42:15-26. Solimano, A. 1993. The post-socialist transitions in comparative perspective: Policy issues and recent experience. World Development 21:1823-1835. Storey D.J., Keasey, K., Watson, R., and Wynarczyk, P. 1987. The Performance of Small Firms. London: Croom Helm. Storey, D.J. 1990. Firm performance and size. In Z.J. Acs and D.B. Audretsch, eds., The Economics of Small Firms: A European Challenge. Dordrecht: Kluwer Academic Publisher, pp. 41-50. The Economist. 1992. A survey on China. November 28, 1992, pp. 1-18. The Economist. 1995. A survey on China. March 18, 1995, pp. 1-24. Tsang, E.W.K. 1994. Threats and opportunities faced by private businesses in China. Journal of Business Venturing 9:451-468. Tschoegl, A.E., and C.M.J., Yu. 1990. Gibrat's Law in marketing: The case of liquor brand sales. International Journal of Research in Marketing 7:249-262. Wagner, J. 1992. Firm size, firm growth and persistence of chance: Testing Gibrat's Law with establishment data from Lower Saxony, 1978-1989. Small Business Economics 4:125-131. Weitzman, M.L. 1993. Economic transition: Can theory help? European Economic Review 37:549-555. Weitzman, M.L., and Xu, C. 1993. Chinese township and village enterprises as vaguely defined cooperatives. Manuscript. Cambridge, MA:Harvard University. Wholey, D.R., Christianson, J.B., and Sanchez, S.M. 1992. Organizational size and failure among health maintenance organizations. American Sociological Review 57:829-842. Yusuf, S. 1993a. The rise of China's non-state sector. Manuscript. Washington, DC: World Bank. Yusuf, S. 1993b. Property rights and non-state sector development in China. Manuscript. Washington, DC: World Bank.
FIRM D Y N A M I C S A N D I N D U S T R I A L I Z A T I O N IN THE CHINESE E C O N O M Y IN TRANSITION: IMPLICATIONS F O R SMALL BUSINESS POLICY CLEMENT CHOW KONG WING Lingnan College, Hong Kong
M I C H A E L F U N G KA Y I U Chinese University o f Hong Kong
This article first presents" a review of the development of different types
EXECUTIVE SUMMARY
of domestic enterprises: state-owned enterprise (SOEs), collective-
owned enterprises (COEs), and other enterprises (OEs), which include mainly private enterprises. Compared with other transition economies, COEs are actually an important source of China's rapid economic growth. They are "vaguely defined cooperative~ without a well-defined ownership structure. There is" no equivalent organizational structure similar to COEs in Western economies. The purpose of this article is to study the empirical relationship between the size of domestic enterprises including state-owned enterprises, collective-owned enterprises, and other enterprises and their firm dynamics (growth) by investigating whether they grow according to Gibrat's Law. The majority of COEs and OEs were formed after the economic reform. The rapid growth of these non-state enterprises, especially C O Es, is actually the driving force behind the rapid development of the industrial sector. Our empirical results suggest that Gibrat's Law does not hold for all types of enterprises (the rejection is especially strong for COEs). Moreover, small enterprises are growing faster than their larger counterparts in terms of employment and output. Address correspondence to Michael Fung, Department of Decision Sciences and Managerial Economics, Faculty of Business Administration, Chinese University of Hong Kong, Shatin, N.T., Hong Kong. The authors thank an anonymous referee and the editor of this journal for their helpful comments on an earlier version of this article. All remaining errors are of course ours. We would also like to acknowledge the support from the Chinese Management Research Group, Faculty of Business Administration, Chinese University of Hong Kong. Journal of Business Venturing 11, 489-505 © 1996 Elsevier Science Inc. 655 Avenue of the Americas, New York. NY 10010
0883-9026/96/$15.00 PII S0883-9026(96)00019-5
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C. CHOW AND M. FUNG
This conclusion leads to (at least) three implications: (1) There has been little research into the dynamics o f SOEs and COEs in an economy in transition. Our results not only help stimuhlte fi~rther research hi this area but also provide infornmtion for designing policies to speed up the economic reforms. (2) Until now, tile Chinese government has not had a comprehensive small business policy. Given our empirical results, there # till implication for goverllment policy prescription. To achieve ~ i n a ' s development targets o f rapid industrialization and lower u,employnlent or lmderemployment, the Chinese economv can reap more clynamic benefits by breaking ltp a giant and inefficient enterprise (especially i,e[ficient SOE) into a number gfsmaller enterprises. In addition, more small enterprises also improve competition ill the marketplace told force other inefficient SOEs to intprove their performance. Further research should he undertaken to examine the viabig O' o f this polio,. (3) Because COEs are the majori O, of small malutfacturing enterprises, the rapid growth o f COEs (~(f_tertheir formation) that are simply the opposite o f the O'pe o f private organizations, the center of" the Central and East European reforms, is rather surprising to academics. Although some hypotheses like "cooperative culturd and "moral Jkameu'ork [br righL¢ are put forward to explain their superior performance, the validi O' o f such hypotheses has not been empirically tested yet. This is" a fruitful area .for fltrther research. © 1996 Elsevier Science Inc.
INTRODUCTION
Since the implementation of economic reform in 1978 in the People's Republic of China. entrepreneurship--once under severe suppression before economic reform-has been slowly receiving more and more attention from the nation. The spirit of economic reform was to introduce regional and sectoral decentralization of economic decision-making to each individual economic unit under the banner of state ownership of resources. Since then the non-state sector where entrepreneurship and small businesses have a key role to play has been booming, making a significant contribution to the national economy. Although the non-state sector in China has been developing rapidly, academic research in this area started fairly recently. The impressive performance of collectiveowned enterprises (COEs) has been confirmed in various studies (Jefferson, Rawski, and Zheng 1992: McGuckin and Nguyen 1993: Prime 1992). Entrepreneurial development in China has attracted the attention of researchers who focus on particular aspects of the development process (Chang and Macmillan 1991: Reeder 1984: Tsang 1994). Rapid growth and expansion of these non-state enterprises are the driving forces behind the rapid development of the Chinese industrial sector. However, little is known about their dynamic behavior. The study of dynamic behavior is important in understanding the development of firms. One dynamic pattern that attracts many researchers is the relationship between firm size and firm growth. It is well known that the size distributions of companies are highly skewed: log-normal distribution is commonly used to approximate the size distribution of firms (Gibrat 1931: Ijiri and Simon 1977). Schmalensee (1989, p. 994) terms this regularity as a stylized fact. Do firms with different sizes face different growth opportunities? Early studies of firm's growth rates in the United States, e.g., Hymer and Pashigian (1962), Ijiri and Simon (1977), and Mansfield (1962), generally supported Gibrat's Law of Proportionate Growth. This law predicts that each firm faces the same distribution of growth possibilities, and each firm's actual growth is determined by random sampling from that distribution (Scherer and Ross 1990, p. 141; Wagner 1992). In other words, firm growth is independent of firm size. However, the empirical literature in this area produces inconclusive evidence. For example, on the
FIRM SIZE A N D D Y N A M I C S IN C H I N A
491
one hand, recent studies by Cardozo et al. (1992), Chesher (1979), Evans (1987a~ 1987b), Kumar (1985), Ranger-Moore et al. (1995), Storey et al. (1987), and Tschoegl and Yu (1990) found that Gibrat's Law is rejected at population level for the small firm sector because growth and size are negatively correlated, even allowing for the exits of slow growth firms. On the other, Singh and Whittington (1975) showed that growth rates are increasing in firm size. On the average, more researchers in this line of study support the presence of a negative relationship between firm growth and its size and conclude that small manufacturing firms have contributed disproportionately to economic growth by generating greater employment growth and technological advancement than their large and established counterparts (Birch 1987: Box, Watts, and Hisrich 1994; Cooper, Woo~ and Dunkelberg 1989). Why is Gibrat's Law important? First, firm size is an important factor in theoretical and empirical research in organizations (Baum 1995: Scott 1992). The validity of Gibrat's Law, which implies a relationship between firm size and firm growth, provides important information in understanding the organizational dynamics of enterprises. For example, in organizational ecology, size distribution is an important element of the competitive process in any market economy, e.g., organizational survival and diversity, which affect the opportunities of employment (Hannan and Freeman 1989). The available evidence suggests that there is a negative relationship between firm size and its death rate (Aldrich and Auster 1986: Birch 1979: Cooper et al. 1989). Moreover~ Ranger-Moore et al. (1995) suggest that there are internal and external effects of firm size on firm growth. The internal effect (within an organization) is mainly the structural inertia proposed by Hannan and Freeman (1984) to explain how the internal organizational structure interacts with the environment. This theory suggests that older and larger firms are slower to change because these changes can undermine their accountability and reliability. The external effect is the competition among organizations of different sizes. Organizations in a population are not equal competitors. For example, organizations similar in size engage in fiercer competition, resulting in increased failure rates (Baum and Mezias 1992: Hannah and Freeman 1977; Wholey et al. 1992). Moreover, the validity of Gibrat's Law has implications for industrial policies. The empirical literature of organizational ecology uses only the data in developed countries, and little is known about how the theories apply to developing countries. Unfortunately, for transition economies, there is no published work in this area at all. Nevertheless, for any transition economy like China, which focuses on economic development~ balanced economic growth and rapid industrialization are always top priority. If Gibrat's Law holds, i.e., firm growth is independent of firm size, firm size is not a concern in designing policy programs to promote business development. However. if Gibrat's Law does not hold, the government can possibly use policy to exploit the situation so as to achieve its development targets. For example, suppose all empirical results, including the impact of size on organization survival and localized competition, unambiguously point to a faster growth rate of smaller firms, the government should provide incentives to encourage the birth and growth of small firms. In most developing countries, unemployment caused by rapid urbanization and surplus labor in the rural sector is the most difficult problem faced by their governments. Encouraging the birth and development of small firms can help in solving the unemployment problem gradually if small firms grow faster and create more jobs. Obviously, at the present primitive stage of research in organizational ecology in China, there is still a lot of work yet to be done before reaching any conclusions for policy prescriptions.
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c. C H O W A N D M. F U N G
The purpose of this article is to study the firm dynamics (growth) of domestic enterprises (including both state and non-state) in the manufacturing sector of Shanghai by investigating whether Gibrat's Law can accurately describe their dynamic behavior. Although Gibrat's Law has been tested previously (mainly by economists), our test is one of the only tests that is conducted in a complete population that includes a wide variety of different organization sizes in a transition economy like China. Our focus is on the effect of size on firm growth (Chesher 1979: Evans 1987a, 1987b: Hall 1987: Kumar 1985: Mansfield 1962: Ranger-Moore et al. 1995) and not on size-localized competition (Baum and Mezias 1992; Hannan and Freeman 1977: Ranger-Moore et al. 1995: Wholey et al. 1992). This study not only provides relevant implications for small business policy but also improves our understanding of the firm dynamics (growth) of state and non-state enterprises in China, which have been neglected in the literature. Therefore, our empirical results can provide some first-hand information of the relationship between firm growth and firm size in a transition economy. Moreover, unlike in Central and Eastern Europe where small businesses were developed only recently, the Chinese economy has been populated by a relatively high proportion of small businesses for the past 15 years of economic reform. The Chinese experience in small business development is actually valuable for Central and East European governments in designing their own policies.
E N T E R P R I S E S IN T H E C H I N E S E E C O N O M Y IN T R A N S I T I O N In 1978, just before reform began, the Chinese state-owned sector accounted for 78% of industrial output. Since then, the state share of industrial production had been declining steadily and at the end of 1990, it accounted for roughly 50% of industrial output. If agriculture and services were included, the state share of China's output was less than 25% (The Economist 1992). Substantial expansion of the non-state sector is probably one of the prominent features of the Chinese economic reform besides spectacular economic growth: average G D P was growing at 9.0% per year during the period of 1978-1992 (Solimano 1993). State-owned enterprises (SOEs) in the state sector were strictly controlled by the central government before economic reforms. They functioned as passive agents of the state economic bureaucracy (Jefferson and Rawski 1994). Managers of SOEs in China were rewarded primarily for their success in meeting physical output targets set by the government (Gordon and Li 1991). Facing so much competition from non-state enterprises, the Chinese government introduced measures to improve the productivity of state enterprises. The reform measures include the following changes: (1) A higher degree of autonomy is given to an enterprise's manager in input, output, and pricing decisions; (2) manager's rewards are linked to the enterprise's performance; (3) bonus out of profits made are given to workers: and (4) more contract workers are employed to replace permanent workers so as to add more flexibility to the labor force in a firm (Groves et al. 1994). After introducing these new measures, the total factor productivity (TFP, a measure of technical efficiency) growth rate in the state-owned enterprises was 3.4% from 1980 to 1984 (Chen et al. 1988). In addition, the TFP of the state-owned sector continued to grow at about 3% from 1984 to 1988 (Jefferson et al. 1992) and 2.5% from 1988 to 1992 (Jefferson and Rawski 1994). As part of the public sector, SOEs can access the supplies of raw materials and inputs through the official distribution systems. On the other hand, they are also facing soft budget constraints, which
FIRM SIZE AND DYNAMICS IN CHINA 493 correspond to loans from state-owned banks at a below-market interest rate (Gordon and Li 1991). The Chinese government develops a non-state sector by introducing different types of ownership into the economy, such as collective-owned, private, and foreigninvested enterprises. Since the economic reform, the non-state sector is dominated by COEs, especially in the industrial sector. These COEs are relatively small and have developed originally from cooperatives before the economic reform. In contrast with SOEs that can easily obtain input materials, energy, and loans from the central government (these inputs are under the control of the central government), these COEs have to rely on the market to obtain these input factors. In addition, products are sold in some form of marketplace rather than being allocated by state planning. Finally, these COEs are ultimately theoretically responsible for their own profits and losses. Therefore, their budget constraints are far from soft (Lockett 1988: The Economist 1992). As a result, these COEs are very much "marketizea except that their ownership structure is different from the western concept of a private firm. On the one hand, the formation of these collective enterprises and their subsequent entry into the market contribute substantially to improving the competitiveness of the economy: on the other, they also increase the flexibility and adaptability of the whole economy because of their small size. Although these COEs are typically controlled by local governments, their performance is much better than SOEs. According to G o r d o n and Li (1991), the allocation decisions of COEs are relatively more efficient, because the local governments effectively owned and controlled these firms and operated in competition with many other local jurisdictions. Their impressive performance is confirmed by their improvement in TFP: The annual average TFP growth rates of the collective sector were about 5% percent from 1980 to 1988 and 4.7% from 1988 to 1992 (Jefferson and Rawski 1994). These TFP growth rates were higher than the TFP growth rate of state enterprises (Jefferson et al. 1992). In a similar study on the collective sector in Jiangsu province from 1981 to 1988, the TFP growth rate of collective enterprises was 6.2%, which was also higher than the national level (Prime 1992). In addition, by using Industrial Census Data, McGuckin and Nguyen (1993) show that the TFP growth rate of collective enterprises was six times higher than that of state enterprises from 1981 to 1984. According to Weitzman (1993), these collective enterprises are actually the driving force behind the rapid economic growth of China. They are now the second biggest sector of the national economy (after the state sector), constituting over 40% of total industrial output, whereas they accounted for only 20% of industrial output before economic reform. Compared with collective and state enterprises, private enterprises in China are enjoying more flexibility in the process of decision-making. These enterprises are behaving like most private companies in other market economies, because they have to recruit resources in the markets, such as employing workers by paying market wages, and compete with all other enterprises in the marketplace. Unlike collective and state enterprises, these companies do not enjoy any privileges or special assistance from the central or local governments. Moreover, in the presence of severe financial repression, these enterprises have difficulties obtaining funding from the government-owned banking system (McKinnon 1994). Enterprises with different ownership structures imply different flexibility in decision-making and different levels of assistance from either the central or local governments. These differences may imply that enterprises of different ownership structure
494
C. C H O W A N D M. FUNG
are facing different growth opportunities. Do we expect different relationships between size and growth of enterprises of different ownership structures? This question will be investigated in this study.
DATA The data set supplied by the Shanghai Economic Commission covers 2083 domestic enterprises, which comprise state-, collective-owned, and other enterprises (mainly private enterprises) in Shanghai and includes the input-output information of these enterprises. This data set contains enterprises" net industrial output values (value-added, measured at current prices in Chinese yuan), gross industrial output values (also measured at current prices in Chinese yuan), original values of fixed assets (book value, in Chinese yuan), net values of fixed assets (book value, in Chinese yuan), and types of ownership. Shanghai, a metropolitan city of 14 million people, was chosen because it has been one of the important commercial and industrial cities before and after 1949. According to different issues of Shanghai's Statistical Yearbooks, the city's share in the national industrial output is very high: It had an average of 15% from 1949 to 1979 and about 7% in the post-reform period, although Shanghai accounts for only 1% of the national population. Despite these superior conditions, the central government gave little autonomy to Shanghai's government in managing its political and economic affairs. One reason for the central government's reluctance to give Shanghai more economic freedom in the earlier part of reform is that Shanghai has a sophisticated and relatively efficient taxation bureau and the highest industrial formation and per capita national income--all of which implies stable and substantial tax revenues for the central government. Although Shanghai only accounted for 1% of the national population, the central government relied on the city for 15% of its revenues (The Economist 1995). This situation changed dramatically when Jiang Zemin (ex-party chief and mayor of Shanghai) was elected as the chairman of the Chinese Communist Party in 1989 after the Tiananmen Square incident. Since 1990, Shanghai has been given a lot of autonomy in making economic and administrative decisions. The Shanghai government can now plan and invest at its own pace. As a result, we chose our sample to begin in 1989 because of the policy changes in Shanghai. Among all Shanghai's manufacturing industries, two labor or less skill-intensive industries (textile, clothing) and two capital or skill-intensive industries (machinery and equipment, electronics and telecommunication equipment) are considered in our study. These four industries were chosen because they are the largest four manufacturing industries in Shanghai. This panel data set covers 629 firms (SOEs:269, COEs:216, OEs:144) in the textile industry, 327 firms (SOEs:33, COEs:192, OEs:102) in the clothing industry, 965 firms (SOEs:421, COEs:435, OEs:109) in the machinery and equipment industry, and 162 firms (SOEs:50, COEs:97, OEs:15) in the electronics and telecommunication equipment industry. This data set tracks these 2083 firms from 1989 to 1992 and therefore is rectangular (i.e., complete panel data set). In addition, this data set allows a very high level of size variability (e.g., the average firm size (from 1989 to 1992) ranges from 13.5 workers to 9886.5 workers). This high variability of size can provide more information in understanding the relationship between firm size and firm growth.
FIRM SIZE AND DYNAMICS IN CHINA 495
METHODOLOGY In studying the relationship between organization size and growth rates, Gibrat (1931) found that organization size distributions were usually log-normal. Therefore, in studying firm growth problems, stochastic growth models are commonly adopted in the empirical literature (e.g., Baum and Mezias 1992; Barnett and Carroll 1987: Evans 1987a, 1987b: Hannan and Freeman 1989: Ranger-Moore et al. 1995; Tschoegl and Yu 1990). To derive empirical tests of firm growth, a simple stochastic growth model in which the growth rate of any firm (say, firm i), ~(i,t), in period t is drawn from a common normal distribution, N(c~,cr2), with mean c~ and variance cr-~,is considered. As a result, the size of firm i in period t follows the following process: S(i,t) = S~(i,t -
1)exp[p~(i,t)]
(1)
where S(i,t) is the size of firm i in period t: [3 is a growth parameter. Since p~(i,t) follows normal distribution, N(e~,cr-~), we can rewrite ~(i,t) as ~ ( i , t ) = e~ + e(i,t) where E[e(i,t)]
= 0
where ~(i,t) has mean zero and variance ¢r-~. This set-up implies that organization size follows log-normal distribution. Following the logic of Gibrat's Law, annual growth rate, g(i,t), is defined as the ratio of current size to previous size, g(i,t) = S(i,t)/S(i,t 1). This definition allows us to visualize growth in the following two ways: If size is measured in absolute terms, growth is multiplicative. However, it is equally valid to imagine additive growth in a situation where growth is measured using a logarithmic scale. Thus~ we can analyze the natural logarithm of growth: logg(i,t)
= log(o, S(i't) ~Jtt,t -
1)
) = logS(i,t) - logS(i,t
1 )
This definition is compatible with the previous empirical research that studies the problems of firm growth (Chesher 1979: Evans 1987a, 1987b: Ranger-Moore et al. 1995: Tschoegl and Yu 1990: Wagner 1992). Taking a logarithm of equation 1, we have the following cross-sectional relationship: logS(i,t)
= [31ogS(i,t -
1) + e~ + ~(i,t)
(2)
If we subtract log S(i,t - 1) from both sides of equation 2, we have logS(i,t)
- logS(i,t -
1) = l o g g ( i , t ) = ([3 - 1) l o g S ( i , t - 1 ) + e~ + e(i,t)
Obviously, if [3 is not equal to 1, the growth rate, g(i,t), is not independent of firm size, S(i,t - 1). Firms that started with a larger size can always grow faster (slower) than firms that started with a smaller size as long as [3 > ( < ) 1. In other words, if [3 is less than 1 (a negative relationship between growth and size), a firm that starts with a smaller initial size can grow faster and larger over time on average. This result is an important outcome derived from the rejection of Gibrat's Law. The acceptance of Gibrat% Law is equivalent to accepting the following three null hypotheses (Tschoegl and Yu 1990): H I : ~ = 1. H 2 (absence of serial correlation): Cov[e(i,t),e(i.t H 3 (homoscedasticity): E[e-~(i,t)] - ~z(t)
1)] - 0.
496
C. C H O W A N D M. F U N G
When firms accept these three null hypotheses simultaneously, these firms are growing according to Gibrat's Law. To avoid bias due to serial correlations in the presence of lagged dependent variables, parameter instability caused by multicollinearity, and to capture the effects caused by different industries, we adopt the following regression model, which can be derived from equation 2, 4
logS(i,t)
= b31ogS(i,t
1) + b 2 [ l o g S ( i , t - 2) - l o g S ( i , t -
1)] + ~ a ( j ) D ( i , j ) I
+ e(i,t)
(3)
I
where b3 = [3 + r - [3r (r is serial correlation coefficient, r = cov(e(i,t),e(i,t - 1))/~r2) and b, = - [3r. Dummy variable D(i,j) represents firm i belonging to industry j and j = 1 . . . . . 4. D0,j) = 1 if firm i belongs to industry j; otherwise, D(i,j) = 0. G is deleted in estimation so as to avoid its perfect collinearity with the four dummy variables. These dummy variables are used to capture the individual industry effect on firm growth as firms in different industries may have different patterns of firm growth. The details of recovering parameter [3 and r and the steps of deriving equation 3 from equation 2 by removing serial correlations, multicollinearity are shown in the Appendix. Equation 3 is our model for testing Gibrat's Law and estimating the parameter [3 and r. In H1 ([3 = 1), restriction b3 = 1 is imposed on equation 3: in H2 (r = 0), restriction b2 = 0 is imposed on equation 3. Mentioned in the previous section, the three types of domestic enterprises (stateowned, collective-owned, and other-owned) differ in terms of government involvement, management style, and access to important factor inputs like credit and materials. As a result, it is relevant to find out how the ownership effect affects the dynamic growth of enterprises. Therefore, equation 3 is applied to each type of ownership in order to test whether Gibrat's Law is accepted or rejected by each type. By conducting this empirical study, one can get a clearer picture of the dynamic performance of these three types of enterprises. Firm size is commonly measured by its employment, e.g., the U.S. Small Business Administration classifies firms that employ fewer than 500 workers as small firms, and by output measured by its value-added, which is net industrial output values of that enterprise. The net industrial output values of each firm are deflated by its product price index (1980 as base year) so as to remove any impact of inflation, which is common in the Chinese economy. Therefore, the output values obtained are measured in real terms. In testing Gibrat's Law, we adopt both definitions of firm size. In order to test hypothesis H3 (presence of heteroscedasticity), Breusch and Pagan (1979) and Godfrey (1978) test for heteroscedasticity is used. The estimation and testing are done by S H A Z A M 6.2.
EMPIRICAL RESULTS Before we discuss the empirical tests of Gibrat's Law, let us consider some descriptive statistics of those three types of domestic enterprises (SOEs, COEs, and OEs). Table 1 reports the means and standard deviations of employment, output (in millions of yuan), and growth rates measured by employment and output of these three types of enterprises. When firm size is measured in terms of either employment or output, state-owned enterprises are the largest, followed by other enterprises, and collectiveowned enterprises are the smallest. For example, in terms of average employment out-
FIRM SIZE AND DYNAMICS IN CHINA 497 TABLE 1
Means and Standard Deviations of Employment, Output, and Firm Growth Rates (in percentage) Measured by Employment and Output State
Year Employment: 1989 1990 1991 1992 Output (millions of yuan): 1989 1990 1991 1992 Growth rates (in %) measured by employment: 89-90 90-91 91-92 Growth rates (in %) measured by output: 89-90 90-91 91-92
Collective
Other
Mean
SD
Mean
SD
Mean
SD
1069.6 1072.9 1067.2 1056.0
1338.8 1343.1 1330.8 1333.5
296.0 295.5 295.8 288.2
242.7 241.6 244.5 240.1
521.8 544.7 554.8 558.2
972.5 1076.7 1091.1 1126.6
7.530 7.092 6.843 7.649
14.40 11.05 10.73 11.48
1.552 1.282 1.526 1.789
2.231 1.501 1.981 2.444
3.043 3.119 3.254 3.588
9.575 11.38 11.46 11.19
0.005 0.685 0.417
9.514 11.92 18.57
2.266 1.454 0.384
28.63 24.77 39.38
7.065 2.196 2.145
22.7 l 19.87 18.15
7.823 25.96 24.44
68.99 305.1 88.02
2.432 32.21 28.04
83.00 124.1 116.9
5.162 13.56 23.07
66.99 67.89 79.07
put, state-owned enterprises are three to four times larger than collective enterprises. This result confirms the observation that most collective enterprises are small. Similarly, the same ranking arises when we measure the standard deviations of firm size in terms of employment or output. When firm growth is measured in terms of employment, state-owned enterprises have the lowest growth rate but other enterprises have the highest growth rate. Since other enterprises include mainly private enterprises, their flexibility in employment decisions give them a higher capacity in creating jobs. Moreover, the growth rates actually declined from 1989 to 1992 for all three types of enterprises. On the other hand, in terms of variability of growth rates, collective enterprises have the highest variances. When growth rate is measured in terms of output, state and collective enterprises now have higher output growth rates relative to those of the other enterprises. Moreover, the variances of their output growth rates are also higher than those for the other enterprises as well. In addition, the output growth rates actually increased from 1989 to 1992 for all types of ownership. Combined with the declining employment growth rates, these enterprises are actually more cost-conscious and work hard to slow the increases in their wage bills while maintaining relatively high output growth. The results of empirical tests are reported in Tables 2 and 3. Since the acceptance of Gibrat's Law requires that HI, H2, and H3 hold simultaneously, all three types of enterprises cannot fully accept all three hypotheses. When firm size is measured by employment, Table 2A reports that almost all enterprises reject HI: 13 = 1. All SOEs strongly reject H1 and the estimated [3~s are all less than 1. This result also indicates that small SOEs are growing faster in terms of employment. For COEs, a similar result is also obtained. On the other hand, OEs have less rejections of Gibrat's Law in comparison with the SOEs and C O E s - - f o r example,
" Joint test of [3 - 1 and r
Firm no.
(33.91"*) 0.926** (37.36) 0.343":: (4(I.64) 0.831
(33.54**) 0.937** (40.34) 0.266** (36.01) 0.830
(8.628**) 0.959** (8.746) 0.175'::* (8.996) 0.839
(4.879*) 0.987** (7.445) 0.089 (2.963) 0.983
1992
(33.74**) 0.953** (15.091 0.244':* (42.26) I).722
(5.180") 0.975** (9.2811 0.086 (0.892) 0.925
1991-92
94[1
(26.50**) 0.931 ** (22.53) 0.262"::: (27.70) 0.739
(2.249) 0.979 (4.778) 0.096 (0.771) 0.938
1991
Collective
110.11"*) 0.975 (1.8611 0.254"::: (15.29) 0.709
(3.748) (I.971 * (6.0311 - 0.080 (0.31/)) 0.912
1992
0. The values tn parentheses are lht: F statistics of the corresponding restrictions. : p <~ .I)5 and " " p "--- .01.
773
(5.188") 0.989** (9.858) 0.1136 (0.173) 0.992
1991
(9.209**) 0.987** (17.92) //.024 ((I.161) 0.988
1991-92
State
R e s u l t s of T e s t i n g G i b r a t ' s L a w ([3 = 1 a n d r = 1)) o n D i f f e r c n t T y p e s o f O w n e r s h i p
A. Size measured by e m p l o y m e n t 13 and r ~ 13 (13 - 1) r (r - 0) Re B. Size measured by output 13 and r ~ 13 ( [ 3 - 11 r (r = 0) Re
TABLE 2
(3.809) 0.954* (5.693) 0.086 (2.852) (I.776
(2.790) 0.984* (5.1541 - 0.061 (0.667) 0.9(~0
1991 92
340
(3.694) 0.948* (4.443) -0.120 (2.962) (/.807
(0.288) 0.999 (0.003) 0.059 (0.572) 0.959
1991
Other
(1.104) (I.961 (1.9181 0.063 (0.623) 0.752
(4.925") 0.977* (6.219) 0.174 (2.329) 0.963
1992 :Z C~
> Z
Z ©
4~
FIRM SIZE AND DYNAMICS IN CHINA 499 TABLE 3 Results of Breusch-Pagan-Godfrey Test for H3 T y p e s of O w n e r s h i p Collective
State Period 1991-92 1991 1992
Other
Employment"
Outpuf'
Employment
Output
Employment
Output
8.333 7.838 4.215
53.37** 26.67** 24.91"*
62.99** 21.44"* 44.25**
30.52** 15.38"* 23.59**
23.46** 6.89 18.32"*
18.52"* 9.80 13.73"*
"Reported values are the Breusch-Pagan-Godfrey test statistics (X-~statistics) of the case in which firm size is measured by its employment. ~'Reported values are the Breusch-Pagan-Godfrey test statistics (?(2 statistics) of the case in which firm size is measured by its output. *p < .05: and **p < .01.
OEs accepted H1, H2, and H3 in 1991 (but rejected H1 in 1992 and the whole sample in 1991 to 1992). Since all estimated [3's are statistically significantly less than 1, this suggests that small enterprises grow faster and create more jobs. Nevertheless, H2 (absence of serial correlation) is accepted by these three types of enterprises. This suggests that the employment growth of firms in the current period is on average independent of the employment growth in the last period. When firm size is measured by outpuk not a single type of enterprise can accept both H1 and H2 for all periods (Table 2B). Both SOEs and COEs strongly reject both H1 and H2. Together with the rejection of H3, they reject all three hypotheses. However, OEs have much less rejection of H1 and H2 and reject only H1 mainly in 1991. After considering H3, OEs still cannot accept all three hypotheses, because they reject at least one hypothesis in any year. This result indicates that output growth of SOEs, COEs, and OEs does not follow Gibrat's Law as well. Since all estimated [3"s are also statistically significantly less than 1, this suggests that the output of all smaller enterprises is growing faster than that of larger enterprises. In addition, the rejection of H2 suggests that the output growth of enterprises in the current period is in general slightly negatively correlated with the output growth in the last period. Moreover, in Table 2A, the estimated [3 of collective enterprises (over the whole sample period) is the smallest among all three types of ownership, suggesting that the negative relationship (measured by [3 - 1 in equation 2) between the growth rates and the size (measured in terms of employment) of collective enterprises is the strongest (in absolute terms). In other words, if collective enterprises start with a smaller initial size measured in terms of employment, they can grow faster and create more jobs than the other types of ownership can. In addition, Table 1 shows that collective enterprises also have the smallest firm size (measured in terms of employment or output). This result gives support to the argument that small firms on average can grow faster and create more jobs than their larger counterparts. On the other hand, when firm size is measured in terms of output, the estimated [3 of state enterprises is the smallest over the whole sample period, similarly suggesting that the output of smaller state enterprises is going to grow faster than other types of ownership (Table 2B). Since accepting Gibrat's Law requires that H1, H2, and H3 are to be accepted simultaneously, our empirical results reject Gibrat's Law when growth is measured in both employment and output. In both cases, size and growth are negatively correlated: the larger the organization, the lower the growth rate. As growth rates are decreasing, this reinforces the view that the accrual of inertial forces reduces organizational performance over time (Hannan and Freeman 1984).
500
C. C H O W A N D M. F U N G
Although the Chinese economy is a transition economy, the growth patterns of manufacturing enterprises in Shanghai are similar to their counterparts in developed countries. Similar to what is obtained by Cardozo et al. (1992), Chesher (1979), Evans (1987a, 1987b), Kumar (1985), Ranger-Moore et al. (1995), Storey et al. (1987), and Tschoegl and Yu (1990), we also find that Gibrat's Law is rejected and smaller firms can grow faster than larger firms. The results here can be regarded as a first report on the firm dynamics of the Chinese enterprises. According to our empirical results, the Chinese economy may have more dynamic gains by forming 10 small enterprises each employing 100 workers than by creating a large enterprise employing 1000 workers. Since small non-state firms are more marketized, it is not very surprising that they resemble their counterparts in developed economies and grow faster. Although enterprises of different ownership structures differ in terms of flexibility and levels of assistance from either central or local governments, their dynamic patterns of the relationship between size and growth rate are very similar. This is implied from the sign and magnitude of [3's of different types of enterprises. The result that even small state-owned firms grow faster has important implications for the economic development policy of China. Despite the inefficiency of SOEs, breaking up a giant and inefficient SOE into several smaller SOEs may have some positive contribution to the economy over time, according to our results. Moreover, such a break-up may also improve the competition among enterprises in the marketplace. Definitely, our present empirical results cannot provide any conclusive evidence to support this policy, but they do indicate the possible benefits. Whether this policy really leads to any unambiguous improvement in the national welfare requires further research and more studies in this area. For example, one should construct models that incorporate the dynamic behavior of industries to measure the changes in social welfare in response to the implementation of this policy. One major problem that has concerned the Chinese government is how to stop the heavy losses made by giant state-owned enterprises. Such heavy losses are commonly the reason for the persistent budget deficits of the central government. To finance these deficits, the government has to resort to printing money, which creates inflation. One solution to ending this nightmare is to close these loss-making SOEs. However, letting these loss-making SOEs go bankrupt will create mass unemployment, which may cause instability in society. Given the possibility that small firms can create more jobs, more studies should be conducted to further examine the job creation ability of small firms in China so as to have a better understanding and measurement of this ability. If more solid evidence is available to confirm the job creation ability of small firms, small firms may help in restructuring the economy by re-allocating workers from the loss-making SOEs to these small firms. As a whole, the Chinese government may need a long-term and well-planned small business policy to promote the birth and development of small firms in order to meet their long-term development targets.
DISCUSSION AND CONCLUSION This article studies the empirical relationship between the size of SOEs, COEs, and OEs and their growth over time by testing whether Gibrat's Law can adequately describe the dynamic behavior of these domestic manufacturing enterprises. The panel data set used in this empirical study includes four industries in Shanghai from 1989 to 1992. Our empirical results obtained are as follows: (1) Small firms are better at creat-
FIRM SIZE A N D D Y N A M I C S IN C H I N A
501
ing jobs than their large counterparts. (2) In terms of output growth, small firms are also growing faster than large firms. Given the result that size and growth are negatively correlated, organizational inertia plays a part here in reducing the growth of larger firm. Based on this result, the Chinese economy may reap more dynamic benefits by creating more small firms than large firms, as small enterprises can create more jobs over time and help solve their unemployment problems. However, further research should be undertaken to understand the sources of faster growth of these smaller manufacturing enterprises. For example, the linkage between product markets and firm growth may provide a reason for faster growth of younger and smaller firms. Cardozo et al. (1993) have studied the relationship between product market strategies and firm growth and find that growth is greater for younger firms that change their product offerings. For the past 15 years of economic reform, the formation and rapid growth of nonstate enterprises, especially COEs, are actually an important source of growth of the industrial sector. Our empirical results do confirm that these small firms (including state-owned small firms) grow faster and can make significant contributions to the industrialization process. Because small state-owned firms also grow faster, the policy of breaking up a giant and inefficient SOE into a number of smaller firms may provide some benefits to the economy. More research should be conducted in this area to determine whether this policy is beneficial to the economy. In spite of the empirical evidence of the superiority of small firms (especially collective enterprises), economists have no comprehensive theory to explain the better performance of small firms (Aiginger and Tichy 1991). Similarly, although Storey (1990) argues that small firms are not scaled down versions of larger firms, no fully accepted theories are provided to explain their behavior. Therefore, Aiginger and Tichy (1991) suggest six hypotheses to account for the superiority of small firms. In examining these six hypotheses, not all of them can be applied to the situation in the Shanghai manufacturing sector. Because of the special organizational features of small manufacturing firms in China (e.g., the presence of COEs), our discussion concentrates on their hypothesis four, the absence of agency costs in small firms, because this hypothesis can be applied in the present context. Agency costs exist not only in the management of private enterprises, in which the principals are owners or shareholders and the agents are managers, but also in the management of SOEs and COEs. According to Groves et al. ( 1994), the communication and coordination between principals (state agencies) and managers can be very costly in SOEs. Such costs are even higher for large SOEs, which are subject to tighter control by government. China's large state enterprises in the early 1990s still found themselves halfway between a bureaucratic command system and a market system (Perkins 1994). In addition, given the large size of SOEs, agency costs are in general quite large. For COEs, managers who are not the owners of enterprises have to be employed to run their daily operation. As a result, agency costs may still be present, but they may be lower in smaller collective enterprises as the coordination costs among a smaller number of managers and local government are lower. However, based on the presence of these special collective enterprises which are more or less absent in western countries, there is no widely accepted theory to explain how their complicated organizational structure affects their operation and profitability. Recent studies do offer some ideas to explain why these small collective enterprises can have better performance: information channels linking principals (local government) and agents (managers) are
502
C. C H O W A N D M. F U N G
shorter and simpler compared with their larger counterparts (Groves et al. 1994). Therefore, the smaller the firms, the less the agency costs. Despite the absence of welldefined property rights, factors like the demographic stability of China's communities promote the emergence of "invisible institutions" to provide a "moral framework for rights" or "cooperative culture" that serves to reduce problems of shirking and monitoring found in most public enterprises (Byrd and Lin 1990: Yusuf 1993a, 1993b: Weitzman and Xu 1993). Obviously, if these collective enterprises were smaller, they would find it easier and less costly to establish the "cooperative culture" so as to keep their productivity high. These results help explain why these small and vaguely defined cooperatives could have performed surprisingly well in the past. Nevertheless, the validity of the hypotheses of cooperative culture and moral framework for rights has not been examined yet. This offers a direction in which to further extend this area of research. When these collective enterprises were still small in size in the early 1980s, the coordination and monitoring costs were still relatively low; therefore, managers could still make quick and accurate decisions in allocating resources. As they have grown larger and exhausted their growth potential for a given size of enterprises, they run into problems of finding sources of growth so as to sustain their rapid development. Moreover, agency costs may start to increase, and the channels linking the principals (local governments) and agents (managers) may be prolonged when they get larger. As a result, coordination and monitoring are now more costly and difficult when the enterprises are larger. More importantly, large firm size may affect the maintenance of cooperative culture within these enterprises. All these factors indicate that collective enterprises rely on their small size for rapid growth. Once they have grown larger, they have to rely on other factors to keep up with their performance. In addition, keener competition drives up the costs of labor and materials and lowers their output prices. These factors suggest that the collective-owned manufacturing sector may have significantly matured in its process of development.
A P P E N D I X
In this Appendix, the derivation of equation 3 from equation 2 is presented. Equation 2 is rearranged to take into account the possibility of first order serial correlation in growth rate (H3). This is done to avoid bias due to serial correlations in the presence of lagged dependent variables (Chesher 1979). The following model is obtained: logS(i,t)
= c~ + f J l o g S ( i , t
1) +
-
ru(i,t
-
1) +
(A1)
e(i,t)
where r is the serial correlation coefficient; u(i,t) = ru(i,t - 1) + ¢(i,t); and ¢(i,t) is the standard stochastic error term. Rewriting equation A1 in terms of lagged dependent variable, we have logS(i,t)
= b,, + b ~ l o g S ( i , t
-
1) +
b~_logS(i,t
2) +
-
(A2)
¢(i,t)
where b,~ = (1 - r)~x, bl = [3+r, b2 = -[3r. The inclusion of log S(i,t - 1) and log S(i,t - 2) may cause the possibility of high multicollinearity. To reduce the numerical problems caused by multicollinearity, we add and subtract [3r log S(i,t - 1) from the righthand side of equation A2. Rearranging terms results in the following equation: logS(i,t)
= bo + b 3 1 0 g S ( i , t
-
1) +
b~_[logS(i,t
-
2) -
logS(i,t
-
1)] +
e(i,t)
(A3)
FIRM SIZE AND DYNAMICS IN CHINA
503
N o w b3 = [3+r - [3r (b~ is easily r e c o v e r e d by b3 - b2 = bl), and b2 still equals -[3r. A l t h o u g h , mathematically, e q u a t i o n A 3 is identical to equation A2, equation A3 has a m u c h lower correlation b e t w e e n its first two explanatory variables. W e pool all four industries t o g e t h e r in the estimation and testing. Therefore, an additional set of d u m m y variables D(ij), j = 1..... 4, is a d d e d to e q u a t i o n A3 in order to capture the effects caused by different industries. Finally our equation 3 is obtained from this step. In H1 ([3 = 1), restriction b3 = 1 is imposed: in H2 (r = 0), restriction b2 = 0 is imposed. [3 and r are r e c o v e r e d by solving the following equations: [3 = [bl + x/b~ + 4be]~2, r : [b, - x/Cb~ + 4b,1/2
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