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Education, experience and productivity of labor in China's township and village enterprises: the case of Jiangsu province
EDUCATION, EXPERIENCE AND PRODUCTIVITY OF LABOR IN CHINA'S TOWNSHIP AND VILLAGE ENTERPRISES: THE CASE OF JIANGSU PROVINCE Liu Yunhua, Chew Soon Beng and Li Wenzhi
ABSTRACT: In this paper, we analyze the productivityof different categories of labor and capital by estimating a production function using micro data of 140 industrial township and village enterprises located in 15 countiesin Jiangsuprovincefor the years 1989and 1990. It is estimatedthat the annual marginal productivityof laborwas 19,282 yuan for tertiaryeducated workers and 2,175 yuan for workers with lower educationat 1990prices. It is also found that there is not much difference in marginalproductivity of labor when work experience is considered. Invitedpersonneland joint operations can help to increase the performanceof firms significantly.The estimatedmarginalproductivityof capital was quite high. The rate of return on capital reached about 30%. JEL Classification Numbers: D24, J24, and 012.
I.
INTRODUCTION
The fast growth of township and village enterprises (TVEs) in China is a special phenomenon. A large part of the success is attributed to the market oriented economic reform, which substantially liberalized the potential of economic forces in rural China. However, to further the development, it is necessary to investigate the productivity of input factors to determine the status of production factors usage. Policy implications can then be drawn regarding the existing constraints to production. In particular, how the education level and work experience of the labor force affect labor productivity in China's rural industry should be of special significance in determining the policy on labor training given the abundant labor force in China. Not many research studies have been made on measuring factor productivity in China's rural industry. One early estimation of rural enterprises production function was made by Svejnar (1990), who employed the Cobb-Douglas function with 415 firm level observations in four counties, Wuxi in Jiangsu, Nanhai in Guangdong, Shangrao in Jiangxi and Jieshou in Anhui for the period 1970-1986. The paper focused mostly on the productivity of capital. Another estimation of production function for rural enterprises was made by Chen Direct all correspondence to: Liu Yunhua, Chew Soon Beng and Li Wenzhi, Divisionof Applied Economics,
Nanyang BusinessSchool, B2C-122, NanyangTechnologicalUniversity,NanyangAvenue,Singapore639798; Tel: 65-799-4949;Fax: 65-792-4217;E-Mail:[email protected]. China Economic Review, Volume9, Number 1, 1998, pages 4 7 - 5 8 Copyright © 1998by JA1 Press Inc. All rights of reproduction in any form reserved. ISSN: 1043-951X.
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(I 995) using a data set of 200 large scale rural enterprises from ten provinces for the period 1986-1991. The major focus of his study was also on the rate of return on capital. Liu (1996) estimated the production function of TVEs using provincial totals of TVEs for the period 1991-1994. Both labor productivity and capital productivity were estimated in his work, with the labor productivity being estimated for one category of labor only. In this paper, we analyze the productivity of different categories of labor and capital by estimating a production function using the micro data set of 140 industrial township and village enterprise located in 15 counties in Jiangsu province for the years 1989 and 1990. The two special features of this study are: (1) The attempt to distinguish the different possible effects of education and work experience of employees on labor productivity in China's rural industry; and (2) The attempt to arrive at a better understanding of labor usage through a comparison of the marginal productivity of labor with the labor wage. Jiangsu province is selected for this study since it is one of the leading provinces in rural industrialization in China. The significance of the rural industry in Jiangsu province can be seen from the following major statistics. The total value-added of the output of its rural industry in 1995 was 164.1 billion yuan (Exchange rate in 1994-95:US$l=8.3 Yuan), which accounted for 32% of the provincial total and was above the national level of 25.1%. 1 The number of rural enterprises increased from 75.6 thousand in 1978 to 1.03 million in 1994, while the total employment in rural enterprises was 9.38 million in 1994, almost three times as in 1978. 2 In this empirical work, the primary analytical tool is the Cobb-Douglas production function, with net value of output as the dependent variable and capital and labor as the independent variables. The GLS method is employed with a cross-sectional data set at the enterprise level, dummy variables are used to capture the different characteristics of the rural enterprises. The rest of this study proceeds as follows. Section II outlines the methodology and models of the research; Section III contains a brief description of the data; Section IV presents the estimation of the model and econometric results; Section V carries out the analysis of the estimated results in terms of factor elasticity, dummy variables and marginal products; and the last section concludes the paper.
II.
THE MODEL
As the research focuses on the marginal productivity of inputs, we employ the production function approach as the principal tool of analysis. The Cobb-Douglas production function with capital and labor as inputs is adopted. Differences in enterprise characteristics are captured by dummy variables. The production function is specified as follows.
Y = AK
O~K
L
(XL
exp(~3D)
(l)
where Y is output, A is technical parameter, K is capital, L is total employment, D is a column vector of dummy variables, ~ is a row vector of coefficients of dummy variables, and O~K a n d ~L are output elasticity of capital and labor, respectively. The generalized form of Cobb-Douglas production function is expressed as follows,
49
Case of Jiangsu Province
n ai
Y = A I-I x i exp(•D)
(2)
i=1
where X i represents the ith input. Differentiating equation (2) with respect to each input X i yields the marginal product of each input: ~Y
Y
The available data allow us to decompose the labor input into two categories with two alternatives, which can provide more information on labor productivity. One form of the labor decomposition is based on the education level of employees, i.e., labor with advanced education, LI, and labor with low education level, L2. The other form of labor decomposition is based on the work experience of employees, L(1) for less work experience and L(2) for sophisticated work experience. The Cobb-Douglas production function for estimation takes the following form:
Y = A K "LI
~'L2 ~'exp[([~D)el
Y = A K a r L ( 1 )c~u1)L(2 )aLI-')exp [ ( [~D )e ]
(4)
(5)
where the variable e is an error term assumed iid. The Cobb-Douglas production function imposes a restriction of unitary elasticity of substitution among the inputs. For comparison reason, the flexible translog function will also be estimated. III.
THE D A T A
A set of micro data at the enterprise level was obtained through a survey jointly conducted by Nanjing University and the Institution of Social Science of Jiangsu. 140 industrial township and village enterprises located in 15 counties with different levels of net output were chosen as the sample at random and the data were gathered for the years of 1989 and 1990. The data set covers a variety of aspects and the 77 variables are divided into following eight categories: (1) output value and revenue, (2) employment, (3) capital stock, (4) equipment, (5) investment and technical exploitation, (6) administration and environment, (7) raw material and energy consumed, (8) joint operation. Table 1 shows the sample means of employment, capital, and gross value of output. The average labor and capital productivity and capital/labor ratio are also listed in the table, which roughly parallel the official data for the rural enterprises in Jiangsu province as reported in the China Statistical Yearbook 1990. The gross value of output in the sampled enterprises in 1989 and 1990 ranged from a high of 123 million yuan to a low of 120 thousand yuan in 1990 prices (Exchange rate in 1990:US$1=5.22 Yuan). Employment ranged from 2,126 workers to 12 workers.
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Table 1 Sample Means of the Surveyed 140 TVEs (1990 Prices)
Employment L (person) Capital K (million yuan)* Net value of output (million yuan) Gross value of output (million yuan) Gross value of output/per worker (thousand yuan) Capital/labor ratio (thousand yuan/per worker) Note:
1989
1990
200 3.08 1.36 6.71 30.2 15.1
215 3.70 1.67 8.34 36.0 16.7
* Net value of fixed capital plus working capital.
Sources:
The survey by Nanjing University and the Institution of Social Science of Jiangsu, 1991.
Total capital ranged from 39.8 million yuan to 29 thousand yuan at 1990 prices. Average labor productivity, defined as gross output value per worker, varied tremendously over the surveyed enterprises, and ranged from 385 thousand yuan per worker to 1.25 thousand yuan per worker. The capital labor ratio ranged from 1.34 thousand yuan to 68.2 thousand yuan.
IV.
ESTIMATION
In selecting the empirical production function, we experimented with alternative specifications of the basic function, the form of the dependent variable and the categories of labor. Ultimately, two versions of Cobb-Douglas production function are adopted since there are two approaches to categorize the labor. Translog functions are estimated only for comparison.
Dependent Variable The net value of output rather than the gross value of output was chosen to be the dependent variable simply because in doing so comparisons are made possible between the costs of inputs and the marginal products of inputs derived from the production function. 3 The dependent variable is measured in monetary terms in million yuan and evaluated at 1990 prices on the grounds that a variety of rural industries producing different kinds of products do not allow aggregation in terms of physical units.
Regressors Based on the belief that net output is generated by the quantities of services of factors of production, we choose capital and labor as the principal regressors and materials inputs are excluded (Caves & Barton, 1991). Capital K is the sum of the net value of fixed assets and the working capital (evaluated at 1990 prices), measured in million yuan. We consider working capital as part of the total capital which also provides services in generating the net output. In doing so, the services provided by material inputs in fact are considered in a different way.
Case of Jiangsu Province
51
Labor is measured by number of employees. Although a better and more accurate measure of labor would be the number of man-hours, adequate information is not available for each firm. The labor quality is assumed to be homogenous. L 1 is the total number employed who have finished tertiary study at universities, colleges or institutions, etc., representing the labor cohort with advanced education. L2 is the total number employed whose education is below tertiary level, including both secondary & primary education and even no schooling. To better determine the effect of education on labor, we initially attempt to extract the laborers who had only finished secondary education as a separate labor input. Its standard deviation, however, is too large to ensure the accuracy of its estimation. L(I) is the total number employed who have worked for less than 10 years, representing less experienced labor. L(2) is the total number employed whose work experience is 10 years or above, representing the sophisticated labor group.
Dummy Variables To capture the differences in TVEs operations and characteristics, two dummy variables are employed, the invited personnel and the joint operation. In the course of selecting dummy variables, experiments were also conducted regarding the firm size, location, and the number of modern management methods employed in the firm. However, these three dummies are dropped from the regression due to insignificance. Invited personnel is the dummy variable equal to 1 if the enterprise invited at least one managerial or technical personnel from outside the enterprise, and to 0 otherwise. Joint operation is the dummy variable equal to 1 when there is at least one entity with which the enterprise established cooperation, and to 0 otherwise. Joint operation refers to cooperation in terms of either production or technology between the enterprise and other entities such as state-owned enterprises, urban collective-owned enterprises, other township and village enterprises as well as scientific or educational institutes. Four models are estimated in total: models (1) and (2) for Cobb-Douglas and translog functions respectively by using the regressors of capital K, labor with categories of more educated labor L1 and less educated labor L2; models (3) and (4) for Cobb-Douglas and translog functions respectively by using the regressors of capital K, labor with categories of more experienced labor L(1) and less experienced labor L(2). The same dummy variables are included in all estimation. The estimated results of models (1) and (2) are reported in Table 2, while models (3) and (4) are in Table 3. Heteroskedasticity was tested by Breusch-Pagan regression and found existent. 4 Generalized least square (GLS) was then employed to correct the problem. Because of the short two years time span, serial correlation is not checked. Nevertheless, the time effect is examined by estimating the coefficient of a time dummy variable, which was not significant and dropped. The F-test for parameters stability does not reject the hypothesis that the coefficients of factors for 1989 and 1990 are the same. 5 From Tables 2 and 3, we can see that all of the estimated parameters of Cobb-Douglas function in model (1) and model (3) are statistically significant with the right signs. All the four models achieve a very good adjusted R 2 value. One good point of the estimation in Cobb-Douglas functions is that the estimated result for the capital input is quite stable no
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Table 2 Estimated Results for Cobb-Douglas Production Function (Based on Labor by Education Level) Cobb-Douglas produc~onfunc6on
Translog production function
Independent Variables
Model (1)
Model (2)
Constant LnK LnL 1 LnL2 (LnK) 2 (LnL1) 2 (LnL2) 2 LnK*InLI LnK*InL2 LnL 1*lnL2 Invited personnel Joint operation Sum of input elasticity Sample size Adj R2
~).541 ( - 1.92) * 0.638(12~0) 0.090(2.19) 0.262(4.05)
-0.084(-0.072) 0.743( 1.58) 0.40(1.14) -0.15(--0.30) -0.005(-0.09) --0.019(-0.53) 0.015(0.20) -0.105(-1.09) 0.024(0.25) 0.084( 1.09) 0.178(2.45) 0.314(4.16)
No~s:
0.205(2.47) 0.332(4.11) 0.99 189 0.842
The mean of estimated output~input elasticity** 0.658 0.199 0,255
1.112 189 0.843
* Numbers in parenthesesare t-values, ** The mean of output/inputelasticity is the mean value of elasticity of output with respect to inputs evaluated at every observation of the output and inputs,calculated by (~)Q/Q).t(o3Xi/Xi).
Table 3 Estimated Cobb-Douglas Production Function and Translog Production Function (Based on Labor by Work Experience) Cobb-Douglas production function
Translog production function
Independent Variables
Model (3)
Model (4)
Constant LnK LnL(1) LnL(2) (LnK) 2 (LnL(1))2 (LnL(2)) 2 LnK*InL 1 LnK*InL2 LnL 1*lnL2 Invited personnel Joint operation Sum of input elasticity Sample size Adj R 2
-0,844(-4.97)* 0.655(16.27) 0.257(5.33) 0.130(4.02)
-0.281(-0.41) 0.644(2.42) 0.081 (0.36) -0.085(-0.38) 0.073(2.10) 0.085(2.89) 0.093(4.13) -0.104(-2.01 ) -0,055(- 1.30) -0.038(- 1.10) 0.274(4.01 ) 0.271 (4.39)
No~s:
0.235(3.28) 0.176(2.314) 1.042 228 0.857
The mean of estimated output/input elasticity** 0.727 0.184 0.135
1.046 228 0.910
* Numbers in parenthesesare t-values. ** The mean of output/input elasticity is the mean value of elasticity of output with respect to inputs evaluated at every observation of the output and inputs, calculated by (G3Q/Q)/(OXi/Xi).
Case of Jiangsu Province
53
matter how the labor is classified. The stable estimate of capital input also means stable estimates of labor inputs, which makes the inference of the contribution of different categories of labor possible. In the estimated translog models, though some of the parameters are not significant as commonly predicted, the calculated elasticities of output with respect to inputs are very supportive to the estimated values of elasticity in the Cobb-Douglas functions. Considering the consistency of the two types of estimated functions, our inferences are then based on the results of Cobb-Douglas functions for the purpose of convenience.
V.
ANALYSIS OF THE EMPIRICAL RESULTS Economy of Scale and Factor Elasticity
In the case of model (1), there is no evidence of economies or diseconomies of scale. The sum of the Cobb-Douglas input elasticities is 0.993, which is close to unity. At the same time, after using an F-test to test constant returns to scale, we find that its value of 0.0651 fails to reject the null hypothesis that rural enterprises in Jiangsu are characterized by constant returns to scale. 6 Where the estimated output elasticity of each input is concerned, the share of capital in net output is 0.638 with a very significant t-value, which shows that capital has the most important contribution to net output. This value is also close to those estimated by other reports. 7 The estimated output elasticity of labor as a whole in net output is 0.352, of which 0.090 goes to employees who have tertiary education and the other 0.262 to employees who have no education or at most finished secondary school. In other words, the contribution of employees with low levels of education to the net output of Jiangsu's rural enterprises is nearly three times as much as that of employees with advanced education due to a dominant proportion of the labor force belonging to the former cohort, since the ratio of the employment of the tertiary educated workers to total employment is only 3%. Similarly, as with model (3), the sum of input elasticities is 1.042, and the F-test value, 1.411, strongly supporting constant returns to scale in TVEs production. The estimated output elasticity of capital stock in net output, 0.655, is also quite close to the one estimated in model (1). The estimated output elasticity of employees whose work experience was 10 years or above is 0.130, while the share of employees who have relatively less work experience is 0.257. Labor's share in net output as a whole, 0.387, is near the one calculated from model (1). It is obvious that less experienced workers contributed nearly twice as much to the net output as relatively sophisticated employees. The ratio of experienced labor to total employment is 30%. This means that there is not much difference in the contribution to output of each experienced or non-experienced worker.
Marginal Productivity of Capital and Different Laborers Using the Cobb-Douglas function results of models (1) and (3), marginal productivity of all inputs are evaluated at the sznnple means. 8 The marginal product of capital in
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Table 4 Marginal Productivity of Capital and Labor (in 1990 Prices) K- rate of return on capital Ll-tertiary educated worker(yuan/workerper year L2-1esseducated workers (yuan/workerper year) L(1)-less experienced workers(yuan/workerper year L(2)-more experienced workers(yuan/workerper person)
Model (1)
Model (3)
0.296 19,282 2,175
0.306 2,755
model (1), 0.296, is very close to the one derived from model (3), 0.306, which suggests a very high rate of return on capital investment compared to the nominal loan rate of 9.22% from banks in 1990. 9 The estimated rate of reiurn on the capital, however, is gross of depreciation. The other estimates on the rate of return on capital in TVEs and in similar China enterprises are 0.145 in Liu (1996) for TVEs in China for the years 1991 to 1994, 0.38 in Fleisher et al (1996) for the paper industry for the years 1985, 1987 and 1990, and 0.058 in Svejnar (1990) for TVEs in four counties of different provinces for the years 1981 to 1986 (Table 4). Considering the inflation rate of 2.1% in 1990 in China, 10 the real loan rate was even lower. The considerable gap between the estimated marginal product of capital and the low real loan rate implies that obtaining capital from state banks could bring a fat profit to the rural enterprises. Credit rationing was obviously a consequence of the non-marketed state banks. In fact, TVEs have been borrowing from the private sector at a very high rate, sometimes said to be 20%, but data are not available in this regard. The high rate of return to capital reflects the fact that capital availability was a great constraint to TVEs in China in the late 1980s and early 1990s. In model (1), the marginal product of labor with tertiary education calculated from the estimates is 19,282 yuan per worker per year at 1990 prices, evaluated at the sample means. The calculated marginal product of employees who have low-level education is 2,175 yuan at 1990 price. In other words, a highly educated employee is approximately nine times as productive as a relatively less educated employee. Fleisher et al (1996) estimated that the marginal product of the educated and professional workers was more than 50 times that of the high school graduates in state owned paper industry. Apparently, rural enterprises can benefit greatly from employing more laborers with advanced education since the wages of these laborers are far below what they can contribute. As for the comparison between experienced and non-experienced workers in model (3), using a similar approach, we estimated that the marginal product of the labor with less than ten years of work experience is 2,755 yuan per worker per year at 1990 prices, while the marginal product of employees whose work experience was ten years or above is 3,015 yuan. The closeness of the two marginal products does not strongly support the hypothesis that the employee with more years of work experience is more productive than the employee with less work experience. The reason could be due to the fact that less experienced workers are usually younger and possess more mental and physical vigor and can produce as much as those who have worked for 10 years or more. Usually, due to the simplicity of the production process of rural enterprises, the newly employed workers can catch up within one year or even a few months. However, the available data do not allow
Case of Jiangsu Province
55
Table 5 Invited Personnel for the Years of 1989 and 1990 for the 140 Sampled Enterprises Years
1989
1990
Personnel Holding a Title General Technical Personnel Managerial Personnel Miscellaneous Total Invited Personnel As % of Total Employment
148 168 63 14 380 1.36
171 159 75 2 414 1.37
us to separate these workers as an independent variable. The average annual wage of workers in TVEs in Jiangsu province in 1990 was 1,245 yuan. ll The difference between the high marginal product of labor and the low average wage shows that more labor could be hired in TVEs in 1990.
Impact of Invited Personnel and Joint Operations In both model (1) and model (3), the estimated parameters of the invited personnel dummy variable are positive and significant. This indicates that the invited personnel has a positive impact on the performance of rural enterprises and they did generate what were expected from them to the corresponding host firms. Invited personnel usually are highly trained and experienced personnel in managerial, financial and technical fields employed from outside the enterprise temporarily and are characterized by practical skills which rural enterprises need urgently. Based on our estimation in model (1), invited personnel can lead to an increase in TVEs net output of about 20-23%, j2 but invited personnel employment is only 1.4 per cent of total employment of the enterprises in our 1990 sample. However, it is quite difficult for rural enterprises to engage these personnel because the majority of them are living in the big cities and prefer to stay at state-owned enterprises where they enjoy a wide range of benefits. Personnel already retired from state-owned enterprises constitute a relatively fertile recruiting ground for rural enterprises. The number of invited personnel of different categories in our 140 sampled rural enterprises are presented in Table 5. Another dummy variable captures the effect of joint operations with other enterprises. The significance of the estimated parameter in model (1) and model (2) suggests that joint cooperation with other entities can markedly increase the net output of rural enterprises by about 18-33%. With cooperation, rural enterprises can easily get access to more practical and advanced technology, experience and know-how, which are important elements for production. In order not to fall behind other enterprises in the same production line, rural enterprises need to break the limitation geographically and search all over the nation for the opportunity to cooperate with state-owned enterprises, urban collective-owned enterprises, and other scientific or educational institutes. Among the sampled enterprises, the distribution of joint operations is listed in Table 6.
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Table 6 Distribution of TVEs with or without Joint Operations in 1989 and 1990 Years
TVEs withoutjoint operation TVEs with onejoint operation partner TVEs with morethan one joint operation partner
Vl.
1989
1990
102 28 10
101 28 l1
CONCLUDING REMARKS
Through the case of Jiangsu province, we estimated the factor productivity in township and village enterprises which constitute the most dynamic part in Chinese economy. Based on the estimated results, a short summary and policy implications are provided as follows. 1.
2.
3.
4.
In TVEs production in Jiangsu province, the high value of output elasticity of capital, 64%, indicates the strong capital constraint to TVEs production in early 1990s, i. e., an increase in capital could lead to more output increase. The output elasticity of labor only has a value about 35%, consistent with the fact of labor abundance in China. Constant returns to scale is proved to exist. The marginal product of capital on the average is 29.6%, which is quite a high rate of return on capital and is much higher than the loan rate of state owned banks. Such a high rate confirms the fact of capital shortage in China and capital availability is an effective constraint on TVEs development. The national average level of governmental capital in TVEs was 45% in 1992 (Liu 1996). TVEs in Jiangsu province achieved a level of self-funding of their investment at about 60% (Liu et al, 1996). But liberalizing the financial market should still be encouraged to form a relatively decentralized financial market to facilitate the efficient use of capital resources. Consistent with the common belief, highly educated labor contributes much more than those who have less advanced education. The marginal product of tertiary educated labor is 19,282 yuan per year, while the marginal product of labor with less than tertiary education is only 2,175 yuan per year in 1990. Labor with more than ten years' work experience does not have much more effect on the production than labor with less work experience; their marginal productivities are 3,015 yuan and 2,755 yuan respectively in 1990. Even though there is a gap between the estimated marginal product of labor with less education and the average wage, labor employment in TVEs in Jiangsu province in fact did not increase very much; the total employment of TVEs only increased by 4.3% from 1991 to 1993 (Liu, 1996). It is obvious that there is abundant less educated labor available. In fact, in Liu's (1996) estimation, the marginal product of labor and the average wage are statistically equal. However, the high marginal product of tertiary educated labor shows that highly educated workers are still needed by TVEs since their average wage could not be ten times that of general workers. Special training of the labor force to fit the needs of TVEs and encouraging policy for tertiary educated people to work in TVEs are therefore suggested. One of the outcomes of the study is that we found that invited personnel in TVEs had a significant impact on the production such that TVEs with invited per-
Case of Jiangsu Province
57
sonnel output increased by about 20%. This m e a n s that a free labor market could facilitate the efficiency o f labor usage and necessary reforms should be carried out to foster such a circumstance. A n o t h e r observation is that we found j o i n t operation with other enterprises to have a very significant impact on the output of the T V E s .
NOTES Beginning from 1996, value added output of TVEs for provinces is reported in China Statistical Yearbook. In 1995, Jiangsu's GDP was 515.5 billion yuan, TVEs value added output was 164. ! billion yuan. China Statistical Year Book 1996, p. 43, p.390. 2. Township and Village Enterprises Yearbook of China 1995, p. 87-89, and Statistical Yearbook of Jiangsu Province 1992. 3. A common belief about choosing the dependent variable is that when gross output is used, inputs should include capital, labor and materials, while when net output is chosen as dependent variable, materials should be excluded from the inputs. When gross output value is used as dependent variable, we also calculate the marginal products of inputs. However, the fact that each value of marginal product is much higher than the corresponding cost of each input because gross output in itself encompasses a large proportion of intermediate inputs such as raw materials and energy makes the comparison not meaningful. The data for material inputs in the survey are not usable. 4. Maddala, p. 162. Glejser test for heteroskedasticity: The left-hand dependent variable is the absolute value of predicted residuals, the right-hand regressors selected are right-hand variables of the Cobb-Douglas function reported in Tables 2 and 3. The null hypothesis is to test Ho: a I = a 2 . . . . . a r = 0, where r is the number of regressors. The estimated parameters for logK is -0.0474 with t-value -1.343, logL1 is 0.023 with t-value 0.768, logL2 is 4). 107 with t-value -2.53, R 2 is 0.113. N i s 189. 5. Maddala, p. 130. The F-test for parameters stability is conducted as follows. F=[(RRSS-URSS)/(k+I)]/[URSS/(nI+n2.2k-2)], where RRSS is the residual sum of squares for the entire period, URSS is the sum of residual sum of squares for the separate two years 1989 and 1990. k is the number of variables, here k=5. nt=91, n2=98. The calculated F=1.26<2.8 (6, 177, 1% significance level). 6. The F-test for the economy scale is done by SAS program automatically specified as a restriction test. Test: H0: K + LI + L2 = 12, The Numerator: 0.1133 DF: 1, Denominator: 1.739373 DF: 183, F value = 0.0651 < 6.63 (1% significance level). See Maddala, pp. 119121. 7. In Liu's (1996) estimates, the capital share of TVEs in net output is 0.55. 8. There are two approaches to calculate marginal products. One method is to derive the individual marginal product for each sampled firm, and then to calculate the mean of marginal product of each firm. Alternatively, we can first calculate sample means of net output and all inputs with which marginal product can be derived. In our empirical work, we employ the latter one because the former one incurs an upward bias. 9. This loan rate is estimated weighted average nominal loan rate. See Liu, 1996. 10. China Statistical Yearbook 1996, p.255. 1 I. Statistical Yearbook (~Jiangsu Province 1992. p. 149. 12. The estimated parameter of the dummy variable is the percentage in output. The calculation is as follows. Y=AK~XLBe~x, dY/dD=yAKaL~:~e'/~=yY, (dY/dD)/yY=. 1.
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REFERENCES Caves, R.E. & Barton, D.R. (1991). Efficiency in US manufacturing industries, p.21-25. Chen J. (1995). Structure of property rights in village and town-owned enterprises and its effects on efficiency of resources allocation. JingJi Yanjiu (Economic Research Journal), 3, 35-39. Beijing, China. China State Statistical Bureau. (1996). China statistical year book 1996, p.43, p.390. Beijing, China: China Statistical Publishing House. Fleisher, B.M., Dong, K. & Liu, Y. (1996). Education, enterprise organization and productivity in the Chinese paper industry. Economic Development and Cultural Change, 44(3) April. Liu, Y. (1996). Capital formation, productivity of capital and labor and labor absorption in China's township and village enterprises. Presented in the Institute of East Asian Political Economy, Singapore, March. Liu, Y. & Zhibiao, L. (1996). The comparative advantages of township and village enterprises in Suzhou, Wuxi and Changzhou in Jiangsu Province. In T.T. Meng, H. Yinxing, & C. Yong (Eds.), Jiangsu economy in China's regional development. (Chinese version) pp. 120-139. Maddala, G.S. (1988). Introduction to econometrics, first edition. Statistical Yearbook of Jiangsu Province 1992. Svejnar, J. (1990). Productive efficiency and Employment. In A. William & L. Qingsong (Eds.), China's rural industry: Structure, development, and reform, Byrd, Oxford University Press. Township and Village Enterprises Yearbook of China 1995, p. 87-89.
ABSTRACT: In this paper, we analyze the productivityof different categories of labor and capital by estimating a production function using micro data of 140 industrial township and village enterprises located in 15 countiesin Jiangsuprovincefor the years 1989and 1990. It is estimatedthat the annual marginal productivityof laborwas 19,282 yuan for tertiaryeducated workers and 2,175 yuan for workers with lower educationat 1990prices. It is also found that there is not much difference in marginalproductivity of labor when work experience is considered. Invitedpersonneland joint operations can help to increase the performanceof firms significantly.The estimatedmarginalproductivityof capital was quite high. The rate of return on capital reached about 30%. JEL Classification Numbers: D24, J24, and 012.
I.
INTRODUCTION
The fast growth of township and village enterprises (TVEs) in China is a special phenomenon. A large part of the success is attributed to the market oriented economic reform, which substantially liberalized the potential of economic forces in rural China. However, to further the development, it is necessary to investigate the productivity of input factors to determine the status of production factors usage. Policy implications can then be drawn regarding the existing constraints to production. In particular, how the education level and work experience of the labor force affect labor productivity in China's rural industry should be of special significance in determining the policy on labor training given the abundant labor force in China. Not many research studies have been made on measuring factor productivity in China's rural industry. One early estimation of rural enterprises production function was made by Svejnar (1990), who employed the Cobb-Douglas function with 415 firm level observations in four counties, Wuxi in Jiangsu, Nanhai in Guangdong, Shangrao in Jiangxi and Jieshou in Anhui for the period 1970-1986. The paper focused mostly on the productivity of capital. Another estimation of production function for rural enterprises was made by Chen Direct all correspondence to: Liu Yunhua, Chew Soon Beng and Li Wenzhi, Divisionof Applied Economics,
Nanyang BusinessSchool, B2C-122, NanyangTechnologicalUniversity,NanyangAvenue,Singapore639798; Tel: 65-799-4949;Fax: 65-792-4217;E-Mail:[email protected]. China Economic Review, Volume9, Number 1, 1998, pages 4 7 - 5 8 Copyright © 1998by JA1 Press Inc. All rights of reproduction in any form reserved. ISSN: 1043-951X.
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(I 995) using a data set of 200 large scale rural enterprises from ten provinces for the period 1986-1991. The major focus of his study was also on the rate of return on capital. Liu (1996) estimated the production function of TVEs using provincial totals of TVEs for the period 1991-1994. Both labor productivity and capital productivity were estimated in his work, with the labor productivity being estimated for one category of labor only. In this paper, we analyze the productivity of different categories of labor and capital by estimating a production function using the micro data set of 140 industrial township and village enterprise located in 15 counties in Jiangsu province for the years 1989 and 1990. The two special features of this study are: (1) The attempt to distinguish the different possible effects of education and work experience of employees on labor productivity in China's rural industry; and (2) The attempt to arrive at a better understanding of labor usage through a comparison of the marginal productivity of labor with the labor wage. Jiangsu province is selected for this study since it is one of the leading provinces in rural industrialization in China. The significance of the rural industry in Jiangsu province can be seen from the following major statistics. The total value-added of the output of its rural industry in 1995 was 164.1 billion yuan (Exchange rate in 1994-95:US$l=8.3 Yuan), which accounted for 32% of the provincial total and was above the national level of 25.1%. 1 The number of rural enterprises increased from 75.6 thousand in 1978 to 1.03 million in 1994, while the total employment in rural enterprises was 9.38 million in 1994, almost three times as in 1978. 2 In this empirical work, the primary analytical tool is the Cobb-Douglas production function, with net value of output as the dependent variable and capital and labor as the independent variables. The GLS method is employed with a cross-sectional data set at the enterprise level, dummy variables are used to capture the different characteristics of the rural enterprises. The rest of this study proceeds as follows. Section II outlines the methodology and models of the research; Section III contains a brief description of the data; Section IV presents the estimation of the model and econometric results; Section V carries out the analysis of the estimated results in terms of factor elasticity, dummy variables and marginal products; and the last section concludes the paper.
II.
THE MODEL
As the research focuses on the marginal productivity of inputs, we employ the production function approach as the principal tool of analysis. The Cobb-Douglas production function with capital and labor as inputs is adopted. Differences in enterprise characteristics are captured by dummy variables. The production function is specified as follows.
Y = AK
O~K
L
(XL
exp(~3D)
(l)
where Y is output, A is technical parameter, K is capital, L is total employment, D is a column vector of dummy variables, ~ is a row vector of coefficients of dummy variables, and O~K a n d ~L are output elasticity of capital and labor, respectively. The generalized form of Cobb-Douglas production function is expressed as follows,
49
Case of Jiangsu Province
n ai
Y = A I-I x i exp(•D)
(2)
i=1
where X i represents the ith input. Differentiating equation (2) with respect to each input X i yields the marginal product of each input: ~Y
Y
The available data allow us to decompose the labor input into two categories with two alternatives, which can provide more information on labor productivity. One form of the labor decomposition is based on the education level of employees, i.e., labor with advanced education, LI, and labor with low education level, L2. The other form of labor decomposition is based on the work experience of employees, L(1) for less work experience and L(2) for sophisticated work experience. The Cobb-Douglas production function for estimation takes the following form:
Y = A K "LI
~'L2 ~'exp[([~D)el
Y = A K a r L ( 1 )c~u1)L(2 )aLI-')exp [ ( [~D )e ]
(4)
(5)
where the variable e is an error term assumed iid. The Cobb-Douglas production function imposes a restriction of unitary elasticity of substitution among the inputs. For comparison reason, the flexible translog function will also be estimated. III.
THE D A T A
A set of micro data at the enterprise level was obtained through a survey jointly conducted by Nanjing University and the Institution of Social Science of Jiangsu. 140 industrial township and village enterprises located in 15 counties with different levels of net output were chosen as the sample at random and the data were gathered for the years of 1989 and 1990. The data set covers a variety of aspects and the 77 variables are divided into following eight categories: (1) output value and revenue, (2) employment, (3) capital stock, (4) equipment, (5) investment and technical exploitation, (6) administration and environment, (7) raw material and energy consumed, (8) joint operation. Table 1 shows the sample means of employment, capital, and gross value of output. The average labor and capital productivity and capital/labor ratio are also listed in the table, which roughly parallel the official data for the rural enterprises in Jiangsu province as reported in the China Statistical Yearbook 1990. The gross value of output in the sampled enterprises in 1989 and 1990 ranged from a high of 123 million yuan to a low of 120 thousand yuan in 1990 prices (Exchange rate in 1990:US$1=5.22 Yuan). Employment ranged from 2,126 workers to 12 workers.
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Table 1 Sample Means of the Surveyed 140 TVEs (1990 Prices)
Employment L (person) Capital K (million yuan)* Net value of output (million yuan) Gross value of output (million yuan) Gross value of output/per worker (thousand yuan) Capital/labor ratio (thousand yuan/per worker) Note:
1989
1990
200 3.08 1.36 6.71 30.2 15.1
215 3.70 1.67 8.34 36.0 16.7
* Net value of fixed capital plus working capital.
Sources:
The survey by Nanjing University and the Institution of Social Science of Jiangsu, 1991.
Total capital ranged from 39.8 million yuan to 29 thousand yuan at 1990 prices. Average labor productivity, defined as gross output value per worker, varied tremendously over the surveyed enterprises, and ranged from 385 thousand yuan per worker to 1.25 thousand yuan per worker. The capital labor ratio ranged from 1.34 thousand yuan to 68.2 thousand yuan.
IV.
ESTIMATION
In selecting the empirical production function, we experimented with alternative specifications of the basic function, the form of the dependent variable and the categories of labor. Ultimately, two versions of Cobb-Douglas production function are adopted since there are two approaches to categorize the labor. Translog functions are estimated only for comparison.
Dependent Variable The net value of output rather than the gross value of output was chosen to be the dependent variable simply because in doing so comparisons are made possible between the costs of inputs and the marginal products of inputs derived from the production function. 3 The dependent variable is measured in monetary terms in million yuan and evaluated at 1990 prices on the grounds that a variety of rural industries producing different kinds of products do not allow aggregation in terms of physical units.
Regressors Based on the belief that net output is generated by the quantities of services of factors of production, we choose capital and labor as the principal regressors and materials inputs are excluded (Caves & Barton, 1991). Capital K is the sum of the net value of fixed assets and the working capital (evaluated at 1990 prices), measured in million yuan. We consider working capital as part of the total capital which also provides services in generating the net output. In doing so, the services provided by material inputs in fact are considered in a different way.
Case of Jiangsu Province
51
Labor is measured by number of employees. Although a better and more accurate measure of labor would be the number of man-hours, adequate information is not available for each firm. The labor quality is assumed to be homogenous. L 1 is the total number employed who have finished tertiary study at universities, colleges or institutions, etc., representing the labor cohort with advanced education. L2 is the total number employed whose education is below tertiary level, including both secondary & primary education and even no schooling. To better determine the effect of education on labor, we initially attempt to extract the laborers who had only finished secondary education as a separate labor input. Its standard deviation, however, is too large to ensure the accuracy of its estimation. L(I) is the total number employed who have worked for less than 10 years, representing less experienced labor. L(2) is the total number employed whose work experience is 10 years or above, representing the sophisticated labor group.
Dummy Variables To capture the differences in TVEs operations and characteristics, two dummy variables are employed, the invited personnel and the joint operation. In the course of selecting dummy variables, experiments were also conducted regarding the firm size, location, and the number of modern management methods employed in the firm. However, these three dummies are dropped from the regression due to insignificance. Invited personnel is the dummy variable equal to 1 if the enterprise invited at least one managerial or technical personnel from outside the enterprise, and to 0 otherwise. Joint operation is the dummy variable equal to 1 when there is at least one entity with which the enterprise established cooperation, and to 0 otherwise. Joint operation refers to cooperation in terms of either production or technology between the enterprise and other entities such as state-owned enterprises, urban collective-owned enterprises, other township and village enterprises as well as scientific or educational institutes. Four models are estimated in total: models (1) and (2) for Cobb-Douglas and translog functions respectively by using the regressors of capital K, labor with categories of more educated labor L1 and less educated labor L2; models (3) and (4) for Cobb-Douglas and translog functions respectively by using the regressors of capital K, labor with categories of more experienced labor L(1) and less experienced labor L(2). The same dummy variables are included in all estimation. The estimated results of models (1) and (2) are reported in Table 2, while models (3) and (4) are in Table 3. Heteroskedasticity was tested by Breusch-Pagan regression and found existent. 4 Generalized least square (GLS) was then employed to correct the problem. Because of the short two years time span, serial correlation is not checked. Nevertheless, the time effect is examined by estimating the coefficient of a time dummy variable, which was not significant and dropped. The F-test for parameters stability does not reject the hypothesis that the coefficients of factors for 1989 and 1990 are the same. 5 From Tables 2 and 3, we can see that all of the estimated parameters of Cobb-Douglas function in model (1) and model (3) are statistically significant with the right signs. All the four models achieve a very good adjusted R 2 value. One good point of the estimation in Cobb-Douglas functions is that the estimated result for the capital input is quite stable no
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Table 2 Estimated Results for Cobb-Douglas Production Function (Based on Labor by Education Level) Cobb-Douglas produc~onfunc6on
Translog production function
Independent Variables
Model (1)
Model (2)
Constant LnK LnL 1 LnL2 (LnK) 2 (LnL1) 2 (LnL2) 2 LnK*InLI LnK*InL2 LnL 1*lnL2 Invited personnel Joint operation Sum of input elasticity Sample size Adj R2
~).541 ( - 1.92) * 0.638(12~0) 0.090(2.19) 0.262(4.05)
-0.084(-0.072) 0.743( 1.58) 0.40(1.14) -0.15(--0.30) -0.005(-0.09) --0.019(-0.53) 0.015(0.20) -0.105(-1.09) 0.024(0.25) 0.084( 1.09) 0.178(2.45) 0.314(4.16)
No~s:
0.205(2.47) 0.332(4.11) 0.99 189 0.842
The mean of estimated output~input elasticity** 0.658 0.199 0,255
1.112 189 0.843
* Numbers in parenthesesare t-values, ** The mean of output/inputelasticity is the mean value of elasticity of output with respect to inputs evaluated at every observation of the output and inputs,calculated by (~)Q/Q).t(o3Xi/Xi).
Table 3 Estimated Cobb-Douglas Production Function and Translog Production Function (Based on Labor by Work Experience) Cobb-Douglas production function
Translog production function
Independent Variables
Model (3)
Model (4)
Constant LnK LnL(1) LnL(2) (LnK) 2 (LnL(1))2 (LnL(2)) 2 LnK*InL 1 LnK*InL2 LnL 1*lnL2 Invited personnel Joint operation Sum of input elasticity Sample size Adj R 2
-0,844(-4.97)* 0.655(16.27) 0.257(5.33) 0.130(4.02)
-0.281(-0.41) 0.644(2.42) 0.081 (0.36) -0.085(-0.38) 0.073(2.10) 0.085(2.89) 0.093(4.13) -0.104(-2.01 ) -0,055(- 1.30) -0.038(- 1.10) 0.274(4.01 ) 0.271 (4.39)
No~s:
0.235(3.28) 0.176(2.314) 1.042 228 0.857
The mean of estimated output/input elasticity** 0.727 0.184 0.135
1.046 228 0.910
* Numbers in parenthesesare t-values. ** The mean of output/input elasticity is the mean value of elasticity of output with respect to inputs evaluated at every observation of the output and inputs, calculated by (G3Q/Q)/(OXi/Xi).
Case of Jiangsu Province
53
matter how the labor is classified. The stable estimate of capital input also means stable estimates of labor inputs, which makes the inference of the contribution of different categories of labor possible. In the estimated translog models, though some of the parameters are not significant as commonly predicted, the calculated elasticities of output with respect to inputs are very supportive to the estimated values of elasticity in the Cobb-Douglas functions. Considering the consistency of the two types of estimated functions, our inferences are then based on the results of Cobb-Douglas functions for the purpose of convenience.
V.
ANALYSIS OF THE EMPIRICAL RESULTS Economy of Scale and Factor Elasticity
In the case of model (1), there is no evidence of economies or diseconomies of scale. The sum of the Cobb-Douglas input elasticities is 0.993, which is close to unity. At the same time, after using an F-test to test constant returns to scale, we find that its value of 0.0651 fails to reject the null hypothesis that rural enterprises in Jiangsu are characterized by constant returns to scale. 6 Where the estimated output elasticity of each input is concerned, the share of capital in net output is 0.638 with a very significant t-value, which shows that capital has the most important contribution to net output. This value is also close to those estimated by other reports. 7 The estimated output elasticity of labor as a whole in net output is 0.352, of which 0.090 goes to employees who have tertiary education and the other 0.262 to employees who have no education or at most finished secondary school. In other words, the contribution of employees with low levels of education to the net output of Jiangsu's rural enterprises is nearly three times as much as that of employees with advanced education due to a dominant proportion of the labor force belonging to the former cohort, since the ratio of the employment of the tertiary educated workers to total employment is only 3%. Similarly, as with model (3), the sum of input elasticities is 1.042, and the F-test value, 1.411, strongly supporting constant returns to scale in TVEs production. The estimated output elasticity of capital stock in net output, 0.655, is also quite close to the one estimated in model (1). The estimated output elasticity of employees whose work experience was 10 years or above is 0.130, while the share of employees who have relatively less work experience is 0.257. Labor's share in net output as a whole, 0.387, is near the one calculated from model (1). It is obvious that less experienced workers contributed nearly twice as much to the net output as relatively sophisticated employees. The ratio of experienced labor to total employment is 30%. This means that there is not much difference in the contribution to output of each experienced or non-experienced worker.
Marginal Productivity of Capital and Different Laborers Using the Cobb-Douglas function results of models (1) and (3), marginal productivity of all inputs are evaluated at the sznnple means. 8 The marginal product of capital in
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Table 4 Marginal Productivity of Capital and Labor (in 1990 Prices) K- rate of return on capital Ll-tertiary educated worker(yuan/workerper year L2-1esseducated workers (yuan/workerper year) L(1)-less experienced workers(yuan/workerper year L(2)-more experienced workers(yuan/workerper person)
Model (1)
Model (3)
0.296 19,282 2,175
0.306 2,755
model (1), 0.296, is very close to the one derived from model (3), 0.306, which suggests a very high rate of return on capital investment compared to the nominal loan rate of 9.22% from banks in 1990. 9 The estimated rate of reiurn on the capital, however, is gross of depreciation. The other estimates on the rate of return on capital in TVEs and in similar China enterprises are 0.145 in Liu (1996) for TVEs in China for the years 1991 to 1994, 0.38 in Fleisher et al (1996) for the paper industry for the years 1985, 1987 and 1990, and 0.058 in Svejnar (1990) for TVEs in four counties of different provinces for the years 1981 to 1986 (Table 4). Considering the inflation rate of 2.1% in 1990 in China, 10 the real loan rate was even lower. The considerable gap between the estimated marginal product of capital and the low real loan rate implies that obtaining capital from state banks could bring a fat profit to the rural enterprises. Credit rationing was obviously a consequence of the non-marketed state banks. In fact, TVEs have been borrowing from the private sector at a very high rate, sometimes said to be 20%, but data are not available in this regard. The high rate of return to capital reflects the fact that capital availability was a great constraint to TVEs in China in the late 1980s and early 1990s. In model (1), the marginal product of labor with tertiary education calculated from the estimates is 19,282 yuan per worker per year at 1990 prices, evaluated at the sample means. The calculated marginal product of employees who have low-level education is 2,175 yuan at 1990 price. In other words, a highly educated employee is approximately nine times as productive as a relatively less educated employee. Fleisher et al (1996) estimated that the marginal product of the educated and professional workers was more than 50 times that of the high school graduates in state owned paper industry. Apparently, rural enterprises can benefit greatly from employing more laborers with advanced education since the wages of these laborers are far below what they can contribute. As for the comparison between experienced and non-experienced workers in model (3), using a similar approach, we estimated that the marginal product of the labor with less than ten years of work experience is 2,755 yuan per worker per year at 1990 prices, while the marginal product of employees whose work experience was ten years or above is 3,015 yuan. The closeness of the two marginal products does not strongly support the hypothesis that the employee with more years of work experience is more productive than the employee with less work experience. The reason could be due to the fact that less experienced workers are usually younger and possess more mental and physical vigor and can produce as much as those who have worked for 10 years or more. Usually, due to the simplicity of the production process of rural enterprises, the newly employed workers can catch up within one year or even a few months. However, the available data do not allow
Case of Jiangsu Province
55
Table 5 Invited Personnel for the Years of 1989 and 1990 for the 140 Sampled Enterprises Years
1989
1990
Personnel Holding a Title General Technical Personnel Managerial Personnel Miscellaneous Total Invited Personnel As % of Total Employment
148 168 63 14 380 1.36
171 159 75 2 414 1.37
us to separate these workers as an independent variable. The average annual wage of workers in TVEs in Jiangsu province in 1990 was 1,245 yuan. ll The difference between the high marginal product of labor and the low average wage shows that more labor could be hired in TVEs in 1990.
Impact of Invited Personnel and Joint Operations In both model (1) and model (3), the estimated parameters of the invited personnel dummy variable are positive and significant. This indicates that the invited personnel has a positive impact on the performance of rural enterprises and they did generate what were expected from them to the corresponding host firms. Invited personnel usually are highly trained and experienced personnel in managerial, financial and technical fields employed from outside the enterprise temporarily and are characterized by practical skills which rural enterprises need urgently. Based on our estimation in model (1), invited personnel can lead to an increase in TVEs net output of about 20-23%, j2 but invited personnel employment is only 1.4 per cent of total employment of the enterprises in our 1990 sample. However, it is quite difficult for rural enterprises to engage these personnel because the majority of them are living in the big cities and prefer to stay at state-owned enterprises where they enjoy a wide range of benefits. Personnel already retired from state-owned enterprises constitute a relatively fertile recruiting ground for rural enterprises. The number of invited personnel of different categories in our 140 sampled rural enterprises are presented in Table 5. Another dummy variable captures the effect of joint operations with other enterprises. The significance of the estimated parameter in model (1) and model (2) suggests that joint cooperation with other entities can markedly increase the net output of rural enterprises by about 18-33%. With cooperation, rural enterprises can easily get access to more practical and advanced technology, experience and know-how, which are important elements for production. In order not to fall behind other enterprises in the same production line, rural enterprises need to break the limitation geographically and search all over the nation for the opportunity to cooperate with state-owned enterprises, urban collective-owned enterprises, and other scientific or educational institutes. Among the sampled enterprises, the distribution of joint operations is listed in Table 6.
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Table 6 Distribution of TVEs with or without Joint Operations in 1989 and 1990 Years
TVEs withoutjoint operation TVEs with onejoint operation partner TVEs with morethan one joint operation partner
Vl.
1989
1990
102 28 10
101 28 l1
CONCLUDING REMARKS
Through the case of Jiangsu province, we estimated the factor productivity in township and village enterprises which constitute the most dynamic part in Chinese economy. Based on the estimated results, a short summary and policy implications are provided as follows. 1.
2.
3.
4.
In TVEs production in Jiangsu province, the high value of output elasticity of capital, 64%, indicates the strong capital constraint to TVEs production in early 1990s, i. e., an increase in capital could lead to more output increase. The output elasticity of labor only has a value about 35%, consistent with the fact of labor abundance in China. Constant returns to scale is proved to exist. The marginal product of capital on the average is 29.6%, which is quite a high rate of return on capital and is much higher than the loan rate of state owned banks. Such a high rate confirms the fact of capital shortage in China and capital availability is an effective constraint on TVEs development. The national average level of governmental capital in TVEs was 45% in 1992 (Liu 1996). TVEs in Jiangsu province achieved a level of self-funding of their investment at about 60% (Liu et al, 1996). But liberalizing the financial market should still be encouraged to form a relatively decentralized financial market to facilitate the efficient use of capital resources. Consistent with the common belief, highly educated labor contributes much more than those who have less advanced education. The marginal product of tertiary educated labor is 19,282 yuan per year, while the marginal product of labor with less than tertiary education is only 2,175 yuan per year in 1990. Labor with more than ten years' work experience does not have much more effect on the production than labor with less work experience; their marginal productivities are 3,015 yuan and 2,755 yuan respectively in 1990. Even though there is a gap between the estimated marginal product of labor with less education and the average wage, labor employment in TVEs in Jiangsu province in fact did not increase very much; the total employment of TVEs only increased by 4.3% from 1991 to 1993 (Liu, 1996). It is obvious that there is abundant less educated labor available. In fact, in Liu's (1996) estimation, the marginal product of labor and the average wage are statistically equal. However, the high marginal product of tertiary educated labor shows that highly educated workers are still needed by TVEs since their average wage could not be ten times that of general workers. Special training of the labor force to fit the needs of TVEs and encouraging policy for tertiary educated people to work in TVEs are therefore suggested. One of the outcomes of the study is that we found that invited personnel in TVEs had a significant impact on the production such that TVEs with invited per-
Case of Jiangsu Province
57
sonnel output increased by about 20%. This m e a n s that a free labor market could facilitate the efficiency o f labor usage and necessary reforms should be carried out to foster such a circumstance. A n o t h e r observation is that we found j o i n t operation with other enterprises to have a very significant impact on the output of the T V E s .
NOTES Beginning from 1996, value added output of TVEs for provinces is reported in China Statistical Yearbook. In 1995, Jiangsu's GDP was 515.5 billion yuan, TVEs value added output was 164. ! billion yuan. China Statistical Year Book 1996, p. 43, p.390. 2. Township and Village Enterprises Yearbook of China 1995, p. 87-89, and Statistical Yearbook of Jiangsu Province 1992. 3. A common belief about choosing the dependent variable is that when gross output is used, inputs should include capital, labor and materials, while when net output is chosen as dependent variable, materials should be excluded from the inputs. When gross output value is used as dependent variable, we also calculate the marginal products of inputs. However, the fact that each value of marginal product is much higher than the corresponding cost of each input because gross output in itself encompasses a large proportion of intermediate inputs such as raw materials and energy makes the comparison not meaningful. The data for material inputs in the survey are not usable. 4. Maddala, p. 162. Glejser test for heteroskedasticity: The left-hand dependent variable is the absolute value of predicted residuals, the right-hand regressors selected are right-hand variables of the Cobb-Douglas function reported in Tables 2 and 3. The null hypothesis is to test Ho: a I = a 2 . . . . . a r = 0, where r is the number of regressors. The estimated parameters for logK is -0.0474 with t-value -1.343, logL1 is 0.023 with t-value 0.768, logL2 is 4). 107 with t-value -2.53, R 2 is 0.113. N i s 189. 5. Maddala, p. 130. The F-test for parameters stability is conducted as follows. F=[(RRSS-URSS)/(k+I)]/[URSS/(nI+n2.2k-2)], where RRSS is the residual sum of squares for the entire period, URSS is the sum of residual sum of squares for the separate two years 1989 and 1990. k is the number of variables, here k=5. nt=91, n2=98. The calculated F=1.26<2.8 (6, 177, 1% significance level). 6. The F-test for the economy scale is done by SAS program automatically specified as a restriction test. Test: H0: K + LI + L2 = 12, The Numerator: 0.1133 DF: 1, Denominator: 1.739373 DF: 183, F value = 0.0651 < 6.63 (1% significance level). See Maddala, pp. 119121. 7. In Liu's (1996) estimates, the capital share of TVEs in net output is 0.55. 8. There are two approaches to calculate marginal products. One method is to derive the individual marginal product for each sampled firm, and then to calculate the mean of marginal product of each firm. Alternatively, we can first calculate sample means of net output and all inputs with which marginal product can be derived. In our empirical work, we employ the latter one because the former one incurs an upward bias. 9. This loan rate is estimated weighted average nominal loan rate. See Liu, 1996. 10. China Statistical Yearbook 1996, p.255. 1 I. Statistical Yearbook (~Jiangsu Province 1992. p. 149. 12. The estimated parameter of the dummy variable is the percentage in output. The calculation is as follows. Y=AK~XLBe~x, dY/dD=yAKaL~:~e'/~=yY, (dY/dD)/yY=. 1.
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REFERENCES Caves, R.E. & Barton, D.R. (1991). Efficiency in US manufacturing industries, p.21-25. Chen J. (1995). Structure of property rights in village and town-owned enterprises and its effects on efficiency of resources allocation. JingJi Yanjiu (Economic Research Journal), 3, 35-39. Beijing, China. China State Statistical Bureau. (1996). China statistical year book 1996, p.43, p.390. Beijing, China: China Statistical Publishing House. Fleisher, B.M., Dong, K. & Liu, Y. (1996). Education, enterprise organization and productivity in the Chinese paper industry. Economic Development and Cultural Change, 44(3) April. Liu, Y. (1996). Capital formation, productivity of capital and labor and labor absorption in China's township and village enterprises. Presented in the Institute of East Asian Political Economy, Singapore, March. Liu, Y. & Zhibiao, L. (1996). The comparative advantages of township and village enterprises in Suzhou, Wuxi and Changzhou in Jiangsu Province. In T.T. Meng, H. Yinxing, & C. Yong (Eds.), Jiangsu economy in China's regional development. (Chinese version) pp. 120-139. Maddala, G.S. (1988). Introduction to econometrics, first edition. Statistical Yearbook of Jiangsu Province 1992. Svejnar, J. (1990). Productive efficiency and Employment. In A. William & L. Qingsong (Eds.), China's rural industry: Structure, development, and reform, Byrd, Oxford University Press. Township and Village Enterprises Yearbook of China 1995, p. 87-89.