Potential sources of productivity growth within Chinese industry

World Development. Vol. Printed in Great Britain.

17, No. 1. pp. 45-57,

03O_S75OXC39 $3.00 + 0.00

1989.

0

1989 Pergamon

Press plc

Potential Sources of Productivity Growth within Chinese Industry GARY H. JEFFERSON* Brandeis University, Waltham, Massachusetts Summary.

- This paper presents an approach for modeling and estimating potential sources of productivity growth within four major sectors of Chinese industry-the state and collective sectors and heavy and light industry. The findings, based upon industrial data from 293 Chinese counties, indicate that substantial productivity gains can be achieved by transferring technology from the state to the collective sector, exploiting enterprise and agglomerative scale economtes, and reallocating investment and labor to take advantage of large disparities between factor returns among the sectors. The implications for the results of several idiosyncracies of Chinese industrial prices and data are examined.

sector tends to specialize in heavy industry production, the collective sector is highly specialized in light industry. While the differences in factor intensity are not so pronounced as between the state and collective sectors, heavy industry relative to light industry also tends to be dominated by larger, more capital-intensive enterprises. One outstanding feature of Chinese industry in the 1980s is the enormous diversity of enterprise types. This diversity is represented by different forms of ownership, administrative and management systems, technology, scale and location. During the period 1980-85, nearly one-third of the equipment installed by large and mediumsized enterprises in the state sector was imported from abroad; within the textile industry this proportion exceeded one-half.3 Within the collective sector, by contrast, virtually all of the equipment is domestically produced and of pre1980s vintage, if not in its date of manufacture, at least in its embodied technique. Chinese industry also reveals remarkable variation in enterprise scale, even among enterprises whose product mix is quite similar. One such example is the Kunming Iron and Steel Company, whose gross output and workforce represent just 1% and 3%. respectively, of the output and workforce of its giant counterpart, the Anshan Iron and Steel Company.’ Finally, enterprises are located in widely differing industrial settings, including

1. INTRODUCTION Following a long period of near stagnant industrial productivity beginning in the late 1950s and continuing to the late 1970s China’s economic reforms focused on raising productivity within the nation’s industrial sector. Among China analysts, attention has focused on whether industrial productivity growth has responded favorably to the economic reform program initiated in the late 1970s’ but little attention has been given to the potential sources of productivity increase within Chinese industry. Such increases can be obtained through technical change, scale economies, a more efficient allocation of resources or through a combination of these three sources. This paper investigates the potential for productivity growth from each of these sources within four sectors of Chinese industry. These sectors represent a two-by-two industrial classification. One classification is by type of ownership state-owned (SOE) and collective-owned (COE); the other is by technological type heavy and light industry. Statistics reported by the Chinese government indicate that in 1984 the state sector accounted for nearly three-quarters of China’s gross industrial production, while the collective sector accounted for most of the balance. During the same year, just over 50% of total industrial output was produced by heavy industry.* Relative to the collective-owned sector, state-owned industry is highly capital-intensive and based on large-scale production. Moreover, while the state

*The author appreciates the valuable comments on earlier drafts provided by Anne Carter, Dwight Perkins, Mao Yushi. Peter Petri, Thomas Rawski, Jeffrey Williams and three anonymous referees.

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46

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Shanghai and other coastal cities which have relatively well-developed communications and long traditions of diverse industrial activity that have nurtured enterprise linkages, highly skilled and adaptable supplies of labor, and other conditions that are conducive to efficient production.’ By comparison, particularly in the provinces of the west and northwest, where large quantities of industrial investment across many industries have been directed in recent decades, few resources, other than natural resources, are available to support an efficient industrial operation. Given these vast differences in technology, scale and location, it should not be surprising that significant productivity gains could be achieved through the dissemination of existing technologies, the exploitation of scale economies and the redirection of factors to industries and sectors that promise higher returns. This paper employs cross-sections data for 293 cities and counties (hereafter simply called “counties”) to estimate the parameters needed to investigate the potential importance of these three sources of productivity gain. The paper finds that, for each of the four sectors, gains in efficiency are attainable from exploiting scale economies - either enterprise scale economies, agglomerative economies, or both. Efficiency gains can also be achieved through shifts in factor allocations that take advantage of higher social returns to labor in state industry relative to collective industry and in heavy industry relative to light industry. Higher returns to capital within the collective sector and within light industry also present substantial opportunities for obtaining overall efficiency improvements within Chinese industry. Finally, estimates of substantially higher levels of multi-factor productivity within the state sector suggest that significant gains can be achieved through the transfer of existing industrial techniques and skills from state enterprises to collective enterprises. This paper also investigates several problems associated with the analysis of Chinese industry that result from various measurement problems which are found in Chinese industrial data, including its non-conformance with standard national income accounting concepts. The use of different measurement concepts and the existence of relative product price distortions complicate the interpretation of some of the paper’s results, while others remain robust. The remainder of the paper consists of five sections. The first section formulates an econometric model that accounts for the opportunities and limitations of Chinese data available for evaluating production technologies and sources of productivity difference within the state and col-

lective industrial sectors. This basic model is then extended to accommodate additional data that are available for comparing heavy and light industry. Section 3 of the paper describes the data and estimation techniques and evaluates the estimation results. In Section 4, the estimation results are used to evaluate the relative levels of multi-factor productivity and relative factor returns within each of the four sectors. Comparisons of relative factor returns should account for disparities between relative domestic prices and world prices. In addition, the sensitivity of these estimates of standard neoclassical measures of relative efficiency to Chinese industrial accounting concepts requires investigation. These issues are addressed in Section 5. The final section of this paper evaluates the potential for achieving higher productivity levels through disseminating existing industrial techniques within China, exploiting scale economies and redirecting investment and labor between sectors. The paper also draws some conclusions with respect to methods and perspectives that can be used to address the idiosyncracies of Chinese industrial data. 2. METHODOLOGY The basic model developed in this section consists of two equations - a production function with an industrial efficiency variable, and a productivity equation that specifies the determinants of this efficiency variable. In this paper, industrial efficiency is determined by a technology parameter, enterprise-level scale economies and economies of agglomeration. An advantage of the model is that it is sufficiently flexible to be able to incorporate and test a number of hypotheses concerning the ways in which productivity growth varies across sectors and evolves over time.6 The model used for the state and collective sector analysis is developed below. Thereafter, this model is adapted to take advantage of the data reported for heavy and light industry. (a) The state-coIlective sector model The production technology used in the model is assumed to be Cobb-Douglas.6 Constant returns to scale are imposed on the sum of the output elasticities. If scale economies are present, they appear in the efficiency variable, a;j, which, in turn, is determined by the productivity equation. In log-linear form: In

qij

=

In

aij

+

aJn

k;j

+

aLln

iij

+

Ulj

In Uij= In a,;j + &In qij + fiNIn NTj + vii,

(1)

(2)

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17

where aK + aL. = 1, and i = 1, . . . N represents the N enterprises in each of the j counties, j = 1 . . . 295. In equation (l), qji is the quantity of net output,’ k, is the net capital stock, lij is the number of workers, and the U;j’S represent stochastic disturbances to the production technology.’ Equation (2) specifies that the level of efficiency or multi-factor productivity of each enterprise depends upon the scale of its own net output, qii, as well as upon the total number of industrial enterprises in the locality in which it is located, Nq. The total number of enterprises is used here as a measure of economies of agglomeration in which the proximity of competitors and suppliers and available trained industrial labor are hypothesized to facilitate efficiency. The term vii represents disturbances to productive efficiency. Substituting equation (2) into equation (l), and solving for q/f, the labor productivity variable, gives:

composition variables. Data on the shares of I5 industry branches allow for the estimation of an efficiency parameter for each of China’s 15 industry branches.’ Unfortunately, neither input data nor data concerning the number of enterprises are reported for heavy and light industry, making it necessary to use dummy variables to obtain separate estimates for the heavy and light industry output elasticities and for the scale parameters. Using this approach, the extended estimation equation can be written as:”

In (q/1), = In LJ,ij + aKIn (kll),j + &In NTi +E ii,

The coefficient Aob represents the efficiency parameter of industry branch b: (b = 1 . . .14) while A,,\, represents the efficiency parameter of the metal industry. The estimate of this parameter is the reference intercept against which the efficiency parameters of the other industry branches can be evaluated. The superscripts h and 1 represent heavy and light industry, respectively. Since data on labor and capital inputs are not reported separately for heavy and light industry, Equation (5) is estimated by using two subsamples, one in which light industry is highly concentrated and one in which heavy industry is highly concentrated. This procedure is further described below.

+

Poln qij (3)

where E li = Uii i- vii. Equation (3) represents the technology of the individual enterprise. Because the data are available only at the county level, equation (3) must be aggregated over all enterprises within the relevant sector of the county. The appropriate index for aggregating equation (3) over all enterprises is a Cobb-Douglas Divisia index. However, because the distribution of output, capital and labor across the N establishments is unknown, the mean value of the relevant variable is used, SO that Qi = (Ziq;i)lNi, Kj = (Zikii)l Ni, and Li = (~ilii)lNi, where i = I . . .N. The resulting county-level productivity equation is: In (Q/L)i = In A, + aKIn (KIL)j + 8oln Qj + BNln NTj + Ei,

(4)

Equation (4) specifies that, at the county level, the average level of labor productivity is a function of the average capital-labor ratio, average enterprise size, and the number of industrial enterprises within the county. (b) The light-heavy industry model A shortcoming of the state-collective sector analysis, is that equation (4) does not control for industrial composition differences across counties. While data for industry composition are not reported by form of ownership, the composition of gross industrial output is reported for each county. In order to take advantage of these data, equation (4) is extended by including industry

In (Q/L)i = In Ao,+t + ES, (In Aob - Ao~) + a:

In (K/L)j

+ (a: + &In

-

a$ln

(K/L):

&In

Qr + &In N7j + (&

Qj + (8: -

1

i$,,)ln NTf+Ej.

3. DATA AND ESTIMATION

-

(5)

METHODS

The data used to estimate equation (4) for the state and collective sectors and equation (5) for the heavy and light sectors are 1984 data reported in State Statistical Bureau (SSB) (1985). The data cover 295 counties in China. Since two observations for labor input are missing for the collective sector, the estimation procedure uses the data from only 293 counties. Together these observations represent 85% of the value of gross output in the state-owned sector in 1984 and 80% of the value of gross output in the collectiveowned sector in that same year. Within each county, for both the state and collective-owned sectors, the data include the values of net and gross output, net value of fixed assets,” the labor force, the number of enterprises and gross industrial output by sector.12 A list of variables and their methods of construction appears in Table 1.

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-18 Table Variable

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1. List of variables Symbol

Method

Table

2. State and collective sector industry estimates

of construction

SOE Efficiency

parameter

Average enterprise

A,

Estimated

Q

Value of net output/no. of establishments

size Labor

productivity

Capital-labor

ratio

Q/L

Average enterprise value of net output/ labor force

KIL

Average enterprise

3.931’ (6.213) 0.309 (3.843) 0.148 (2.896) 0.119 (5.143) 0.943

value of net assets/labor

force Economies of agglomeration Industry

shares

NT

Sh

Total number of industrial enterprises the county level

at

Value of gross industrial output of the industry branch/AQ

Because the data for each of the county observations are grouped from individual enterprise data and the number of enterprises varies considerably across counties, the error structure, i.e. the Ei’s, may violate the assumption of homoskedasticity. Using the standard assumption that Var(Ej) = a2/nj, where nj is the number of establishments in county i, an appropriate estimator is the weighted least squares method in which n’e is used to weight the errors. In addition, productivity errors and the mean enterprise size may be correlated, i.e. Cov(ln Q,, Ej) = 0. Since this correlation violates one of the classical assumptions of the ordinary least squares estimator, an instrument is generated for In Q using a two-stage least squares (ZSLS) estimator. Within the model, the number of establishments and stocks of capital and labor are assumed to be fixed at the beginning of the period and are therefore invariant with respect to disturbances to productivity. Only the output of the individual enterprises adjusts to productivity disturbances. The predetermined variables used to generate an instrument for In Q include the exogenous variables in the regression equation and the logarithmic values of population, area, number of service units and the total value of retail sales. ”

(a) Estimates

of the state-collective

sector model

The estimation results shown in Table 2 indicate that, within both the state and collective sectors, capital deepening and scale economies

COE 1.604 (6.683) 0.722 (22.928) 0.164 (;:;A) (i.;;;) ,

*HO: coefficient estimates SOE = estimates COE. F(4.580) = 43.117 (critical value at the 5% level = 2.37).

significantly raise labor productivity. Economies of agglomeration appear to be significantly associated with higher levels of labor productivity only within the state sector. With the exception of the economies of agglomeration coefficient in the collective sector, all of the estimates are statistically significant above the 99% level. An F-test rejects the hypothesis that the coefficient estimates are identical between the state and collective sectors. A significant difference between the two sets of estimates is that the capital-output elasticity within the collective sector is approximately 2.3 times as large as that of the state sector. ” Estimates of the scale economies parameters do not differ significantly between the two sectors, but as mentioned above, the economies of agglomeration coefficient, highly significant within the state sector, is insignificant within the collective sector.15 Finally, the efficiency parameter of the state sector is considerably larger than that of the collective sector.16 In Sections 4 and 5, these results will be used to calculate the levels of multi-factor productivity and the marginal revenue products of capital and labor within the state and collective sectors. (b) Estimates of the light-heavy

sector model

The data used to estimate equation (5) are aggregate industry data constructed by combining the state and collective sector data series for net output, capital and labor.” To estimate the dummy variables a$, @. and fi,$ in equation (5). a subsample was created consisting of those counties for which a majority of gross output value originates with heavy industry. One shortcoming of using this method is that

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the heavy and light industry subsamples are each “contammated” by data from the opposite sector. In order to investigate the effect of this contamination, the stability of equation (5) was tested by estimating the equation using two different subsamples. The first of these (Subsample A) omitted the observations for which the ratio of light to total gross industrial output lies within the range 0.333-0.667. This subsample, therefore, includes only the countries that are highly specialized in heavy or light industry. The second subsample (Subsample B) consisted of the observations that were excluded from the first, i.e. those observations for which the same ratio falls within the range 0.333-0.667. These are the counties that are only moderately specialized in either light or heavy industry. A stability test, reported in Table 3, is unable to reject the null hypothesis that the coefficients are identical over the two subsamples. As a result, the estimation results reported in Table 3 are those based upon the full sample. As reported in Table 3, an F-test is also unable to reject the joint hypothesis that the production coefficients taken as a group are identical between the two sectors. However, because the two subsamples and the full sample yield consistently different estimates of the parameters of the two sectors, rather than restrict the two sets of parameter estimates to be equal, their point estimates are reported. The estimation results yield values of aK for the light and heavy industrial sectors that are very similar. Both sectors exhibit significant enterprise-level scale economies, although these scale economies are somewhat more pronounced within heavy industry than within light industry. Finally, economies of agglomeration exhibit statistically significant effects of similar magnitudes in both sectors. Also shown in Table 3 are the relative estimates of the efficiency parameters of the 15 industrial branches. Since, as shown in equation (2), multi-factor productivity also depends on economies of scale and agglomeration, as well as the value of the efficiency parameter, these estimates reported in Table 3 cannot be interpreted as the actual industry multi-factor productivity ranking. The respective efficiency parameters for light and heavy industries are output shareweighted indexes of the relevant branch industry parameters. These indexes are evaluated below.

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Using the estimation results for equations (4) and (S), it is possible to evaluate whether alloca-

49

Table 3. Heavy and ligh industry estimates”

aK

Light

Heavy

0.538

0.524 (6.913) 0.183

(8.061) 0.127 (1.801) 0.149 (5.249)

BQ

BN

(Z? (5.410)

Heavy industries: Metal

O.OQO

Coal$

0.000

Petroleum

0.000

Chemicals

0.000

Machine

0.000

Power

0.000

Building materials

0.000

1.336 (2.236) 0.997 (1.537) 2.159 (3.327) 2.485 (3.771) 1.613 (2.532) 1.219 (2.126) 0.907 (0.921)

Light industries: 3.201 (5.768) 1.0% (1.141) 1.043 (1.685) 1.710 (2.464) 1.961 (3.453) -0.309 @;I;)

Food Leather Forest Paper Textile Clothing Educational Other R2

0.000 0.000 0.000 0.000 0.000 O.OQO 0.000

materials (4.816) 0.217 (0.266) 0.979

0.000

‘H,,: (stability test) coefficient estimates for Subsample A = estimates for Subsample B; F(21.251) = 1.11 (critical value = 1.60). tH,: coefficient estimates heavy industry = estimates light industrv N3.272) = 0.336 (critical value = 3.04). *Based on ihe‘estimate of the efficiency parameter (AoM) and its standard error for the metal industry, the efficiency parameters and f-statistics have been computed for each of the other industries.

holds between the state and collective sectors as well as between heavy and light industry. This evaluation is made by calculating and comparing the values of the marginal revenue product of capital and labor between the state and collective sectors and also between heavy and light industry. tive efficiency

4. INTERSECTORAL ALLOCATIVE EFFICIENCY

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Using the estimates obtained in the previous section and the data from Table 4, the values of the marginal product of capital (VMPK) and labor (VMPL) can be calculated from the expressions:

Using the estimation results from Tables 2 and 3 and the mean values reported in Table 4, multifactor productivity levels can be calculated for each sector from the expression:

VMPK

=

(@In Q/@ln K) (Q/K)

In Aj = In A, + ppln Qi + f3,dn NT,-

VMPL

=

(@In Q/@ln L) (Q/I!,) =

=

[taKi(l-~Q)l

where aL = 1 -

(Q/K)

ox.

These calculations, reported in Table 5, show that the marginal revenue product of capital in the collective sector is approximately four times that of the state sector. By comparison, calculations of the marginal revenue product of labor within the state and collective sectors indicates an overwhelming advantage for the state sector.‘s As between the light and heavy industrial sectors, the marginal revenue product of capital is higher within light industry while that of labor is higher within heavy industry. The disparity between the returns to capital and labor between these two sectors is not nearly so pronounced as the differences found between the state and collective sectors.

The results, reported in Table 5, indicate that the level of multi-factor productivity within the state sector is substantially higher than that found in the collective sector. Part of this difference reflects differences in the scale of production between the two sectors. However, even if enterprise scale within the collective sector were equal to that of the state sector, the disparity would only decline from a factor of 2.6 to 2.2. Using the estimate of the efficiency parameter for each of the industry branches, Aobr an industry share-weighted efficiency parameter can be calculated for both the heavy and light industrial sectors. Based upon these composite measures of the efficiency parameters for heavy and light industry, the relevant estimates from Table 3 and statistics from Table 4, the levels of multi-factor productivity can be calculated for light and heavy industry. These results, reported in Table 5, indicate that multi-factor productivity within light

Table 4. Mean values for the “average” enterprise 1984’

SOE Qt (RMB ~‘A~) Q/L (RMB/worker) Q/K (RMBIRMB)

167.670

COE 12.267

Heavy

Light

162.569

71.215

4283.680 2669.916 4372.790 3845.093

In Q

In (K/L) In NT

0.535

0.915

0.472

0.702

5.122

2.506

5.091

4.266

8.961 5.999

7.890 5.999

8.992 6.007

8.598 5.968

*These statistics are based on data reported in SSB (1985). ?A11 output figures (Q) are measured as net output.

Table 5. Measures of total factor productivity

and

allocative efficiency

In Ai (TFP)

Absolute levels: State sector Collective sector Light industry Heavy industry Relative levels: State/collective Light/heavy

VMPK

VMPL

5.403

0.194

3474.205

2.063

0.790

887.843

3.703 4.057

0.383 0.303

2034.860 2547.672

2.619 0.913

0.246 1.264

3.913 0.799

industry is somewhat higher than that of heavy industry. The difference, however, is not nearly as pronounced as that between the state and collective sectors. 5. IMPLICATIONS OF CHINA’S INDUSTRIAL PRICE STRUCTURE AND DATA CONSTRUCTION METHODS It is critical that the results reported above be evaluated in view of the limitations of the data. These limitations include: (1) many industrial prices continue to be administered; (2) input data

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include resources used for non-industrial purposes; and (3) the fixed asset data are based upon the sum of investment valued in current (original) prices rather than in constant prices. The implications of each of these data problems are discussed below.

(a)

Administered

prices

The results reported in Table 5 depend critically upon the assumption that output prices reflect their true scarcity values. Since many industrial output prices in China are set by administrative fiat, the relative marginal revenue products and productivity levels reported in Table 5 may accordingly be distorted. Some indication of the likely magnitude of the price distortions can be obtained from relative price indexes calculated by Taylor (1986b), Ahmed (1983), Wharton Econometric Forecasting Associates (WEFA) (1984) and the World Bank (1985).‘9 Each of these authors has estimated yuanl dollar purchasing power parities for retail or producer prices within major Chinese industry groups. In Table 6, the average of the three retail price series has been calculated for each industry. From these industry price averages, a geometric average has been calculated for both heavy and light industry. 2o Geometric means were also calculated for light and heavy industry using the

Table

Industry

Taylor 1979’

Metal Power Coal

0.483 N/A 0.393

Petroleum Chemicals Machine Building Heavy index Forest Food Textiles Paper Other Light index Light/Heavy

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51

producer price series prepared by Taylor and the World Bank. Finally, the relative price ratios of the light-heavy price indexes were calculated and are reported at the bottom of Table 6. Adjusting relative Chinese prices in the light and heavy industrial sectors by these price ratios yields relative price relationships which approximate those of the United States and the world economy during 1979-81. According to these indexes, relative to the US price structure, China’s domestic light industry prices in 1979-81 were high relative to heavy industry output prices. The ratio of the former to the latter, relative to the United States, are estimated to be in the vicinity of 1.10 for retail prices and 1.30 to 1.35 for producer prices. In order to relate these price indexes to the relative prices implicit in the results reported in this paper using 1984 data, we examine the change in relative prices between heavy and light industry within China during 1980-84. This is done by calculating the ratio of the rise in nominal output relative to real output in the two sectors as follows:

Q

N.&l heavy /

Q

N780 heavy

Q

Retail prices Ahmed WEFA 1981’ 1981’

1979-81

Producer prices AVG.

Taylor 1979’

World Bank 1981

0.483 N/A

1.359 N/A

1.003 0.744

l

N/A

N/A

1.041 0.852 2.244 N/A

N/A 1.934 0.498 3.001 N/A

0.216 0.207 0.523 3.581 0.193

0.305 3.182 0.624 2.942 0.193 0.519

0.474 0.772 1.892 0.501 0.606 0.635

0.512 0.574 1.219 1.017 1.073 0.885

1.148 1.497 0.540

0.793 0.530 0.291

0.141 2.805 0.967 0.979 0.948

0.141 1.582 0.998 0.979 0.593 0.575 1.087

0.964 1.277 1.574 1.356 0.317 0.834 1.313

1.153 1.153 1.305 1.153 1.153 1.191 1.346

were

Q

>I

R,80 heavy

In this relationship, 80 and 84 refer to the years 1980 and 1984, respectively, and N and R refer to

6. Relarive price smcrure

Source: Taylor (1986). *Year during which data

R.84 heavy

collected.

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nominal and output only in output Substituting

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real. The Chinese report nominal in terms of net output and real terms of gross output only.ZL available data gives: [(891.32/673.16)/(3484/2332)]*’

[Plight~Pheavyl = [(1354.71/975.19)/(3546/2639)] = 0.857 This result indicates that the light/heavy industry price distortion found by Taylor (1986b), the World Bank (1985) and others eroded during 1980-84.23 The convergence of relative prices in China with those prevailing in the world is consistent with the qualitative economy changes that we would expect from the early phase of industrial price reform during this period.24 Two conclusions can be drawn from the above analysis: 1. Adjustments required to account for differences in relative actual prices and relative scarcity prices between light and heavy industry are not large. 2. To the extent that such adjustments are required, they should reflect the upward bias in light industry prices relative to heavy industry prices. These results tend to reinforce the finding that labor’s marginal revenue product in heavy industry exceeds its productivity in light industry. At the same time, viewed within the context of relative world prices, the initial finding that the return to capital in light industry exceeds that of heavy industry becomes more ambiguous. Data are not available to evaluate the relative price structure of the state and collective sectors. However, because the relative marginal factor efficiencies between these two sectors differ by factors of 3.9 or more, it is unlikely that output or input price differences between the two sectors can account for such large disparities in the relative efficiency levels.

(b) Non-industrial resources A second major shortcoming of the data set used in this paper and other Chinese industrial data is the failure to distinguish between industrial and non-industrial (or “nonproductive”) capital and labor.2s Since, ceteris par&us, this reporting method causes enterprises that allocate resources to housing. education, health and other employee services to appear to be less productive, either the input data should

be adjusted to reflect the division between productive and non-productive inputs. or. alternatively, the output data should be expanded to reflect the provision of these services by the enterprise. In order to evaluate the effect of the inclusion of non-productive resources in the capital and labor series on the estimates of the relative sectoral measures of efficiency, we consider four possibilities concerning the configuration of nonproductive resources within the aggregate data at the county level. These are (a) capital and labor are used in the same proportion, (b) capital and labor are used in fixed but different proportions, (c) capital and labor are used in randomly distributed proportions across counties, and (d) a systematic relationship exists between the proportion of resources used for non-productive activities and the true capital-labor ratio or errors in the measurement of labor productivity. We examine the consequences of each of these possibilities: a. From equation (4). if the proportion of capital and labor used for non-productive activities is identical across counties, the capitallabor ratio, (K/L),, will be unaffected by the presence of non-productive resources. That is, the true capital-labor ratio is multiplied by a scalar, U, equal to unity. b. If capital and labor are used for nonproductive activities in fixed but different proportions, then the observed capital-labor ratio is the true ratio multiplied by a scalar, U, not equal to unity. The result is that the estimated efficiency coefficient, 4,: is the product of the true efficiency coefficient and PK. The estimated efficiency parameter is biased upward if U > 1 and downward if U < 1. and labor are used for c. If capital non-productive purposes in different, but randomly distributed, proportions acres: counties, i.e. the error equals (/I, then there is an errors in measurement problem. Using an instrument which is highly correlated with the actual capital-labor ratio but uncorrelated with the error can, in principle, correct for the estimation bias that would result from this measurement error. d. If there exists a systematic relationship between Uj and (K/L), or errors in the measurement of labor productivity. then additional data concerning the nature of the relationship are required to correct this problem. Among these four possibilities, the first implies no bias for any of the parameter estimates. For case (b) applied to the light-heavy industry estimates, if the relevant scalar for each sector were rf and clh, then the bias reflected in the light-

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htzy industry efficiency ratios would be U’Y i . For the light-heavy industry efficiency comparisons, if the value of I/ for the two sectors were similar, because the estimates of aK for the two sectors are virtually identical, the bias would, in effect, cancel out. This is not true for the state and collective sectors in which the estimated output elasticities are significantly different. In light of the large estimated efficiency differences between these two sectors, however, the values of U within the two sectors would have to be surprisingly large or different to overturn the relative efficiency measures reported in Table S.*’ Possibilities (c) and (d) imply more serious problems because they can lead to biased and inconsistent estimates of capital’s output elasticity. The errors in measurement problem can be addressed through the use of an appropriate instrument which is both uncorrelated with the measurement error and highly correlated with the true capital-labor ratio. Unfortunately, within the data set used in this paper, a suitable set of instruments for the capital-labor ratio could not be found.*’ This paper, therefore, has not been able to correct for possible errors in measurement in the capital-labor series. The degree of bias and inconsistency in the parameter estimates of aK will, if this measurement problem exists, depend upon the ratio of the variance of the measurement error in the capital-labor ratio and the variance of the regression disturbance (which includes the error in measuring the capital-labor ratio). All else equal, the bias in the estimates of the relative efficiency parameters will depend upon the relative size of these ratios between the sectors. In conclusion, although case (c) will lead to biased and inconsistent errors, because these biases are in the same direction, their effect on the results is mitigated by the use of the estimates to construct relative measures of efficiency. Finally, as described in case (d), it is possible that there is a systematic relationship between the variable Vi and the true capital-labor ratio or errors in the measurement of the labor productivity variable. Only the provision of additional or more refined data can solve this problem.

(c) Capital stock undeflated The final serious data problem concerns the construction of the capital stock. For the purposes of productivity analysis, the major drawback associated with the capital stock is that its construction is the sum of each period’s invest-

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53

ment measured in current rather than constant prices. Changes in the price of investment goods, therefore, will bias measures of the capital stock. A rise in the inflation rate of capital goods prices will, for instance, cause an overestimate of the actual growth in the capital stock. Chen et al. (1988a), who have constructed price indexes for components of industrial investment, find that during the thirty-year period preceeding 1981, as equipment prices fell, construction prices rose with negligible effect on overall investment prices. Beginning in 1981, however, with substantial inflation in construction and land costs and modest inflation in equipment prices, fixed investment costs rose dramatically. These findings lend support to Rawski’s earlier conjecture that “the dramatic surge in (reported) investment activity (since 1980) may be largely a monetary phenomenon.“” Within the model used in this paper, this issue of capital stock valuation can be treated in a manner similar to the treatment of the problem of non-productive resources. The inflationary comoonent of the change in capital stock valuations is .malogous to non-productive investment which is erroneously included in the reported value of the capital stock. If changes in ca ital goods prices are the same across counties, 2s then the relative efficiency measures between the heavy and light industry sectors will be substantially unaffected. For the state and collective sector efficiency comparisons, however, even if changes in capital goods prices are constant across counties, relative measures of efficiency will be affected, since the capital-output elasticity estimates of the two sectors differ markedly. It is unlikely, however, that, in combination with the adjustments discussed above, that a correction for differences in investment price deflators between the state and collective sectors would be sufficient to overturn any of the relative efficiency measures. Alternatively, the deflator problem may be viewed as an errors in measurement problem or as a systematic measurement problem. Lacking a proper instrument, this analysis is unable to correct for a possible errors in measurement problem. However, one potential method of addressing the problem of systematic mismeasurement is to use the age structure of the capital stock as a proxy for the investment price deflator. Since the age structure of the fixed assets is reflected in the ratio of the net original value of the capital stock, the larger the proportion of recent, inflated investment, the larger this ratio. As a result, the capital-stock series were adjusted by this deflator proxy and the econometric equations were re-estimated. This adjustment,

54

WORLD

DEVELOPMENT

however, did not significantly change the original set of estimates.30 In conclusion, this section has examined the consequences of three problems with the data. Using available international price studies, it is possible to account for disparities between relative administered prices and relative scarcity prices. This adjustment, by itself, affects the relative measure of multi-factor productivity between light and heavy industry but none of the other results. If the inclusion of non-productive resources in the capital and labor series and the absence of an investment price deflator transform the measured aggregate county-level capital-labor ratio by either (a) a scalar or (b) a normally-distributed error, then, the bias, whether a transformation bias or an econometric bias, is likely to be in the same direction. The effect will be to mitigate the influence of the bias on measures of relative efficiency. This result is, particularly true for the light-heavy industry comparison, since the estimated capital-output elasticities are not significantly different. In the one instance in which it is anticipated that measurement error might have a systematic effect - the tendency of a post-1980 surge in investment costs to overinflate reported quantities of fixed assets with a large proportion of new investment - an adjustment for the age structure of the capital stock had no significant effect on the estimation results. One possible explanation of these statistically insignificant results is that, as a measure of the age composition of the capital stock, newer vintages of capital may be more efficient, so as to warrant the higher prices or more recent vintages. If this is true then the omission of an investment price deflator does not seriously affect the results reported in Table 5.

6. CONCLUSIONS

AND OBSERVATIONS

This paper investigates the potential sources of productivity growth within Chinese industry. In particular, it shows that substantial efficiency gains can be achieved through technology transfer from the state to the collective sector, the exploitation of enterprise and agglomerative scale economies and the redirection of investment and labor between the state and collective sectors, and to a far more limited extent, between heavy and light industry. Whether factor returns are valued according to Chinese prices or world prices, extensive investment within the state sector appears to have depressed the rate of return on capital within that

sector to levels substantially below the returns that could be expected from collective sector investments. Also, the relatively high capitaloutput ratio within heavy industry correlates with a lower rate of return on capital than that within light industry. These findings are not surprising, given the deep subsidies to state industrial investment and, until recently, the virtual absence of non-state sources for capital for the collective sector. By contrast, the value of labor’s marginal product is substantially higher within the state sector than within the collective sector. As between the light and heavy sectors, new hiring promises to generate higher returns within stateowned heavy industry. Measures of multi-factor productivity within the state and collective sectors show that the state sector enjoys a considerable advantage in overall efficiency. Between light industry and heavy industry, the estimates indicate that multi-factor productivity in the latter sector may slightly exceed that of light industry. Adjusting the relative overall productivity of these sectors to account for differences between Chinese and world prices accentuates the small advantage of heavy industry reported in Table 5. Because the difference in measured multi-factor productivity is not large between heavy and light industry, the finding of substantially higher multi-factor productivity within the state sector relative to the collective sector cannot be accounted for by differences in industry mix between these two sectors. The results reported in this paper suggest important policy directions for the Chinese reforms. First, the large disparity between estimates of multi-factor productivity between the state and collective sectors indicate that substantial efficiency gains can be achieved by disseminating techniques and skills to the collective sector. The Spark Plan, in which technicians from more sophisticated state enterprises assist the more backward collective enterprises, is one example of a program whose returns are likely to be quite high. Second, consistent findings of scale economies, within the state and collective sectors and within both light and heavy industry,3’ underscore the need for the Chinese government to control the proliferation of inefficient enterprises, including the persistent tendency of lower levels of government to establish and maintain a complete set of key industrial activities within their jurisdictions. Emphasis should be on opportunities and incentives to promote interregional linkages and to expand production through the growth of existing efficient, small and medium-sized enterprises. A further implication of these findings con-

PRODUCTIVITY

GROWTH

terns the critical contribution that economies of agglomeration make to enterprise efficiency, at least within the state-owned industrial sector. While there are many factors that must inform China’s regional development policy, the importance of economies of agglomeration lends support to the reform element which argues for the concentration of industrial development and foreign investment along China’s coastal region. Finally, the finding of large disparities in the returns to capital and labor between the rapidly growing collective sector and the state sector underscores the potential benefits to accelerating the development of capital and labor markets. Disparate factor returns among China’s industrial sectors indicate that, even without technical change, substantial increases in industrial output can be obtained through a more efficient allocation of labor and capital throughout the industrial system. The paper shows that gains from factor reallocation will also require further rationalization of industrial product prices. Analysis of the returns to capital between light and heavy industry demonstrates how sensitive judgments concerning efficient factor reallocations can be to distortions in administered product prices. Moreover, the results of Taylor, Ahmed and the World Bank indicate that at the branch industry level some of these price distortions are quite large (see Table 6) and therefore have the ability to impair substantially the contribution that evolving factor markets can make to industrial growth and efficiency. The data set used in this paper provides a useful starting point for a systematic examination of the comparative performance of various sectors of Chinese industry. However, because of the shortcomings of the data, before the quantitative results reported in this paper can be accepted with a high degree of confidence, the following conditions should be satisfied:

IN CHINESE

INDUSTRY

55

1. The results should be confirmed by timeseries data as well as by cross-section data. 2. Satisfactory instruments should be found for the capital-labor ratio under the assumption that random measurement errors exist in the reported capital and labor series. 3. The results should be corroborated by other model specifications including those which allow for a more flexible functional form and also incorporate short-run dynamics, such as costs of adjustment. The increase in the collective sector’s share of gross industrial output from less than 20% in 1979 to nearly 30% in 1986 underscores the need for a more detailed investigation of the production characteristics and sources of productivity change within that sector. In particular, it will be useful to undertake comparisons of state and collective enterprises within the same branch industries such as textiles, apparel and machinebuilding, in which there are large concentrations of collective enterprises. Such industry studies will reduce the problem of different industry mixes between the two sectors which could not be accounted for in this study. A further important area of investigation concerns the effect of certain reform-related innovations on industrial efficiency. These measures include the expanding role of foreign investment and joint ventures, and different regional effects, including the effect of special economic zone designations and the vigor with which various reforms are known to have been implemented within different locations. The year 1984 is the first year for which data for the 295 cities and counties have been tabulated and reported in their current form. Future statistical releases, for 1985 and beyond, should provide the basis for a most valuable time-series perspective on productivity change and questions of allocative efficiency within Chinese industry.

NOTES 1. See, for example, chi et al. (1985), and

World Bank (1983), Shi QingChen er al. (1988b).

2.

SSB (1986).

p. 276.

3.

SSB (1987),

pp.

4.

MIM

(1987).

116-131.

pp. 357-548.

5. See, for example, the discussion of the evolution of Shanghai industry in Rawski (1980). 6. For a detailed discussion of this approach including possible extensions based on U.S. manufacturing data, see Jefferson (1989). The choice of a Cobb-

Douglas functional form, of course, implies a unitary elasticity of substitution. If the purpose of this paper were to estimate the substitution elasticity between capital and labor, this choice of functional form would clearly be inappropriate. Since the analysis requires estimates of sectoral output elasticities, not elasticities for each observation, the Cobb-Douglas assumption is unlikely to affect substantially the results obtained in this paper. Moreover, other estimates of Chinese industrial production functions using constant elasticity of substitution (CES) and translog forms (Chen ef al., 1988b and Jefferson, 1988) are unable to reject the hypothesis of a unitary elasticity. 7.

Following Soviet convention,

depreciation

is not

56

excluded sources.

WORLD from the net output

series reported

DEVELOPMENT

in Chinese

8. In order to retrieve the scale parameter in terms of ah., aL and BUT equation (3) below can be solved for q,,. The result IS ah-* = a&l-&>) and uL* = aLI(l-@Q). 9. According to Chinese classification, there are, excluding the “other” category, 14 industry branches. Seven of these are heavy industries and seven are light industries. 10. See Maddala (1977. pp. 132-36) for a discussion of the intercept and slope dummy variables used in this extended form of equation (1). 11. Data on the value of assets are only available for independent accounting units in industry, which exclude the output of industrial enterprises subordinate to units primarily in other fields (e.g. workshops engaged in prototype production with research institutes). In 1979, the units for which asset data are reported cover 96% of total net industrial output (World Bank. 1983, p. 127). For a discussion of the shortcomings of the reported measures of net fixed assets see Rawski (1986) and Chen et al. (1988a). 12. These 312 (state), (1985).

data appear respectively on pp. 297. 264, 328 (collective), 648, 280 and 2% in SSB

13. These data appear respectively 488. and 464 in SSB (1985).

on pp. 208, 216,

14. The size of this difference may surprise some analysts. The author is not familiar with any other production function estimates using Chinese collectiveowned industry data that can be compared with the estimates reported here. However, using time-series data for the state sector, Chen et al. (1988b) obtain estimates of capital’s output elasticity in the vicinity of 0.55. Using a translog function, they also find some evidence of a declining capital output elasticity which for 1984 they estimate to be 0.46 (using unrevised data) and 0.38 (using revised data). 15. One possible explanation of the absence of economies of agglomeration in the collective sector is that large variations in labor skills between counties may largely affect labor force quality within state enterprises, but have little effect on labor force quality within collective enterprises if the latter find it difficult to recruit highly skilled labor, regardless of proximity. In this case, the agglomeration variable should be interpreted as a labor quality variable. 16. F-tests consistently rejecf the null hypotheses: c, (SOE) = c, (COE), aK (SOE) = a1 (COE), l3v (SOE) = B,., (COE). The F-test, however, cannot reject the hypothesis &, (SOE) = l3o (COE).

index is a divisia index in which 17. The aggregation the weights are the shares in total industrial net output. For a discussion of the aggregation bias implicit in this commonly used method of aggregation see Muellbauer (1981). 18. Note that even if the capital output elasticity in the state sector were substantially underestimated (e.g. assume the estimate of aR = 0.55 obtained by Chen er al.), the qualitative import of these results concerning the substantial difference in relative returns to capital and labor between the two sectors would not be seriously affected. 19. The results of these studies (1986a).

are reported

in Taylor

20. The industry shares used in the calculation 1981 gross output value shares adjusted to reflect relative price structure of the United States.

are the

Using these different series assumes that rhe mrio 21. of the value added ratios within the light and heavy industry sectors remained constant over 19SO-84. Source: SSB (1986): fixed price gross output (ad22. justed for 1980 comparable prices), p. 27-l; net output in current prices, p. 278. 23. This conclusion, of course, assumes that within the United States and world economy the light/heavy industry price ratio remained essentially unchanged over this period. Chinese sources do not specify the method of con24. struction of official price indexes. It is not possible, therefore. to know with precision the extent to which official price indexes reflect actual price conditions or officially-announced prices. In either case, however. the prices implicit in the data used to calculate relative returns and those used to compare relative Chinese and U.S. (world) prices are consistent. This does not preclude the real possibility that actual relative prices may deviate substantially from those based on reported prices. 25. Chinese data sources sometimes distinguish between “productive” and “non-productive” capital and labor. This distinction, however, is not made in the reporting of the industrial data in SSB (1985). If, for example, the ratio of the proportion of non26. productive capital to non-productive labor were 2.0 for both sectors, then the bias would be 2.0”3”“ = 1.239. For the collective sector, the bias would be 2.0”‘” = 1.649. Because the relative efficiencies are the critical values of interest, it is the ratio of these two terms. i.e. 1.331. which constitutes the actual bias. If the value of Lr were 5.0 within both sectors, then the bias in relative efficiency would still only approach 2.0. 27. Several different combinations were attempted, but in each case the correlation between the instrument generated by the first-stage procedure and the original series in K/L was less than 0.40.

PRODUCTIVITY 28.

Rawski

(1986).

GROWTH

p. 8.

29. This condition requires that equipment-construction composition and age structure of the capital stock are relatively constant across counties. 30. One possible explanation of these statistically insignificant results is that. as a measure of the age composition of the capital stock, newer, more efficient

IN CHINESE

INDUSTRY

57

vintages of capital may positively affect labor’s productivity. so as to counter the negative effect of the inflated measures in later vintages. is also consistent 31. This finding of scale economies with the results obtained by Jefferson (19%) in his investigation of the iron and steel industry using enterprise data.

REFERE JNCES Ahmed, Sultan, “International Comparison of Chinese Unpublished paper (Washington, DC: Prices,” Comparative Analysis and Data Division. Economic Analysis and Projections Department, World Bank, 1953). Chen, K., G. Jefferson, T. Rawski, H. C. Wang, and Y. K. Zhenu, “New estimates of fixed capital stock for Chinese”state industry,” China Quat&ly, No. 114 (June 1985) pp. 24.3-266. Chen, K.. ernl., “Productivity change in Chinese industry 1953-1985,” forthcoming, Journal of Compamrive Economics (198Sb). of productivity variation Jefferson, G. H., “Sources within China’s iron and steel industry: An econometric evaluation of 120 Enterprises,” Brandeis University Department of Economics Working Paper #I89 (Waltham, MA: 198X). Jefferson, G. H., “The Aggregate Production Function and Productivity Change: A Reevaluation of VerOxford Economic Prrpers doorn’s Law,” forthcoming (April 1989). Maddala, G. S., Economefrics (New York: McGrawHill Book Co., 1977). Ministry of Metallurgical Industry (MIM), Zhongguo Gangtie Gongye ?&njian. 1986 [Chinese Iron-and Steel Industry Yearbook, 19861 (Beiiine: Metalluraical Industry Publishing House,‘ 1987). Muellbauer, John, “Aggregate production functions and productivity measurement: A new look” (London: Centre for Economic Policy Research, 1984). Rawski, Thomas G., China’s Transition IO Hindusuialism: Producer Goods and Economic Developmem (Ann Arbor, MI: University of Michigan Press, 1980). Rawski, Thomas G., “Productivity change in Chinese industry: Problems of measurement,” Paper presented to the American Council of Learned Societies-Social Science Research Council (ACLSSSRC) sponsored conference on Price and Wage Reform in the People’s Republic of China (Washington, DC: 17-18 June 1986).

Reynolds. Bruce (Ed.), Reform in China: Challenges and Choices (White Plains, NY: East Gate Books. 1987). Shi Qingchi. Qin Baoting, and Chen Jing. Jishu Jinbu yu Jingji Zengjunq [Technical Progress and Economic Growth], (Beijing: Kexue Jishu Wenxian Chubanshe, 1985). State Statistical Bureau (SSB), China Urbnn Suzfistics f98.5 (Lcndon: Longman Group Ltd. and China Statistical Information and Consultancy Service Center, 1985). State Statistical Bureau (SSB), Zhongguo rongji nianjinn I986 [China Statistical Yearbook l9S6) (Beijing: _ Zhonggud tongji chuban she. 1986). _ State Statistical Bureau (SSB) Zhongguo renmin Eonghe guo yijiubarvu nian~gongye pucuh;; ziliao [Chinese People’s Republic Industrial Census Materials, 19S5] (Beijing: Zhongguo tongji chuban she. 1987). Taylor, Jeffrey R., “China’s price structure in international perspective,” Paper presented to the American Council of Learned Societies-Social Science Research Council (ACLS-SSRC) sponsored conference on Price and Wage Reform in the People’s Republic of China (Washington, DC: 17-18 June 1986a). Taylor, Jeffrey R., Input-Output Tables for rhe People’s Republic of China, 1958 and 1980 (Washington, DC: US Department of Commerce, Bureau of the Census, 1986b). Appendix H. Associates Forecasting Whar:on Econometric (WEFA). China Macroeconomic Documenration Dam Bunks, Vol. 3: Foreign Trade (Washington. DC: US Department of State, 198-l): World Bank, China: Socialist Economic Development (Volume I: The Economy, Statistical System. and Basic Data) (Washington; DC: World Bank 1983). World Bank, China: Long-rerm Development Issues and Options, Annex 5: China - Economic Structure in International Perspective (Washington, DC: World Bank, 1985).