China's iron and steel industry

Journal

of Development

Economics

CHINA’S

33 (1990) 329-355.

IRON AND

North-Holland

STEEL

INDUSTRY

Sources of Enterprise Efficiency and the Impact of Reform* Gary Brandeis Received

H. JEFFERSON

University,

Waltham, MA 02254-9110,

July 1988, final version

received

March

USA 1989

Using Chinese data which by standards of western industrial analysis is deficient but in other respects is quite rich, this paper corrects for these deficiencies and exploits the richness of the data set by (i) investigating the technological properties of China’s iron and steel industry, (ii) identifying the sources of productivity variation among enterprises within that industry, and (iii) evaluating the industry’s historical productivity performance. The paper explains over eighty percent of the variance in efficiency among 120 enterprises and finds that during the reform period productivity growth within this key industry has accelerated significantly.

1. Introduction Ten years after the inauguration of China’s economic reforms, there remains a surprising paucity of research on the productivity performance of key industries and the extent and causes of productivity differences among enterprises within these industries. This absence of quantitative productivity research is particularly surprising, since China’s reform program intends to achieve economic growth and rising living standards by emphasizing productivity gains rather than, as in the past, by mobilizing large injections of new capital and labor into the production process. The extent to which China is succeeding in achieving its reform objectives within the industrial sector remains an open question.’ This lack of quantitative research reflects, in large part, the absence of sufficiently detailed historical data during the 195Os, 1960s and 1970s. Productivity research is also complicated by the failure of much of the *This work is supported by research grants from the National Science Foundation and from the Joint Committee on Chinese Studies of the American Council of Learned Societies and the Social Sciences Research Council with funds provided by the Andrew W. Mellon Foundation and the Ford Foundation. The author appreciates the helpful comments and suggestions of Peter Petri, staff of the Economic Research Institute of China’s Ministry of Metallurgical Industry, including Zhang Xinchuan, Xue Chuanzhao and Gao Xiangling, and two anonymous referees. ‘Studies concluding that Chinese industrial productivity has continued to stagnate into the 1980s include the World Bank (1985) and Lardy (1987). A recent study that takes issue with this conclusion is Chen et al. (1988b). 03043878/90/$03.50

0

199&Elsevier

Science Publishers

B.V. (North-Holland)

330

G.H. Jefferson, China’s iron and steel industry

historical and currently reported data to conform to standard concepts of national income accounting. Inconsistencies between Chinese and standard accounting conventions include the reporting of undeflated fixed assets and the failure to distinguish between industrial and non-industrial factor inputs, where the latter are used to support housing, education, health and other services for employees. During the 1980s rising capital goods prices and substantial increases in non-industrial capital and labor have resulted in overestimates of the levels of industrial capital and labor inputs and their growth over time.’ One purpose of this paper is to investigate the conditions that give rise to striking differences in measured multifactor productivity among 120 enterprises within China’s iron and steel industry. Because the problems of data measurement and the economic conditions which account for much of the variation in measured efficiency among China’s iron and steel enterprises can be found in each of China’s major branch industries and within reported statistics from the level of the enterprise on up, the modeling approach used in this paper has wide-ranging application to analyses of Chinese industry at all levels. In addition to this enterprise-level cross-sectional analysis, a second purpose of this paper is to examine the productivity performance of China’s iron and steel industry during the period 1952-85. Whether productivity growth has accelerated during the reform period is of particular interest. Because the output of the iron and steel industry represents less than 6% of China’s total gross value of industrial output, the productivity performance of this industry provides only limited evidence concerning Chinese industrial productivity growth under the reform program. From a methodological perspective, the importance of this study is to develop a prototype for carrying out the numerous industry studies that are necessary to: (i) identify a pattern of China’s overall industrial productivity performance and (ii) provide an empirical foundation for investigating the range and sources of performance variation among and within key industrial branches. Using cross-sectional iron and steel enterprise data, this paper shows that estimating conventional production functions with raw Chinese input data leads to implausible results. The official measure of capital must be augmented with variables that account for important price and quality differences among different vintages and types of investment. The paper finds that substantial differences in measured levels of multifactor productivity among enterprises within China’s iron and steel industry can be largely explained by differences in scale, product mix, the level of enterprise supervision, and the vintage and composition of capital. The paper also finds that, once proper adjustments have been made to the factor input data to account for inflation 2The implications of Chinese industrial accounting procedures analysis have been extensively analyzed in Chen et al. (1988a, b).

for

industrial

productivity

G.H. Jefferson,

China’s

331

iron and steel industry

and the presence of non-industrial resources, compared with a trend of stagnating or deteriorating productivity during much of the pre-reform period, China’s iron and steel industry shows a pronounced increase in multifactor productivity during 198&85. Finally, this paper presents several policy perspectives concerning efficient patterns of investment within the iron and steel industry, the effect of price reform on measures of relative enterprise efficiency, and the potential for gains in allocative efficiency resulting from factor market reform. 2. Other work Few estimates of Chinese production functions appear in the published literature. Chow (1985) estimates a constant-returns-to-scale Cobb-Douglas production function for the aggregate state industrial sector. The author uses time-series data for which no effort is made to correct for distortions in the measurement of factor inputs discussed above. Chen et al. (1988b) estimate a translog production function for state industry using input data which they deflate and adjust for non-industrial capital and labor. Employing this data set, Lau and Brada (n.d.) estimate a frontier translog production function for the purpose of distinguishing between technical progress and deviations from best practice. Jefferson (1989) uses cross-sectional county-level data to estimate differences in factor returns between state and collective industry and between heavy and light industry and to investigate the sources of productivity change within each of these four sectors. A major shortcoming of each of the econometric studies discussed above is their reliance on highly aggregated data where the methods of aggregation are not consistent with an underlying representation of production.3 By focusing on a specific industry and using individual enterprise data, while not eliminating the aggregation problem, the approach used in this paper does, in principle, substantially reduce the potential for aggregation bias. Moreover, unlike most earlier econometric studies of Chinese industry, which use standard production functions, this analysis focuses on specific enterprise conditions - differences in vintages of capital, types of investment, product mix, and supervisory regimes - which account for differences in enterprise performance. 3. Statistical

profile of the industry and data description

In 1985, ferrous

metal

(iron

and

steel) production

in China

stood

at 44

3Normal procedures for aggregating inputs and outputs consist of adding up share-weighted inputs and outputs in which the weights represent the output contribution of each category of asset, worker or product. By comparison, aggregate Chinese data are typically constructed by adding up the values of assets and products, where the prices represent combinations of fixed state prices and market prices.

G.H. Jefferson,

332

China’s iron and steel industry Table

Statistical

1

profile of the 120 enterprises, Means All

Sample

size

Net output (Q: million yuan)

‘Medium small’

‘Key’ 120 118.5

48 249.1

and

72 43.1

Capital-labor ratio (K/L: yuan/worker)

14,296

19,046

11,129

Labor productivity (Q/L: yuan/worker)

5,902

6,951

5,202

Capital productivity (Q/r<: yuan/yuan)

0.542

0.469

0.591

Renovation capital productivity (Q/R: yuan/yuan)

0.895

1.103

0.751

Renovation invest/net capital stock (R/K: yuan/yuan)

0.721

0.657

0.773

billion yuaq4 representing close to two-thirds of the gross output of China’s metallurgy industry, which ranks the fifth largest among China’s fifteen ‘branch’ industries. In that year, the output of the metallurgy industry accounted for 8.0% of total gross industrial production.’ This paper uses data for 120 iron and steel enterprises reported in the Chinese Iron and Steel Industry Yearbook 198h6 Important statistical characteristics of these 120 enterprises are shown in table 1. Although these 120 enterprises account for only 9.5 percent of the 1,318 iron and steel enterprises under the jurisdiction of the Metallurgical Industry Ministry in 1985, because the largest plants are included in the sample, the 120 enterprises together account for approximately 80 percent of China’s total 1985 ferrous metal output.7 While biased toward the largest of the enterprises, the sample nevertheless 4The average Rmb-S exchange rate in 1985 was 2.93 Rmb yuan to the dollar [SSB (1986, p. 580)). ‘SSB (1986, p. 275). 6MMI (1986a). The Yearbook is the second in a series of such yearbooks published by the Metallurgy Industry Publishing House. The first issue appeared in 1985. Unfortunately, the enterprises for which data are reported in the 1985 and 1986 Yearbooks are sufficiently different to limit the opportunity to use panel data for analyzing certain dynamic issues or for diflerencing the data to account for fixed effects. The categories of enterprise data reported in the Yearbook are described in Appendix B. ‘The Yearbook reports data on 125 state-owned enterprises. However, since the data are incomplete for three of the enterprises and implausible factor input ratios suggest significant measurement or reporting errors for two of the remaining enterprises, this paper omits five enterprises from the sample.

G.H. Jefferson, China’s iron and steel industry

333

The average gross output of the contains numerous small enterprises. enterprises included in the sample is 118.5 million yuan, while that for the iron and steel industry as a whole is just 13.3 million yuan. Among the 120 enterprises, however, 26 enterprises report gross output levels below this latter figure in 1985. The smallest of these produced only 1.4 million yuan of output in 1985 - just 0.004 the size of the largest enterprise in the sample. It would appear, therefore, that if significant scale economies exist within the iron and steel industry, the sample contains sufficient diversity of enterprise scale to allow these economies to be captured in econometric estimates. The China Iron and Steel Industry Yearbook distinguishes between so-called ‘key’ and ‘medium and small’ enterprises. The former category applies to those enterprises that are supervised directly by the central government, while the ‘medium and small’ enterprises are supervised by local government, i.e. by provincial or municipal government.* The former are, on average, live times as large as the latter, but within both groups there exists a great deal of size variation. Net output of The Gannan Iron and Steel Factory, for example, the largest of the ‘medium and small’ enterprises, exceeds the net output of all but eleven of the 48 ‘key’ iron and steel enterprises reported in the sample. Table 1 shows a comparative statistical profile of the ‘key’ and ‘medium and small’ enterprises. It will be shown in the paper that there exist significant differences in the measured efficiency of these two enterprise types that have important implications for price reform.

4. Model

4.1. Functional form of the production function The enterprise tive form:

Qi=Ag(Ki,

production

4)&i,

technology

is assumed

to be of the multiplica-

i=l )...) 120,

where Qi represents the 1985 value of net output of enterprise i, Ai is a technology variable, Ki and L, represent, respectively, the net capital stock and labor force of the enterprise, and ai represents stochastic shocks to production. The translog production function is used to obtain estimates of the output elasticities of capital and labor. These elasticities are used for two purposes. First, they are used to construct measures of returns to capital and labor. Their second function is to serve as weights for combining separate indexes “Throughout the paper, ‘key’ and centrally-supervised and medium’ and locally-supervised.

are used interchangeably

as are ‘small

334

G.H. Jefferson, China’s iron and steel industry

of capital and labor into a composite input measure used to calculate time-series of multifactor productivity for the entire iron and steel industry. For the two-factor case, the translog function can be written as In Qi = In A,+ aK In Ki + aI. In Li + (1/2)cc,,(ln + (1/2)a,,(ln

a

Ki)*

I+)* + aKL In Ki In Li + e,,

(1)

where e, is assumed to satisfy all the basic assumptions of the linear regression model (iid). The assumption of constant returns to scale requires the following restrictions on the parameters of the production function: (i)

cr,+a,=

1,

(ii)

UKK+ UKL= 0,

(iii)

@LL+ c(KL= 0,

(2)

Alternatively, if aKK = txLL= aKL = 0 the output elasticities are simply c(~ and ~1~. The output elasticities of the resulting Cobb-Douglas function may or may not sum to unity. Whether the technology is constant returns to scale can be determined by testing the hypothesis ~1~+ ~1~= 1. 4.2. Model of the efficiency

parameter

The efficiency index, Ai, represents the efficiency level for each enterprise. For reasons discussed below, this index is not a pure measure of efficiency in the technical sense. The efficiency variable is modeled in the following way: lnAi=p,+P~*D+CPjsij+Pv~+BclnCi+Pc’lnci*D+ui,

(3) j

where &, is a constant and ui is iid. Within eq. (3), /I,$ is a dummy intercept where D assumes a value of unity for all locally-supervised (i.e. ‘medium and small’) enterprises and zero for centrally-supervised (i.e. ‘key’) enterprises. A to sign PA. The relative accessibility of the priori, it is not possible centrally-supervised enterprises to newer vintages of equipment suggests that the measured efficiency of these industries may be higher than that of locallysupervised enterprises. On the other hand, it is widely recognized that among the locally-supervised enterprises, as compared with the centrally-supervised enterprises, a greater share of output is sold at market prices that exceed administered state prices.g Consequently, because output is measured in value terms, even if the technical efftciency of both sets of enterprises were ‘See, for example, the discussion of the proportion of output subject to the mandatory by governments of different levels during 1984 in Reynolds (1987, pp. 55-56).

plan

G.H. Jefferson, China’s iron and steel industry

335

identical, under the dual pricing system, higher prices commanded by the ‘medium and small’ enterprises would cause these enterprises to appear to be more efficient. In this case, the estimate of /I; would be positive. The /Ij are efficiency parameters for the major product groups within the iron and steel industry (j = 2,. . . ,7). The parameter sij represents the share of each product type, measured in tons of total output, within each enterprise. The purpose of the product efliciency parameters is to capture differences in technical efficiency and price regulation among the seven product types.” Finally, the term c represents the vintage structure of investment and the terms Ci and Ci* D are proxies for the composition of investment. The motivation for and construction of these variables are described below.

4.3. Specification

of the capital

stock

A critical problem associated with using Chinese data on capital stock is that these data (i) do not adjust for inflation and (ii) include significant amounts of non-industrial investment. These issues of factor input measurement have been investigated at the aggregate state industry level by Chen et al. (1988a, b). Chen and his colleagues find that, especially since 1980, inflation in the capital goods sector has caused official measures of the capital stock to overstate the rateof-growth of capital substantially. They find that these overestimates do not significantly bias estimates of the output elasticities of capital and labor. Rather the effect of overestimating the rate of growth of capital is to bias downward estimates of the rate of technical change. For cross-sectional analyses at the enterprise level, different degrees of inflation in each enterprise’s purchased capital stock and differences in the mix of productive (i.e. industrial) and non-productive (i.e. non-industrial) capital cannot be captured by a trend variable. Specifically, errors-invariables will impart a downward bias to the estimate of capital’s output elasticity and an upward bias to labor’s elasticity estimate.” It can be seen from table 2, column (A) that estimates of eq. (1) alone give a point estimate of capital’s output elasticity that is negative and an estimate of labor’s output elasticity that is unrealistically large. Once the model has been fully specified and estimated, this bias can also be interpreted as resulting from omitted variables misspecification. For purposes of completing the model specification, therefore, it is necessary to formulate variables that capture the inflationary component of the capital stock and differences in the composition “These seven major product lines are steel, pig iron, steel products, iron ore, industrial coke, ferroalloys and refractories. “The intuition for this result is that the measurement error within the right-hand-side capital variable will be negatively correlated with the regression error. For a more complete explanation of the result, see Griliches and Ringstad (1971, pp. 194198).

336

G.H. JefSerson, China’s iron and steel industry Table 2 Estimation

Constant

(PO)

results.

(4

(B)

CC)

9.521 (10.536)

9.050 (10.465)

5.470 (4.384)

Constant (local) Mb) Capital Labor

(aJ

- 0.054 (0.614)

0.355 (2.759)

0.649 (5.880)

1.309 (11.036)

1.287 (11.911)

0.784 (5.214)

0.523 (4.103)

- 0.027 (0.114)

- 0.474 (2.542)

0.287 (3.974)

0.352 (6.316)

0.352 (3.763)

0.194 (2.171)

(/lJ

Composition (local) (Bt) Pig iron (In AZ) (In A3)

Iron ore (In A4) Industrial

0.320 (2.814)

- 0.098 (1.029)

Vintage (8,)

Steel products

2.351 (2.154)

-0.045 (0.361)

(c+)

Composition

0

coke (In A,)

-0.088 (0.303)

-0.131 (0.564)

0.626 (2.446)

0.511 (2.511)

-0.333 (1.172)

-0.473 (3.094)

-0.619 (1.556)

-0.568 (1.833)

Ferroalloys

(In Ab)

0.534 (2.173)

0.495 (2.584)

Refractories

(In A,)

0.295 (0.984)

0.420 (1.794)

R2 adj R2 H,: H,: H,: H,:

0.882 0.880

Translog CRS: aKK = ciLL= czKL= 0 CobbDouglas CRS: stability

of investment. The construction following two sections.

0.924 0.918

0.9 17 0.913

0.956 0.951

F(3, 104)=265.58 (3.10) F(4,108) = 1.78 (2.47) F( 1,107) = 16.91 (3.95) F(26,94) = 0.40 (1.65)

of these

two variables

is described

in the

4.3.1. Deflating the capital stock In order to adjust for undeflated capital stock, the age structure of the capital stock is used as a proxy for inflation. The age or vintage structure, in turn, is measured as the ratio of the net to original capital stock.12 The higher the ratio I$ which lies in the range zero to one, the more youthful the “The original capital net capital stock adjusts

stock is the cumulation of original the original series for depreciation.

investment

values, adjusted

for scrap;

~G.H. Jefferson, China’s iron and steel industry

337

capital stock. Since the rate of inflation in investment goods has been positive, and has been particularly pronounced during the 1980s enterprises with a young investment age structure will, relative to enterprises with an old investment structure, tend to overstate the quantity of productive capital on hand. This will be the case unless recent vintages of capital have become sufficiently more productive than older vintages to warrant higher prices for the newer vintages.’ 3 4.3.2. Eliminating non-industrial fixed assets During the past decade, most industrial investment in China’s state sector has been classified as basic construction or technical renovation. Prior to 1978, no such distinction was made. Basic construction investment is now hospitals, shops and other nonused extensively for housing, schools, industrial investments, as well as for industrial structures. By comparison, since technical renovation investments are largely restricted to plant renovations and equipment purchases, they are, for the most part, pure industrial investmentsi To account for the presence of substantial quantities of non-industrial capital, an investment composition variable has been constructed. Within eq. (3), this variable is represented by Ci, where Ci = SRJK,. In the expression above, Ri represents accumulated renovation investment, Ki is the net capital stock and 6 represents a parameter with value 0<6< 1. The parameter, 6, assumed to be constant across all enterprises is necessary to depreciate the stock of accumulated renovation investment in order for the measure of renovation capital to be consistent with the net stock of total is If as anticipated, technical renovation investcapital in the denominator. ment contains a higher proportion of industrial investment than basic construction investment which comprises some fraction of total fixed assets represented by Ki, then we expect a positive estimate of the parameter 8,. Due to persistent reports that locally supervised enterprises, in particular, are allocating substantial quantities of their investment capital to ‘non13That efficiency improvements more than compensate for price inflation is plausible for the case of equipment, since, according to the estimates of Chen et al. (1988), between 1978 and 1985, equipment prices rose by only 10 percent. By comparison, construction and land prices nearly doubled during the same period. 14The Yearbook [SSB (1986a, p. 520)] reports that for 1985, most investment goods purchases using technical renovation capital were for productive equipment. For the minor cases where ambiguities exist (e.g. ‘other’) in the published data, representatives of the Ministry of Metallurgical Industry indicate that these expenditures were also principally for industrial equipment. ‘sSince relatively little renovation investment was made prior to the 198Os, cumulative depreciation of the renovation investment is comparatively small

338

G.H. Jefferson,

China’s iron and steel industry

productive’ uses, eq. (3) includes a dummy variable, Ci * D, for the set of locally-supervised enterprises. The dummy is used to test the hypothesis that the proportion of net capital investment used for non-industrial purposes among the ‘medium and small’ enterprises differs significantly from that of the ‘key’ enterprises. Substituting eq. (3) into eq. (1) gives

+ a,’ In Ci * D + LXKIn Ki + CZ,_ In Li + (1/2)cr,,(ln + ( 1/2)c(,,(ln Li)2 + uKL In K i In Li + si,

KJ2 (5)

where si=ei+ ui. Eq. (5) is used to estimate the production technology of the iron and steel industry and to identify the sources of productivity differences among enterprises within the industry.

5. Data issues and estimation

results

Estimation results for eq. (5) are reported in table 2. As reported above, column (A) reports the estimation results for a version of eq. (5), in which all but the coefficients on capital and labor are restricted to zero. For purposes of comparison, eq. (5) has also been estimated with coefftcients restricted to equal zero on the vintage and investment composition variables [column (B)] and on the product mix variables [column (C)]. The estimation results for the full model are shown in column (D).16 As indicated by the F-tests reported at the bottom of table 2, it is not possible to reject the hypothesis that all of the second-order terms, cxKK,aLL and aKL, are zero. An F-test does, however, decisively reject the hypothesis of constant returns to scale in favor of increasing returns.17 The estimation results in table 2, column (D) show that the estimated output elasticities are 0.649 for capital and 0.523 for labor. Together these imply a scale parameter of 1.172. The estimation results also show statistically significant estimates for ‘%I order to test for heteroskedasticity, a Park-Glejser test was used in which the log of the square of the residuals obtained from eq. (5) column (D) was regressed on a constant and the log of net output. The test rejects the hypothesis that errors in the model are correlated with enterprise size, and, consequently, no heteroskedasticity correction was used in the estimation procedure. “In order to test the stability of estimates of eq. (5) over the full sample, the sample was evenly split into two subsamples: one consisting of large enterprises (net output levels in excess of 44 million yuan); the other consisting of smaller enterprises (net output less than 44 million yuan) during 1985. A Chow test (results reported in table 2) is unable to reject the null hypothesis that the relationship is stable across the two sets of enterprises.

G.H. Jefferson, China’s irpn and steel industry

339

capital’s vintage effect. The negative estimate of this parameter value indicates that recent inflation in capital goods prices has resulted in a substantial overestimate of the real quantities of productive capital within many enterprises. Large price increases in capital goods prices since 1980 have not been warranted by efficiency improvements in recent vintages of capital. At both the ‘key’ and ‘medium and small’ enterprise levels, the investment composition effect is also signiticant. The greater the proportion of technical renovation capital, i.e. pure industrial capital, the larger the output elasticity of the enterprise’s stock of capital. The results also show that this parameter estimate is significantly larger for locally-supervised enterprises than for centrally-supervised enterprises. As shown in Appendix A, the economic significance of this result is that it is not possible to reject the hypothesis that the marginal product of basic construction investment at the local level is zero. The dummy intercept in eq. (5) /?A, measures the relative efficiency of the ‘key’ and ‘medium and small’ enterprises, holding constant other factors, including scale, capital’s vintage structure and investment composition. The estimate of this dummy intercept indicates that the measured efficiency of enterprises that are locally supervised is typically higher than those of enterprises under direct central government supervision. While this difference may reflect a technical efficiency advantage of locally-supervised enterprises, once scale and investment composition differences have been accounted for, the difference in measured efficiency between the two levels of enterprises is more likely to reflect the relative freedom that locally-supervised enterprises have in setting their product prices above state-administered price levels.18 Finally, the seven product mix parameters show that product mix can significantly affect measured efficiency, In particular, relative to steel, represented by the reference intercept, PO, measured multifactor productivity is higher in enterprises specializing in steel products, ferroalloys and refractories than for those specializing in iron ore, pig iron and industrial coke. This relative efficiency ranking most likely reflects the fact that, in general, the former group of products are sold outside the iron and steel industry while the latter group is consumed internally within the producing enterprises or transferred to other iron and steel enterprises. While some portion of the ‘“If, as may be the case, enterprises with relatively large shares of self-marketed output also purchase relatively large shares of their inputs on the market, then opportunities to substitute capital and labor against higher cost materials should cause the true measure of value-added multifactor productivity to be lower than for those enterprises which obtain material inputs at state-administered prices. Nonetheless, since opportunities to substitute capital and labor against materials are limited, unless the mark-up on material inputs purchased on the market is substantially greater than for self-marketed output, the measured multifactor productivity of enterprises facing relatively unregulated product and material input prices should continue to be higher than that of the enterprises facing regulated prices.

340

G.H. Jeferson,

China’s iron and steel industry

distributed products are self-marketed at above state-regulated prices, the value of the products that are produced and consumed internally within the iron and steel industry are accounted for at relatively low administered prices. An important issue, addressed in the tinal section of this paper, is the effect further price liberalization may have on these measures of relative product efficiency and on the variance of enterprise efliciency within the iron and steel industry.

6. Sources of variation in multi-factor

productivity

In order to investigate differences in multifactor productivity within the enterprise sample and the sources of these differences, a multifactor productivity index is calculated for each of the enterprises using the expression MFP = exp [ln Qi - X: In Ki - cc: In LJ,

(6)

where LYEand LX: are the estimated output elasticities normalized to sum to productivity calcuunity. i9 The sample mean of the indexes of multifactor lated for the 120 enterprises is 33.3 with a standard deviation of 20.3. Among the enterprises, the Shenyang Steel Rolling Mill reports the highest level of multifactor productivity (125.1), while the Zhongshan Iron Mine (Anhui Province) reports the lowest level (3.4). These results show considerable variation in multifactor productivity levels among the 120 enterprises. It would be useful to know, therefore, how much of the difference in these estimated levels of factor productivity can be explained by economies of scale, the level of supervision, product mix, and investment composition and vintage differences. In order to investigate the sources of variation in enterprise multifactor productivity, the multifactor productivity variable constructed by the procedure described above is regressed on each of the potential sources of productivity variation. 2o Because two of the regressions include the scale variable (In Q) as an explanatory variable which is likely to be positively correlated with the regression error representing productivity shocks [i.e. Cov(ln Qi,si)>O], a two-stage estimation procedure is used to obtain the results reported in columns (A) and (E) in table 3.2’ The explanatory power of each source taken independently is measured by the R2 statistic, Because these included explanatory variables are correlated with the “That is, a$ = ax/(ak + aL) and zz = aL/(aK + ar). This procedure results in weights of 0.554 capital and 0.446 for labor. *‘Each of the equations is estimated in log-linear form. ‘IThe instruments used in the lirst-stage procedure are the factor inputs - the net value of fixed assets and the labor force. Within Chinese industry, both capital and labor may assumed to be fixed factors whose contemporaneous quantities are generally unaffected productivity shocks.

for

the be by

G.H. Jefferson,

China’s iron and steel industry

341

Table 3 Sources

of differential

Constant

2.564 (8.187)

MFP (dependent

2.847 (32.976)

variable:

3.621 (71.345)

Dummy constant for ‘medium and small’ Output

(scale)

In MFP).

3.375 (17.600)

2.012 (6.651)

0.060 (0.527)

0.273 (3.079)

0.092 (2.489)

0.147 (5.365) - 1.146 (5.195)

Vintage

- 0.405 (2.930)

Composition

0.347 (6.260)

0.300 (7.297)

Composition (local)

0.398 (4.322)

0.166 (2.190)

Pig iron

-0.318 (0.891)

-0.115 (0.598)

0.562 (1.835)

0.436 (2.557)

-0.595 (2.592)

- 0.403 (3.070)

- 0.027 (0.057)

-0.485 (1.877)

Ferroalloys

0.180 (0.655)

0.422 (2.687)

Refractories

-0.088 (0.266)

0.358 (1.848)

0.318 0.276

0.812 0.793

Steel products Iron ore Industrial

R2 Adj. R2

coke

0.165 0.158

0.186 0.179

0.458 0.449

omitted variables, each of the R2 statistics must bound on the explanatory power of the included reported on table 3 and summarized below:

be interpreted as an upper variables. These results are

(i) Scale (A) ‘explains’ 16.5 percent of the variation in multifactor productivity. This result mirrors the finding of increasing returns to scale reported in table 2. (ii) Vintage (B) ‘explains’ 18.6 percent of the variation. The newer the vintage of capital, the lower the productivity level, since the prices of vintages are inflated relative to older vintages, even after efficiency differences have been accounted for. (iii) Composition of the capital stock (C) ‘explains’ 45.8 percent of the variation in multifactor productivity. The greater the share of renovation capital, the greater capital’s productivity. The result reported in table 3 suggest that, among locally-supervised enterprises, this composition effect is

G.H. Jefferson,

342

China’s iron and steel industry Table 4

Comparisons

of enterprise-level

Mean (m) Standard Coeffkient

deviation

(sd)

of variation

(sd/m)

multifactor

productivity.

Unadjusted

Adjusted

33.3

21.2

20.32

10.6

0.661

0.391

significantly more pronounced than within the centrally-supervised sector. This difference is not surprising in light of the finding that the productivity of basic construction capital within the local-supervised sector is negligible. (iv) Product mix and level of supervision (D) ‘explains’ 31.8 percent of the productivity variation. These coefficients capture price/efficiency differences among enterprises supervised at the central and local levels and among the seven product groups. The fact that the estimated coefficients on steel products, ferroalloys and refractories are significantly larger than that for steel (i.e. the intercept), while the coefficients on iron ore and industrial coke are significantly lower than the steel coefficient, reflects different pricing arrangements for the former set of products, a substantial portion of which is sold at market prices outside the industry, and the latter set, a substantial portion of which is transferred at administered prices within the industry. In order to identify the total explanatory power of these four factors, they are combined into a single regression equation. The results, reported in column (E) of table 3, show that these five factors account for 81% of the variation in multifactor productivity among the 120 enterprises. Since investment goods inflation, non-industrial investment and the dual pricing regime are spurious sources of efficiency difference, the multifactor productivity indexes are recalculated to adjust for these differences. Stripped of these factors, the mean of the adjusted indexes, reported in table 4, declines to 27.2, while the standard deviation falls to 10.6. The coefficient of variation therefore declines from 0.611 based on the unadjusted indexes of multifactor productivity to 0.391 based on the adjusted indexes. Moreover, the index for the Shenyang Steel and Rolling Mill falls from 125.1 to 53.0 while that for the Zhongshan Iron Mine rises from 3.4 to 10.2. While the Zhongshan Iron Mine, the smallest of the enterprises in the sample, continues to rank as the least efficient of the 120 enterprises, according to the series of revised efficiency indexes, the Shoudu (Capital) Iron and Steel Company is the most efficient (80.0). By adjusting for spurious sources of differences in measured efficiency, the spread between the most and least efficient enterprises therefore declines from a factor of 37 to less than 8. These results, combined with those obtained for returns to industrial capital reported in Appendix A, indicate the substantial potential for gains in allocative efficiency within China’s iron and steel industry.

343

G.H. Jefferson, China’s iron and steel industry

7. Productivity growth: 195245 As reported in the introduction to this paper, a number of productivity studies have concluded that since the inauguration of the industrial reforms in 1978, the growth of productivity within China’s state-owned industrial sector has been negligible. One explanation of such a finding is the failure of reported Chinese industrial statistics to deflate the capital stock or to exclude non-industrial capital and labor, such as enterprise housing, health facilities, schools and the personnel who staff these facilities. Chen et al. (1988b) found that when the capital stock is appropriately deflated and the non-industrial resources removed, the growth of multifactor productivity in state-owned industry over the 1978-85 period was approximately 2-3% per annum greater than estimates obtained using the unadjusted input data. In order to investigate the performance of the iron and steel industry during 1952-85, estimates of capital and labor’s output elasticities obtained from cross-sectional enterprise data are used to implement a growth accounting exercise based on the time-series data reported for the total iron and steel industry reported in the iron and steel industry yearbooks [MM1 (1985, 1986a, b)]. This method assumes that the constant elasticities found in cross-sectional data also obtain over time and that these elasticities based on a sample of enterprise data are appropriate for the entire industry.22 While the analysis requires these assumptions, due to the benefit of a large sample size, the diversity of enterprise types within the sample, consistency between input and output data, and the ability to control for a variety of relevant factors, these cross-sectional estimates are believed to be substantially better than those that may be attainable from time-series data. In the two sections that follow, trend rates of multifactor productivity growth are calculated for the years 1952-57, 1957-80 and 198&85 using two sets of data,23 an unadjusted set and one for which the capital stock has been deflated and both non-industrial capital and labor have been excluded. (A) Unadjusted data. The rate of growth of multifactor productivity, conventionally defined as the difference between the growth of output and the weighted sum of the rates of growth of the inputs, can be calculated using the following expression:

mfpr= 4, where

~121 +a:

4% - 6%

= 1 and

mfp,

(7) q,

k

and

1 represent

the

average

*‘In 1985, the 120 enterprises within the sample used for the cross-sectional paper represented approximately 80% of total iron and steel production. 23Although the reforms began to be implemented in 1978, the 1980 division data used to adjust the capital stock are unavailable before that year.

analysis

annual in this

is used because

344

G.H. Jefferson, China’s iron and steel industry

exponential rates of growth of multifactor productivity, output,24 capital and labor, respectively. Because the rates of growth of original and net fixed assets are comparable during the periods under comparison and the data used to adjust the capital stock in the following section are for the original value of fixed assets, the original fixed asset values are used for the growth accounting exercise. 25 Gross output, labor input and the original value of fixed assets for the reference years are reported in the iron and steel industry yearbooks and appear in table 5. 26 Using the relevant annual growth rates and the same resealed output elasticities as those used for calculating the level of multifactor productivity gives the following rate of productivity growth for the 198&85 period:

1.1%=5.0%-0.554(5.3”/,)-0.446(2.2%).

(8)

Table 6 shows that this unrevised rate of productivity growth during 1980-85 compares unfavorably with the spurt of productivity growth within the iron and steel industry during 1952-57 (6.7%) but is a notable improvement over the -2.4% rate estimated for 1957-70 and the -0.7% rate for 1970-80. This pattern of productivity change reflects the extraordinary high rate of output growth as the steel industry recovered from the SineJapanese War and the Civil War. Subsequent dislocations of the Great Leap Forward during the late 195Os, the withdrawal of Soviet technicians during the early 196Os, a proliferation of small, inefficient plants during the late 196Os, and a coincidence of other factors2’ substantially depressed productivity growth during the two decades bracketed by economic reconstruction and the recent reforms. (B) Adjusted dutu. The methods used for adjusting the labor and capital input series are described in Appendix B. Adjusting the fixed assets of the iron and steel industry for non-industrial investment results in a substantial downward adjustment in the annual growth rate of the capital stock during 1952-57 and a 0.7% downward adjustment for 198@85. These adjustments 240utput is gross value of industrial output measured in terms of constant 1980 prices. 25During the period 198%85, the growth of the original value of fixed assets somewhat exceeds the growth rate of net fixed assets. This disparity may exist due to the increase in accounting rates of depreciation, in which case the growth of original fixed assets would be the more appropriate measure. ‘%ince the labor and capital series are end-of-year figures, the figures reported in table 5 are averages of the reference year and previous year figures. “These factors include the initiation of the Third Front program involving a shift in investment to the less productive interior and the depletion of engineers and technical personnel during the Cultural Revolution.

G.H. Jefferson,

China’s iron and steel industry

345

Table 5 Input and output

data; selected

years.

Unit

1952

1957

1970

1980

1985

Gross output (constant 1980 prices)”

Rmb 100 million

13.9

52.4

153.4

342.2 (306.4)

440.3

Staff and workersb

10 thousand persons

11.1

38.6

146.5

236.4

263.2

Gross

Rmb 100 million

12.3

38.8

161.1

438.8

572.0

Productive staff and worke&

10 thousand persons

17.7

33.1’

na.

200.9

2 16.8

Productive gross capital’

Rmb 100 million

12.3

33.99

n.a.

365.1

462.8

Deflated productive gross capital’

Rmb 100 million

12.3

33.9s

na.

364.4

430.8

capital’

“MM1 (1985, p. 699) for 1952, 1957 and 1970; MM1 (1986b, p. 155) for 1970, 1980 and 1985. Figures for 1952 and 1957 are calculated according to a 1952 price base; 1970 and (1980) according to a 1970 price base; 1980 and 1985 according to a 1980 price base. ‘MM1 (1985, p. 726) for 1952 and 1957; MM1 (1986a) p. 523 for 1970, 1980 and 1985. Figure are averages calculated as (L, + L,_ ,)/2. ‘MM1 (1985, p. 720) for 1952, 1957 and 1980; MM1 (1986a, p. 522) for 1985. Figures are averages calculated as (K, + K, ,)/2. ‘See labor section of Appendix B. ‘See capital section of Appendix B. ‘Estimated by assuming that the ratio of unproductive labor to capital is the same as that calculated for 1980 and 1985, i.e. a virtually constant ratio with value 1.022. Therefore, the factor used to adjust the 1957 labor force figure=0.858 x 1.022=0.877. See following footnote. gEstimated by subtracting 530 million Rmb in non-productive investment [MM1 (1986b. p. lOS)] from total investment during 1953-57 and calculating the ratio of productive/total stock of capital in 1957 (i.e. 0.858).

reflect declines in the proportion of productive fixed assets from SS.S”/, in 1957 to 83.2% in 1980 and finally in 1985 to 80.1%.28 For labor, the share of industrial labor in 1957 is estimated to have been 87.7o/,,29 which then declined to 85.0% in 1980 and again to 81.9% in 1985. These declining proportions of industrial employment result in downward adjustments in labor force growth of 2.6% for 1952-57 and 0.7% during 1980435. Based on these new estimates for the growth of capital and labor, the rate of growth of multifactor productivity during 198&85 rises to 1.8%. The same “See table 5 and Appendix B for details of how these ratios are calculated. The entire stock of Iixed assets is assumed to have been industrial in 1952, since enterprises at that time had not yet assumed the substantial range of social service functions that they later assumed. “%ee table 5, footnote f.

G.H. Jefferson, China’s iron and steel industry

346

Table 6 Exponential 1952-57 Gross

output

rates of growth. 1957-70 8.3%

26.5%

Staff and workers

15.6

Gross capital

23.0

Multifactor productivity (unadjusted)

6.7

197tHO 6.9%

5.0%

4.8

2.2

10.0

5.3

10.3 11.0

198c-85

-2.4

-0.7

1952-57

1957-80

198C-85

Productive staff and workers

13.0%

Productive capital

19.9

10.4

4.6

Deflated productive capital

19.9

10.4

3.4

9.7

- 1.6

1.8

9.7

- 1.6

2.5

Multifactor productivity adjusted for: non-productive capital and labor Non-productive capital and labor and inflation

7.7%

1.1

1.5%

adjustment procedure results in upscaling the 1952-57 estimate of productivity growth to 9.7”/, and an estimate for the entire 1957-80 period of - 1.6%. The second stage of the data revision procedure requires deflating the industry’s fixed assets. For fixed assets prior to 1980, no adjustment is made for inflation, since data regarding the composition of investment are unavailable and the price indexes reported in Chen et al. (1988a) suggest that during this period, falling equipment prices and rising construction costs combined to maintain substantial price stability for investment goods as a whole.30 Adjusting the growth of the capital stock during 1980-85 for rising investment goods prices results in a further downward adjustment in the growth of the industry’s fixed assets from 4.6% (industrial assets only) to 3.4%. This 30As Chen et al. indicate, while this conclusion represents their best judgement using the data available at the time of their investigation, a considerable degree of uncertainty underlies the conclusion. Direct enterprise survey work now underway in China may help to clarify the degree of overall asset price stability during the pre-reform period.

G.H. Jefferson, China’s iron and steel industry

347

final adjustment implies a rate of multifactor productivity growth during 1980-85 of 2.5x, an increase of 1.4% over the unadjusted rate, and a considerable improvement over the negative rates of productivity growth recorded during the previous two decades.31 Observers [e.g. World Bank (1983)] contend that the tendency toward selfsufficiency among municipal and provincial authorities within China has caused the proliferation of inefficient small scale plants. Further investigation of the enterprise sample indicates that significant economies are attainable at least up to an enterprise size of 70 million yuan net output, since it is only after omitting the enterprises below this threshold that the estimation results can no longer reject the hypothesis of constant returns to scale.32 This result implies that substantially more than two-thirds of the 120 enterprises in the cross-section sample and more than 95% of all plants within the iron and steel industry are operating on the downward sloping section of their longrun average cost curves. The Yearbook data report a decline in the number of iron and steel enterprises from 1,334 in 1980 to 1,318 in 1985,33 corresponding to an annual increase in average enterprise size, measured in gross output, of approximately 5.3%. Combined with the estimated elasticity of multifactor productivity with respect to scale of 0.147 reported in table 3, this increase in scale implies an average annual increase in productivity of 0.8%, or approximately one-third of the total annual productivity increase during 1980-85. The presence of many enterprises operating well below minimum optimal scale underscores the substantial potential for further exploiting scale economies within this industry.

8. Conclusions and policy issues

Following a poor productivity performance during the period 1957-80, China’s iron and steel industry has, during the reform period, achieved a respectable rate of productivity growth. The rate of 2.5%, based on adjusted input data, is substantially less than the estimate of 5.2% that can be calculated from the results reported by Chen et al. (1988b) for state-owned ‘iNote that the choice of 1980 as a reference year is critical. In that year output of the iron and steel industry was at the peak of a production cycle. Iron and steel production fell sharply in 1981 and only recovered its 1980 level of production in 1982. Calculated from 1981-85, productivity grew at a rate in excess of 5%. If the industry in both 1985 and 1980 approximated some ‘normal’ rate of capacity utilization then the choice of these as reference years is appropriate. 3ZThis result was obtained by repeatedly increasing the minimum size (i.e. net output) of enterprises included within the sample. Conceptually, this experiment entails eliminating smaller enterprises from the sample that lie along the downward sloping section of the long-run average cost curve until it is no longer possible to reject the hypothesis of a linear, horizontal average cost curve. 33MMI (1985, p. 697 and 1986b, p. 2).

348

G:H. Jefferson, China’s iron and steel industry

industry as a whole during 198&85. However, a substantial proportion of total investment during these years was concentrated on the construction of a new mammoth steel complex, Shanghai’s Baoshan Iron and Steel Works.34 Moreover, during the first half of the 1980s China’s iron and steel industry digested an exceptionally high proportion of imported equipment. While large and medium-sized state industry enterprises as a whole imported 26% of their 1980s vintage equipment, the ratio for the iron and steel industry was 43%. 35 These two conditions suggest that during the next five years potential learning curve effects within China’s iron and steel industry may be very substantial. The estimation results in table 3 show that once differences in scale, the age and investment structure of the capital stock, and product mix have been accounted for, substantial differences in measured productivity persist that can be attributed to the two-tier pricing system. Since measured productivity tends to be lower among centrally-supervised enterprises and among enterprises that specialize in raw material production (controlling for other factors), price reform should, at the margin, have the effect of further narrowing these productivity differences. Large differences in multifactor productivity, and consequently in returns to capital and labor, underscore the fact that, even in the absence of technical change, substantial efficiency gains are attainable from a more rational allocation of labor and investment resources with a view toward achieving scale economies and allocative efficiency. Important preconditions to achieving improved levels of allocative efficiency, however, are the development of product markets to signal meaningful scarcity prices and national capital and labor markets that can serve to allocate capital and labor to the more efficient enterprises. As future editions of the annual Iron and Steel Industry Yearbook are published, time-series data for each of the enterprises used in this sample will become available. The availability of a panel data set will create analytical opportunities that cannot be handled by a single year of cross-sectional data. These opportunities include the following: (1) The use of fixed components estimators that make it possible to account for fixed, systematic differences among the enterprises. (2) A more complete investigation of errors-in-variables issues, including those associated with measures of the capital stock examined in this paper.j6 3“During 1984-85, the doubling of the level of fixed assets of this steel complex represented approximately one-quarter of the increase in fixed assets for the industry as a whole. In 1985, capital’s productivity within this enterprise was just one-quarter the level of average capital productivity for all ‘key’ iron and steel enterprises. ‘sconstructed from SSB (1987, pp. lW115 and 116131). s6See, for example, Griliches and Hausman (1986).

G.H. Jefferson, China’s iron and steel industry

349

(3) An investigation of certain dynamic issues, including adjustment costs associated with major investment programs and learning curve effects3’ In addition, the availability of panel data will allow for systematic investigations of the sources of productivity change and the impacts of specific reform measures. In particular, this panel data will make it possible to investigate: (i) sources of scale economics, since it will be possible to track changes in the size distribution of enterprises; (ii) efficiencies that result from factor reallocations from less to more efficient enterprises; and (iii) the efficiency impacts of specific reform measures, such as various forms of the director responsibility system, labor contracting and the new ‘optimal labor combination’ program.38 One shortcoming of the present analysis is its reliance on price deflators for the fixed assets of aggregate state industry to deflate the capital stock of the state-owned iron and steel industry. To improve upon the time-series productivity analysis of this paper and to extend the analysis to other industrial branches, it will be necessary to develop a set of asset price deflators for China’s other key industrial sectors, since each branch incorporates different mixes of structures and equipment and different equipment types. Preliminary analysis of China’s industrial census [SSB (1987)] indicates that during 198&85, various key industries turned in dramatically different productivity performances. High rates of both labor and capital productivity growth within China’s machine building industry indicate that this industry substantially outperformed both the iron and steel industry and state industry as a whole, while, at the other end of the spectrum, stagnant labor productivity growth within the food processing and textile industries combined with large negative rates of growth of capital’s productivity imply disappointing performances for these industries. The methods set forth in this paper provide a perspective and strategy for investigating such substantial differences in the growth and productivity performances of these industries.

Appendix A: Differential

returns to capital

This appendix uses the estimation results reported in table 2 to: (i) compare the returns to investment of centrally- (‘key’) and locally-supervised (‘medium and small’) enterprises and (ii) compare differential returns to pure industrial investment at the enterprise level. 37For an illustration of how costs of adjustment and learning curve effects are estimated within the framework of a production function; see Jefferson (1988a). “Investigating the impact of these reforms will require supplementary data collection regarding the timing and quality of implementation of each of the reforms within the enterprises included in the sample.

350

G.H. Jefferson, China’s iron and steel industry

A.1. Comparison of parameter estimates between state and locally-supervised enterprises One interpretation of the investment by the following specification:

composition

variable

can be shown

K:=Ci*Ki,

64.1)

where K: measures the quantity ing eq. (4) into eq. (A.l) gives Kr=(GRi/Ki)

* Ki.

of productive

investment

capital.

Substitut-

(A-2)

According to this specification, if R,=O, then K: = 0. The implication of this specification is that non-renovation investment is not productive. This specification can be tested by entering the ratio, GRJKi, and Ki separately into the estimation equation and testing whether, as implied by the specification, the estimated output elasticities are equal. For the ‘key’ enterprises, the estimated composition parameter of 0.352 is both significantly greater than zero and significantly less than capital’s estimated output elasticity. By comparison, for the ‘medium and. small’ enterprises, the estimate of the investment composition parameter (/IC++i) is 0.546. An F-test indicates that this estimate is not significantly different from the estimate of capital’s output elasticity (i.e. ~1~=0.649).~~ That is, the specification shown by eq. (A.2) cannot be rejected; this result leads to the conclusion that a negligible part of basic construction investment within locally-supervised enterprises is actually being used for productive industrial purposes. That is, the marginal product of basic investment construction is not significantly greater than zero.

A.2. Capital’s output elasticity Starting with eq. (5) setting LX~~=CL~~=C(~~=Oto be consistent with the estimation results and partially differentiating output with respect to the right-hand-side capital variables enables us to investigate the output elasticities of different types of capital. 4o While the following discussion applies to centrally-supervised enterprises (i.e. the /I, parameter estimate), it can be 39F(1,107)=0.83 (3.95). @‘Since the effect of a one percent increase in the stock of any type of investment on the vintage variable is the same, the vintage variable is excluded from the following analysis. Moreover, since no estimates are available for separate technical renovation and basic construction vintage effects, including the composite vintage effect in estimates of the output elasticity of different types of capital may be misleading.

351

G.H. Jefferson, China’s iron and steel industry

easily extended to the set of locally-superivsed the estimate of B,+p,‘). (a) Investment that is neutral respect to In Ki gives

enterprises

in its composition:

(i.e. by substituting

Differentiating

alnQi/alnKi=a,+p,alnRi/alnKi-p,.

Ri=(a,-p,)(aln

with

(A.3)

For new investments that embody the non-renovation capital, the second and eq. (A.3) cancel, since total capital and proportions (i.e. C?In R,/c? In Ki = 1). The is therefore aK. (b) Renovation investment: The output dlnQ,/aln

In&

same composition of renovation and third terms on the right-hand side of renovation capital change in the same output elasticity of neutral investment

KJaln

elasticity

of renovation

RJ+B,.

investment

is

(A.4)

For the general case in which investment consists of both renovation and non-renovation investment and both types of investment are productive (P,#aJ, capital’s output elasticity must be evaluated by the composition of investment within each enterprise. In the event that the entire capital stock consists of renovation investment (i.e. Ki= R,), the output elasticity once again simplifies to aK. If, as in the case of the ‘medium and small’ enterprises, the estimates of aK and p, are identical, so that the entire effective increase in the capital stock is due to the addition of renovation investment, then the output elasticity is also aK (or PC). (c) Non-renovation investment: The output elasticity of investment in nonrenovation capital, Ni, is a In Q/a In Ni = (aK - /?,)(a In KJa In Ni).

(A.5)

For the case in which Pc=aK, as with the ‘medium and small’ enterprises, the output elasticity of non-renovation investment is just zero, since the quantity of renovation investment is unaffected by additions of non-renovation investment. Otherwise, for the case in which Pc#aK, the output elasticity of renovation capital must be evaluated knowing the investment composition of each enterprise. A.3.

Variation

in returns to pure industrial equipment

investment

Using estimates of the output elasticity of pure industrial equipment investment, we can evaluate the measured social returns to this investment type. Returns to technical renovation investment - a relatively pure form of industrial investment - are calculated separately for centrallyand locallysupervised enterprises using eq. (A.4), the formula for computing the output elasticity of renovation investment. The results shows a mean rate of return during 1985 of close to 50% with a standard deviation of 39%. Among the 120

352

G.H. Jefferson, China’s iron and steel industry

enterprises, returns range from a low of 6% at the Shuicheng Iron and Steel Works (Guizhou) to 162% at the Shenyang Steel Rolling Mill. These results indicate the presence of very large differences in returns to pure industrial investment among enterprises within the iron and steel industry. By using the output elasticity calculated in eq. (A.4) and limiting the analysis to industrial equipment investment, the analysis controls for the vintage and composition effects. Further adjustments based on product mix differences are unlikely to overturn the conclusion that substantial efficiency gains can result from the development of capital markets and the shift of industrial investment toward those enterprises with high rates of return to capital. Appendix B: Description B.1.

of data sources and revision procedures

Yearbook enterprise data

in the Iron and Steel Industry in constant 1980 prices, net output in current prices, net and original capital stock, labor productivity, and separate series on current basic construction and technical renovation investment. Also, the Yearbook reports data on wages, profits, taxes, per unit cost reduction and certain other items. The 1985 Yearbook reports timeseries data for the total industry for the period 1952-84. Among

the enterprise

data

reported

Yearbook 1986 are 1985 data for gross output

B.2. Factor input adjustment procedures

The labor and capital input data are adjusted for inflation and nonindustrial workers and investment according to the following methods: (a)

Labor. The Iron and Steel Industry Yearbook reports data for the composition of the workforce for the years 1980, 1984 and 1985 [MM1 (1986, p. 523, table 24)]. Categories include industrial workers, apprentices, technicians, managers, supporting personnel and other. Since the latter two categories consist principally of non-industrial labor, these are excluded from the labor input totals to obtain estimates of the industrial labor force. Using these data and assuming that in 1952 the presence of non-industrial labor within Chinese industrial enterprises was negligible and using the method described in table 5 (footnote f) to estimate the quantity of industrial labor for 1957, it is possible to estimate the rates of growth of industrial labor for the periods 1952-57, 1957-80 and 1980-85. (b)

Capital. Chen et al. (1988a) reestimate the capital stock for the state industrial sector by disaggregating fixed assets according to major asset types, deflating the individual series and reaggregating the categories of

353

G.H. Jefferson, China’s iron and steel industry

industrial assets while omitting non-industrial investment. This paper uses a similar procedure, including the investment goods deflators they report for state industry. The major difference in the two procedures is that Chen et al. ignore adjustments for scrap which is a relatively small component for total state industry but not for the iron and steel industry. The specific steps used to develop estimates industrial capital within the iron and steel industry (i) Calculate

of the deflated are as follows:

value

of

scrap (S):

OVFA,=OVFA,_l+GZ,-S,,

(B.1)

GI, = TRI, + BCZ,.

(J3.2)

where

Rearranging

eq. (B. 1) gives

S,=GZ,-(OVFA,-OVFA,_,),

(B-3)

where GI = gross fixed BCZ = basic construction of fixed assets.

investment, investment,

(ii) Calculate

0 VFA (PO VFA):

productive

TRI = technical renovation investment, and OVFA = end-of-year original value

PO VFA, = 0, * 0 VFA,,

U3.4)

where 8= POVFA/OVFA calculated from SSB (1987, pp. 400 and 401) for the years 1980, 1984 and 1985 and is interpolated for 1981-1983. Note. 19~~~~=0.832, 8,,,,=0.818 and, 0,985= 0.801. It is assumed that the proportion of productive capital within all state-owned iron and steel enterprises is similar to that of the large and medium enterprises within the sector. Productivity growth accounting requires the somewhat less restrictive assumption that the rate of change in this ratio for all enterprises is the same as that computed for the large and medium-size enterprises in the Census. (iii) Calculate

productive

POVFA,

PBCZ:

= PO VFA, _ 1 + TRZ, + PBCZ, - S,,

(B.5)

where PBCZ, = BCZ, - NPBK,.

u3.6)

354

G.H. Jefferson, China’s iron and steel industry

Substituting

eq. (B.6) into (B.5) and rearranging

gives (B.7)

Note. All scrap is assumed to be productive capital. This is because most non-productive investment has been made during the past 30 years in structures (e.g. houses, day care centers, shops, etc.) whose lifetime is relatively long-lived. Also, all technical renovation investment is assumed to be productive equipment. See, for example, the breakdown of technical renovation investment by type in MM1 (1986a, p. 20, table 20). Where some ambiguity exists concerning the type of purchases for some minor categories, a representative of the Ministry of Metallurgical Industry reports that the investment expenditures in question are for industrial purposes. (iv) Deflate TRI by the equipment deflator (DEFL”) and PBCZ by the (non-residential) construction deflator (DEFL”), where the deflators are calculated from Chen et al. (1988a, p. 261) using 1980 as a base: DTRI,=

DEFL; * TRI,,

(B.8)

DPBCI, = DEFL; * PBCI,,

(B.9)

where DTRI and DPBCI are, respectively, the deflated series of technical renovation investment and productive basic construction investment. Note. The deflators constructed for the state-owned assumed to be relevant for the iron and steel industry. (v) Reconstruct for 1979-85:

a series of deflated

productive

sector

as a whole

gross fixed assets (DPGFA,)

DPGFA, = DPGFA, _ 1 - DTRI, + DPBCI, - S,, where DPGFA 198,, = 37.230 billion (vi)

Convert

the end-of-year

are

(B.lO)

yuan.

estimates

DPGFA,=(DGFA,+DPGFA,_,)/2.

to annual

estimates,

such that (B.11)

Chen, K., G. Jefferson, T. Rawski, H.C. Wang and Y.X. Zheng, 1988a, New estimates of fixed investment and capital stock for Chinese state industry, China Quarterly 114, 243-266. Chen, K., G. Jefferson, T. Rawski, H.C. Wang and Y.X. Zheng, 1988b, Productivity change in Chinese industry: 1953-1985, Journal of Comparative Economics 12, 57&691.

G.H. Jefferson, China’s iron and steel industry

355

Chow, Gregory, 1985, The Chinese economy (Harper and Row, New York). Griliches. Z. and V. Rinnstad. 1971. Economies of scale and the form of the production function (North-Holland, Amsterdam). Griliches, Z. and J. Hausman, 1986, Errors in variables in panel data, Journal of Econometrics 31, 988118. Intriligator, Michael D., 1978, Econometric models, techniques and applications (Prentice Hall, New Jersey). Jefferson, G.H., 1988a, The aggregate production function and productivity change: A reevaluation of Verdoorn’s law, Oxford Economic Papers 40, 671-191. Jefferson, G.H., 1988b, A decade of reform in China: Pitfalls and lessons in evaluating industrial productivity, Presented at the panel on China’s Economic Reforms, annual meetings of the American Economic Association, New York, Dec., 27730. Jefferson, G.H., 1989, Potential sources of productivity growth within Chinese industry, World Development 17, no. 1, 45-57. Lardy, Nicholas R., 1987, Technical change and economic reform in China: A tale of two sectors (unpublished). Lau, K.T. and J. Brada, n.d., Technological progress and technical efficiency in Chinese industry: A frontier production function approach, China Economic Review, forthcoming. MM1 (Ministry of Metallurgical Industry), 1985, Zhongguo gangtie gongye nianjian, 1985, China iron and steel industry yearbook (Metallurgical Industry Publishing House, Beijing). MM1 (Ministry of Metallurgical Industry), 1986a, Zhongguo gangtie gongye nianjian, 1986, China iron and steel industry yearbook (Metallurgical Industry Publishing House, Beijing). MM1 (Ministry of Metallurgical Industry), 1986b, Zhongguo gangtie gongye nianjian, 1986, Statistics of iron and steel industry in China (Economic Information and Agency, Hong Kong). Reynolds, Bruce L., 1987, Reform in China: Challenges and choices (Sharpe, New York). 1986, Zhongguo tongji nianjian, 1985, Chinese statistical SSB (State Statistical Bureau), yearbook, 1985 (China Statistical Publishing House, Beijing). SSB (State Statistical Bureau), 1987, Zhonggua renmin gonghe quo 1985 nian gongye pucha ziliao, Chinese People’s Republic Industrial Census Materials 1, 1985 (China Statistical Publishing House, Beijing). World Bank, 1983, Size and regional pattern of industry. China: Socialist economic development II, 141-146 (World Bank, Washington, DC). World Bank, 1985, China, long-term development issues and options (Johns Hopkins University Press, Baltimore, MD).