Earlier in the study we identified two areas of economic reform: market reform and institutional reform. In chapter 3, our survey data confumed that govemment-determined prices for goods were increasingly replaced with market prices; consequently, the share of both inputs and output traded on the market increased significantly during the 1980s.
Chapter 4 described institutional reform in state enterprises in three key areas: financial autonomy, financing of working capital and fixed assets, and remuneration of
179
workers. The expansion of enterprises' financial autonomy was instimtionalised in the profit retention scheme, a tax system and the Contract Management Responsibility
System. Despite numerous modifications and amendments, the share of profit retained by state enterprises in total profit increased substantially from 1980 to 1988 (Tables 4.1-4.3 in chapter 4).
The major instrument of reform in the financing of state enterprises was to replace free government grants with interest-bearing bank loans, to fund both working capital and fixed assets. Again, we used survey data to demonstrate that the share of bank loans in total working capital increased consistendy (Table 4.5 in chapter 4). However, the share of bank loans in investment in fixed assets was relatively small.
Reform of the labour management system was carried out in two ways: by restoring the bonus system, and by recruiting workers on a contractual and seasonal basis. While the bonus system represented a significant change (because bonuses were related to the performance of workers) and constituted an important attempt to break away from the egalitarian tradition of remuneration, employment of contract and seasonal workers was found to have had little impact on the management of labour due to various forms of govemment intervention.
In terms of the above review of reform policies, our test of the impact of economic reform is equivalent to estimating quantitatively the relationship between the technical efficiency index and share of goods traded on the market in total output {market share), the share of retained profit in total profit {share of retained profit), the share of bank loans in total working capital {share of bank loans) and the share in total remuneration of performance-related bonuses {bonus share).
This gives us the following equation:
Te, = fiMkt,, Bon,, Pr o f , , Loan,) (6.9)
where Te,, Bon,, Prof and Loan, are respectively technical efficiency index, market share, bonus share, share of retained profit and share of bank loans for the /t^ enterprise in the t^^ period. This is the general form of the various functions described in our discussion of testing procedures.
The hypothesis tested in this model is that the four explanatory variables on the right- hand side will be positively and significantly correlated with technical efficiency if economic reform as a whole was successful; some of the variables will be significantiy positive if reform was partially successful; and otherwise economic reform has been a complete failure. According to the proposition, the four coefficients are believed a ^r/on to be positive.
In the estimation, technical efficiency is an independent variable obtained from the estimation of the stochastic production frontier model in chapter 5. The market shares of each enterprise in the six periods are used as a proxy for the process of market reform.
180 This variable was summarised in Tables 3.5-3.7 in chapter 3. The share of retained profit, the share of bank loans and the bonus share in equation (6.9) are proxies for reform in the areas of financial autonomy, financing of state enterprises and labour management.
There are three types of variable causing variation in intercepts and slopes in the panel data: individual time-invariant, period individual-invariant and individual time- varying variables. All the variables representing economic reform may a priori be any of these three types. To test the significance of their impact on the stability of the coefficients of the model, economists use either fixed effect models or random effect models. In the former, the effects of variables on intercepts and slopes are treated as constants; in the latter, they are assumed to follow certain distributions.^
Both fixed effect and random effect models are widely applied in economic analysis. For example, Hausman (1978b) used both models in estimating wage equations for US data, and Lin (1992) applied a fixed effect model to the estimation of the impact of economic reform on the productivity of Chinese agriculture. Both models have pros and cons. In this study we choose a fixed effect model for estimating and testing the homogeneity of intercepts and slopes, for two reasons. First, the model represented by equation (6.9) is based on Komai's soft budget constraint hypothesis, which is a conceptual rather than mathematically formulated model. Specific functional forms based on equation (6.9), Uke Cobb-Douglas or translog functions, always impose a certain degree of arbitrariness on the relationship between the dependent variable and explanatory variables. The random-effect model requires that we impose an arbitrary distribution on the residual term. It is therefore better to avoid the unnecessary arbitrariness the random effect model may possibly bring about. Second, a simple procedure is required in fixed effect models to test for model selection. These models are therefore straightforward to estimate and the results are easier to interpret.
When using a fixed effect model, it is common practice to introduce a dummy variable for each cross-sectional unit and each period. Model selection is based on the result of an F-test on the linear restriction of the most general model following the three- step procedures for intra and inter-heterogeneity described earlier.
A linear functional form is used in the estimation of equation (6.9). Variables of economic reform expressed as a percentage are entered into the equation for estimation. This functional form is chosen because the coefficients thus estimated are easy to interpret. Since variables on both sides of the equation are percentages, coefficients are the elasticities of technical efficiency in response to changes in economic reform measures. A two-way fixed effect model is represented by equation (6.10):
^The stochastic production frontier model used in chapter 5 for estimating technical efficiency can be classified as a random effect model.
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T e , = a + p . M ^ r , + + P3 P r o / , +
N 4 N T 4 T (6 10)
'•=2 k=l 1=2 1=2 k=l 1=2
where D p " and Dpf are dummy variables and k is the index of four indicators of economic reform. The following are the specifications of these variables:
^ f i ^ — ^ f t ^ — 1 for all observations of the enterprise; = 0 for other observations.
Dp'^ = Dp^ = 1 for all observations of in the r^^ period; = 0 for other observations.
Equation (6.10) is equivalent to the overall unrestricted model and serves as a starting point for the testing procedure and model selection. As in the estimation of technical efficiency in chapter 5, the testing procedure was carried out separately for heavy and light industrial sectors. The results of the analysis of covariance and associated information are presented in Table 6.1.
In Table 6.1, six alternative models are shown for each of the two industrial sectors. Analysis of covariance is carried out for each model by testing the linear restriction on dummy variables associated with intercept and/or slope. Model selection is based on these tests. The first model (H^ and Lj) is an unrestricted model equivalent to equation (6.10). A test is carried out on the restrictions on both intercepts and slopes along cross- sectional and longitudinal dimensions. The F-statistic is 4.30 for the heavy industrial sector and 3.32 for the light industrial sector. Both are significant at the 99 per cent confidence interval, and the null hypothesis is decidedly rejected. These results indicate that the pooled regression model is not appropriate for either sector; in other words, heterogeneity does exist in both panels of data.
Given the existence of heterogeneity, the next question is whether it occurred intra- temporally among cross-sectional units or inter-temporally among observations in different time periods. The tests of the linear restriction (H2 and L2) suggest that intercepts and slopes are not heterogenous inter-temporally, since the F-values in both sectors are not sufficiendy large to reject the null hypothesis. The results imply that the same intercept and slope coefficients should be applied to enterprises inter-temporally. The results are not surprising given that, under the Chinese gradualist approach to reform, technical efficiency on the left-hand side of the equation and reform indicators
Table 6.1 Results of Analysis of Covariance for Heavy and Light Industrial Sectors
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Heavy Industrial Sector
Code Model Null Hypothesis Degree of Freedom F-statistic Decision
(Numerator) (Denominator)
H i Te, = a , + + a , + p ' . x , a , = c x , = p , = p , = 0 for all i atid i 7 5 4 139 4 . 2 9 6 6 * Rejected
H 2 Te„ = a , + p . x , + a , + p ; x , a , = p , = 0 for all t 2 5 139 0 . 8 2 9 1 Not Rejected
H3 Te„ =a,+ p ' . x , + M, a , = p , = 0 for all i 7 2 9 164 3 . 8 8 9 6 * Rejected
H 4 P , = o for all i 5 6 8 3 2 5 3 . 3 0 7 1 * Rejected
H 5 Te, = a, + p . x , + u,, P , = o for all /• 5 8 6 164 1 . 0 3 4 0 Not Rejected
H 6 a , = 0 for all i 161 7 3 2 9 . 3 9 0 5 * Rejected
Light Industrial Sector
Code Model Null Hypothesis Degree of Freedom F-statistic Decision
(Numerator) (Denominator)
L l Te, =a,+ p : x , + a , + p ; x , for all / and i 5 1 1 104 3 . 3 2 2 9 * Rejected
L 2 Te, - a , + + ot, + p ; x , for all t 2 5 104 1 . 3 2 2 0 Not Rejected
L 3 Te, = a, + p.jc,, + o c - P - 0 for all / 4 8 6 129 3 . 2 2 4 6 * Rejected
U P - 0 for all i 3 7 7 2 3 8 3 . 3 1 9 3 * Rejected
L s Te, = a, + p . x , + u. P - 0 for all i 3 7 7 129 1 . 2 0 0 3 Not Rejected
L 6 = a + p x , + «„ a = 0 for all i 109 5 0 6 8 . 1 5 9 9 * Rejected
Notes:
Source:
1) In the testing process, perfect coUinearity arose among dummy variables due to the fact that certain reform measures were either completely implemented or not implemented at all in some enterprises. Correction is made by deleting the perfectly correlated variables, and this is reflected in the degrees of freedom.
2) Asterisk indicates the decision of rejection is evaluated at the 99 per cent confidence interval. Others are evaluated at the 95% confidence interval. Estimated from survey data.
183 changed only marginally from year to year, and therefore the structure of their relationship was not likely to have experienced drastic variation during the period under study. The results do indicate, though, that the response of enterprise-specific technical efficiency to economic reform was fairly consistent over time.
Given that intra-temporal heterogeneity is the major concern, we proceed with the three-step testing procedure described earlier in this chapter, based on homogenous inter-temporal intercepts and slopes. In the first step for overall homogenous intercepts and slopes (H3 and L3), we obtain an F-statistic of 3.89 for heavy industry and 3.22 for light industry. Both are highly significant, far exceeding the critical value evaluated at the 99 per cent confidence interval. This suggests that the heterogeneity already indicated by the test on the fnst model (H^ and L^) does exist, but intra-temporally.
The next question, corresponding to step 2 of the testing procedure, concerns naturally the whereabouts of heterogeneity: in intercepts, or in slopes? Since heterogeneity in slope is far more important from an economic point of view, we start by testing homogenous slopes conditionally on homogenous intercepts (H4 and L4) and unconditionally (H5 and L5). The null hypothesis of the restricted models (H4 and L4) are rejected for both sectors. This does not necessarily mean that slopes are heterogeneous; rather, the result may have been caused by inappropriate restrictions on intercepts. This is confirmed by the unconditional test. The F-statistics for the unconditional model are 1.03 for the heavy industrial sector and 1.20 for the light industrial sector. These are not sufficiently large for us to reject the null hypothesis that the slopes of explanatory variables are jointly homogenous in both sectors. These results, taken together with tests in previous models (H^ and L^; H3 and L3), suggest that the cell-mean corrected regression model (equation 6.2) is most likely to be the appropriate specification.
Finally, the linear restriction is imposed on the intercepts of the cell-mean corrected regression model (H5 and L5), given homogenous slopes. The F-statistics for both sectors are very large, far greater than the critical value at the 99 per cent confidence interval. The results of this test have two implications. First, they confirm that we should use the cell-mean corrected regression model for estimating equation (6.9) and, as earlier tests also indicated, that the pooled regression model is extremely unlikely to be the correct specification; second, the uncomfortable situation described in the preceding section does not arise for this data set, and the estimates are likely to be reliable and believable.
Based on the results of our analysis of covariance and testing procedures for model selection, the following equation will be used to estimate the impact on technical efficiency of reform in the areas of marketing, government-enterprise financial relationships, financing of enterprises and labour management:
Te^, = a + + + P3 Pr of^^ + f>,Loan„ + 1 D f ;
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(6.11)
1=2
A summary of the estimation results for this cell-mean corrected regression model for both heavy and light industrial sectors is given in Table 6.2. Full estimates are presented in Tables A6.1 and A6.2 in Appendix 6.
Table 6.2 Summary of Estimation Results of Cell-mean Corrected Model
Heavy Industrial Sector Light Industrial Sector
Coefficient S. Error T-ratio Coefficient S. Error T-ratio
Intercept (a) 60.7800 4.5172 13.4550 61.8956 3.3138 18.6780
Market share ((3^) 0.1035 0.0515 2.0110 0.1964 0.0979 2.0060
Bonus share (P^) 0.3118 0.0698 4.4670 0.2644 0.1030 2.5670
Retained Profit Share (pj) -0.0118 0.0128 -0.9210 0.0189 0.0206 0.9180
Bank Loan Share (p^) 0.0096 0.0186 0.5170 0.0073 0.0253 0.2890
F-Statistic 10.6950 8.0410
R-squared 0.7068 0.6623
Adjusted R-squared 0.6407 0.5624
Source: Estimated from survey data.
According to this table, the F-statistic has a value of 10.70 for the heavy industrial sector and 8.04 for the light industrial sector. Both are significant at the 99 per cent confidence interval. These results suggest that the indicators of economic reform in the model collectively have strong explanatory power for technical efficiency in both sectors. The value of the adjusted R-squared in both sectors exceeds 0.55, which is reasonably high for the estimation of panel data.
Of the four important explanatory variables, two have reasonably large estimated coefficients and are statistically highly significant The magnitude of the coefficients of market share in the light industrial sector is nearly 0.2, double the value in the heavy industrial sector. Both, though, are significant at the 95 per cent confidence interval. The coefficients of the share of workers' bonus in total remuneration have values of 0.31 and 0.26 respectively and are significant at the 99 per cent confidence interval.
However, the coefficients of share of retained profit and share of bank loans in working capital for both industrial sectors are not significant when evaluated at conventionally accepted confidence levels. The magnitudes of the coefficients are very small, typically less than 10 per cent of those for market share and even less when compared with those of the bonus share.
185 The combined results shown in Table 6.2 show that economic reform in state enterprises was only partially successful, a proposition put forward earlier. Generalisation about the success of reform based solely on the finding of improvements in the efficiency index will fail to take account of the highly complex nature of the process and may, therefore, be both imprudent and unrealistic.
The results echo the findings of chapters 3 and 4. Specifically, market reform was highly successful in enhancing the technical efficiency of state enterprises. The marketisation process was realised through the development of a competitive goods market, in which state enterprises were allowed or forced to take part. The intense market competition faced by state enterprises — together with various other institutional changes that made managers pursue the profit objective — had a double-edged effect. On the one hand, state enterprises had to develop strategies to sell their products, such as improving quality and offering the lowest possible price, because customers were now free to choose among alternative goods and suppliers;^ on the other hand, as govemment protection in the form of price subsidies and guaranteed purchase of output gradually weakened (or, according to Komai, budget constraint became harder), they had to look at their production costs. It was in the interest of enterprises to decrease production costs and increase efficiency so that they could achieve profit and other objectives. As a result, they not only started to bring out new and improved products and services but also showed continual improvements in efficiency over time.
Although state enterprises rarely faced bankruptcy and in that sense were not compelled to improve efficiency, the material incentives generated by the reform process seem to have been strong enough to translate the effects of market competition into increased efficiency. This proposition is strongly supported by our estimation results, which indicate that the impact of marketisation on the efficiency of both heavy and light industrial sectors was strong, consistent and statistically significant
Share of bonus in total remuneration also has a statistically significant coefficient In the neoclassical framework, there is a straightforward interpretation for this result. Since the distribution of bonuses is closed related to the effort and performance of workers, the restoration of the bonus system simply incorporated one more variable into the utility function of workers: it gave workers the chance to choose between working harder for increased material gain or working less hard for a dwindling basic wage. When properly implemented, the bonus system was likely to alter the equilibrium position of the workers' utility function established in the traditional egalitarian income distribution system and induce workers to make a greater effort in return for material rewards. Despite earlier studies in this area arguing that the bonus system was seriously flawed because bonuses simply became a wage supplement (Byrd and Tidrick 1987, pp. 73-4)
^This effect can easily be derived from neoclassical economic theory and is evident in international and Chinese experience. Analysis of this effect is beyond the scope of this thesis and will not be discussed further. However, our analysis of the business objectives of state enterprises (chapter 4) does shed some light on it: developing new products ranked high among economic and non-economic objectives.
186 or that there was a tacit alliance between managers and workers to retain more profit (Walder 1987), our study shows that the bonus system was not only effective but the most important factor in the observed improvement in the technical efficiency of state enterprises. In view of the overall institutional changes taking place when the bonus system was restored, our results are consistent with those of Lee Kuen (1990), who argued that improvements in efficiency were due to the combined effect of various institutional reforms and that any one-dimensional advance in labour management was unlikely to be successful.
Institutional reform in the areas of financial autonomy and the financing of state enterprises was a complete failure as far as technical efficiency is concerned. In Table 6.2, the coefficients of the share of retained profit in total profit and the share of bank