The pyramid structure of the SOEs along with the process of continuous reform has significantly affected the business activities and the deci sion-making behaviors of enterprises. Based on the data from 2007 to 2015, the paper validates the influence of the length of the control chain of SOEs on the R & D investment behavior of enterprises. Through analysis, we find that, with the control chain being lengthened, it suggests the significant decrease in R & D investment, but this correlation is not obvious for the central SOEs. Further research finds that, although there is no significant effect between the length of the control chain of the central SOEs and the R & D investment in the case of the whole sample, when the control layer is greater than median, the elongation of the control chain significantly inhibits the R & D investment of central SOEs and local SOEs. What’ s more, we find that the negative correlation of the number of control layers and R & D investment for local SOEs is only significant when the agent cost is lower.
Before the reform and opening up in 1978, China’s state-owned enterprises (SOEs) were directly controlled by the central government or local governments. The decisions of SOEs were directly made by the government departments. Managers only had limited decision-making rights in the business activities of the enterprises. In the situation, the lack of incentives for managers and the political tasks of SOEs make the production efficiency of SOEs tend to be relatively lower [
In June 2015, the State Council issued “the Suggestion on Several Policies and Measures to Vigorously Promoting Public Innovation”, which points that China’s resources and environmental constraints are increasingly tightened, the driving force of factors has gradually weakened, and the traditional high input, high consumption and extensive mode of development are unsustainable. We need to shift from factor-driven and investment-driven to innovation-driven. On one hand, SOEs continue to head for the stage of deepening reform; on the other hand, the country actively advocates innovation to lead the economic development. And so, in the process of the continuous reforms of SOEs, what kind of impact will they have on the enterprise innovation? The paper explores the problem from the perspectives of government intervention and agency costs. We hope that the paper could provide a supplementation study for the economic consequences of the pyramid holding structure of SOEs and give some references for the reform of SOEs. However, the paper is just focused on the R&D investment which only represents the input. Maybe, paying more attention to innovation output is a direction worth to study in the future.
The article is organized as follows: the first part is the introduction, in this part, we introduce the background, purposes and significances of the study. The second part is the theoretical analysis and hypotheses, in this part, based on the literature review and theoretical analysis; we put forward the research hypotheses of the article. The third part is the study design, in this part, we introduce the process of sample selection and data source of the study, and the key variables are defined and the regression model is also constructed. Then, in the fourth part of the article, we make specific analyses of the regression results. Finally, the fifth part is the conclusion.
Studies have shown that state-owned enterprises in China have granted more effective control right to managers in the process of reforms. This kind of decentralization reduces the cost of government intervention. At the same time, under the Pyramid-holding structure, the organization becomes more complex, the acquisition and transmission costs of information increase, and it is difficult for ultimate controller to directly supervise managers, resulting higher agency costs of managers. The R & D investment usually has higher uncertainty and the investment cycle is always longer, that is, the R & D investment is relatively inefficient and is difficult to meet the short-term target of the government. Therefore, governments will force companies to postpone or reduce technological innovation investment through direct and indirect ways. Ma and Liu believe that enterprises controlled by governments lack the incentive to strengthen their R & D investment [
H1a: As the number of control layers increasing, the SOEs’ R & D investment tends to increase.
H1b: As the number of control layers increasing, the SOEs’ R & D investment tends to decrease.
The study of Wang and Xiao is based on the perspectives of political costs and agency costs, which finds that the decentralization of control rights increases the value of SOEs, but this influence is only significant for local SOEs, not for those controlled by the central government [
H2: Distinguishing the SOEs controlled by central government and local governments, there is a significant difference in the influence of the number of control layers on the R & D investment.
The study selects the listed companies data from 2007-2015 as the sample, and the sample is screened as follows: 1) removing financial industry; 2) eliminating ST and *ST companies; 3) eliminating the data that key variable values are missing; 4) In order to avoid the effect of extreme values on the empirical results, we winsorize all continuous variables on the 1% and 99% positions. The financial data used in the paper are from the CSMAR database. The control layers data used in the paper are manually collected from the actual controller map in the annual reports. The paper uses Excel and Stata12.0 for data analysis.
1) R & D Investment (RD): Referring to the research of Zhang, Liu and Yang [
2) Control Layers (Layer): Learning from the studies of Fan, Wong and Zhang [
3) Agency Cost (Dcost): Referring to the research of Wang, Xu and Wang [
In addition to above variables, the paper also controls the company size, asset-liability ratio (Lev), company’ s growth (Growth), operating cash flow (Cash), shareholding proportion of the largest shareholder (Top1), industry (Industry) and year(Year).
With reference to existing researches, the paper uses the following model for empirical testing.
RD = χ 0 + χ 1 Layer + χ 2 Size + χ 3 Lev + χ 4 Growth + χ 5 Cash + χ 6 Top 1 + ∑ Industry + ∑ Year + ε
The paper conducts the entire sample and sub-samples descriptive statistics. According to the descriptive statistics, we can see the mean of the R & D investment in the entire sample of SOEs is 0.0186, with a median 0.00734. The mean of the R & D investment of the central SOEs is 0.0214, with a median 0.0105. And the mean of the R & D investment of the local SOEs is 0.0155, and the median is 0.00473. It can be seen that the R & D investment of central SOEs is generally higher than that of local SOEs. The reason may be that, as the most important part of the national economy, central SOEs often are more active to respond to the government’ s innovation and development policies and then to increase the companies’ investment in new technology research and development. About controlling layers, under the full sample, the mean of the control layer of SOEs is 2.797, and the median is 3; under the subsamples, the mean control layer of central SOEs is 3.083, and the median is 3; the mean control layer of local SOEs is 2.478, and the median is 2. We can see that the mean control layer of central SOEs is higher than that of local SOEs. This is due to the fact that central SOEs is generally larger than local SOEs. In recent years, central enterprises tend to conduct horizontal mergers and acquisitions, such as the merger of China South Locomotive Group and China North Locomotive Group, conduct the industrial chains reorganization and carry out cross-border mergers and acquisitions and so on. Through a series of measures, central SOEs are becoming more and more complicated. For the variable of agency costs, the total asset turnover ratio (Dcost 2) of central SOEs is 0.748, which is slightly lower than that of local SOEs (0.753). In terms of administrative fee ratio (Dcost 2), the average value of central SOEs is 0.0917 which is slightly lower than that of local SOEs (0.0924).That is to say, both Dcost 2 and Dcost 2 of central SOEs is smaller than local SOEs. (
The result of the full sample regression in sheet 3 column (1), suggests that the control layer does not have the significant impact on the R & D investment of the SOEs (
Variables | N | mean | max | min | p50 | sd |
---|---|---|---|---|---|---|
RD | 1455 | 0.0186 | 0.150 | 0 | 0.00734 | 0.0272 |
Layer | 1455 | 2.797 | 8 | 1 | 3 | 1.104 |
Size | 1455 | 22.41 | 26.48 | 19.86 | 22.24 | 1.337 |
Lev | 1455 | 0.515 | 0.943 | 0.0664 | 0.518 | 0.202 |
Growth | 1455 | 2.404 | 9.594 | 0.909 | 1.932 | 1.560 |
Cash | 1455 | 0.0383 | 0.202 | −0.128 | 0.0353 | 0.0634 |
Top1 | 1455 | 38.48 | 74.30 | 9.799 | 37.79 | 15.03 |
Dcost 2 | 1455 | 0.750 | 3.058 | 0.108 | 0.622 | 0.496 |
Dcost 2 | 1455 | 0.0920 | 0.320 | 0.00893 | 0.0826 | 0.0569 |
Sample | Variables | N | mean | max | min | p50 | sd |
---|---|---|---|---|---|---|---|
Central SOEs | RD | 767 | 0.0214 | 0.150 | 0 | 0.0105 | 0.0275 |
Layer | 767 | 3.083 | 8 | 1 | 3 | 1.221 | |
Size | 767 | 22.37 | 26.48 | 19.86 | 22.23 | 1.419 | |
Lev | 767 | 0.506 | 0.943 | 0.0664 | 0.511 | 0.204 | |
Growth | 767 | 2.593 | 9.594 | 0.909 | 2.085 | 1.616 | |
Cash | 767 | 0.0359 | 0.202 | −0.128 | 0.0327 | 0.0623 | |
Top1 | 767 | 40.79 | 74.30 | 11.37 | 41.05 | 13.94 | |
Dcost 2 | 767 | 0.748 | 2.875 | 0.108 | 0.632 | 0.459 | |
Dcost 2 | 767 | 0.0917 | 0.320 | 0.00893 | 0.0821 | 0.0580 | |
Local SOEs | RD | 688 | 0.0155 | 0.150 | 0 | 0.00473 | 0.0265 |
Layer | 688 | 2.478 | 8 | 1 | 2 | 0.849 | |
Size | 688 | 22.44 | 26.48 | 19.86 | 22.25 | 1.240 | |
Lev | 688 | 0.526 | 0.943 | 0.0664 | 0.527 | 0.199 | |
Growth | 688 | 2.194 | 9.594 | 0.909 | 1.758 | 1.467 | |
Cash | 688 | 0.0409 | 0.202 | −0.128 | 0.0370 | 0.0645 | |
Top1 | 688 | 35.92 | 74.30 | 9.799 | 33.76 | 15.77 | |
Dcost 2 | 688 | 0.753 | 3.058 | 0.108 | 0.610 | 0.533 | |
Dcost 2 | 688 | 0.0924 | 0.320 | 0.00893 | 0.0831 | 0.0557 |
(1) | (2) | (3) | |
---|---|---|---|
Variables | Full Sample | Central SOEs | Local SOEs |
RD | RD | RD | |
Layer | −0.000331 | −0.000198 | −0.00356*** |
(−0.524) | (−0.240) | (−3.022) | |
Size | −0.000546 | −5.87e−05 | −0.00122 |
(−0.774) | (−0.0594) | (−1.180) | |
Lev | −0.0183*** | −0.0183*** | −0.0161*** |
(−4.541) | (−3.290) | (−2.690) | |
Growth | 0.00155*** | 0.000363 | 0.00207** |
(2.746) | (0.454) | (2.446) | |
Cash | −0.0261** | −0.0311* | −0.0143 |
(−2.276) | (−1.894) | (−0.880) | |
Top1 | −9.46e−05* | −0.000254*** | −7.37e−05 |
(−1.905) | (−3.421) | (−1.010) | |
Year | controlled | controlled | controlled |
Industry | controlled | controlled | controlled |
Constant | 0.0182 | 0.00765 | 0.0412* |
(1.129) | (0.333) | (1.750) | |
Observations | 1455 | 767 | 688 |
R-squared | 0.121 | 0.155 | 0.116 |
Note: t-statistics is in parentheses; ***: p < 0.01, **: p < 0.05, *: p < 0.1.
companies’ short-term performance is more intense, such as pursuing higher working consumption, so that they are not willing to increase the R & D investment. For the executives of central SOEs, the odds of getting the political promotion are greater, and the pursuit of working consumption is weaker. Wang, Fu, Huang and Wang [
As we can see from the regression results in
Dcost 1 | Dcost 2 | |||
---|---|---|---|---|
Variables | (1) Lower | (2) Higher | (3) Lower | (4) Higher |
RD | RD | RD | RD | |
Layer | −0.00520*** | −0.00228 | −0.00208** | −0.00308 |
(−3.020) | (−1.368) | (−2.064) | (−1.558) | |
Size | −0.00211* | 0.000817 | −0.00146** | 0.000300 |
(−1.803) | (0.425) | (−2.008) | (0.135) | |
Lev | −0.0222*** | −0.0138 | −0.00596 | −0.0159 |
(−2.750) | (−1.554) | (−1.191) | (−1.550) | |
Growth | 4.49e−05 | 0.00320*** | −0.00107 | 0.00305** |
(0.0348) | (2.687) | (−1.302) | (2.280) | |
Cash | 0.0261 | −0.0297 | −0.0235** | 0.0173 |
(1.283) | (−1.085) | (−2.057) | (0.537) | |
Top1 | 4.33e−05 | −0.000151 | 9.26e−05* | −0.000178 |
(0.464) | (−1.344) | (1.651) | (−1.364) | |
Year | controlled | controlled | controlled | controlled |
Industry | controlled | controlled | controlled | controlled |
Constant | 0.0814*** | −0.00610 | 0.0403** | 0.00693 |
(2.980) | (−0.140) | (2.265) | (0.140) | |
Observations | 344 | 344 | 344 | 344 |
R-squared | 0.187 | 0.128 | 0.140 | 0.107 |
Note: t-statistics is in parentheses; ***: p < 0.01, **: p < 0.05, *: p < 0.1.
Conversely, for the groups that the agency cost is higher, the correlation is not so significant. That is, under different agency costs, there are significant differences in the effect of the number of control layers of local SOEs on the R & D investment. The main reason is that, in the case of low agency costs, the personal benefits of managers are not met, and with the control chain becoming longer, it provides a chance for management to perform self-interested behavior, and the managers have strong motivation to improve the personal welfare, working consumption, and personal reputation. In the situation, managers are often more sensitive for the chance and they are prefer to the companies’ short-term performance, so that they will reduce the R & D investment which is of higher risk and longer investment return period. On the contrary, when the agency cost is relatively higher, managers’ sensitivity to the chance is lower.
Further, to test whether there is a turning point, the paper conduct the regression grouping the sample using the median of the number of control layers (
Variables | Central SOEs | Local SOEs | ||
---|---|---|---|---|
(1) Layer > 3 | (2) Layer ≤ 3 | (3) Layer > 2 | (4) Layer ≤ 2 | |
RD | RD | RD | RD | |
Layer | −0.00586*** | −0.000948 | −0.00473*** | 0.00120 |
(−2.621) | (−0.509) | (−2.957) | (0.204) | |
Size | −0.00194 | −0.000892 | −0.000699 | −0.00193 |
(−0.721) | (−0.848) | (−0.631) | (−1.215) | |
Lev | −0.0470*** | −0.00818 | −6.27e−05 | −0.0312*** |
(−3.603) | (−1.371) | (−0.00883) | (−3.461) | |
Growth | 0.00234 | −0.000897 | 0.00444*** | −0.000615 |
(1.178) | (−1.048) | (4.944) | (−0.475) | |
Cash | −0.0119 | −0.0317* | −0.0479** | 0.0202 |
(−0.285) | (−1.893) | (−2.596) | (0.832) | |
Top1 | −0.000184 | −0.000230*** | −3.45e−05 | −3.31e−05 |
(−1.132) | (−2.790) | (−0.428) | (−0.287) | |
Year | controlled | controlled | controlled | controlled |
Industry | controlled | controlled | controlled | controlled |
Constant | 0.0515 | 0.0270 | 0.00850 | 0.0610 |
(0.729) | (1.133) | (0.328) | (1.646) | |
Observations | 226 | 541 | 265 | 423 |
R-squared | 0.225 | 0.174 | 0.298 | 0.112 |
Note: t-statistics is in parentheses; ***: p < 0.01, **: p < 0.05, *: p < 0.1.
for the group which the control layer is more than 3, there is a significant negative correlation between the control layer and the R & D investment. That is, for the central SOEs, when the control layer is more than 3, with the increasing of the control layer, the company’s R & D investment is decreasing. On the other hand, from the regression results of columns (3) and (4), we can see that, for the local SOEs, there is a significant negative correlation between the control layer and the R & D investment for the group which the control layer is more than 2, and the correlation coefficient is −0.00586, compared with the coefficient −0.00473 in the sheet1, we can find the negative effect is bigger when the layer is more than 2. The negative effect is caused by the significant increase in agency costs, which is consistent with the finding of Zhong, Ran and Wen [
In order to ensure the accuracy and robustness of the results, considering that the operating revenues may have the risk of earnings manipulation, we use the ratio of the R & D expenditures to the total assets as the explanatory variable referring to the study of Liu and Liu [
Variables | (1) | (2) | (3) |
---|---|---|---|
Full Sample | Central SOEs | Local SOEs | |
RD | RD | RD | |
Layer | −0.000365 | −0.000439 | −0.00153** |
(−1.002) | (−0.934) | (−2.210) | |
Size | −0.000916** | −0.000760 | −0.00121** |
(−2.254) | (−1.351) | (−1.985) | |
Lev | −0.00333 | −0.00169 | −0.00246 |
(−1.430) | (−0.534) | (−0.696) | |
Growth | 0.000416 | 0.000357 | −1.83e−05 |
(1.278) | (0.784) | (−0.0368) | |
Cash | 0.00805 | 0.00810 | 0.0142 |
(1.219) | (0.867) | (1.481) | |
Top1 | 1.82e−05 | −9.42e−05** | 8.59e−05** |
(0.635) | (−2.224) | (1.996) | |
Year | control | control | control |
Industry | control | control | control |
Constant | 0.0189** | 0.0151 | 0.0282** |
(2.028) | (1.152) | (2.031) | |
Observations | 1455 | 767 | 688 |
R-squared | 0.098 | 0.118 | 0.097 |
Note: t-statistics is in parentheses; ***: p < 0.01, **: p < 0.05, *: p < 0.1.
Variables | Dcost 1 | Dcost 2 | ||
---|---|---|---|---|
(1) Lower | (2) Higher | (3) Lower | (4) Higher | |
RD | RD | RD | RD | |
Layer | −0.00298** | −0.000634 | −0.000852 | −0.00191 |
(−2.152) | (−1.006) | (−0.821) | (−1.634) | |
Size | −0.00226** | −0.000526 | −0.00161** | −0.00156 |
(−2.401) | (−0.724) | (−2.162) | (−1.193) | |
Lev | −0.00981 | −0.00163 | 0.000205 | −0.00337 |
(−1.512) | (−0.486) | (0.0399) | (−0.557) | |
Growth | −0.00169 | 0.00119*** | −0.00244*** | 0.000712 |
(−1.624) | (2.648) | (−2.877) | (0.901) | |
Cash | 0.0233 | −0.00536 | 0.00808 | 0.0398** |
(1.421) | (−0.517) | (0.689) | (2.097) | |
Top1 | 0.000204*** | −3.44e−05 | 0.000239*** | −3.93e−05 |
(2.716) | (−0.809) | (4.140) | (−0.510) | |
Year | −0.00140 | 0.00116 | −0.00395 | 0.00414 |
Industry | −0.0173 | 0.00753 | −0.000724 | 0.00500 |
Constant | 0.0691*** | 0.0101 | 0.0398** | 0.0337 |
(3.142) | (0.612) | (2.174) | (1.154) | |
Observations | 344 | 344 | 344 | 344 |
R-squared | 0.175 | 0.142 | 0.136 | 0.108 |
Note: t-statistics is in parentheses; ***: p < 0.01, **: p < 0.05, *: p < 0.1.
Variables | Central SOEs | Local SOEs | ||
---|---|---|---|---|
(1) Layer > 3 | (2) Layer ≤ 3 | (3) Layer > 2 | (4) Layer ≤ 2 | |
RD | RD | RD | RD | |
Layer | −0.00214* | 0.000118 | −0.00488*** | 0.00395 |
(−1.819) | (0.104) | (−3.793) | (1.277) | |
Size | −0.00117 | −0.00117* | −0.000529 | −0.00162* |
(−0.824) | (−1.824) | (−0.593) | (−1.941) | |
Lev | −0.0154** | 0.00400 | 0.00736 | −0.0120** |
(−2.234) | (1.102) | (1.291) | (−2.544) | |
Growth | 0.00149 | −0.000173 | 0.000515 | −0.00118* |
(1.429) | (−0.332) | (0.713) | (−1.744) | |
Cash | 0.00764 | 0.0113 | −0.00943 | 0.0349*** |
(0.349) | (1.110) | (−0.637) | (2.741) |
Top1 | −8.34e−05 | −8.34e−05* | 0.000247*** | 3.30e−05 |
---|---|---|---|---|
(−0.975) | (−1.662) | (3.801) | (0.544) | |
Year | −0.00127 | 0.000404 | 0.00423 | −0.00128 |
Industry | 0.0224 | 0.0141*** | 0.000616 | 0.00555 |
Constant | 0.0230 | 0.0219 | 0.0116 | 0.0360* |
(0.618) | (1.515) | (0.554) | (1.853) | |
Observations | 226 | 541 | 265 | 423 |
R-squared | 0.174 | 0.134 | 0.242 | 0.117 |
Note: t-statistics is in parentheses; ***: p < 0.01, **: p < 0.05, *: p < 0.1.
The results in
The paper explores the influence of the number of control layers under pyramid structure that is formed during the reform of SOEs on the R & D investment from the views of government intervention and agency costs. Through the analysis above, the study finds that: 1) With the increasing of the number of control layers, agency costs of SOEs have increased significantly, Which leads to the significant reduction in the R & D investment of local SOEs. However, this negative correlation is not obvious for central SOEs; 2) Further study has found that, when agency costs (using total asset turnover ratio and administrative fee ratio as substitute variables) are lower, the R & D investment of local SOEs tends to decrease as the control chain is lengthening; 3) Although, for the sample of full central SOEs, the length of the control chain and the R & D investment do not show a significant correlation, for the group which the control layer is more than 3 in central SOEs; the R & D investment is significantly weakened with the control layer increasing. And then, for the local SOEs, when the control layer is more than 2, as the control chain lengthening, the R & D investment of target local SOEs tends to be decreasing.
National Natural Science Fund (71771105, 71473180, 71201010).
Lu, X. and Li, R.H. (2018) A Research on Control Layers of SOEs and R & D Investment. Modern Economy, 9, 924-936. https://doi.org/10.4236/me.2018.95059