Abstract
This study aims to investigate the effects and mechanisms of biased technological progress on China’s industrial overcapacity. Using a time-varying stochastic frontier model, standardized supply-side system method, and the production function method, we measure the capacity utilization rate, biased technological progress index, and factor price distortion index in China’s industrial sector. Additionally, we employ a mediation effect model to analyze the impact of biased technological progress on overcapacity through factor price distortion. The findings suggest that biased technological progress can alleviate overcapacity, while factor price distortion exacerbates the problem. Biased technological progress alleviates overcapacity through capital price distortion, with a more significant effect in heavy industries than in light industries. Based on the results, we propose a series of policy recommendations, including optimizing the bias towards technological progress, improving the factor market, and enhancing the original innovation capability.
1. Introduction
Overcapacity refers to the situation where preexisting production capacity exceeds the equilibrium quantity required, resulting in idle resources and being one of the primary factors causing resource misallocation, efficiency loss, and economic downturn [1]. Industrial development is the foundation of China’s high-quality economic development. However, China’s traditional industrial sectors, such as manufacturing [2] and coal [3, 4], and emerging industries such as new energy [5, 6], have been facing long-standing and widespread overcapacity problems. For example, in the manufacturing industry, statistics indicate that 19 out of the industries’ capacity utilization rate is below 79%, with seven industries below 70%, indicating a severe overcapacity state and leading to increased economic risks. Therefore, studying and analyzing the causes of overcapacity and finding key points to alleviate them have significant practical significance for China’s sustainable economic development.
Currently, scholars have explored the causes of overcapacity in China’s industrial sector mainly from perspectives such as the “market failure hypothesis,” “system distortion hypothesis,” “structural imbalance hypothesis,” and “weak demand hypothesis.” Among them, the “structural imbalance hypothesis” explains the causes of overcapacity from a technological perspective, with “biased technological progress” [7–10] and “factor price distortion” [11–13] representing the focus of academic attention.
Biased technological progress is a source of technological progress that refers to a situation where technological progress leads to unequal increases in the marginal output for different factors, and the technology tends to favor the factor with the highest marginal output [14]. Existing studies have shown that under the context of importing technology from developed countries, China’s industrial sector exhibits a similar capital-biased technological progress feature as developed countries [15–17], leading to a sharp rise in the investment scale [18, 19], forming an “investment-induced effect” [20], resulting in excessive investment and affecting overcapacity.
Factor price distortion often manifests as the deviation of factor prices from their marginal output, i.e., shadow prices [21], and is one of the main causes of overcapacity in China’s industrial sector. The level of marketization in China’s factor market is not high [22], and factor price distortion has existed for a long time [23], promoting redundant construction [24, 25], reducing technical efficiency [26], suppressing technical investment motivation [27], and ultimately worsening overcapacity.
The biased technological progress and factor price distortions are closely related, as the direction of technological progress needs to match the structure of factor inputs to develop together [28]. Factor price distortions are a critical factor affecting the structure of factor inputs. However, previous studies have mainly focused on the capital bias of technological progress and used the input-output of capital factors as the main explanation for overcapacity. This neglects the fact that biased technological progress represents a change in the direction and degree of technological progress.
Moreover, it is unclear whether there is an intermediate link between the impacts of biased technological progress on overcapacity, particularly in the context of factor price distortions. Few scholars have explored the impact of biased technological progress in China’s industrial sector on factor price distortions and whether this affects overcapacity through factor price distortions as an intermediary.
Therefore, this study uses the standardized supply-side system method to measure the index of biased technological progress in China’s industrial sector and uses the mediation effect model to examine the impact of biased technological progress on overcapacity through factor price distortions. This study makes several contributions to the literature. Firstly, it confirms that biased technological progress and factor price distortions are important factors affecting overcapacity in China’s industrial sector. Secondly, it verifies that biased technological progress can alleviate overcapacity by improving capital price distortions. Thirdly, it reveals the unique impact mechanism of overcapacity, providing a pathway for alleviating overcapacity.
This study is structured as follows: firstly, the theoretical framework and basic assumptions are presented in Section 2 to explain the impact mechanism of biased technological progress and factor price distortions on overcapacity in the industrial sector and to establish the basic assumptions. Section 3 introduces the measurement indicators and the mediation effect model. The data and results analysis are presented in Section 4 to verify the basic assumptions of this study based on the results of the mediation effect model. Finally, the conclusions are drawn in Section 5.
2. Theoretical Framework and Basic Assumptions
2.1. Impacts of Biased Technical Progress on Overcapacity
The impact of biased technological progress on overcapacity mainly stems from its alteration of factor input structure. Biased technological progress means that a certain factor’s marginal output is greater than that of other factors. As a result, the production side will increase the input scale of that factor to expand capacity and gain excess profits [7, 8, 18]. Previous studies have shown that the biased technological progress in the Chinese industrial sector has shifted from a bias towards capital to a bias towards labor, with capital efficiency declining while labor efficiency has increased [16, 17]. Based on the analytical framework proposed by [17], we believe that this change in biased technological progress has caused the production side to gradually shift from expanding capital input to expanding labor input and to rely on increasing labor input and improving labor efficiency to enhance the total factor productivity and alleviate overcapacity. Therefore, we propose the following hypothesis: Hypothesis 1a: biased technical progress in China’s industrial sector alleviates overcapacity
2.2. Impacts of Biased Technical Progress on Factor Price Distortions
Capital price distortions in China’s industrial sector have been in a state of severe negative distortions [26]; i.e., the marginal output of capital is significantly higher than the nominal price of capital. At present, the nominal price of capital is still maintained at a low level under government supervision and is decreasing, which deepens the negative distortions of capital prices. The convergence of capital-biased technological progress, i.e., the growth of the marginal output of capital gradually slows down, has a certain easing effect on the deepening of capital price distortion caused by capital-biased technological progress on nominal capital prices.
Compared with capital price distortions, the labor market in China’s industrial sector operates in a perfectly competitive environment, and the ratio of the marginal output of labor to nominal wage is relatively stable; i.e., labor price distortions are not significant[26, 29]. At present, labor demand is strong, and the minimum wage has also increased significantly, leading to rapid increases in labor prices [15], which has caused changes in labor price distortions. The convergence of capital-biased technological progress, i.e., the acceleration of marginal output growth of labor, matched with the rise of nominal labor prices, moves in the same direction and thus eases the distortion of labor prices caused by the increase in nominal labor prices. Therefore, we propose the following hypothesis: Hypothesis 2a: biased technical progress in China’s industrial sector mitigates capital price distortions Hypothesis 2b: biased technical progress in China’s industrial sector mitigates labor price distortions
2.3. Impacts of Factor Price Distortions on Overcapacity
The impact of distorted factor prices on industrial overcapacity is mainly manifested in two aspects. Firstly, at the enterprise level, the subsidies and tax measures implemented by the government lead to severely underestimated factor prices, which actually means that the government bears the direct or indirect costs of the enterprise. This leads to enterprises continuously investing in low-cost factors while the technology conditions remain unchanged, resulting in underutilized production capacity [13, 30]. At the same time, low-cost factors enable enterprises to increase factor inputs and obtain profits, which suppresses the motivation for research and development and technological investment [26, 27]. This lack of effective improvement in the technological level results in overcapacity. Secondly, at the industry level, zombie enterprises with overcapacity, in order to avoid the losses caused by exiting the industry, continue to invest in low-cost factors to further expand the production capacity, maintain the market size and profits of products, and set up “entry barriers” [31]. Other enterprises, affected by the distortion of factor prices and the disruption of price mechanisms, still choose to enter the industry, resulting in a number of firms higher than the industry’s optimal level, causing a “surge phenomenon” [32], leading to sustained repeated construction and resulting in industry overcapacity. Therefore, we propose the following hypothesis: Hypothesis 3a: capital price distortions in China’s industrial sector worsen overcapacity Hypothesis 3b: labor price distortions in China’s industrial sector worsen overcapacity
2.4. Impacts of Biased Technical Progress on Overcapacity through Factor Price Distortions
In summary, the core logic of the theoretical analysis is that “capital-biased technological progress and factor price distortions both affect overcapacity, with the former having a positive effect and the latter having a negative effect. However, the former can indirectly have a positive effect on overcapacity by influencing the latter.” Specifically, the convergence of capital-biased technological progress reduces the degree of capital price distortion, thereby reducing the dependence of enterprises on low-cost capital investment and further slowing down the overcapacity caused by overinvestment in capital. Similarly, the increasing bias of labor towards technological progress reduces the degree of labor price distortion, thereby increasing the demand and investment of enterprises in high-skilled labor, further promoting the mitigation of overcapacity through the improvement of labor efficiency (as shown in Figure 1). Therefore, we propose the following hypothesis: Hypothesis 4a: biased technical progress affects overcapacity through capital price distortions Hypothesis 4b: biased technical progress affects overcapacity through labor price distortions
3. Data and Method
3.1. Capacity Utilization Measurement Method
Overcapacity is a state of production capacity utilization, generally measured by the capacity utilization rate. The measurement methods on the capacity utilization rate include the peak method, cost function method, and nonparametric methods such as data enveloped analysis (DEA) and stochastic frontier analysis (SFA) [5, 13]. Compared with other measurement methods, SFA in nonparametric methods is an improved estimation method after DEA. It can not only consider the impact of random errors on decision unit efficiency but also perform the likelihood ratio (LR) test on the model itself, with stability and other characteristics [33, 34]. This article refers to Kirkley et al. [34] and uses the Translog production function model with time-varying stochastic frontiers to calculate the capacity utilization of China’s industrial sectors by year, based on the two-factor model of capital and labor. We relax the assumption of constant elasticity of substitution between factors and allow for non-neutral technical progress. The model is as follows:where represents the industrial output, represents the scale of capital input, represents the scale of labor input, represents the industry, and represents time. is the random error term, which represents the uncontrollable influence factor and has ; u is the technical loss error term, which is used to calculate the technical inefficiency and has ; the variable represents the rate of change of the technical efficiency index ; denotes the proportion of technical inefficiency in the random disturbance term, of which the estimate can be obtained using the maximum likelihood method. If is close to 1, it indicates that the error mainly originates from , namely, the gap between the actual output and the frontier output is mainly caused by technical inefficiency; represents technical efficiency, that is, the capacity utilization rate.
3.2. Biased Technological Progress Index Measurement Method
In this study, we adopt the two-factor constant elasticity of substitution (CES) production function framework referring to Acemoglu [14] and use the standardized supply-side system approach developed by Klump et al. [35, 36] to estimate the capital-labor elasticity of substitution for China’s industrial sectors. In addition, we calculate the directional technology bias index for China’s industrial sectors by year, using the index equation for the factor-specific technical progress rate proposed by Sato and Morita [37] and the directional technology change index equation proposed by Diamond [38]. The model is presented as follows.
In the first step, we follow Acemoglu’s [14] approach and specify the two-factor constant elasticity of substitution (CES) production function as follows:where represents the industrial output, represents the scale of capital input, represents the scale of labor input, and represents time. The parameter represents the generalized technology level, which is assumed to be constant over time and is generally set to 1. and represent capital and labor efficiency, respectively. Finally, represents the capital-labor elasticity of substitution.
In the second step, we follow the approach proposed by Klump et al. [35, 36] to estimate the capital-labor elasticity of substitution for each industrial sector.
In the third step, we refer to Sato and Morita [37] to calculate the factor-specific technical progress rate for both capital and labor ( and , respectively) in each industrial sector by year:
In the final step, we refer to Diamond’s [38] approach to calculate the directional technology bias index () for each industrial sector by year:
3.3. Factor Price Distortion Measurement Method
The production function method has been widely used because it can measure various factors and multiple levels of price distortions and is not subject to time span restrictions [21]. In this study, following Rader’s [39] approach and using the Cobb–Douglas production function, we specify the two-factor model of capital and labor to estimate the capital and labor price distortions for China’s industrial sectors by year. The model is presented as follows:where represents the industrial output, represents the scale of capital input, represents the scale of labor input, represents the industry, and represents time. The parameters and represent the elasticity of capital output and labor output, respectively, and A represents the total factor productivity. and are the estimated values of and obtained from regressing the logarithm of the Cobb–Douglas production function. represents the price of capital, represents the price of labor, and and represent the distortions in capital and labor prices, respectively.
3.4. Methodology
In this study, we follow the mediation model proposed by Baron and Kenny [40] and Wen and Ye [41] to test the impacts of biased technology process on overcapacity through factor price distortion. The regression model is specified as follows:where , , , and represent the capacity utilization, the biased technological progress index, the distortions in capital (labor) prices, and the control variables for each industry and year, respectively. The mediation effects are tested in four steps: if the coefficient in equation (6) is significant, it indicates that biased technological progress impacts capacity utilization; if the coefficient in equation (7) is significant, it indicates that biased technological progress affects factor price distortion; if the coefficient in equation (8) is significant, it indicates that factor price distortion influences capacity utilization; if the coefficient in equation (9) is significantly lower in magnitude and significance compared to the coefficient in equation (6), it indicates that the mediation effect of factor price distortion on the relationship between biased technological progress and capacity utilization is established.
In this study, control variables include production factors, demand factors, industry competition, and government intervention, as shown in Table 1.
3.5. Data
According to the “National Economic Industry Classification” released by the National Bureau of Statistics in 2011 and based on the availability and accuracy of data, this study deleted five industries with serious data gaps: auxiliary mining activities, other mining industries, other manufacturing industries, comprehensive utilization of waste resources, and metal products and machinery and equipment repair industries. The automotive manufacturing industry and the railway, shipbuilding, aerospace, and other transportation equipment manufacturing industries were combined into one industry, namely, the transportation equipment manufacturing industry, following the examples of Han et al. [42] and Huang et al. [43]. Ultimately, 35 industrial subsectors were selected and classified into heavy and light industries.
This study uses industrial value-added as the output data. Since 2009, the National Bureau of Statistics of China has not published industrial value-added data, but only the growth rate of industrial value-added excluding price factors, and data for 2004 are missing. To ensure the timeliness and continuity of data, this study estimated the industrial value-added for 2004 using the average growth rate of industrial value-added from 1999 to 2003. In addition, the industrial producer price index (1997 = 100) published by the National Bureau of Statistics was used to remove price factors, and the cumulative annual growth rate of industrial value-added was used to estimate the industrial value-added data from 2009 to 2017.
This study uses capital stock as the data for capital input. Following Chen [44], we estimated the capital stock for each of the 35 industrial subsectors from 1997 to 2017 using the perpetual inventory method. The formula for estimating capital stock is , where is the capital stock for subsector in year , is the comparable investment for the subsector in the current year, calculated by first converting the original value of fixed assets for the subsector using the price index for fixed assets (1997 = 100) and then subtracting the comparable value of fixed assets for the subsector in the previous year from the comparable value of fixed assets in the current year, and is the depreciation rate for the subsector in the current year, calculated by dividing depreciation in the current year by the original value of fixed assets in the current year, where depreciation in the current year is the difference between cumulative depreciation in the current year and cumulative depreciation in the previous year.
The 3- to 5-year fixed deposit interest rates in the “China Financial Statistical Yearbook” were used as the capital price data in this study.
The employment data for the industrial subsectors from 1997 to 2017 in the “China Labor Statistical Yearbook” were used as the data for labor input.
The wage data for the industrial subsectors from 1997 to 2017 in the “China Labor Statistical Yearbook” were used as the labor price data and were adjusted using the consumer price index (1997 = 100).
4. Results
4.1. Descriptive Statistics
The data for this study were sourced from the “China Statistical Yearbook,” “China Industrial Statistical Yearbook,” “China Labor Statistical Yearbook,” “China Financial Statistical Yearbook,” and “China Science and Technology Statistical Yearbook.” Descriptive statistics are presented in Table 2.
4.2. Regression Results
Table 3 presents the regression results of the effect of biased technical progress on overcapacity through capital (labor) price distortion, which were obtained using the fixed-effects panel data regression method in Stata 14.
As shown in Table 3, column (1) shows that the regression coefficient of biased technological progress on the capacity utilization rate is significantly positive. Columns (2) and (3) indicate that the regression coefficients of capital (labor) price distortion on the capacity utilization rate are significantly negative. Columns (4) and (5) reveal that the regression coefficient of biased technological progress on the capital price distortion is significantly positive, while the regression coefficient on the labor price distortion is not significant. By comparing column (6) with column (1), the value and significance of the regression coefficient of biased technological progress on the capacity utilization rate decrease, indicating that the mediation effect of biased technological progress on overcapacity through capital price distortion is valid. By comparing column (7)with column (1), the value and significance of the regression coefficient of biased technological progress on the capacity utilization rate do not decrease significantly, suggesting that the mediation effect of biased technological progress on overcapacity through labor price distortion is not valid.
As shown in Figure 2, China’s industrial sector has biased technical progress toward capital but is gradually shifting towards labor. This is due to the diminishing marginal returns of capital factor, where the scale of capital input continues to expand, but the marginal output growth of capital is slowing down. On the other hand, expanding labor input in the production process mainly reflects the improvement of labor quality (as shown in Figure 3, the proportion of skilled personnel in total employment has been increasing year by year), which significantly increases the marginal output growth of labor. The positive impact of the expanding scale of capital input and the slowing marginal output growth of capital on overcapacity is gradually weakening, while the negative impact of improving labor quality and accelerating marginal output growth of labor on overcapacity is strengthening. Ultimately, biased technical progress toward labor in China’s industrial sector has a positive effect on overcapacity, mitigating overcapacity. Hypothesis 1 is supported.
Under the premise that the nominal price of the capital factor is regulated and capital prices have not shown significant fluctuations (with China’s 3–5 year fixed deposit rates at 0.087 and 0.048 in 1998 and 2017, respectively), the persistently low growth of marginal output of capital exhibited by the technological bias in China’s industrial sector has led to a slowing trend in the ratio of marginal output of capital to the price of capital, i.e., capital price distortion. This demonstrates that China’s industry-biased technological progress has mitigated capital price distortions. Moreover, under the premise of enhanced labor mobility and bargaining power and continuous growth of labor prices (with China’s industrial sector average wages at RMB 6,400 and RMB 45,200 in 1998 and 2017, respectively, after excluding price factors), the growth of marginal output of labor exhibited by the technological bias in China’s industrial sector continues to be high. The ratio of the marginal output of labor to labor prices, i.e., labor price distortion, does not show significant changes, proving the insignificant impact of industry-biased technological progress on labor price distortion in China. Hypothesis 2a is supported, while hypothesis 2b is not supported.
Based on the results of this study, there are varying degrees of price distortions for both capital and labor factors in different periods in China’s industrial sector, which lead to misallocation of production resources, reduced consumer demand, and insufficient technological innovation, ultimately resulting in lower capacity utilization and exacerbating overcapacity. Hypotheses 3a and 3b are supported.
In summary, biased technical progress in China’s industrial sector has a significant impact on overcapacity through the capital price distortion. Hypothesis 4a is supported, while hypothesis 4b is not supported.
4.3. Robustness Test
In this study, we referred to Yang [18] and used the overcapacity index, which is defined as follows:
In this formula, represents the overcapacity index, where is the production-side capacity utilization rate, which is the capacity utilization rate index calculated earlier, and is the demand-side capacity utilization rate, represented by the ratio of industrial sales value (representing market demand) to the total industrial output value (representing market supply). We conducted the regression analysis using panel data fixed effects in Stata 14 and performed robustness tests. The results are shown in Table 4.
As shown in column (1) of Table 4, biased technological progress has a significantly negative regression coefficient on overcapacity. Column (2) shows that capital price distortion has a significantly positive regression coefficient on overcapacity. Column (3) indicates that biased technological progress has a significantly negative regression coefficient on capital price distortion. Compared with column (1), column (4) shows a significant decrease in the value and significance of the regression coefficient of biased technological progress on overcapacity. Overall, it can be concluded that biased technical progress and the mitigation of overcapacity in China’s industrial sector through capital price distortion are robust based on the early results obtained.
4.4. Heterogeneity Test
The regression results in the previous section show that the impact of biased technological progress on overcapacity is mediated by capital price distortion, but not by labor price distortion, indicating that the mediation effect is significantly different between the two factors. To further verify these results, this study divides the industrial sectors into heavy and light industries, which have different characteristics in terms of the input factors of capital and labor, and conducts the mediation regression analysis using panel data fixed effects. The results are presented in Table 5.
Columns (1) and (5) of Table 5 show that biased technological progress has a significantly positive regression coefficient on capacity utilization in both heavy and light industries. Columns (2) and (6) show that biased technological progress has a significantly negative coefficient on capital price distortion in both heavy and light industries. Columns (3) and (7) show that capital price distortion has a significantly negative coefficient on capacity utilization in both heavy and light industries. Columns (4) and (8) show that a decrease in the value and significance of the regression coefficient of biased technological progress on capacity utilization in both heavy and light industries, but the regression coefficient of biased technological progress on capacity utilization decreases significantly in heavy industry compared to light industry.
Therefore, biased technological progress increases capacity utilization through capital price distortion. Compared with light industry, biased technological progress and capital price distortion have more significant impact in statistics and economics on capital utilization in heavy industries.
5. Conclusion
This study proposes a theoretical hypothesis that biased technological progress affects overcapacity through capital (labor) price distortion and uses a mediation effect model to verify this hypothesis. Based on robustness and heterogeneity tests, the following conclusions are drawn: (1) biased technological progress in the Chinese industrial sector can alleviate overcapacity; (2) price distortions of factors in the Chinese industrial sector worsen overcapacity; (3) biased technological progress in the Chinese industrial sector affects overcapacity through capital price distortion; (4) in capital-intensive heavy industries, the mediation effect of biased technological progress affecting overcapacity through capital price distortion is more significant than that in light industries.
The results of this study are based on China’s industrial sector, but the theoretical framework and methods used can be applicable to other countries and industries with similar characteristics, because the interaction mechanism between biased technological progress and factor structure has a significant impact on the changes in factor prices and technological efficiency across different countries and industries. Particularly for developing countries, when following the technological progress of developed countries, it is necessary to consider their own factor endowments and adjust the factor input structure to keep up with the technological progress. However, such adjustments may lead to price distortions in capital or labor, affecting production efficiency and leading to overcapacity. Therefore, different countries need to consciously choose the technological progress direction that matches their factor input under the consideration of their own factor endowments, in order to prevent and mitigate overcapacity problems.
Combining the results above, we make the following policy recommendations: (1) optimize the bias of technological progress. Policymakers should fully utilize price adjustment mechanisms, rationally guide production factors input, develop a technology progress structure that is suitable for the country’s economy, couple factor endowment structure with industry characteristics, and improve factor allocation efficiency and production efficiency; (2) improve factor markets. Policymakers should deepen the market-oriented reform of factors, improve the mechanism for determining factor prices, and ensure that factor prices reflect market supply and demand, thereby reducing the negative impact of price distortions on excess production capacity; (3) enhance original innovation capability. Policymakers should encourage independent innovation, strengthen the research and development of key technologies, rely on technological progress to improve the production efficiency of factors such as labor and capital, and promote high-quality economic development.
Data Availability
The datasets generated and analyzed during the current study are available from the first author on reasonable request.
Conflicts of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.
Authors’ Contributions
Peng Xiao proposed methodology, provided software, performed formal analysis and data curation, wrote the original draft, reviewed and edited the manuscript, validated the data, and visualized the study; Baoxi Li wrote the original draft, validated the data, and wrote, reviewed, and edited the manuscript; Yuhang He wrote, reviewed, and edited the manuscript.
Acknowledgments
This study was funded by the Key Program of the National Social Science Fund (grant numbers: 19AJL004 and 19AJL016), the General Program of the National Social Science Fund (grant number: 20BJL140), the Youth Program of National Social Science (grant number: 21CJL021), and the Hubei Provincial People's Government Research Office Project (grant number: HBZB-2019-03) .