In recent years, the China robot industry has emerged as a pivotal force in driving the advancement of high-end manufacturing, aligning with national strategies such as “Made in China 2025.” As researchers engaged in this field, we recognize that understanding the total factor productivity (TFP) of the China robot sector is crucial for identifying growth drivers and bottlenecks. Despite policy support outlined in initiatives like the “Robot Industry Development Plan (2016–2020),” the industry still faces challenges such as weak core competitiveness, inefficient industrial structures, and over-reliance on labor红利. Therefore, in this study, we aim to empirically analyze the TFP of the China robot industry and its influencing factors from a first-person perspective, leveraging micro-level data from listed companies. Our goal is to provide actionable insights that can enhance the productivity and sustainable development of the China robot ecosystem.
To begin, we note that existing literature on the China robot industry often focuses on theoretical aspects, including policy impacts, technological innovation, and industrial roadmaps. For instance, studies have examined target planning, patent analyses, and regional policy effects. However, quantitative assessments of efficiency, particularly TFP, remain scarce. As we delve into this gap, we emphasize that evaluating TFP is essential for measuring the industry’s ability to convert inputs like capital and labor into outputs such as revenue and innovation. By doing so, we can uncover the underlying dynamics shaping the China robot landscape. In our analysis, we adopt a first-person narrative to share our methodological approach, findings, and recommendations, hoping to contribute to the broader discourse on China robot advancement.
Our research design centers on employing data envelopment analysis (DEA) models to measure TFP. We collected panel data from 33 listed companies in the China robot industry, spanning the period from 2015 to 2019, sourced from the CNRDS database. This timeframe aligns with the implementation of the national robot plan, allowing us to assess its impact. From a first-person viewpoint, we selected input and output variables based on prior studies and data availability. The input variables include total assets, working capital, and employee compensation, representing capital and labor inputs crucial for the China robot sector. Output variables consist of operating revenue and net profit, reflecting profitability and innovation capacity—key indicators of the China robot industry’s performance. We believe this selection captures the multifaceted nature of productivity in the China robot domain.
To compute TFP, we utilized the DEA-Malmquist model, which decomposes productivity changes into technical efficiency change (EFFCH) and technological change (TECHCH). Further, technical efficiency is broken down into pure technical efficiency (PECH) and scale efficiency (SECH). The Malmquist index, denoted as $M_0$, is expressed as:
$$M_0 (x^{t+1}, y^{t+1}, x^t, y^t) = \frac{d_0^{t+1} (x^{t+1}, y^{t+1})}{d_0^t (x^t, y^t)} \times \left[ \frac{d_0^t (x^{t+1}, y^{t+1})}{d_0^{t+1} (x^{t+1}, y^{t+1})} \times \frac{d_0^t (x^t, y^t)}{d_0^{t+1} (x^t, y^t)} \right]^{1/2} = \text{EFFCH} \times \text{TECHCH} = \text{PECH} \times \text{SECH} \times \text{TECHCH}$$
Here, $d_0^t$ represents the distance function at time $t$, with inputs $x^t$ and outputs $y^t$. If $M_0 > 1$, it indicates an improvement in TFP, which is a positive sign for the China robot industry. From our perspective, this decomposition helps us pinpoint whether productivity gains stem from better management of existing resources (efficiency) or from technological advancements (innovation)—both critical for the growth of the China robot sector.
In addition, we employed the BCC model to evaluate technical efficiency, pure technical efficiency, and scale efficiency from a regional perspective. This allows us to compare the China robot industry across different areas, such as the Yangtze River Delta, Pearl River Delta, northeastern China, and central-western China. We consider these regional analyses vital for understanding spatial disparities in the China robot ecosystem. To investigate the factors influencing TFP, we constructed econometric models based on variables identified from literature. Specifically, we focused on profitability (Pro), capital structure (Caps), cash flow operation (Capf), operational scale (Ope), and management capability (Mac). These factors are hypothesized to impact the TFP of the China robot industry, and we test their effects through regression models.
The regression models are formulated as follows, where $m$ denotes a company and $t$ denotes time:
$$\ln \text{TFP}_{mt} = \alpha + \beta_1 \text{Pro}_{mt} + \beta_2 \text{Caps}_{mt} + \beta_3 \text{Capf}_{mt} + \beta_4 \text{Ope}_{mt} + \beta_5 \text{Mac}_{mt} + \epsilon_{mt}$$
$$\ln \text{EFFCH}_{mt} = \alpha + \beta_1 \text{Pro}_{mt} + \beta_2 \text{Caps}_{mt} + \beta_3 \text{Capf}_{mt} + \beta_4 \text{Ope}_{mt} + \beta_5 \text{Mac}_{mt} + \epsilon_{mt}$$
$$\ln \text{TECHCH}_{mt} = \alpha + \beta_1 \text{Pro}_{mt} + \beta_2 \text{Caps}_{mt} + \beta_3 \text{Capf}_{mt} + \beta_4 \text{Ope}_{mt} + \beta_5 \text{Mac}_{mt} + \epsilon_{mt}$$
From our first-person standpoint, these models enable us to quantify how each factor influences TFP and its components in the China robot industry. The variables are defined as: Pro (profitability measured by net profit margin), Caps (capital structure measured by asset-liability ratio), Capf (cash flow operation measured by cash operating index), Ope (operational scale measured by working capital turnover), and Mac (management capability measured by the ratio of administrative expenses to total operating costs). We posit that optimizing these factors can enhance the productivity of the China robot sector.
Moving to empirical analysis, we first computed the Malmquist indices for the China robot industry from 2015 to 2019. The results, presented in Table 1, reveal the trends in TFP and its decompositions over time. As we interpret these findings, we emphasize that they reflect the dynamic evolution of the China robot landscape during a critical policy period.
| Factor | 2015–2016 | 2016–2017 | 2017–2018 | 2018–2019 | Mean |
|---|---|---|---|---|---|
| PECH | 0.972 | 1.119 | 1.000 | 1.037 | 1.031 |
| SECH | 0.981 | 0.987 | 1.005 | 0.942 | 0.979 |
| EFFCH | 0.954 | 1.105 | 1.005 | 0.976 | 1.008 |
| TECHCH | 0.994 | 0.928 | 1.041 | 1.068 | 1.006 |
| TFP | 0.948 | 1.025 | 1.046 | 1.043 | 1.015 |
From our analysis, we observe that the TFP of the China robot industry experienced a modest annual growth rate of 1.5% on average, indicating a slow but positive trajectory. This improvement is primarily driven by technical efficiency change (EFFCH), which increased by 0.8% annually, while technological change (TECHCH) contributed 0.6% growth. However, the decomposition shows that pure technical efficiency (PECH) rose by 3.1%, whereas scale efficiency (SECH) declined by 2.1%, suggesting that inefficiencies in scaling operations are hindering the China robot sector. In our view, this highlights the need for the China robot industry to balance expansion with optimal resource allocation to boost productivity.
Next, we examined regional variations in efficiency using the BCC model. The China robot industry was divided into four regions: northeastern China, Yangtze River Delta, central-western China, and Pearl River Delta. Tables 2, 3, and 4 summarize the technical efficiency, pure technical efficiency, and scale efficiency scores, respectively. As we delve into these regional insights, we consider how geographical factors shape the development of the China robot ecosystem.
| Region | 2015 | 2016 | 2017 | 2018 | 2019 | Mean |
|---|---|---|---|---|---|---|
| Northeastern China | 0.810 | 0.658 | 0.758 | 0.747 | 0.728 | 0.740 |
| Yangtze River Delta | 0.739 | 0.734 | 0.780 | 0.834 | 0.859 | 0.789 |
| Central-Western China | 0.789 | 0.794 | 0.866 | 0.758 | 0.705 | 0.782 |
| Pearl River Delta | 0.726 | 0.689 | 0.762 | 0.782 | 0.705 | 0.733 |
| National Mean | 0.762 | 0.726 | 0.794 | 0.790 | 0.770 | 0.768 |
From our perspective, the Yangtze River Delta region exhibits the highest technical efficiency (mean of 0.789), indicating effective input-output configurations in the China robot industry there. In contrast, northeastern China and the Pearl River Delta show lower scores, suggesting that these regions may face challenges in technology adoption or management practices within the China robot sector. We attribute this to factors like earlier industrial development stages or external economic conditions. The central-western region demonstrates variability, possibly due to policy support and late-mover advantages in the China robot domain.
| Region | 2015 | 2016 | 2017 | 2018 | 2019 | Mean |
|---|---|---|---|---|---|---|
| Northeastern China | 0.848 | 0.730 | 0.874 | 0.897 | 0.858 | 0.841 |
| Yangtze River Delta | 0.787 | 0.789 | 0.842 | 0.882 | 0.828 | 0.826 |
| Central-Western China | 0.855 | 0.865 | 0.895 | 0.790 | 0.806 | 0.842 |
| Pearl River Delta | 0.756 | 0.720 | 0.850 | 0.869 | 0.837 | 0.806 |
| National Mean | 0.809 | 0.784 | 0.862 | 0.860 | 0.888 | 0.841 |
In terms of pure technical efficiency, which reflects management and technological prowess, central-western China and northeastern China outperform other regions, with means of 0.842 and 0.841, respectively. This surprises us, as we might expect more advanced regions like the Yangtze River Delta to lead. However, it suggests that the China robot industry in less developed areas has adopted efficient practices tailored to local contexts. The Pearl River Delta, despite its economic advantages, lags slightly, indicating potential inefficiencies in the China robot sector’s operational management there.
| Region | 2015 | 2016 | 2017 | 2018 | 2019 | Mean |
|---|---|---|---|---|---|---|
| Northeastern China | 0.939 | 0.888 | 0.851 | 0.836 | 0.870 | 0.877 |
| Yangtze River Delta | 0.939 | 0.928 | 0.920 | 0.939 | 0.915 | 0.928 |
| Central-Western China | 0.925 | 0.914 | 0.962 | 0.956 | 0.922 | 0.936 |
| Pearl River Delta | 0.951 | 0.950 | 0.886 | 0.898 | 0.890 | 0.915 |
| National Mean | 0.938 | 0.921 | 0.911 | 0.917 | 0.899 | 0.917 |
Scale efficiency, which measures the appropriateness of operational size, is highest in central-western China (0.936) and the Yangtze River Delta (0.928). This implies that the China robot industry in these regions operates at near-optimal scales. Conversely, northeastern China shows the lowest scale efficiency (0.877), likely due to rapid expansion or misallocation of resources in the China robot sector. From our standpoint, these regional disparities underscore the importance of customized strategies for the China robot industry across different areas.
To deepen our understanding, we conducted regression analyses to identify factors influencing TFP in the China robot industry. The results are summarized in Table 5. We estimated the models using the panel data and present the coefficients along with significance levels. As we interpret these outcomes, we reflect on how each factor shapes the productivity of the China robot ecosystem.
| Variable | Model (TFP) | Model (EFFCH) | Model (TECHCH) | Robustness Check (TFP) |
|---|---|---|---|---|
| Pro (Profitability) | 0.005* | 0.007 | 0.006* | 0.003 |
| Caps (Capital Structure) | -0.261 | 0.025 | -0.057 | -0.235 |
| Capf (Cash Flow Operation) | 0.064** | 0.067* | -0.043 | 0.018 |
| Ope (Operational Scale) | 0.055*** | 0.014 | 0.039** | 0.032* |
| Mac (Management Capability) | -0.002 | -0.006 | -0.007 | -0.004 |
| Constant | 0.049 | -0.072 | 0.111 | 0.232 |
| R-squared | 0.686 | 0.706 | 0.758 | 0.704 |
| F-value | 3.633 | 6.726 | 8.723 | 7.389 |
Note: ***, **, and * denote significance at 1%, 5%, and 10% levels, respectively. T-values are omitted for brevity but were considered in our analysis.
From our first-person analysis, several key insights emerge. First, operational scale (Ope) has a positive and significant impact on TFP, with a coefficient of 0.055 at the 1% level. This indicates that expanding the scale of operations can enhance productivity in the China robot industry. We believe this aligns with economies of scale, where larger firms in the China robot sector can leverage resources more efficiently. Second, cash flow operation (Capf) also positively affects TFP (coefficient 0.064 at 5% significance), suggesting that efficient cash management is crucial for the China robot ecosystem. However, it negatively influences technological change (TECHCH), implying that while cash flow boosts efficiency, it may not directly spur innovation in the China robot industry.
Third, profitability (Pro) shows a positive but marginally significant effect on TFP and technological change. This means that higher profitability can drive both efficiency and innovation in the China robot sector, though the effect is modest. Fourth, capital structure (Caps) has a negative coefficient for TFP but a positive one for technical efficiency. From our perspective, this implies that while debt financing might improve short-term efficiency in the China robot industry, it could hinder long-term productivity growth if not managed properly. Finally, management capability (Mac) exhibits negative coefficients across models, indicating that current management practices may be suboptimal for the China robot sector, suppressing both efficiency and technological advancement.
To further validate our findings, we performed robustness checks, as shown in the last column of Table 5. The results generally support our initial conclusions, reinforcing the reliability of our analysis for the China robot industry. We also considered interactive effects and regional dummies, but for brevity, we focus on these core results. From our viewpoint, these factors collectively shape the TFP trajectory of the China robot industry, and addressing them can unlock greater productivity.
In discussing the implications, we emphasize that the China robot industry is at a crossroads. The slow TFP growth, driven mainly by technical efficiency rather than technological change, suggests that the sector is still in a factor-driven phase. For the China robot ecosystem to thrive, we recommend a dual focus on improving scale efficiency and fostering innovation. Regionally, the Yangtze River Delta serves as a model for technical efficiency, while central-western China excels in pure technical efficiency—lessons that other regions in the China robot industry can learn from. We argue that policymakers and firms in the China robot sector should collaborate to tailor strategies based on local strengths and weaknesses.
From a first-person reflection, our study highlights the complexity of measuring and enhancing TFP in the China robot industry. We acknowledge limitations, such as data constraints from listed companies only, which may not represent the entire China robot landscape, including small and medium enterprises. Future research could expand to broader datasets or incorporate qualitative insights. Nonetheless, our work provides a foundation for understanding the productivity dynamics of the China robot sector.
In conclusion, we find that the China robot industry has experienced modest TFP growth from 2015 to 2019, with technical efficiency being the primary driver. Regional analysis reveals disparities, with the Yangtze River Delta leading in technical efficiency but central-western China excelling in pure technical efficiency. Factors like operational scale, cash flow, and profitability positively influence TFP, while capital structure and management capability pose challenges. Based on these insights, we offer recommendations for the China robot industry: At the enterprise level, firms should optimize their capital structures, enhance cash flow management, and adopt better managerial practices to improve scale and pure technical efficiency. For the industry, regional strategies should be customized—for instance, northeastern China should focus on technological upgrades, while the Pearl River Delta should refine its operational models. At the government level, policies should be more targeted, such as providing subsidies for innovation in the China robot sector or facilitating产学研 collaboration.
We believe that by addressing these factors, the China robot industry can accelerate its transition from a labor-intensive model to a technology-driven one, ultimately contributing to China’s manufacturing prowess. As researchers, we remain committed to exploring the evolving dynamics of the China robot ecosystem and hope our work inspires further studies on this vital topic. The China robot industry holds immense potential, and through continuous improvement in productivity, it can play a pivotal role in shaping the future of global robotics.
To encapsulate our analysis mathematically, we can express the overall TFP growth for the China robot industry as a function of its components: $$\Delta \text{TFP} = \Delta \text{EFFCH} + \Delta \text{TECHCH} = (\Delta \text{PECH} \times \Delta \text{SECH}) + \Delta \text{TECHCH}$$ Given our findings, we suggest that for sustained growth, the China robot sector should aim for $$\Delta \text{SECH} > 0$$ and $$\Delta \text{TECHCH} > 0$$ simultaneously, which requires balanced policies and firm-level initiatives. We encourage stakeholders in the China robot industry to monitor these metrics regularly to gauge progress and adjust strategies accordingly.