Service-Manufacturing Integration and the “Baumol’s Disease” Trap: Experience from China and Global Patterns

2.1 Research on the Concept, Measurement, and Impact of the Integration of the Service and Manufacturing Sectors

The integration of the service and manufacturing sectors is characterized by mutual penetration, interaction, and deep integration within the industrial chain and value chain, ultimately leading to the formation of new industries and business models. Academic discussions focus primarily on two categories. The first is the servitization of the manufacturing sector, which involves increasing the use of service elements in the production process and shifting from selling products to offering “products and services”. This concept was first proposed by Vandermerwe and Rada (1988), who argued that the servitization of manufacturing integrates goods and services to enhance the value of core products and achieve a higher market share. Liu et al. (2016) referred to the transformation from a manufacturing-centered to a service-centered approach as manufacturing servitization; Liu and Ni (2018) posited that this transformation reflects the embedding of service-added value in manufacturing enterprises, an important form of integration between the service sector and manufacturing. Building on these concepts, quantitative methods for measuring the servitization of the manufacturing sector have become relatively established. Some scholars have employed the direct or complete input coefficient of manufacturing to the service sector for measurement (Liu et al., 2016; Xu et al., 2017). With advancements in the decomposition method of trade value-added, some studies have depicted the servitization of manufacturing from the perspective of value-added proportions (Peng et al., 2017). Additionally, some research has used indices such as the proportion of operating income from firm services to total revenue to assess the level of servitization in the manufacturing sector (Crozet and Milet, 2017). The second category relates to the manufacturingization of services, which has sparked debate over its definition. Some scholars assert that it involves the introduction of manufacturing production methods and products into the service sector, while others contend that the service sector extends the industrial chain and engages with the original manufacturing sector (Yu et al., 2021). Du and Hou (2021) emphasized the reverse integration from manufacturing to the service sector, proposing that manufacturingization is measured by the contribution of the manufacturing sector to the service sector.

From the perspective of the economic impact of integrating the service and manufacturing sectors, many scholars have focused on the servitization of the manufacturing sector, typically examining the performance of manufacturing industries or enterprises. At the industrial level, Xu and Sun (2009) found that integrating the information and manufacturing sectors enhances the performance of the manufacturing sector. At the enterprise level, Liu and Ni (2018) demonstrated that the servitization of the manufacturing sector improves total factor productivity (TFP) and promotes technological progress. Liu et al. (2016) and Crozet and Milet (2017) studied the effects of manufacturing servitization on value chain upgrading and firm performance, respectively. However, there are relatively few studies on the impact of service manufacturingization. Du and Hou (2021) noted that the effect of service manufacturingization on service productivity remains uncertain. In summary, the current concepts and measurements of the integration of the service and manufacturing sectors remain incomplete, and research examining the impact of the service sector on manufacturing input from the perspective of the service sector is lacking. This paper aims to explore the level of input from the service sector to the manufacturing sector, thereby supplementing the concepts and measurement methods related to the integration of these two industries.

2.2 Discussion of “Baumol’s Disease” and Research on Factors Influencing Productivity in the Service Sector

The essence of “Baumol’s disease” lies in the low productivity of the service sector compared to the manufacturing sector. Several studies have provided empirical evidence for the existence of “Baumol’s disease.” Baqaee and Farhi (2019) found that stagnant sector sales growth exacerbated “Baumol’s disease,” leading to a decline in overall TFP growth in the United States. Sen (2020) found that “Baumol’s disease” caused only a minor decline in economic growth. In China, Cheng (2004) observed that labor productivity in the service sector lagged and developed unevenly. Wang (2021) reported that changes in the price structure of the tertiary industry negatively impacted economic growth. Lin and Xu (2023) found that technological progress in manufacturing led to a decline in the domestic share of manufacturing and a drop in the relative prices of manufacturing products in China, further verifying the existence of “Baumol’s disease”.

Regarding the current state of service productivity, most of the literature indicates that China’s service sector productivity is low, as noted by Tan and Zheng (2012) and Chen and Hou (2021). Previous studies have also explored paths for improving service sector productivity. Factors such as service openness (Chen and Wei, 2018; Chen et al., 2022), digital transformation (Peters et al., 2018; Jiang and Luo, 2019; Li et al., 2022), and human capital (Li, 2016), and migration (Ottaviano et al., 2018) all positively influence service TFP. However, few studies have examined the effect of the integration of the two industries on service sector productivity improvement. Du and Hou (2021) indicated that integrating the service and manufacturing sectors can alleviate “Baumol’s disease”. Indeed, alleviating “Baumol’s disease” involves not just enhancing service sector productivity but improving its productivity relative to the manufacturing sector—a topic that has been largely overlooked in current research.

3.1 Impact of Service Sector Input to the Manufacturing Sector Regarding the “Baumol’s Disease” Trap

The deep integration of the service and manufacturing sectors is crucial for improving service sector productivity. According to the theory of “demand compliance,” the development level of the manufacturing sector—an important demand side for the service sector—directly affects the service sector’s development, particularly regarding market size and growth potential for producer services. As the manufacturing sector expands, the demand for efficient and professional services increases, stimulating continuous innovation and efficiency improvements in the service sector. Additionally, technology spillovers from the manufacturing sector help modernize and intelligently transform the service sector, thereby enhancing productivity. Simultaneously, the value-added portion of the manufacturing sector is gradually shifting to the service sector, increasing the demand for service intermediate inputs in manufacturing, which promotes the growth of the service sector and the enhancement of service quality, ultimately improving productivity. In summary, this paper proposes the following hypothesis:

Hypothesis 1: The service sector’s input to the manufacturing sector can improve the relative productivity of the service sector and help overcome the “Baumol’s disease” trap.

3.2 Mechanism by which Service Sector Input to the Manufacturing Sector Overcomes the “Baumol’s Disease” Trap

First, consider the competitive effect. Currently, the enhancement of the core competitive advantage of the service sector drives the manufacturing sector to absorb service sector inputs to boost its competitiveness. This enriches the value chain of the manufacturing sector, increases the added value of products and services, and enables it to respond flexibly to market changes and achieve sustainable development. From the perspective of manufacturing production, service sector inputs (such as systems, software, talent, and technological innovation) improve production efficiency and reduce costs. From the sales perspective, the combination of service and product sales enhances overall product competitiveness. However, increased competition in the manufacturing sector also raises the demand for high-quality services, promoting the refinement of labor division, efficiency improvements, and quality optimization within the service sector, intensifying internal competition and fostering innovation and upgrading. In the long run, the service sector’s input into the manufacturing sector under this integration intensifies competition in the manufacturing sector, which expands the demand for services and further intensifies competition among service providers. This two-way interaction increases the competitive dynamic between the service and manufacturing sectors, serving as a vital pathway to improve service sector productivity and helping to overcome the “Baumol’s disease” trap. In summary, we propose the following hypothesis:

Hypothesis 2a: The input of the service sector to the manufacturing sector enhances the relative productivity of the service sector by improving its competitive dynamics, serving as a crucial mechanism to overcome the “Baumol’s disease” trap.

Second is the scale effect. In economics, the scale effect arises from factors such as the application of new inputs, specialized division of labor, and spatial agglomeration. The service sector’s contributions to the manufacturing sector can significantly enhance the scale effect, which manifests in several ways. First, it introduces new management concepts, systems, financial capital, human capital, and R&D innovation, thereby improving production efficiency and promoting the scale effect. Second, it generates new manufacturing formats, refines the division of production, and strengthens specialized production. Third, it breaks geographical space segmentation, enabling “cloud agglomeration” of the industrial chain, which forms the scale effect. Furthermore, as the manufacturing and service sectors interact and share resources, the scale effect in manufacturing also boosts the scale effect in the service sector. Specifically, first, the scale effect in manufacturing drives down costs in the service sector, facilitating large-scale production and eliminating inefficient enterprises. Second, the specialization within the manufacturing sector creates personalized demands for the service sector, enhancing its degree of specialization. Third, the concentration of physical space allows the service sector to provide services to more manufacturing industries, resulting in factor agglomeration. Following integration with the manufacturing sector, the relative scale effect of the service sector increases, ultimately enhancing its productivity compared to the manufacturing sector. In summary, this paper proposes Hypothesis 2b.

Hypothesis 2b: The service sector’s input to the manufacturing sector enhances the relative scale effect of the service sector and improves its relative productivity, serving as a crucial mechanism to overcome the trap of “Baumol’s disease.”

Third is the innovation effect. In chain production modes, innovation requires the full mobilization of resources from all parties to create a synergistic effect. The service sector, as a vital input for the manufacturing sector, enhances its competitiveness and productivity. Simultaneously, the development of the manufacturing sector also fosters innovation in the service sector. First, it generates new demands for the service sector, such as high-end talent, information connectivity, and knowledge spillover, which drive deeper innovation and enhance research and development. Second, it provides application scenarios, experimental spaces, and mediums for transmitting innovation insights, facilitating the exchange of ideas and sparking inspiration through production networks. Third, it offers learning, training, and financial support for service sector innovation, creating more learning opportunities and funding for innovation activities. Innovation is fundamentally essential for improving enterprise productivity, and enhancing the relative innovation effect of the service sector is a key pathway to boosting its relative productivity. In summary, this paper proposes Hypothesis 2c.

Hypothesis 2c: The service sector’s input to the manufacturing sector enhances the relative innovation effect of the service sector, leading to improved relative productivity, which is an important mechanism to overcome the trap of “Baumol’s disease.”

4.1 Index Measures

4.2 Methodological Model Setting

In this study, the data sample is constructed in the form of “industry pairs” formed by various service sectors and manufacturing sectors in China. The relative productivity of the service sector and its input to the target manufacturing sector is used as the explained and explanatory variable, and the econometric model is constructed as follows:

In Equation (9), i, j, and t denote the service sector, manufacturing sector, and time period, respectively. This study focuses on samples across the “time-service-manufacturing” dimension, consistent with prior definitions. RTFPijtc represents the relative productivity of Chinese service sector i to its target manufacturing sector j (as previously defined). SRijtc captures the input intensity from Chinese service sector i to manufacturing sector j, where SRijtc∈Sratio1ijtc,Szratio2ijtc. Fijtc represents a set of control variables selected for this study, all derived from enterprise-level weighted data. The construction of these control variables follows the same methodology as the relative productivity measures for the service sector described earlier. The underlying enterprise-level data are sourced from administrative tax records. In the equation, the “industry-to-industry” fixed effect ( v_ij ) and time fixed effect ( v_t ) of the service-manufacturing sector, along with robust standard errors, are included. To test Hypothesis 1, the coefficient α₁ is of key concern. If α₁> 0 significantly passes the test, it indicates that, an increase in the level of service sector input to the manufacturing sector leads to a significant increase in the relative productivity of the service sector.

4.3 Data Sources and Data Processing

This study primarily utilizes two databases. The first is the OECD Inter-Country Input-Output (ICIO) database, which provides input-output tables covering 67 countries/regions and 45 sectors (17 manufacturing sectors, 20 service sectors) from 2000 to 2020. The OECD-ICIO database was selected because it contains input-output information among industries in China and many other countries, allowing for a clear measurement of China’s service sector input to the global manufacturing sector over a long and continuous time span. The second database, China’s National Tax Survey Database, covers the period 2007–2016 and is mainly used to measure enterprise-level TFP and industry-weighted productivity. This database includes over 200 indices reflecting enterprise characteristics, with a sample size of more than 310000 enterprises in 2007, over 610000 in 2016, and more than 700000 in subsequent years. We clean the data in the Tax Adjustment Database. ^[1] We match the OECD-ICIO input-output table database with the National Tax Survey database. ^[1] Additionally, this paper uses the database of listed companies to measure the relative productivity of the service sector, with results presented in the robustness test. The TFP data and control variables of listed companies are sourced from the statistics of the characteristic indices of listed companies in the Guotaian database.

Furthermore, the follow-up study is tested at the cross-country level, using the 2016 edition of the World Input-Output Tables database (2000–2014, 43 countries, 56 sectors) and the Socio-Economic Accounts Database (SEA) to calculate TFP.

5.1 Benchmark Regression Results Analysis

Columns (1) and (2) of Table 1 demonstrate that an increase in the level of service sector input to the manufacturing sector significantly enhances the relative productivity of the service sector and narrows the TFP gap between the two industries, thereby verifying Hypothesis 1. In fact, alongside the low productivity of the service sector relative to the manufacturing sector, there exists the issue of higher wage costs in the service sector compared to the manufacturing sector, which contributes to “Baumol’s disease.”

To address this, we use the previously measured relative wage cost of the service sector as an expanded estimate of the explained variable in Equation (9). The results are presented in columns (3) and (4) of Table 1. The findings indicate that as the level of service sector input to the manufacturing sector increases, there is no significant increase in the relative wage cost of the service sector, and it even exhibits a downward trend to some extent. This alleviates the problem of high relative wage costs in the service sector associated with “Baumol’s disease.” According to the results in Table 1, increasing the level of service input to manufacturing can help overcome the “Baumol’s disease” trap in terms of both productivity and wage costs. Since the issue of relative productivity is central to “Baumol’s disease,” and relative wages and prices are fundamentally determined by productivity (Song and Zheng, 2017), the examination of relative productivity will be used in subsequent articles to address the “Baumol’s disease” trap unless otherwise specified.

5.2 Endogeneity Discussion^[1]

In benchmark regression, we control for sector pairs and time fixed effects while addressing the data dimension mismatch between the core explanatory variable and the explained variable. However, an endogeneity problem persists. To verify the accuracy of the results, we employ two instrumental variable methods to perform two-stage least squares estimation. First, following Liu and Ni (2018)’s study, we use Japan’s service sector input to the manufacturing sector as the instrumental variable for China’s service sector’s input to the manufacturing sector. ^[2] The results indicate that the instrumental variable is effective, ^[3] and the service sector’s input to the manufacturing sector significantly enhances the relative productivity of the service sector. Second, referring to Fallah et al. (2021)’s study, the Bartik IV method is used to construct instrumental variables, and the estimation results further support the reliability of the baseline regression.

6.1 Mechanism Test Model

This paper examines the mechanism by which service sector input to the manufacturing sector improves the relative productivity of the service sector from three perspectives: the relative competition effect, relative scale effect, and relative innovation effect. The econometric model is constructed using a step-by-step regression method as follows:

In Equations (10) and (11), the variables i, j, t, and c retain their previous meanings. Channelijtc are the three mechanism variables studied in this paper, ^[1] and the control variables and fixed effect selection remain consistent with benchmark regression. The economic significance of Equation (10) lies in testing the influence of service sector input to the manufacturing sector on the mechanism variables. The economic significance of Equation (11) is to examine the impact of mechanism variables on the relative productivity of the service sector.

6.2 Testing the Mechanism of Competitive Effect

Drawing on Choi’s (2023) study, this paper uses the Herfindahl-Hirschman index ^[2] to measure the level of competition within the industry. A value closer to 1 suggests stronger monopoly and lower competition. We quantify the relative competitive effects of the service sector by defining the relative competitive effect of service sector i on its input target manufacturing sector j as RCEijtc=HHIitc−HHIjtc, where RCEijtc represents the relative competitive effect of the service sector, measuring the degree of competition of the service sector relative to the input target manufacturing sector. A smaller RCEijtc indicates a stronger relative competitive effect of the service sector. The test results are presented in Table 3, columns (1) and (2). The increase in input from the service sector to the manufacturing sector promotes the enhancement of the relative competitive effect of the service sector (Column 1), and the greater the relative competition of the service sector compared to the manufacturing sector, the greater the effect on improving the relative productivity of the service sector (Column 2). Therefore, the relative competitive effect of the service sector constitutes an important mechanism through which service sector input to the manufacturing sector promotes improvements in relative productivity.

6.3 Testing the Mechanism of Scale Effects

To measure the scale effect at the sector level, we use the proportion of enterprise output as a weight to aggregate the enterprise scale to the sector level SEitc ^[1]. Next, we calculate the relative scale effect of the service sector to its input target manufacturing sector, RSEijtc, where RSEijtc=lnSEitc/SEjtc. ^[2] RSEijtc measures the scale effect of service sector i relative to the input target manufacturing sector j. A larger RSEijtc indicates a greater relative scale effect of the service sector. Table 3, columns (3) and (4), show that inputs from the service sector can enhance the relative scale effect (column 3), and that this relative scale effect positively impacts the improvement in the relative productivity of the service sector (column 4). Therefore, the relative scale effect of the service sector is an important mechanism for enhancing its relative productivity.

6.4 Testing the Mechanism of Innovation Effect

Based on the measurement methods for scale effects, the adjusted basic index is the logarithm of the enterprise’s R&D expenses. This index is used to calculate the innovation indices for both the service sector and the input target manufacturing sector, further measuring the relative innovation effect of the service sector. The specific measurement process aligns with the previously mentioned competitive effects and will not be reiterated here. Table 3, columns (5) and (6), show that inputs from the service sector can enhance its relative innovation effects (column 5) and that these relative innovation effects positively impact the relative productivity of the service sector (column 6). Therefore, the relative innovation effects of the service sector are a crucial mechanism for enhancing its relative productivity.