Study on the Mechanism of Wage Growth in China’s Logistics Industry: The Roles of Government and Market

Abstract

Government policies and market forces have created new possibilities for wage growth in the logistics industry, which can reshape the development direction and labor reward of enterprises. The inclusive financial policy implemented by the Chinese government is effective, and the inputs of inclusive finance can affect the intelligent and low-carbon operations, the technical economic benefits and labor productivity in the logistics industry, thereby promoting wage growth. Meanwhile, the government-led industrial structure transformation and transportation infrastructure have brought a large number of new workers, transport individuals and enterprises into the logistics industry, which intensify the homogeneous service competition of enterprises, thereby hampering wage growth. In the market force, with the scale expansion of Internet access and logistics delivery vehicles and freight volume, the scale effects may enhance the wage level in the logistics industry. In addition, the moderating effect between policy and market forces can also confirm the existence of a positive spillover effect. The heterogeneity of wage growth varies across the eastern, central and western regions, as well as between the northern and southern regions. These findings highlight the importance of promoting the growth of labor wage income by policy implementation in inclusive finance, preferential measures on agricultural product logistics, integrated operation in the manufacturing and logistics field and the Belt and Road Initiative.

1. Introduction

Since China’s accession to the WTO (World Trade Organization), the rapid development of China’s economy and trade has contributed to the global economy, but it has also widened the income gap among various industries in China, which has faced challenges between high-quality development and improved income distribution. At present, under the background of China’s implementation of the “carbon peaking and carbon neutrality” strategic program (dual-carbon strategy), the logistics industry has a shortage of highly skilled workers, and the lack of senior talents slows down the development process of intelligent and low-carbon logistics in China’s logistics industry. The homogenization service of a large number of logistics enterprises is serious, and price competition has become the main means of competition. Therefore, the revenue growth of the logistics industry faces challenges. However, the Chinese government needs a low-cost, high-efficiency and low-carbon logistics industry. Although the government has reduced social logistics costs by implementing high-quality transport infrastructure, the rising price of energy factors has increased logistics costs. In order to promote the development of the logistics industry and improve the wage income of workers through inclusive finance, industrial structure adjustment and upgrading, some policy mechanisms also need to be studied. In the market link, the scale of Internet connectivity is huge, vertical e-commerce and network broadcast sales of point-to-point (producer to consumer) freight reduce the transit costs and the growth of the e-commerce transaction volume and freight volume lays the foundation of scale effect in the logistics industry. Therefore, under the market mechanism, the impact of the scale effect on the wage income of the logistics industry is also crucial.

At present, there is some literature on the studies of wage income in China’s industries and regions. The first branch is the influence of inclusive finance on wage level. Researchers learned that financial agglomeration had a significantly positive impact on regional wage level (J. J. Wang & Yue, 2016), but with the improvement of the economic development level, its positive promoting effect gradually weakened. Digital inclusive finance could significantly promote the increase in wage levels (H. X. Cheng, 2022; Lin, 2023), and there was a lag effect on the impact of wage levels (H. X. Cheng, 2022), but the promoting effect of digital inclusive finance on wage income was more obvious in the service industry (Lin, 2023). On the transmission channels of digital inclusive finance affecting the labor income share, the development of digital inclusive finance significantly increased the labor income share of regular employees and decreased the labor income share of non-regular employees through three paths: credit constraint, labor employment scale and the impact of credit constraint on labor employment scale (Z. X. Xu & Li, 2024). In terms of heterogeneity, digital inclusive finance contributed to the income increase in high-income groups, and there was a Matthew effect on disadvantaged groups, which existed in regional, educational and financial heterogeneity (Demir et al., 2022).

The second branch is the influence of industrial structure changes on wage level. First, the industrial agglomeration of different industries had different effects on wage level. Several studies analyzed the internal relationship and found that there was a significant negative influence between industrial agglomeration and regional wage gap (R. F. Yang, 2013; Z. H. Cheng & Yu, 2014; X. H. Wang et al., 2016), and industrial agglomeration could also enhance productivity through the density effect and agglomeration effect and raised workers’ wages through knowledge spillover (Lu et al., 2017). Moreover, industrial integration has affected wage levels. Government deregulation was one of the main driving factors for industrial integration (Wan et al., 2011), and the reform of “three networks integration” (broadcast television network, telecommunications network and Internet) significantly improved the wage level (Z. H. Yang et al., 2021). Second, the wage premium of the industrial structure was considered: industrial structure optimization increased wage income and wage growth could also force the transformation and upgrading of the industrial structure (Bao & Lin, 2020). Under the selection effect of the migrant labor force and the economic effect of urban agglomeration, the wage premium effect from urban industrial structural adjustment and upgrading was significantly positive (Hu et al., 2021).

The third branch is the influence of economic growth on wage level. Since Kuznets (1955) proposed the inverted-U hypothesis regarding the relationship between economic development and income distribution, the number of studies on income inequality have gradually increased. Until recently, there was mixed evidence in the literature on the relationship between income inequality and economic growth (Fawaz et al., 2014; Amarante, 2014): there was a negative relationship between income inequality and economic growth in LIDC (low-income developing countries), in stark contrast with a positive inequality–growth relationship for HIDC (high-income developing countries), and both correlations were statistically significant across multiple econometric specifications (Fawaz et al., 2014). On the issue of wage income, China’s development on the issue is different than that of other countries. Many Chinese scholars have shown that economic growth could promote the growth of wage income (J. X. Li et al., 2021). At the same time, wage income could also affect economic development (M. Deng & Cao, 2019). The rapid development of the digital economy has also contributed to the increase in the share of wages or labor income (X. Zhang et al., 2022; J. Wang & Zhang, 2024; Han & Tian, 2024). The digital economy enhanced the degree of the urban wage premium by increasing the technological innovation capability of cities (X. Zhang et al., 2022). J. Wang and Zhang (2024) found that a wage increase could be achieved mainly by promoting investment in labor and human capital and broadening access to employment information, while Ai et al. (2024) found that the influence of China’s digital economy development on labor income share also depended on the level of human capital. In terms of the wage gap or labor income inequality, the development of the digital economy could significantly reduce labor income inequality (Han & Tian, 2024; Bu et al., 2024). Moreover, the digital economy has significantly improved the ability to narrow the low- and middle-income gap between low- and highly skilled groups (Han & Tian, 2024), and it could also reduce labor income inequality by improving the levels of urban human capital and reducing the differences of urban labor productivity (Bu et al., 2024).

The fourth branch is the wage income level and gap in the logistics industry. First, transportation costs affected wage income (Hanson, 2005). Song and Su (2009) also found that wages in a region were raised with the rise of incomes and fell with the rise of transportation costs. Second, the high-quality development of the logistics industry could drive the narrowing of the urban–rural income gap. The development of the logistics industry, labor efficiency of the logistics industry and regional structure of the labor force had static, dynamic, long-term and evolutionary effects on narrowing the income gap between urban and rural areas (J. Li, 2021). On the development of the logistics industry, as a labor-intensive industry in contemporary China, its size of labor force is very important. However, there is a dearth of data analysis between the labor force income and labor structure in the logistics industry. Third, the minimum wage system has affected the development of the logistics industry. The minimum wage system is an important policy instrument for the government to regulate the labor market, which has been adopted widely around the world. The intangible, low marginal costs and personalized attributes of logistics services determined that logistics development was more dependent on system arrangements, and the minimum wage system significantly promoted the growth of the logistics industry (B. Y. Zhang et al., 2021).

From the above point of view, although the existing literature analyzed the wage income, there are still some research gaps: (1) There was relatively rare discussion on wage income by the difference mechanism of inclusive finance from government demand, especially in the logistics industry. Several studies explained the transmission channel from digital financial inclusion, to financing constraints and then labor income share (Z. X. Xu & Li, 2024), in which the analysis of government demand was relatively rare. In this paper, inclusive finance is policy-oriented, and it also acts as a typical policy tool, where China’s implementation of inclusive finance can help or improve logistics enterprises. Specifically, under the Chinese government’s demand for intelligent and low-carbon logistics, through the government’s policy transmission, banks and other financial institutions provide inclusive financial services, the investments and co-constructions of logistics’ applications, the technical level and service efficiency of the logistics industry to be improved under the support of the government’s policy and inclusive financial inputs. By the operations of intelligent and low-carbon technologies and technical economic benefits in the logistics industry, the operational costs of the logistics industry will be reduced, and the wage income will be improved. (2) There are relatively rare studies on the effect of the industrial structure on wage income in the logistics industry. The early literature analyzed the inhibition mechanism of the industrial structure in some industries (R. F. Yang, 2013; Z. H. Cheng & Yu, 2014; X. H. Wang et al., 2016), but in the process of the transformation and upgrading of China’s manufacturing industry in the past decade, high-quality manufacturing has reduced the demand for low-skilled employees, driving a large number of low-skilled employees to flow to labor-intensive industries such as the logistics industry, which is bound to affect the wage level of the logistics industry, and there are few in-depth analyses of this phenomenon. This study will focus on the inhibition of the industrial structure on the wage level in the logistics industry. (3) There are still relatively rare mechanisms to explore how transport infrastructure inhibits wage income. Most studies focused on its promotion effects. Although the literature discussed the inhibition of transport infrastructure, the object of the discussion was not the issue of wage income but the issue of foreign direct investment (Zhao & Xiang, 2012). On the discussion of the urban–rural income gap by transport infrastructure, Sun (2020) found that the construction of transport infrastructure could effectively promote the flow of agricultural labor to non-agricultural sectors and then affect the urban–rural income gap; with the further increase in the rural labor transfer rate, its negative impact on the urban–rural income gap would increase. Obviously, there are not enough discussions on the inhibition mechanism of transport infrastructure, and the underlying reasons still need to be explored, especially regarding the wage income of the logistics industry.

In general, the contributions of this study are mainly reflected in the following points: (1) This study focuses on the inclusive finance mechanism and its differentiation based on Chinese government demand and examines the spillover effect between inclusive finance and GDP. Regarding the aspect of government demand, China’s government has the motivation to use inclusive finance to achieve the reduction in logistics costs in the whole of society and the realization of the dual-carbon strategy. First, the government needs intelligent logistics to achieve high efficiency. Second, the government needs the logistics industry to decarbonize in order to execute the dual-carbon strategy. Moreover, the government has laid the foundation for the scale of intelligent and low-carbon logistics through sustained economic growth and financial inclusion, in order to avoid the homogenization of services of financial institutions. The government also hopes that financial institutions can provide differentiated inclusive financial services for logistics enterprises through differentiated business processing, low interest rates and credit loans, etc. In this way, the logistics industry can realize the industrialization of science and technology, reduce cost and improve efficiency and labor productivity and increase the wage income under the support of inclusive finance. (2) The paper examines the inhibition mechanisms of the industrial structure and transportation infrastructure to make up for the shortcomings of the existing studies. The former inhibits wage growth because of an increased size of the logistics labor force through the transmission mechanism from the increased use of machinery in manufacturing, the reduction of employees in the manufacturing industry and the flow of employees into the logistics industry. The latter makes it easier for new logistics resources to enter the logistics industry through the upgrading of the transportation infrastructure, and the homogenized services intensify the competition and inhibit wage growth. (3) This study examines the promotion effect of wage income growth in the logistics industry from the perspective of the Internet access scale and logistics vehicle scale. The scale of Internet access has promoted the great development of e-commerce logistics, and the growth of the e-commerce transaction volume and freight volume has provided a basis for logistics scale operations. The market force of the “invisible hand” plays its role, and the scale effect generates benefits to the logistics labor market and promotes wage growth in the logistics industry.

The rest of this paper is organized as follows: the section “Research framework and hypothesis” builds the research framework and puts forward the research hypothesis. The section “Construction and empirical analysis of econometric model” introduces the models, variables, data and reports and discusses the main results. The section “Conclusions and policy implications” presents the main conclusions and makes relevant suggestions.

2. Research Framework and Hypothesis

2.1. Research Framework

The research framework of this paper is shown in Figure 1.

In Figure 1, the paper expands the discussion on several aspects:

• The roles of government and market. As far as the government is concerned, in addition to conventional monetary and fiscal policies, it has also provided inclusive financial policies, transport infrastructure, etc., and is also actively promoting the transformation and upgrading of the industrial structure. As far as the logistics market is concerned, it needs a large number of logistics personnel, whether it is low- skilled workers or highly skilled workers, as this phenomenon stems from two aspects: first, the scale of the freight market continues to expand; second, the development of intelligent logistics is thriving. Both the government’s macro-control and the market’s free regulation are playing their respective roles. China’s economic resilience is still strong, and both the size and the speed of development are good, but this does not mean that the real wage level of all sectors can keep up with the speed of economic growth. Therefore, the factors related to government and market warrant a systematic analysis and consideration in our study. In the construction of the econometric model, policy tools, economic and scale variables will be used, such as inclusive finance, transport infrastructure, economic growth increment, logistics vehicles scale, Internet access scale and other variables; each variable can reflect its significance and value from the perspective of the government and the market.
• Wage echelons. In China’s major industries, the highest wage levels are found in sectors such as computer science, finance and related industries, followed by electricity, health and culture and education industries. The logistics industry is ranked lower, while agriculture remains the lowest. According to CSY (China Statistical Yearbook) data, the average salary of urban units employed in the logistics industry was USD 7288.97 in 2011 and USD 17,027.20 in 2021, with an annual growth rate of 8.85%, ranking in the middle and lower reaches of various industries, slightly lower than the annual growth rate of 8.94% of GDP in the same period. Through the real reflection of the wage echelon, in order to improve the wage level of the logistics industry, we first need to know the inhibiting factors and promoting factors of the wage level in China’s logistics industry. Therefore, the positive and negative impact mechanisms of the variables mentioned above will be discussed.
• Industrial structure changes. China’s industry has been divided into three categories: the main bodies of the primary, secondary and tertiary industries are the agriculture, industrial industry and service industry, respectively. The logistics industry is a part of the tertiary industry, and is known as the artery of China’s economy, penetrating various industries and serving transportation and warehousing in various industries. Indeed, from the perspective of the total output value of various industries, the total output value of three categories of industry has risen, respectively, in recent years. From the perspective of the proportion of the total output value and GDP of various industries, during the period from 2011 to 2021, the proportion of the agriculture, industrial industry and service industry was 9.2%, 46.5% and 44.3% in 2011, and 7.2%, 39.3% and 53.5% in 2021, respectively, in the CSY data. These data showed that the proportion of the agriculture or industrial industry was declining, while the proportion of the service industry was rising. The change in the industrial structure means a change of talent flow and talent concentration, which affects the change in wage level. In 2011, the number of people employed in the secondary industry was 225.39 million, and in 2021 it was 217.12 million, a total decrease of about 8.27 million. However, the number of people employed in the logistics industry (railway, road, water, air, pipeline and posts) was 7,149,213 in 2021, an increase of 46% (about 2.25 million) from 2011. We believe that the shift towards high-quality manufacturing in the manufacturing industry has driven low-skilled workers to enter the logistics industry, thus affecting the level of wages in the logistics industry. Therefore, the paper will focus on the influence mechanism of the change of industrial structure on the wage level of China’s logistics industry.

2.2. Research Hypothesis

• Industrial structure. For the purpose of economic development, local governments intend to guide the development of their own advantageous industries and improve the industrial structure, so the performances of wage levels in various industries in the market are different. For example, R. F. Yang (2013), Z. H. Cheng and Yu (2014) and X. H. Wang et al. (2016) analyzed the inhibition mechanism of the industrial structure on wage income in some industries, and Bao and Lin (2020) found the promoting role of the industrial structure on wage income, but until now, the analysis on the inhibition effect of the industrial structure on wage income has been absolutely rare in the logistics industry. In the process of transformation and upgrading of China’s manufacturing industry, low-skilled workers in high-quality manufacturing have been gradually replaced by machines, and they have begun to flow to the logistics industry, which lowers the average wage level of the logistics industry. Therefore, this paper believes that the industrial structure will affect the wage level of China’s logistics industry, and hypothesis H1 is proposed: The increase of the industrial proportion will squeeze the wage level of China’s logistics industry.
• Inclusive finance. The mechanism analysis revealed the transmission channel from digital inclusive finance, to financing constraints and then labor income share (Z. X. Xu & Li, 2024). We believe that technological progress and labor productivity could also be enhanced by the implementation of an inclusive financial policy. With the support of inclusive financial inputs, the technological process of China’s logistics industry will be accelerated, intelligent logistics and logistics efficiency will be improved and the level of low-carbon and energy-saving logistics will also be improved. Specifically, the Chinese government also needs an intelligent and low-carbon logistics industry. Under China’s dual-carbon strategy, China’s logistics industry needs to strengthen the process of low carbon, and it also needs capital investments to improve the level of scientific and technological energy-saving. Through the Chinese government’s implementation of inclusive finance, financing institutions offer the credit support for logistics technology and the investments and co-constructions for logistics’ applications. Under the support of the Chinese government, with the concentration of intelligent and energy-saving technology’s logistics talents, the industrialization effect of science and technology logistics can be realized. This is conducive to raising the average wage level of the entire Chinese logistics industry. Hypothesis H2 is proposed: Inclusive finance will promote the increase in wages in China’s logistics industry.
• Transport infrastructure. The improvement of infrastructure can not only drive economic growth, but also has an impact on income inequality (S. J. Xu, 2024). Road and port infrastructure increased income inequality, while rail and air infrastructure helped reduce income inequality (S. J. Xu, 2024). Meanwhile, Pei and Gao (2024) found that digital infrastructure could significantly promote the increase in labor wages. In terms of transport infrastructure, the data analysis studies on its impact on wages in the China’s industry are rare. China has vigorously developed transport infrastructure such as roads and railways, and the freight transport volume has achieved rapid growth. According to China’s CSY data, the freight volume of railways and roads increased from 36,969.61 million tons in 2011 to 52,984.99 million tons in 2021. The freight volume of railways and roads achieved positive growth, among which, the freight volume of road transport was the largest, reaching more than 70%. Rail transport accounted for nearly 10% of the freight transport. The better the road and rail transport infrastructure, the higher the transport efficiency, but this will also lead to a large number of individuals and enterprises entering the logistics industry and becoming new transport individuals and transport enterprises. The new entrants have led to the expansion of the market, and freight price competition is inevitable. Difficult-to-manage private logistics resources are also increased, which further intensify the competition. Therefore, hypothesis H3 is proposed: Good transport infrastructure leads to the increase of new entrants in the logistics industry, intensifies the price competition in the industry and is not conducive to the improvement of wages in China’s logistics industry.

2.3. Construction and Empirical Analysis of Econometric Model

2.3.1. Variable Definitions

The variables are described in

Table 1. Description of variables.

Classification of Variables	Variable Abbreviation	Logarithmic Variable	Original Data Description
Logistics industry wage level	Average wages in the logistics industry	ln LRealS	The real average salary of employees in urban units of logistics industry in each province (CNY 10,000 per capita)
Economic and structural variables	Economic growth	ln GDP	Real GDP per capita of each province (CNY 10,000 per capita)
	Industrial structure (proportion of industry)	ln Secind	Ratio of industrial output value to GDP by province (%)
Inclusive finance variables	Digital financial inclusion index	ln Dig	Digital financial inclusion index
	Index_aggregate of digital finance	ln Agg	Index_aggregate of digital finance
	Insurance index	ln Insu	Insurance index
Logistics vehicles scale and Internet access scale variables	Logistics vehicles scale	ln Truck	Possession of trucks per capita by province (trucks per capita)
	Internet access scale	ln BSPI	Broadband subscribers port of Internet per capita in each province (per hundred people)
Transport infrastructure variables	Railway mileage	ln Railmile	Railway operating mileage (10,000 km)
	Road mileage	ln Roadmile	Road mileage (10,000 km)
Dummy variables	Eastern region variable	East	Dummy variable, It is 1 for the eastern provinces and 0 for the other provinces
	Central region variable	Central	Dummy variable, It is 1 for the central provinces and 0 for the other provinces
	Western region variable	West	Dummy variable, It is 1 for the western provinces and 0 for the other provinces
	Southern and northern region variable	North	Dummy variable, It is 1 for the northern provinces and 0 for the southern provinces

below, in which the Classification of Variables, Variable Abbreviation, Logarithmic Variable and Original Data Description are shown.

In Table 1, lnLRealS acts as a dependent variable; the main independent variables include the following variables.

• Economic and structural variables (lnGDP and lnSecInd). We refer to the literature of Bao and Lin (2020) and choose the regional GDP as a measure of the regional economic development level, which can drive wage growth. The index selection of industrial structure and industrial upgrading is different, and we also refer to the literature of L. M. Zhang (2012) to use the proportion of the industry to study the impact of changes in the industrial structure on wages. L. M. Zhang (2012) found that the change in industrial structure had no major impact on the regional wage level, and the possible reason was that the regional labor market mechanism was not sound. However, industrial agglomeration or industrial integration had different effects on the wage level (R. F. Yang, 2013; Z. H. Cheng & Yu, 2014; X. H. Wang et al., 2016; Lu et al., 2017; Z. H. Yang et al., 2021).
• Inclusive finance variables (lnDig and lnAgg). H. X. Cheng (2022) used the digital financial inclusion index and the index aggregate of digital finance, respectively, to study their impacts on wages. We refer to the study of (H. X. Cheng, 2022) and use inclusive finance variables to further study the wage growth problem in the logistics industry.
• The variables of logistics vehicle scale and Internet access scale (lnTruck and lnBSPI). The existing literature studied the development of freight logistics under industrial transformation and upgrading (S. F. Deng et al., 2019). However, there is very little literature that considers the impact of the logistics vehicle scale on the wage level. Regarding the aspect of Internet use, there was a significant correlation between it and labor income, and Internet use promoted wage growth (S. G. Wang & Kuang, 2022). The variable of the Internet access scale was used in some literature, but we did not find that this variable was used to analyze the wage growth problem in the logistics industry; therefore, we will use it in this paper.
• Transport infrastructure variables. Ma et al. (2023) studied the income gap in rural areas through the use of transport infrastructure (density of road network and density of railway network), but most scholars have used the indicators to study other issues. Many Chinese scholars measured transport infrastructure by route mileage, such as Shao et al. (2024), or by route density (such as Zhao & Xiang, 2012; Ma et al., 2023).We use the variable adopted by Shao et al. (2024), which is reflected by railway mileage (lnRailmile) and road mileage (lnRoadmile), respectively.

2.3.2. Data Source and Description

The panel data in this paper are from the Peking University digital financial inclusion index and China Statistical Yearbook (CSY) 2011–2021. The descriptive statistics of the provincial data are shown in

Table 2. Descriptive statistics of provincial data.

Variable	Obs	Mean	Std. Dev.	Min	Max
ln LRealS	341	1.856	0.242	1.284	2.538
ln GDP	341	1.210	0.397	0.464	2.293
ln Secind	341	3.678	0.234	2.771	4.126
ln Truck	341	−4.041	0.369	−4.837	−2.856
ln BSPI	341	3.706	0.575	2.156	4.677
ln Dig	341	5.556	0.681	2.026	6.136
ln Agg	341	5.276	0.677	2.786	6.129
ln Insu	341	5.960	0.916	−1.386	6.859
ln Railmile	341	−1.146	0.730	−2.996	0.351
ln Roadmile	341	2.471	0.840	0.191	3.686
East	341	0.355	0.479	0	1
Central	341	0.258	0.438	0	1
West	341	0.387	0.488	0	1
North	341	0.484	0.500	0	1

.

The empirical regression uses the panel data for analysis. By keeping all variables without missing data, 341 effective observations are obtained. There are a total of 31 provincial-level regions (province or municipality) in the Chinese mainland.

Before the regression analysis, it is necessary to show the regional differences in real wages. China’s economy is developed in the eastern region and underdeveloped in the central and western regions. In the regional wage differences of China’s logistics industry in 2021, the real average wage level of the logistics industry in the eastern region is higher, followed by the western region, and the central region ranks last, as shown in

Table 3. Regional differences of real wages in China’s logistics industry (base year is 2011).

Year	Area	East	Central	West	North	LRealS (RMB:CNY 10,000)	LRealS (US:USD 10,000)
2021	Beijing(bj)	1	0	0	1	10.928	1.694
2021	Fujian(fj)	1	0	0	0	9.086	1.408
2021	Guangdong(gd)	1	0	0	0	9.819	1.522
2021	Hainan(hain)	1	0	0	0	9.283	1.439
2021	Hebei(heb)	1	0	0	1	8.024	1.244
2021	Jiangsu(js)	1	0	0	0	8.837	1.370
2021	Liaoning(ln)	1	0	0	1	7.755	1.202
2021	Shandong(sd)	1	0	0	1	8.682	1.346
2021	Shanghai(shai)	1	0	0	0	12.656	1.962
2021	Tianjin(tj)	1	0	0	1	9.243	1.433
2021	Zhejiang(zj)	1	0	0	0	9.909	1.536
2021	Anhui(ah)	0	1	0	0	7.877	1.221
2021	Henan(hen)	0	1	0	1	7.103	1.101
2021	Heilongjiang(hlj)	0	1	0	1	7.768	1.204
2021	Hubei(hub)	0	1	0	0	8.406	1.303
2021	Hunan(hun)	0	1	0	0	7.981	1.237
2021	Jilin(jl)	0	1	0	1	7.11	1.102
2021	Jiangxi(jx)	0	1	0	0	7.697	1.193
2021	Shanxi(sx)	0	1	0	1	8.424	1.306
2021	Gansu(gs)	0	0	1	1	7.964	1.234
2021	Guangxi(gx)	0	0	1	0	8.184	1.269
2021	Guizhou(gz)	0	0	1	0	8.445	1.309
2021	Neimenggu(nmg)	0	0	1	1	8.675	1.345
2021	Ningxia(nx)	0	0	1	1	7.979	1.237
2021	Qinghai(qh)	0	0	1	1	9.039	1.401
2021	Sichuan(sc)	0	0	1	0	8.45	1.310
2021	Shaanxi(shx)	0	0	1	1	8.138	1.261
2021	Xijiang(xj)	0	0	1	1	9.309	1.443
2021	Xizan(xz)	0	0	1	0	10.48	1.624
2021	Yunnan(yn)	0	0	1	0	8.787	1.362
2021	Chongqing(cq)	0	0	1	0	8.133	1.261

Note: In order to be consistent with the data files in the stata software (version 11.1), Chinese pinyin abbreviations are shown in parentheses in Area column.

.

2.3.3. Theoretical Model and Empirical Model

(1)

Theoretical model

This paper adopts the Cobb–Douglas production function. Let Y be output; K and L be capital input and labor input, respectively; A be technological progress; $\alpha$ and $\beta$ be the output elasticity coefficients of capital and labor, respectively. We suppose $P_K$ and $P_L$ are the capital price and labor price, respectively, and set the minimum function and constraint conditions.

$$\begin{array}{ll}\min & f=P_{K}K+P_{L}L\\ st.& Y-A{K}^{\alpha }{L}^{\beta }=0\end{array}$$

(1)

Let the Lagrange function be:

$$f(K,L,\lambda )=P_{K}K+P_{L}L+\lambda (Y-A{K}^{\alpha }{L}^{\beta })$$

(2)

The first-order condition is:

$$\begin{array}{c}\partial f/\partial K=P_K-\lambda A\alpha K^{\alpha -1}{L}^{\beta }=0\\ \partial f/\partial L=P_L-\lambda A\beta K^{\alpha }{L}^{\beta -1}=0\\ \partial f/\partial \lambda =Y-A{K}^{\alpha }{L}^{\beta }=0\end{array}$$

(3)

$L$ and $P_L$ are obtained:

$$\begin{array}{c}L={({\displaystyle \frac{Y}{A}})}^{({\displaystyle \frac{1}{\alpha +\beta }})}{({\displaystyle \frac{\alpha P_L}{\beta P_K}})}^{{\displaystyle \frac{-\alpha }{\alpha +\beta }}}\\ P_L={({\displaystyle \frac{Y}{A{L}^{\alpha +\beta }}})}^{{\displaystyle \frac{1}{\alpha }}}({\displaystyle \frac{\beta P_K}{\alpha }})\end{array}$$

(4)

From this, it can be known that there is an inverse restrictive relationship between labor price and labor input. That is, the more labor input is made in the logistics industry, the lower the labor price will be. Let wages be a function of the labor price over a period of time $t$: $w=[{({\displaystyle \frac{Y}{A{L}^{\alpha +\beta }}})}^{{\displaystyle \frac{1}{\alpha }}}({\displaystyle \frac{\beta P_K}{\alpha }})](t)$, then wages will be affected by the price of capital.

We continue to adopt the core periphery model to analyze the transportation cost of icebergs, which refers to the fact that if the selling price of a manufactured product at the production location $r$ is $p_r^M$, then $p_{rs}^M$ is the C.I.F. price at location $s$, and only a part of it ($1/T_{rs}^M$) arrives (Samuelson, 1952). Similarly, it holds true for agricultural products as well. We suppose M is the comprehensive index of the consumption of manufactured products and A is the consumption of agricultural products. The following equation holds:

$$\begin{array}{c}{p}_{rs}^M=p_r^M{T}_{rs}^M, T_{rs}^M>1\\ p_{rs}^A=p_r^A{T}_{rs}^A, T_{rs}^A>1\end{array}$$

(5)

To simplify the problem, the total costs of iceberg transportation in agriculture and manufacturing are equal to the output value $Y$ in the logistics industry, $Y=T_{rs}^{M}M+T_{rs}^{A}A$.

If the unit transportation costs of agriculture and manufacturing are equal, according to the racetrack economy proposed by Krugman, the following formula holds: $T_{rs}^M=T_{rs}^A=e^{\tau |r-s|}$. $Y$ can be changed into $Y=T_{rs}^M(M+A)=e^{\tau |r-s|}(M+A)$.

If the unit transportation costs of agriculture and manufacturing are not equal, then we set $T_{rs}^A=\theta T_{rs}^M(\theta \ne 1),T_{rs}^M=e^{\tau |r-s|}$, and $Y$ can be changed into $Y=T_{rs}^M(M+\theta A)=e^{\tau |r-s|}(M+\theta A)$. The wages in the logistics industry can be expressed as:

$$w=[{({\displaystyle \frac{e^{\tau |r-s|}(M+A)}{A{L}^{\alpha +\beta }}})}^{{\displaystyle \frac{1}{\alpha }}}({\displaystyle \frac{\beta P_K}{\alpha }})](t), (\theta =1)$$

(6)

Or

$$w=[{({\displaystyle \frac{e^{\tau |r-s|}(M+\theta A)}{A{L}^{\alpha +\beta }}})}^{{\displaystyle \frac{1}{\alpha }}}({\displaystyle \frac{\beta P_K}{\alpha }})](t), (\theta \ne 1)$$

(7)

The $w$ expression indicates that inclusive finance is a kind of capital with low interest rates, which helps to improve the wage level in the logistics industry whether it is $M+\theta A$ or $M+A$, which is a part of economic growth output. In addition, the racetrack economy has an impact on the wage level of the logistics industry, so the influence of vehicles cannot be ignored. In general, these factors such as inclusive finance, economic growth output and logistics delivery vehicles will all affect the wage level in the logistics industry.

(2)

Basic regression model

The paper sets up a linear regression model I, in which the dependent variable is lnLRealS and the independent variables including inclusive finance ($\ln Dig$), economic growth output ($\ln GDP$), logistics delivery vehicles ($\ln Truck$), industrial structure ($\ln \mathrm{Sec}ind$) and Internet access scale ($\ln \mathrm{BSPI}$) are also added into model I.

$$\begin{array}{c}\ln {\mathrm{LRealS}}_{it}=\alpha +{\beta }_1\ln Di{g}_{it}+{\beta }_2\ln GD{P}_{it}+{\beta }_3\ln \mathrm{Sec}in{d}_{it}+{\beta }_4\ln Truc{k}_{it}\\ +{\beta }_5\ln {\mathrm{BSPI}}_{it}+{\mathit{u}}_{\mathit{i}\mathit{t}} \text{(model I)}\end{array}$$

(8)

In model I, $i$ is regarded as the province and $t$ as the year; $u$ is regarded as the error term. Model I is a logarithmic model, and the possible heteroscedasticity in the model is eliminated. In order to test the effect of the economic growth increment, we construct model II.

$$\begin{array}{c}\ln {\mathrm{LRealS}}_{it}=\alpha +{\beta }_1\ln Di{g}_{it}+{\beta }_2\Delta \ln GD{P}_{it}+{\beta }_3\ln \mathrm{Sec}in{d}_{it}+{\beta }_4\ln Truc{k}_{it}\\ +{\beta }_5\ln {\mathrm{BSPI}}_{it}+{\mathit{u}}_{\mathit{i}\mathit{t}} \text{(model II)}\end{array}$$

(9)

In the above econometric models, considering the endogeneity problem that may exist in the models, an instrumental variable method (TSLS) is needed for estimation. Because inclusive finance is a typical policy tool, it is reasonable to use the method of instrumental variables to deal with the endogeneity problem. The estimation results are shown in

Table 4. Basic regression.

	Model I (TSLS Estimation)	Model II (TSLS Estimation)
ln Dig	0.153 ***(4.70)	0.073 ***(2.60)
Δln GDP		0.741 ***(3.70)
ln GDP	0.120 ***(4.78)
ln Secind	−0.16 ***(−5.89)	−0.169 ***(−6.21)
ln Truck	0.126 ***(6.48)	0.083 ***(4.13)
ln BSPI	0.185 ***(7.61)	0.262 ***(15.04)
Constant	1.252 ***(6.38)	1.423 ***(7.59)
Number of obs	310	310
Wald test	997.24 ***	1001.13 ***
R-squared	0.7528	0.7344
Instrument variable	L.lnDig	L.lnDig

Note: L.lnDig is one-phase lag of lnDig; the values in parentheses of the TSLS estimation are the z-statistic; *** indicates significance levels of 1%, these are similar in the following tables.

. Additional details are provided in Supplementary Material.

As shown in Table 4 above, the estimation results of model I show that inclusive finance, economic growth, logistics vehicle scale and Internet access scale have significant promoting effects on the wage growth of China’s logistics industry, while the variable of industrial structure shows inhibition, which verifies the research hypotheses H1 and H2. There are many reasons that they have been analyzed in the research hypothesis section; if any reason is more critical, in simple terms, it is that in China’s industrial upgrading to high-quality manufacturing, low-skilled workers can only enter labor-intensive industries such as logistics, where the number of employees is in high demand, diluting the average wage level of the logistics industry.

After the endogenetic test, the influence of inclusive finance is positive. The demand of the Chinese government is to develop an intelligent and low-carbon logistics industry to achieve a reduction in logistics costs and the advancement of the dual-carbon strategy. Through the transmission of government policies, inclusive finance has been used in the science and technology projects of the logistics industry, which has promoted the development of the logistics industry and the improvement of wage incomes. Moreover, the differences in transmission are as follows: one is the strong support of a credit loan, the other is from financial institutions to invest in logistics applications, or financial institutions and enterprises to co-construct logistics applications (such as vehicle service or management apps). The study is also different from the studies of H. X. Cheng (2022), Lin (2023) and Z. X. Xu and Li (2024). What makes the study different from others is that it takes into account the demand of the Chinese government. Therefore, the inputs of inclusive finance have received more policy support.

This study also addresses gaps in the extant literature. J. Li (2021) believed that it was necessary to strengthen the high-quality development of the logistics industry, increase the scale of the logistics industry’s labor force, improve its labor efficiency and optimize the structure of the logistics industry’s labor force. However, how to increase the scale of the logistics industry’s labor force and where the new labor force came from had not been discussed in detail from the industrial structure and talent adaptability selection in Li’s study (2021). This study believes that the newly added logistics labor force mainly comes from the shift of low-skilled workers from the industrial industry to the logistics industry after industrial adjustment and upgrading. Therefore, this study is regarded as a supplement to the study of J. Li (2021). In the literature, the research results varied depending on the objects of focus, for example, the wage growth and the optimization of the industrial structure had a mutually reinforcing effect (Bao & Lin, 2020), the industrial structure had a significantly positive influence on wage premium (Hu et al., 2021; H. X. Cheng, 2022) and H. X. Cheng (2022) also examined how industrial structure optimization played a mediated role. This study analyzes the inhibition of the industrial structure, which can be regarded as an extension of the studies of Bao and Lin (2020), Hu et al. (2021) and H. X. Cheng (2022). Indeed, the difference is that this study mainly focuses on the change of the industrial structure (the proportion of industry), especially the development of China’s high-quality manufacturing industry, resulting in the transfer of the labor force to the logistics industry, and then affecting the wage level of the logistics industry. It is fundamentally different from the studies of Bao and Lin (2020), Hu et al. (2021) and H. X. Cheng (2022).

In model II, the paper uses the variable of economic growth increment to replace the variable of economic growth and also achieves a good regression effect. The empirical evidence of economic growth increment shows that it also has a statistically significant effect. If the economic development maintains zero growth and there is no increment, the prices of consumer goods and resource factors rise for a variety of reasons (such as external wars, trade disputes, etc.), which will cause the real wage level of the logistics industry to decline. Therefore, it is of economic significance to adopt the index of economic growth increment.

(3)

Endogeneity and heterogeneity test

Next, the regional heterogeneity test is discussed. There are four regional dummy variables, East, Central, West and North, which are added in model II, and the estimation results are shown in

Table 5. Regional heterogeneity test based on TSLS.

	Model I (TSLS Estimation)	Model II (TSLS Estimation)	Model II (TSLS Estimation)	Model II (TSLS Estimation)	Model II (TSLS Estimation)
ln Dig	0.146 ***(4.47)	0.098 ***(2.94)	0.101 ***(3.54)	0.062 **(2.24)	0.054 **(1.96)
Δln GDP		0.868 ***(4.42)	0.874 ***(4.66)	0.679 ***(3.53)	0.583 ***(2.92)
ln GDP	0.157 ***(5.39)
ln Secind	−0.167 ***(−5.92)	−0.161 ***(−5.79)	−0.144 ***(−5.39)	−0.165 ***(−6.16)	−0.176 ***(−6.29)
ln Truck	0.131 ***(6.69)	0.088 ***(4.38)	0.060 ***(2.90)	0.068 ***(3.26)	0.137 ***(7.23)
ln BSPI	0.189 ***(7.64)	0.237 ***(10.47)	0.246 ***(14.41)	0.279 ***(15.25)	0.266 ***(15.48)
East	−0.044 **(−2.50)	0.035 *(1.87)
Central			−0.073 ***(−5.12)
West				0.034 ***(2.69)
North					−0.085 ***(−6.68)
Constant	1.295 ***(6.47)	1.349 ***(6.81)	1.156 ***(5.95)	1.337 ***(7.14)	1.805 ***(9.51)
Number of obs	310	310	310	310	310
Wald test	992.08 ***	1036.81 ***	1071.33 ***	1013.57 ***	1181.35 ***
R-squared	0.7572	0.7395	0.7539	0.7385	0.7639
Instrument variable	L.lnDig	L.lnDig	L.lnDig	L.lnDig	L.lnDig

Note: *, ** and *** indicate significance levels of 10%, 5% and 1%, respectively.

.

In Table 5, the paper tests the rationality of model I and model II in the heterogeneity test. The results of model II show that the eastern region is developed, and its average wage in the logistics industry is higher than that of other regions, which is in accordance with the actual situation and is therefore more reasonable. This is consistent with the research view of B. Y. Zhang et al. (2021), which is that the average wage of logistics workers in the eastern region is higher. However, model I shows that the wage level in the logistics industry in the eastern region is lower than that in other regions, which is unreasonable, so model I will not be discussed below.

The regional heterogeneity of model II shows the following: (1) There are differences between the eastern, central and western regions in terms of wage level in China’s logistics industry. The real wage level of the logistics industry in the eastern region is the highest, followed by the western region and the central region is the lowest. In order to confirm the correctness of this conclusion, the paper analyzes the original data again (shown in Table 3 above) and finds that the regression results are consistent with the original data. (2) There is also a North–South difference in the wage growth of China’s logistics industry, and the wage level in the logistics industry in the North is lower than that in the South. The South has a more developed economic and financial system, and the Yangtze River Delta and Pearl River Delta are located in the South. The two Deltas are the gathering places for technical talents and also the better regions for intelligent and low-carbon logistics industrialization in China, and they help promote the wage level of the logistics industry in the southern region. (3) The transportation hub distributions are more developed in the eastern or southern provinces, where the provinces have higher wage levels in the logistics industry, while the transportation hub distributions are less developed in the central, western or northern regions, so these regions have lower wage levels in the logistics industry.

(4)

Spillover test between inclusive finance and economic growth

As can be seen from model II, inclusive finance has a significant promoting effect. In order to further investigate the moderating effect between inclusive finance and economic growth, cross-variables lnGDP* lnDig, lnGDP* lnAgg, lnGDP* lnInsu and lnGDP* lnBSPI are added into model II. The regression results are shown in

Table 6. Spillover test of inclusive finance and economic growth.

	Extended Model II(TSLS Estimation)	Extended Model II(TSLS Estimation)	Extended Model II(TSLS Estimation)	Extended Model II(TSLS Estimation)
ln Dig	0.126 ***(3.95)	0.140 ***(4.39)	0.131 ***(4.10)	0.257 ***(10.28)
Δln GDP	0.900 ***(4.63)	0.879 ***(4.53)	0.884 ***(4.56)	1.134 ***(5.42)
ln Secind	−0.155 ***(−5.95)	−0.151 ***(−5.82)	−0.152 ***(−5.89)	−0.155 ***(−5.46)
ln Truck	0.122 ***(6.33)	0.126 ***(6.59)	0.125 ***(6.57)	0.153 ***(7.51)
ln BSPI	0.172 ***(6.84)	0.153 ***(5.87)	0.167 ***(6.56)
lnGDP* lnDig	0.022 ***(5.22)
lnGDP* lnAgg		0.025 ***(5.83)
lnGDP* lnInsu			0.020 ***(5.57)
lnGDP* lnBSPI				0.051 ***(13.21)
Constant	1.412 *** (7.35)	1.39 ***(7.26)	1.404 ***(7.35)	1.349 ***(6.13)
Number of obs	310	310	310	310
Wald test	1135.07 ***	1161.55 ***	1148.93 ***	898.99 ***
R-squared	0.7662	0.7727	0.7702	0.7445
Instrument variable	L.lnDig	L.lnDig	L.lnDig	L.lnDig

Note: *** indicates significance levels of 1%.

below.

As shown in Table 6, the estimated results of extended model II show that the moderating effect between inclusive finance and economic growth is positive. This indicates that the integration of inclusive finance and economic growth is conducive to the improvement of wage levels in China’s logistics industry, no matter the previous literature, such as the positive impact of financial agglomeration on the regional wage level studied by J. J. Wang and Yue (2016), the lagging effect of digital inclusive finance on the wage level studied by H. X. Cheng (2022) and the influence mechanism of digital inclusive finance discussed from different industrial levels by Lin (2023). The literature rarely explained the moderating effect on a concrete implementation object by inclusive finance and economic growth. The difference in this study is as follows: economic growth has driven the freight growth in the logistics industry, and the government’s demand for intelligent and low-carbon logistics has provided more policy support for inclusive financial inputs. The moderating effect between inclusive finance and economic growth further supports the scale of intelligent and low-carbon logistics, which lays the foundation for the logistics industry to reduce costs and improve efficiency, thereby promoting wage growth. For the governmental incentives, many Chinese banks issued inclusive finance products for logistics enterprises to ensure the efficient and low-carbon development of the logistics industry and economic growth. The interaction between inclusive finance and economic growth shows a positive spillover effect on the wage level of the logistics industry.

Notably, the moderating effect between economic growth and Internet access scale is also significantly positive, showing promotion and indicating that the integrated development of the two is conducive to further improvement of the wage level in China’s logistics industry. For the market forces, since 2011, intelligent payment technology has developed rapidly, making online shopping more convenient and increasing the scale of Internet access users in cities and towns. In 2017, China implemented the rural revitalization strategy, and the scale of rural e-commerce and rural broadband network access increased significantly. The scale of Internet access, as a powerful market force, serves as the foundation for the development of various vehicle applications (APPs), effectively facilitating the freight business in the logistics industry and providing new opportunities for the business growth of logistics enterprises. Therefore, the interaction between the Internet access scale and economic growth is also a positive spillover effect. After eliminating the endogenous influence of digital inclusive finance, these conclusions are trustworthy.

(5)

Inhibition test of transport infrastructure

Moving to the next part, the impact of transport infrastructure is discussed, and model III and model IV are constructed as follows:

$$\begin{array}{c}\ln {\mathrm{LRealS}}_{it}=\alpha +{\beta }_1\ln Di{g}_{it}+{\beta }_2\ln GD{P}_{it}+{\beta }_3\ln \mathrm{Sec}in{d}_{it}+{\beta }_4\ln Truc{k}_{it}\\ +{\beta }_5\ln {\mathrm{BSPI}}_{it}+{\beta }_6\ln X_{it}+{\mathit{u}}_{\mathit{i}\mathit{t}} \text{(model III)}\end{array}$$

(10)

$$\begin{array}{c}\ln {\mathrm{LRealS}}_{it}=\alpha +{\beta }_1\ln Di{g}_{it}+{\beta }_2\ln GD{P}_{it}+{\beta }_3\ln \mathrm{Sec}in{d}_{it}+{\beta }_4\ln Truc{k}_{it}\\ +{\beta }_5\ln {\mathrm{BSPI}}_{it}+{\beta }_6\ln GD{P}_{it}\ast \ln X_{it}+{\mathit{u}}_{\mathit{i}\mathit{t}} \text{(model IV)}\end{array}$$

(11)

Here, lnX includes lnRailMile or lnRoadMile, lnGDP* lnX includes lnGDP* lnRailMile or lnGDP* lnRoadMile and the estimated results of model III and model IV are shown in

Table 7. Inhibition test of transport infrastructure.

	Model III (TSLS Estimation)	Model III (TSLS Estimation)	Model IV (TSLS Estimation)	Model IV (TSLS Estimation)
ln Dig	0.126 ***(4.93)	0.118 ***(4.01)	0.177 ***(6.93)	0.076 ***(3.02)
Δln GDP	0.885 ***(4.41)	0.885 ***(4.43)	1.032 ***(5.19)	0.768 ***(3.89)
ln Secind	−0.065 **(−2.22)	−0.093 ***(−2.71)	−0.045 *(−1.68)	−0.109 ***(−3.11)
ln Truck	0.078 ***(4.21)	0.073 ***(3.55)	0.119 ***(6.34)	0.071 ***(3.31)
ln BSPI	0.256 ***(16.56)	0.242 ***(14.41)	0.192 ***(10.99)	0.278 ***(15.00)
lnRailMile	−0.069 ***(−6.04)
lnRoadMile		−0.039 ***(−3.00)
lnGDP* lnRailMile			−0.051 ***(−8.61)
lnGDP* lnRoadMile				−0.019 **(−2.10)
Constant	0.658 ***(3.05)	1.017 ***(4.43)	0.707 ***(3.87)	1.128 ***(4.94)
Number of obs	310	310	310	310
Wald test	1128.74 ***	1000.46 ***	1340.58 ***	991.13 ***
R-squared	0.7755	0.7505	0.7995	0.7409
Instrument variable	L.lnDig	L.lnDig	L.lnDig	L.lnDig

Note: *, ** and *** indicate significance levels of 10%, 5% and 1%, respectively.

below.

As shown in Table 7, model III indicates that the increase in railway mileage or road mileage restricts the wage growth in China’s logistics industry to a lesser extent, which verifies the research hypothesis H3. The reason is that transportation facilities have become better, and more and more new entrants have resulted in increased competition for all types of transportation and a general decline in logistics profits. Moreover, a large number of private logistics resources enter into the logistics industry, and they are scattered and difficult to manage, which further intensifies competition and restricts the wage growth of the logistics industry. With the rapid development of China’s economy, the moderating effect between economic growth and transport infrastructure also shows a slight inhibition, but no positive spillover effect is shown.

The prior literature such as Song and Su (2009) showed that transportation costs affected wage levels. High transportation costs might come from many aspects. On the one hand, China’s early transport infrastructure was poor, which affected transportation efficiency and increased transportation costs; on the other hand, energy prices and labor costs were driving higher and higher. Nowadays, although China’s transport infrastructure is in good condition, the factors of energy costs and labor costs still have great impacts on the transportation costs. Therefore, J. Li (2021) proposed to increase the scale of the logistics labor force, improve the labor efficiency of the logistics industry and optimize the labor structure of the logistics industry. What is different in this study is that the improvement of the transport infrastructure increases the number of new transport individuals and transport enterprises, thus intensifying competition and affecting the rise of the average wage level in the logistics industry.

2.3.4. RobustnessTest

In order to test the robustness of the above regression models, the TSLS method is used to test the endogeneity of each model. The robustness testing conducted in this paper includes adding and subtracting variables, adding dummy variables, changing regression methods and instrumental variables, etc., and it is found that the conclusions of each model are robust and reliable. The paper also uses another instrumental variable method (GMM method) and new instrumental variable to re-examine the robustness of the basic regression, heterogeneity, inclusive finance and transport infrastructure, as shown in

Table 8. Robustness test of basic regression and heterogeneity.

	Model II (GMM Estimation)	Model II (GMM Estimation)	Model II (GMM Estimation)	Model II (GMM Estimation)	Model II (GMM Estimation)
lnAgg	0.182 ***(4.96)	0.207 ***(5.07)	0.222 ***(6.00)	0.176 ***(4.93)	0.138 ***(3.62)
Δln GDP	0.535 ***(2.73)	0.638 ***(3.36)	0.635 ***(3.50)	0.479 **(2.54)	0.454 **(2.29)
ln Secind	−0.167 ***(−6.66)	−0.159 ***(−6.21)	−0.139 ***(−5.76)	−0.163 ***(−6.62)	−0.173 ***(−6.59)
ln Truck	0.086 ***(4.40)	0.093 ***(4.82)	0.062 ***(3.14)	0.068 ***(3.41)	0.130 ***(6.85)
ln BSPI	0.167 ***(6.11)	0.134 ***(4.13)	0.134 ***(5.05)	0.184 ***(6.70)	0.194 ***(6.77)
East		0.038 **(2.36)
Central			−0.083 ***(−6.66)
West				0.037 ***(3.20)
North					−0.071 ***(−5.35)
Constant	1.217 ***(6.84)	1.189 ***(6.44)	0.945 ***(5.19)	1.088 ***(6.24)	1.593 ***(8.24)
Number of obs	310	310	310	310	310
Wald test	1103.12 ***	1144.79 ***	1205.96 ***	1132.59 ***	1253.77 ***
R-squared	0.7681	0.7758	0.7961	0.7729	0.7831
Instrument variable	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg

Note: L.lnAgg is an instrumental variable for lnAgg. ** and *** indicate significance levels of 5% and 1%, respectively.

and

Table 9. Robustness test of inclusive finance and transport infrastructure.

	ModelII (GMM Estimation)	ModelII (GMM Estimation)	ModelII (GMM Estimation)	ModelII (GMM Estimation)	ModelII (GMM Estimation)	ModelII (GMM Estimation)	ModelII (GMM Estimation)	ModelII (GMM Estimation)
lnAgg	0.218 *** (5.76)	0.223 *** (5.86)	0.215 *** (5.69)	0.278 *** (13.16)	0.22 *** (6.03)	0.236 *** (5.79)	0.252 *** (6.84)	0.19 *** (5.25)
Δln GDP	0.629 *** (3.37)	0.592 *** (3.17)	0.608 *** (3.25)	0.587 *** (3.10)	0.615 *** (3.09)	0.626 *** (3.20)	0.689 *** (3.56)	0.557 *** (2.86)
ln Secind	−0.156 *** (−6.41)	−0.153 *** (−6.30)	−0.155 *** (−6.35)	−0.151 *** (−6.23)	−0.07 *** (−2.62)	−0.081 *** (−2.61)	−0.059 *** (−2.42)	−0.098 *** (−2.98)
ln Truck	0.126 *** (6.87)	0.129 *** (7.09)	0.128 *** (7.01)	0.135 *** (7.35)	0.085 *** (4.85)	0.078 *** (4.06)	0.127 *** (7.09)	0.072 *** (3.51)
ln BSPI	0.079 ** (2.47)	0.068 ** (2.10)	0.082 *** (2.58)		0.157 *** (6.04)	0.129 *** (4.52)	0.096 *** (3.49)	0.182 *** (6.83)
lnGDP* lnDig	0.021 *** (5.54)
lnGDP* lnAgg		0.022 *** (5. 96)
lnGDP* lnInsu			0.018 *** (5.66)
lnGDP* lnBSPI				0.033 *** (8.62)
lnRailMile					−0.066 *** (−6.33)
lnRoadMile						−0.045 *** (−3.84)
lnGDP* lnRailMile							−0.046 *** (−8.80)
lnGDP* lnRoadMile								−0.022 *** (−2.61)
Constant	1.334 *** (7.31)	1.35 *** (7.29)	1.348 *** (7.36)	1.315 *** (7.01)	0.613 *** (3.04)	0.835 *** (3.83)	0.806 *** (4.43)	0.869 *** (3.76)
Number of obs	310	310	310	310	310	310	310	310
Wald test	1235.70 ***	1254.01 ***	1241.42 ***	1190.94 ***	1260.79 ***	1112.12 ***	1448.92 ***	1099.24 ***
R-squared	0.7996	0.8035	0.8002	0.8049	0.8097	0.7932	0.8317	0.7774
Instrument variable	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg	L.lnAgg

Note: L.lnAgg is an instrumental variable for lnAgg. ** and *** indicate significance levels of 5% and 1%, respectively.

.

After the GMM test, in Table 8 and Table 9, the results of each model are still consistent with those results estimated by TSLS. Therefore, the regression results in this paper are proven to be robust and reliable.

2.3.5. Discussion

The alignment or divergence of the empirical results with relevant national and international scholarship are as follows: (1) The performances of inclusive finance and GDP are basically consistent with the other literature and have positive impacts, while the logistics vehicle scale also has a positive impact. According to a large number of studies, increasing the number of vehicles can expand the revenue of enterprises. From the perspective of rational economics, vehicles are production tools and are used by enterprises to create value. Furthermore, the existing literature showed Internet use promoted wage growth (S. G. Wang & Kuang, 2022), but we did not find that the Internet access scale is used to analyze the wage growth problem in the logistics industry, so we examine its positive impact on wage growth. (2) The influence of the industrial structure varies in various fields (L. M. Zhang, 2012; R. F. Yang, 2013; Z. H. Cheng & Yu, 2014; X. H. Wang et al., 2016; Lu et al., 2017; Z. H. Yang et al., 2021). According to the industrial upgrading of the industry, machines in manufacturing replaced manual labor, and personnel in the logistics industry showed an inflow. This was the change in China’s industrial structure during the period from 2011 to 2021, resulting in the growth of logistics personnel and suppressing the growth of wages. (3) The impact of the transport infrastructure also varies. China’s infrastructure is conducive to economic development, but its impact on the various industries varies in different periods. In the 1990s or the early 21st century, the income of a freight driver could support a family, but now it is very difficult. The reason lies in the intensified competition. Good transport infrastructure has attracted a large number of new entrants (new individual transporters and enterprises), hindering the growth of wages in the logistics industry. This is a phenomenon that was only discovered from 2011 to 2021, and it is difficult to find similar evidence in other countries.

The manuscript contributes to industrial economics, labor economics and financial economics. In different periods, the demand for labor in China’s industrial development also varies. During the period from 2000 to 2010, with the development of China’s industry, the number of laborers in the industrial sector generally increased. During the period from 2011 to 2021, the upgrading of the industrial sector reduced the use of labor. Although the upgrading of the logistics industry was also underway, considering the rapid development of China’s economy, the rapid growth in the variety of goods and the freight volume, the number of employees in the logistics industry actually increased during this period. At the level of express delivery enterprises, intensified competition has led to a decline in the average revenue of per parcel express business, hindering the growth of wages in the logistics industry. At the employee level, compared with the past, doing the same amount of work tasks makes it difficult for wages to increase. If wages are to increase, more work tasks need to be completed. The above findings are a useful supplement to industrial economics and labor economics. At the policy level, China’s inclusive finance system has truly contributed to the development of the logistics industry. This is because China needs a low-cost, low-carbon and efficient logistics industry to enhance the operational efficiency of the Chinese economy and achieve China’s dual-carbon strategy. This paper focuses on the differentiated research of inclusive finance at the industry level and expands the research field of financial economics.

In general, we use panel data, propose assumptions and limitations and test the robustness of the econometric models. Our research was concluded during the sample data period from 2011 to 2021 based on the industrial upgrading of China’s industry and the logistics, personnel mobility, the government’s long-term strategic vision and the implementation of existing inclusive financial policies. It has a time limitation from 2011 to 2021, but it also shows differences from other times (and other countries). This also reflects the research value of this article.

2.4. Conclusions and Policy Implications

Research on the mechanism of wage growth in China’s logistics industry is an unconventional issue, because the industry has a large demand for low-skilled workers, and it is also eager for high-tech talents, especially in the development of intelligent and low-carbon logistics. The objective of this study is to analyze the transmission mechanisms from governmental policies and market forces on the wage income for the logistics industry, and to expand the research field of wage income growth. This study draws the following conclusions:

• Inclusive finance, economic growth increment and the synergistic development of inclusive finance and economic growth are conducive to the improvement of wage growth in China’s logistics industry. It is to be believed that the inclusive financial policies have played their roles. Based on the government demand to reduce the national logistics costs and develop the low-carbon logistics under a dual-carbon strategy, the inclusive finance inputs are transmitted to the logistics industry through the credit channels of financial institutions, the investments and co-constructions of logistics’ applications and the projects of intelligent technology and energy saving which have been better carried out, which is very different from other industries. In the process of the science and technological industrialization of the logistics industry, the innovation and technology indicators are starting to attract attention. For example, data from China’s national key industries’ patent information service platform showed that Chinese utility model patents and Chinese invention patents in China’s logistics industry were about 9.58 hundred thousand in 2011, rising to 4.14 million in 2021. As China’s economy grows larger, so does the volume of freight, and the scale effect of intelligent and low-carbon logistics has emerged. The wage income of logistics industry can also be improved.
• The improvements of the industrial structure and transport infrastructure have significant inhibitory effects on the wage growth of China’s logistics industry. In the adjustment and upgrading of the industrial structure, China’s manufacturing industry is oriented to the development of high-quality manufacturing, and low-skilled workers can only enter labor-intensive industries. A decrease of 8.27 million employees in the secondary industry (industrial industry) between 2011 and 2021 is the evidence of a change in the industrial structure. Due to a large number of new workers, the average wage level of the logistics industry grows slowly. For other reasons, the rising prices of resource factors squeeze the profits of the manufacturing industry, thus affecting the profit and wage growth of the logistics industry because logistics are inseparable from the manufacturing industry. The inhibition mechanism of the transport infrastructure mainly comes from the growth of new transport individuals and transport enterprises, and difficult-to-manage private logistics resources are also increased. Homogenized services intensify competition in the logistics industry. China’s logistics market is close to the perfect competition market, and the ultimate consequence of the price competition after market saturation is that the enterprises have no super profits, and can only maintain a low-profit level, which ultimately affects the wage growth of China’s logistics industry. The CSY data show that the average revenue of per parcel express business has also been declining, about USD 3.2 in 2011 and USD 1.48 in 2021.The main reasons are the homogenized services and price competitions; another reason is that the bargaining power of logistics service providers is weak, especially the logistics service providers cooperating with large manufacturing enterprises and e-commerce platforms, as they have to accept low-price contracts with small profit margins.
• In the market force, there are some scale effects: the logistics vehicle scale, Internet access scale, etc. The first one means that logistics resources can achieve large-scale operation, and the second one means the rise of the scale of the Internet economy; both of them are conducive to promoting the wage growth in China’s logistics industry. Moreover, the development of technological industrialization in the logistics industry has also been affected by market force; another market force manifested by the integration between the Internet access scale and economic growth is also conducive to further promoting the wage growth in China’s logistics industry.
• The regional heterogeneity of wage growth in China’s logistics industry reveals that the eastern region has the highest wage level, followed by the western region, with the central region being the lowest. Additionally, wages in the southern region arehigher than those in the northern region. The eastern and southern regions have economic centers such as the Yangtze River Delta and the Pearl River Delta: the two Deltas are also the most developed core industrial clusters of digital finance and science and technology in China. The Yangtze River Delta has world-class digital finance and e-commerce enterprises such as Alibaba, and the Pearl River Delta has digital technology enterprises such as Huawei and Tencent. It is precisely because of the existence of these great enterprises that there is a drive in the rapid development and technological advancement of e-commerce logistics, catering logistics, retail logistics, etc. Moreover, the Yangtze River Delta and Pearl River Delta have obvious geographical advantages, better industrial composition and infrastructure investment, remarkable implementation effects of various policies and greater support for the logistics industry. With the support of world-class digital science and technology enterprises, the eastern and southern regions have accelerated the constructions of digital logistics and intelligent logistics systems in recent years, gathered a number of scientific and technological logistics talents and promoted the wage growth of the logistics industry and the regional differences.

In general, these conclusions are still valid after the endogeneity test, and through the study above, some policy implications are proposed.

• Inclusive financial policies are good and should continue to be implemented. The Chinese government should continue to encourage financial institutions to offer low-interest credit, optimize resources for logistics enterprises to reduce costs and enhance efficiency. Under the implementation of China’s dual-carbon strategy and the support of inclusive financial inputs, for better development of the logistics industry and prosperity of the economy, the local governments should issue attractive talent policies to gather more high-tech talents to improve the scientific and technological level of logistics enterprises, so as to further achieve wage growth in the logistics industry.
• The scale of Internet access is still to be improved, especially in rural China, which is conducive to the development of agricultural product logistics. The Chinese government should continue to expand the scale of Internet access, continue to implement preferential measures such as high-speed free agricultural products, strengthen circulation efficiency and promote the further development of the agriculture and logistics industry. Policies should be implemented to foster the collaboration between transport individuals and logistics transport enterprises, optimize the competitive environment of logistics and then improve the wage level of China’s logistics industry.
• The adjusting and upgrading of the industrial structure should be continued, orienting to high-quality industrial manufacturing should be encouraged and an integrated operation in the manufacturing–logistics field should be supported by policies. By promoting high-value-added products, Chinese manufacturing and logistics industries can win high profit margins. At the end of the last century, China’s production and export of 100 million shirts to buy a foreign aircraft era did not bring high profits for China’s manufacturing and logistics industry, and now China is exporting a large number of integrated circuit products, new energy vehicles and other high-value-added products to win high profit space. Therefore, an integrated operation in the manufacturing–logistics field is very necessary, and the logistics industry can benefit and the level of wages can be improved from the integrated operation.
• The Belt and Road Initiative and high-quality opening to the outside world are good, and their influences are wide and far-reaching. The Chinese government should continue to spread the Belt and Road Initiative, and further promote high-quality opening to the outside world. This can continue to expand the scale of logistics and transportation, improve and optimize the development of China’s logistics industry and promote the multi-win development of the logistics, industrial industry and foreign trade to ultimately benefit the labor reward in China’s logistics industry.