Research on Price Formation Based on Resource Optimization Allocation

Abstract

Global economic growth has weakened under the guidance of equilibrium price theory, which focuses on price competition. Therefore, developing a price formation mechanism that aligns with sustainable economic development is imperative. Based on a scientific definition of the boundaries of the constituent factors of wealth creation, this paper proposes a price formation mechanism centered on resource optimization allocation and constructs a price measurement model using the Leontief input–output analysis method. To test the price formation mechanism, this paper uses panel data from 31 provinces in China from 2003 to 2020 to overcome the limitations of single-year data, and the 2020 input–output table of China to calculate the product prices for 153 sectors (covering the entire industrial system) at the average social production level in 2020. These estimated prices are then compared with the approximate market prices of 72 sector products for the same year. The research shows that the following: (1) The price is determined by the techno-economic relationships among the labor, land, technology, and capital that constitute a commodity. It can objectively and fairly reflect the value information of the factors and the quantity of factors embedded in the commodity. (2) The balance and degree of deviation between the price measured by the techno-economic relationships among a commodity’s constituent factors and the market price of the sector’s products depend on effective market competition centered on commodity quality. Price, through the market’s effective competition mechanism, will incentivize and guide resources towards enhancing labor skills, technological upgrades, and the development of new technologies, products, and markets, thereby achieving sustainable economic growth.

1. Introduction

According to the “World Employment and Social Outlook: Trends 2024” report released by the International Labour Organization in 2024 (https://www.ilo.org/publications/flagship-reports/world-employment-and-social-outlook-trends-2024, accessed on 13 June 2024), the global GDP growth rates for 2024 and 2025 are projected to be 2.9% and 3.2%, respectively. Moreover, the number of countries with inflation rates exceeding 6% is significantly higher than those with inflation rates below 6%. The global unemployment rate remains above 4%, indicating an overall sluggish trend in global economic growth. From the perspective of resource allocation, the fundamental cause of this phenomenon lies in the operation of market price competition for allocating resources under equilibrium price theory. Factors such as price wars, profit compression, overinvestment, asset bubbles, and credit expansion lead to resource misallocation, hinder technological innovation, and result in economic issues like cyclical growth, economic crises from overproduction, and other economic problems. These issues severely constrain sustainable economic development. Economic sustainable development refers to intensive economic growth characterized by low input, high output, low consumption, and sustainability. Since price serves as the foundation for promoting economic sustainable development, constructing a price formation mechanism that aligns with economic sustainable development has become an urgent issue that this paper aims to address.

Price is a fundamental and perpetual topic in economic research, and it has evolved alongside the continuous development of economic theory. In the classical period, scholars like Adam Smith (1972) [1], David Ricardo (1972) [2], and Karl Marx (2008) [3] emphasized the dominant role of labor in commodity production, advocating that labor is the source of commodity value. With the rapid economic development driven by the Industrial Revolution and the increasing diversification of goods and consumer demands, the classical labor theory of value struggled to meet the new requirements for sustainable economic development in the neoclassical period. As a result, Alfred Marshall’s equilibrium price theory emerged accordingly [4].

After the 1930s, the theoretical exploration of equilibrium price theory was extensively completed, and its focus shifted to applied theory, namely formalistic and eclectic model building (Landreth et al., 2018) [5]. For instance, Stigler (1990) [6] studied the impact of information asymmetry and search costs faced by consumers and producers on market prices and resource allocation; Friedman (2011) [7] explored how market freedom and limited government intervention in bidding distribute resources; Sraffa (1991) [8] measured commodity prices with standard commodities and determined that commodity prices depend on the method of production, distribution conditions, and the lifespan of fixed assets; Hirshleifer (2009) [9] combined individual choice behavior with market prices, finding that individual choice behavior influences prices through the market price competition mechanism, and prices, in turn, affect individual choices; Milgrom (2020) [10] designed effective auction rules under information asymmetry to promote the efficient formation of auction prices and improve the efficiency of resource allocation. Paul Jusselin (2021) [11] determined the price that maximizes exchange volume during liquidation, based on each market participant’s supply and demand relationship. Some scholars assert that price is an external characteristic faced by market participants (Durkheim E, 1992) [12], the result of social and political forces (Bourdieu, 2005) [13], and the influence of power (Weber M, 1978) [14] in the market domain. Price is also shaped by institutional regulation, the social structure of the market, and meaning rather than the overall result of individual supply and demand interactions (Beckert, 2011) [15]. In addition to supply and demand, price is influenced by power, market structure, customs, rules, practices, and conventions, among other factors (De Andrade, 2019) [16]. Research based on information asymmetry explores relaxing the assumptions of a perfectly competitive market (Yan Xiaoting et al., 2017) [17] and follows pricing principles in an imperfectly competitive market (Peng Guofu et al., 2017) [18].Some scholars, from the perspective of assessment and evaluation, argue that price is not only the non-intermediary result of rights relations or trial and error and gradual adjustments (Garcia-Parpet, 1986) [19] but also describe the formation and discovery of price as a process where transaction prices adjust to new information (Brünner, 2019) [20] and are determined through multiple evaluations and tests based on commitments to exchange property (Maude Plante, 2022) [21], among others.

The price measurement methods reflect the essence of the equilibrium price formation idea, mostly starting from J.-M. Lasry and P. L. Lions’ mean-field games (MFGs), covering price formation under constrained MFGs (Gomes et al., 2021) [22], price models evolved from MFGs and containing random variables, cumulative distribution functions, and terms related to parameter R (Deng, 2020) [23], as well as the Boltzmann-type price models driven by parabolic free boundary models (Burger et al., 2013) [24], and dynamic pricing models aimed at maximizing profits and capital gains or minimizing expenditures (Solow, 1959; Morishmann, 1958) [25,26], among others.

Meanwhile, some socialist countries led by the Soviet Union attempted to construct an economic system based on de-commodification and founded on Marx’s labor theory of value, aiming to eliminate the exploitation and injustice of the market economy. The plan price, which does not reflect value and lacks substantive form, exists merely as a means of accounting, emerged in this context (Li Huizhong, 1993) [27]. The plan price was set based on the comprehensive considerations of the government or relevant planning departments regarding the labor cost of commodity production, social demand, and the overall economic plan. The price measurement models entailing the logic of the determination of Marx’s labor value include the input–output price model (Leontief, 1999; Bazzazan, 2003; Zhang Hongxia et al., 2022) [28,29,30], the traction price model (Zhang Limin et al., 1988) [31], the optimal plan price model (Ji Yushan, 1991) [32], the shadow price model for solving dual solutions of optimal planning models (Geng Jidi, 1994) [33], and the transformation formulas for commodity values, such as Bortkiewicz’s “coefficient method” (Zhang Zhongren, 2015) [34], Winternitz’s Win-style model, and the expansion of the value transformation algorithm that relaxes the equality of surplus value rates among various departments (Wang Yiming et al., 2019) [35], as well as the value transformation based on the TSSI model extension (Li Mengfan, 2020) [36].

The formation of the labor theory of value and the equilibrium price theory was a theoretical response to the economic conditions and development of their respective times, promoting the sustainable economic development of those eras. Currently, we are in an era where technological innovation drives resource optimization and allocation to promote sustainable economic development. The mechanism of optimizing resource allocation driven by technological innovation to promote sustainable economic development is that technological innovation improves the quality of individual factors, especially the level of labor knowledge, skills, and inventive abilities, as well as technological progress. It promotes the dynamic optimization and adjustment of the combination of production factors and their quantity ratios, resulting in a reduction in production costs, an increase in output and efficiency, an increase in product-added functions or utility, the further enhancement of product-added value, and the realization of sustainable economic development. The equilibrium price theory, which centers on market price competition, is incompatible with the new requirements for sustainable economic development in this era. In this context, this paper attempts to construct a price formation mechanism centered on resource optimization and allocation to meet the demands of sustainable economic development in the new era. It aims to effectively address a series of economic problems caused by the operation of the market price competition mechanism under equilibrium price theory, such as resource misallocation, cyclical economic growth, and economic crises due to overproduction.

The main contributions of this paper are as follows:

(1)

Unlike equilibrium price formation, which is determined by supply and demand competition, the price in this study is determined by the techno-economic relationships among a commodity’s constituent factors. This price not only reflects the quantity of factors embedded in the commodity but also conveys the value information of these factors.

(2)

Unlike the equilibrium price theory, where market competition is primarily based on price competition, this study emphasizes effective market competition centered on the quality of commodities.

The structure of the remaining sections is arranged as follows: After the Introduction, Section 2 applies Marx’s historical materialism methodology to analyze the evolution law of the constituent factors of wealth creation and scientifically define labor, land, technology, and capital. Based on this foundation, it proposes a price formation mechanism centered on resource optimization allocation and elucidates the role of price in sustainable economic development. Section 3 constructs a price measurement model based on Leontief’s input–output theory. Section 4 empirically calculates the prices of products across 153 sectors in China in 2020 (covering the entire industrial system) using the price measurement model and further discusses the prices. Section 5 presents the research conclusions.

2. Mechanism of Price Formation

2.1. Constituent Factors of Wealth Creation and Their Scope Boundaries

Throughout the history of economic development, the process of economic growth is the process of resource optimization allocation. Resource is a synthesis of the factors of production that constitute the value of a product (the main body of wealth creation), and factor is the physical manifestation of a resource in the process of wealth creation; these two aspects are two sides of the same coin. The optimal allocation of resources is the result of improved efficiency in the use of factors, the integration of factors in the production process, and the optimal combination of factors in an open economy, which is manifested in the improvement of the efficiency of the comprehensive use of resources and the techno-economic connections among the factors of labor, land, technology, and capital. The resource optimization allocation process is dynamic and involves quantitative increases, qualitative enhancements, and the optimal combination and proportional adjustment of the quantity and quality of factors.

Since this paper studies the price formation mechanism, it is necessary to explain the resource optimization allocation of wealth creation at the enterprise level. As a fundamental economic unit for resource aggregation, capital scientifically combines resources to achieve resource optimization allocation. The basic allocation of resources refers to the basic composition and quantity ratio of production factors that constitute products in the production process, that is, the basic technical and economic connections. The basic configuration of resources forms the foundation of wealth creation and is the prerequisite for the commencement of the enterprise production process. Based on this foundation, the dynamic optimization of the quantity and quality of each factor achieves resource optimization allocation in product production. Under the premise of producing qualified products, economic growth stems from the effective utilization of factors and their optimal allocation. The technological improvement and efficiency enhancement of labor, machinery, raw materials, and production processes, along with the improved efficiency of factor allocation, not only bring about economies of scale through the expansion of product quantities but also create spillover effects in product series expansion. For example, developing different performance specifications and models of mobile phone series based on wireless communication technology or computer series based on microelectronics technology generates sustainable economic growth effects through the optimal combination of resources.

Based on a clear understanding of the concept of resource optimization allocation, the constituent factors of wealth creation can be further identified. In the historical process of wealth creation, along with the evolution of production organization modes from the gathering and hunting of pre-agricultural societies to the agriculture, animal husbandry, and handicraft production of agricultural societies, to the small-scale mechanized production of the small factory system of the First Industrial Revolution, to the assembly line-based large-scale standardized output of the extensive factory system of the Second Industrial Revolution, to the flexible, collaborative production of automated and informational manufacturing of the Third Industrial Revolution and today’s intelligent production, the constituent factors of wealth creation have evolved from labor to labor and land, then to labor, land, and technology, and eventually to labor, land, technology, and capital, with technology becoming the dominant factor in wealth creation.

Labor is the transformative activity of the natural world to meet the needs of human survival and development, generally divided into physical and intellectual labor. It is the fundamental source of wealth creation and the critical driving force behind invention, technological progress, and innovation. The essence of science and technology lies in the discovery or invention of connections between things in human productive practice and the application of these discoveries or inventions in production, representing the crystallization of advanced and complex human labor. As technology has shifted from being controlled by labor to directly acting upon the object of labor, replacing labor as the primary productive force in wealth creation, and given the amplification effects of scientific and technological reuse based on economies of scale, as well as the amplifying effects of technology collaboration in reshaping and optimizing value chains and scope economies, the importance of science and technology as the primary productive forces is further consolidated. Capital, as wealth that creates wealth, originates from wealth accumulation. It has the function of scientifically and effectively organizing production factors to create wealth and aggregates and controls resources on a macro level, with its control over resources being regulated and guided by the state. Land is the sum of resources and conditions provided by nature for humanity and the various products and materials formed through human processing and improvement of these resources and conditions. Land primarily constitutes the functions or utilities of products, evolving from the sum of resources and conditions initially provided by nature to humans into products (such as components, products requiring further processing, and products serving production), new materials (such as synthetic materials), and subsequently more complex and advanced products.

Over the past 70 years since the founding of the People’s Republic of China, economic development has fully illustrated the objective laws governing the evolution of the constituent factors of wealth creation. From the composition ratios of GDP across the three sectors from 1952 to 2022 (see Figure 1), the growth rate of employment and the composition ratios of the three sectors (see Figure 2), and the annual growth rate of fixed asset investment and the composition ratios of the three sectors (excluding data before 2002, see Figure 3), it is evident that over these 70 years, Chinese society has transitioned through agricultural, industrial, and modern social stages. This transformation has seen China evolve from a traditional agricultural country to a modern nation and the world’s second-largest economy.

As shown in Figure 1, the changes in the composition ratios of GDP across the three sectors from 1952 to 2022 can roughly be divided into three stages. This division is based on the absolute proportion of the three industries in GDP from the perspective of effective utilization and evolution of factors (for detailed classifications of China’s economic development stages, please refer to relevant literature). The stages are as follows: Society was agriculture-dominated from 1952 to 1969, where the primary sector was dominant. The secondary industry was dominant in an industry-dominated society from 1969 to 2012. From 2012 to 2022, the tertiary industry became dominant in modern society.

An agricultural society is a typical self-sufficient natural economic form, where labor directly acts on land (natural resources) and utilizes natural forces to create wealth. The improvement of labor skills mainly comes from practical experience and experimentation. Handicrafts serve as an essential supplement to the agricultural economy. From 1952 to 1969, the employment composition across the three sectors remained largely unchanged. The primary industry accounted for over 80% of employment, while the secondary and tertiary sectors accounted for approximately 10%. Analyzing the changes in the composition ratios of GDP among the three sectors, this period is characterized as a typical agricultural society. The main features of this era include labor directly acting on work objects or using simple tools on work objects. The products constituting wealth were primarily primary products formed by direct labor on work objects or processed primary industrial products.

An industrial society is characterized by large-scale production dominated by the use of large machinery and specialized processes. It features advanced science and technology, leading to increased production efficiency. The primary avenues for economic growth are the improvement of labor efficiency, the technological upgrading and efficiency enhancement of machinery and equipment, the optimal matching of labor and machinery (technology), the effective utilization of natural resources (land, particularly raw materials), and the integration of resources. From 1969 to 2012, during the entire industrial society phase, the changes in employment composition across the three sectors were similar to the changes in the composition of GDP. Throughout China’s industrialization process, industrial technology has largely been in a state of technology importation, learning, assimilation, and imitative innovation. The dominance of the secondary sector was unstable, especially in the later period (post-2002), where the GDP proportions of the secondary and tertiary sectors were nearly equal, contributing almost equally to economic growth.

The third stage, modern society, is characterized by an economy dominated by technological production. Its notable features include modern service industries—centered on scientific research and development, productive services, and financial services—becoming the main drivers of economic growth. High-tech industries lead economic development, and high-quality development permeates all aspects of economic growth. In 2012, the tertiary sector’s share of GDP was 45.46%, while the secondary sector’s share was 45.42%. The tertiary industry began to surpass the secondary sector, becoming the dominant force in economic growth. The gap between the two industries has since widened. In 2011, the proportion of employment in the tertiary sector exceeded that in the primary industry for the first time, with 35.68% and 34.74%, respectively. This upward trend continued, and the gap gradually increased, reaching 47.15% by 2022. In the same year, the secondary sector’s employment proportion was 28.77%, and the primary sector’s was 24.08%. Regarding the composition of total fixed asset investment (excluding households), from 2012 to 2022, the tertiary sector’s share of fixed asset investment consistently remained around 65%, the secondary sector’s share around 33%, and the primary sector’s share around 1%.

Thus, in 1949, China began its economic development from a state of extreme poverty and has, in over 70 years, transitioned from an agricultural society to an industrial society and finally into a modern society. China has become the world’s second-largest economy, forming a relatively complete modern industrial system and a relatively independent scientific and technological system centered on wireless communication network technology, e-commerce network technology, and aerospace technology. The evolution of the constituent factors of wealth creation has also been reflected in this 70-year economic development process.

2.2. Mechanism for the Transmission of Factor Values

The continuous improvement and optimization of the substitution and complementarity mechanisms of factors in commodity production constitute the types and proportions of factors in commodities, thereby determining the functions or utilities of commodities and triggering price changes. The creation of wealth and value compensation by factors determines the price, which primarily reflects the contributions of labor, land, technology, and capital in the production process of a commodity. The contribution of labor to the price is determined by the amount of labor consumed in the commodity, the ratio of the economic value created by labor, and the compensation for the value created by labor. The compensation for labor-created value includes direct monetary wages and benefits presented by GDP re-expenditure, such as health insurance and pensions. The confirmation of the value contribution of technological innovation to commodity price is essentially compensation for technological research and development and application, including patent licensing income, technology transfer, compensation for the combined use of machines and equipment as technology carriers, tax incentives, and funding for research and development. The unit value of technology is determined by the compensation for the consumption of technology research and application and its economic contribution to GDP, combined with the amount of technology consumed in the commodity to clarify the contribution of technology to the price. Land’s contribution is reflected through compensation for land value and its economic contribution to GDP, revealing the unit value of land. Compensation for land-created value includes various types of economic compensation such as taxes, fines, land rent, ecological restoration, regeneration to restore land environmental functions and biodiversity, and strategies for resource recycling to reduce resource consumption and waste production. The contribution of capital is determined by the amount of capital consumed in the commodity and the ratio of the economic value created by capital and the compensation for capital occupation, such as investment returns, dividends, interest, enhanced market competitiveness, and expanded market share.

The level of price is determined by the value created by the factors during the production process of a commodity and the compensation paid for these factor values. Higher wealth creation typically implies higher value, which may lead to higher costs due to technological innovation, scarcity of resources, high-skilled labor, or a high-quality combination of factors, thereby pushing up the price. If the efficiency of commodity production increases, the same value can be realized at a lower cost, which may reduce the price. The total compensation for the value of factors constitutes the production cost of a commodity. Theoretically, the price is higher than or equal to the production costs of a commodity.

Starting from the perspective of resource optimization allocation, the quantitative ratio of a commodity’s constituent factors under certain technical conditions determines the price. The level of technology determines the efficiency of resource utilization and possible modes of output. Resource allocation under certain technological constraints forms a specific quantitative ratio of the factors producing a commodity, constituting the core of the price.

2.3. Foundation for Promoting Sustainable Economic Development

In the market, where there is a vast variety of goods with different functions (utilities), prices formed around resource optimization allocation can effectively regulate market mechanisms. The price not only facilitates the sharing of commodity value information between the supply and demand sides but also makes the quantitative information of the constituent factors of commodities explicit among enterprises. This enables producers and consumers to make rational decisions, ensuring objective fairness and justice in commodity exchange.

More importantly, the sharing of commodity value information between the supply and demand sides, along with the sharing of the quantitative information of constituent factors among enterprises, limits the scope of market mechanisms. The market shifts its main focus towards effective competition for commodity quality. The competition for product quality is based on the expansion of product functionality and utility, which is driven by technological progress. The essence of market competition is a game of resource control driven by interests. Starting from the core goal pursued by economics, enterprise profit is based on the premise of minimizing resource consumption and maximizing output, while the game is the competition of commodity quality between enterprises based on the premise of enterprise profit. The balance and deviation between price and market price, with resource optimization and allocation as the core, will depend on effective market competition dominated by quality competition.

The long-term operation of effective market competition for allocating resource mechanism not only incentivizes the enhancement of labor skills and technological upgrades but also guides resources towards the development of new technologies, new products, and new markets. This development, in turn, further stimulates the emergence of new industries, new models, and new drivers of economic growth of traditional technologies, the penetration, intersection, and integration of various technologies, and disruptive breakthroughs in original technologies such as artificial intelligence, quantum technology, life sciences, digital intelligence, and green technology, and thus, further optimization of resource allocation is achieved, leading to a revolutionary leap in total factor productivity. The development of new products will further create new markets, manifested as geographically new markets brought about by existing products and new market domains led by new products.

3. Empirical Research Design

This study employs a combination of quantitative and qualitative research methods to explore price formation based on resource optimization allocation. The research design includes model specification, sample data and data sources, and variable description. Relying on Leontief’s input–output theory, this study constructs a price econometric model. Using panel data on regional output and regional factor inputs from 31 provinces in China from 2003 to 2020 and the 2020 China input–output table, the study calculates the prices of 153 sector products representing the entire industrial system of China in 2020 at the average social production level. These prices are then compared with the approximate market prices of 72 sector products in the same year to verify the price formation mechanism.

3.1. Model Specification

Based on the price formation mechanism centered on resource optimization allocation proposed in this paper, the following equation can be derived:

$$\mathrm{p}={\mathrm{v}}_{\mathrm{L}}{\mathrm{q}}_{\mathrm{L}}+{\mathrm{v}}_{\mathrm{T}}{\mathrm{q}}_{\mathrm{T}}+{\mathrm{v}}_{\mathrm{R}}{\mathrm{q}}_{\mathrm{R}}+{\mathrm{v}}_{\mathrm{K}}{\mathrm{q}}_{\mathrm{K}}$$

(1)

In Equation (1), P represents the price, while L, T, R, and K, respectively, denote the quantities of labor, land, technology, and capital inputs.${\mathrm{v}}_{\mathrm{L}},{\mathrm{v}}_{\mathrm{T}},{\mathrm{v}}_{\mathrm{R}},\mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{v}}_{\mathrm{K}}$ represent the unit values of labor, land, technology, and capital, respectively. ${\mathrm{q}}_{\mathrm{L}},{\mathrm{q}}_{\mathrm{T}},{\mathrm{q}}_{\mathrm{R}},\mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{q}}_{\mathrm{K}}$ indicate the quantities of labor, land, technology, and capital embedded in the commodity.

However, obtaining data for the unit value of each factor and the number of each factor consumed in commodities is challenging due to influences from statistical standards, the degree of advancement in digital technology, privacy, and confidentiality policies, and legal and regulatory restrictions.

Therefore, this paper, on the one hand, aims to determine the contribution rate of labor, land, technology, and capital in wealth creation based on their functional differences in creating wealth and the resulting differences in their contributions to total wealth. This paper draws inspiration from the approach of Guo Han et al. (2014) [37], constructing an extended Cobb–Douglas production function to calculate the unit value of each factor. The extended Cobb–Douglas (C–D) production function assumes the following: the production function includes labor, land, technology, and capital; there are constant returns to scale; and labor, land, technology, and capital are variable and exogenous.

Consequently, the relationship between total output and the factors (labor, land, technology, and capital) can be expressed as:

$$\mathrm{Y}=\mathrm{A}{\mathrm{L}}^{\mathsf{\delta }}{\mathrm{T}}^{{\mathsf{\delta }}_{\mathrm{T}}}{{\mathrm{R}}^{{\mathsf{\delta }}_{\mathrm{R}}}\mathrm{K}}^{{\mathsf{\delta }}_{\mathrm{K}}}$$

(2)

In Equation (2), Y represents the total output. ${\mathsf{\delta }}_{\mathrm{L}},{\mathsf{\delta }}_{\mathrm{T}},{\mathsf{\delta }}_{\mathrm{R}},\mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\delta }}_{\mathrm{K}}$, respectively, represent the output elasticity of labor, land, technology, and capital, reflecting the current contribution rates of these factors. A is the constant term, representing the level of technology.

Taking the logarithm of both sides of Equation (2) yields:

$$\mathrm{I}\mathrm{n}\mathrm{Y}=\mathrm{I}\mathrm{n}\mathrm{A}+{\mathsf{\delta }}_{\mathrm{L}}\mathrm{I}\mathrm{n}\mathrm{L}+{\mathsf{\delta }}_{\mathrm{T}}\mathrm{I}\mathrm{n}\mathrm{T}+{\mathsf{\delta }}_{\mathrm{R}}\mathrm{I}\mathrm{n}\mathrm{R}+{\mathsf{\delta }}_{\mathrm{K}}\mathrm{I}\mathrm{n}\mathrm{K}$$

(3)

After estimating ${\mathsf{\delta }}_{\mathrm{L}},{\mathsf{\delta }}_{\mathrm{T}},{\mathsf{\delta }}_{\mathrm{R}},\mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\delta }}_{\mathrm{K}}$, the values of labor, land, technology, and capital contributions to the total wealth are further determined. Thus, the unit value of each factor is presented in Equation (4):

$$\begin{gathered} {\mathrm{v}}_{\mathrm{L}}=\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{L}}}{\mathrm{L}} \\ {\mathrm{v}}_{\mathrm{T}}=\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{T}}}{\mathrm{T}} \\ {\mathrm{v}}_{\mathrm{R}}=\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{R}}}{\mathrm{R}} \\ {\mathrm{v}}_{\mathrm{K}}=\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{K}}}{\mathrm{K}} \end{gathered}$$

(4)

In Equation (4), $\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{L}},\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{T}},\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{R}},\mathrm{a}\mathrm{n}\mathrm{d} \mathrm{Y}·{\mathsf{\delta }}_{\mathrm{K}}$, respectively represent the values of labor, land, technology, and capital contributions to the total wealth.

On the other hand, the Leontief input–output theory is introduced to determine the quantitative ratio among a commodity’s constituent factors. The Leontief input–output theory is an economic analysis tool that describes and analyzes the interdependence and interactions among various economic sectors. In its mathematical model, each industry’s total output is composed of its inputs to other industries and the final demand for its products. Therefore, according to input–output theory, a commodity is constituted by the inputs of various intermediate products and initial factors, manifested in the relationships of the columns formed by the first and third quadrants of the input–output table. Thus, the quantitative ratio relationship among a commodity’s constituent factors can be formed by combining the quantitative ratio relationship among products constituted by intermediate products and the quantitative ratio relationship among factors constituted by initial factors. Since initial factors are only directly and partially transformed into products, the quantitative ratio relationship among factors constituted by the initial factors is determined by the direct consumption coefficients of this part. The effective linkage of initial factors, in turn, fully transforms the intermediate products into another part of the commodity. The quantitative ratio relationship among products constituted by intermediate products is represented by the complete consumption coefficients of this part, further utilizing the direct consumption coefficients of the initial factor part to transform it into the quantitative ratio relationship among factors, thereby obtaining the quantitative ratio relationship among a commodity’s constituent factors as expressed in Equation (5):

$${\mathrm{a}}_{\mathrm{L}}:{\mathrm{a}}_{\mathrm{T}}:{\mathrm{a}}_{\mathrm{R}}:{\mathrm{a}}_{\mathrm{K}}$$

(5)

${\mathrm{a}}_{\mathrm{L}},{\mathrm{a}}_{\mathrm{T}},{\mathrm{a}}_{\mathrm{R}},\mathrm{a}\mathrm{n}\mathrm{d} {\mathrm{a}}_{\mathrm{K}}$ represent the unit of labor, land, technology, and capital embedded in a unit commodity, respectively. Combining Equations (4) and (5), the price can be calculated as:

$$\begin{gathered} \mathrm{p}=\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{L}}}{\mathrm{L}}{\mathrm{a}}_{\mathrm{L}}+\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{T}}}{\mathrm{T}}{\mathrm{a}}_{\mathrm{T}}+\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{R}}}{\mathrm{R}}{\mathrm{a}}_{\mathrm{R}}+\frac{\mathrm{Y}·{\mathsf{\delta }}_{\mathrm{K}}}{\mathrm{K}}{\mathrm{a}}_{\mathrm{K}} \\ ={\mathrm{v}}_{\mathrm{L}}{\mathrm{a}}_{\mathrm{L}}+{\mathrm{v}}_{\mathrm{T}}{\mathrm{a}}_{\mathrm{T}}+{\mathrm{v}}_{\mathrm{R}}{\mathrm{a}}_{\mathrm{R}}+{\mathrm{v}}_{\mathrm{K}}{\mathrm{a}}_{\mathrm{K}} \end{gathered}$$

(6)

3.2. Sample Data and Data Sources

This article takes the calculation of product prices in 153 sectors of China in 2020 under the average level of social production as an example, for the following reasons: Firstly, 2020 was chosen because it is the most recent year for which China’s input–output tables were updated. Due to the update cycle limitations of the input–output tables, selecting the latest update year ensures a certain degree of timeliness and accuracy for the research. Secondly, this study involves 153 sectors, covering various aspects of China’s economy, including agriculture, industry, and services, representing the entire industrial system of China. These sectors collectively constitute China’s GDP in 2020. By treating each industry as a sector product, the estimated prices of the 153 sector products at the average social production level can comprehensively and objectively reflect the price level of China’s entire industry.

The data used in this study are sourced from the “China Statistical Yearbook”, “China Urban and Rural Construction Statistical Yearbook”, “China Science and Technology Statistical Yearbook”, and the statistical yearbooks of various provinces. The data cover the total output and factor input panel data of 31 provinces in China (excluding the Hong Kong Special Administrative Region, Macao Special Administrative Region, and Taiwan Province due to data availability) from 2003 to 2020 and the 2020 China input–output table.

3.3. Variable Description

Due to the limitations of using data from a single year, it is not easy to estimate the factor contribution rates for 2020 directly based solely on China’s total output and factor input data for that year. Therefore, this study uses panel data from 31 provinces in China (excluding the Hong Kong Special Administrative Region, Macao Special Administrative Region, and Taiwan Province due to data availability) from 2003 to 2020. These data serve as a sample to estimate the factor contribution rates for these provinces over this period. The average social production level factor contribution rates for 2003 to 2020 are then calculated, which allows the estimation of the factor contribution rates and unit values for 2020 at the average social production level. The panel model used to estimate the factor contribution rates for the 31 provinces from 2003 to 2020 is as follows:

$$\mathrm{I}\mathrm{n}{\mathrm{Y}}_{\mathrm{u}\mathrm{j}}=\mathrm{I}\mathrm{n}{\mathrm{A}}_{\mathrm{u}\mathrm{j}}+{\mathsf{\delta }}_{{\mathrm{L}}_{\mathrm{u}\mathrm{j}}}\mathrm{I}\mathrm{n}{\mathrm{L}}_{\mathrm{u}\mathrm{j}}+{\mathsf{\delta }}_{{\mathrm{T}}_{\mathrm{u}\mathrm{j}}}\mathrm{I}\mathrm{n}{\mathrm{T}}_{\mathrm{u}\mathrm{j}}+{\mathsf{\delta }}_{{\mathrm{R}}_{\mathrm{u}\mathrm{j}}}\mathrm{I}\mathrm{n}{\mathrm{R}}_{\mathrm{u}\mathrm{j}}+{\mathsf{\delta }}_{{\mathrm{K}}_{\mathrm{u}\mathrm{j}}}\mathrm{I}\mathrm{n}{\mathrm{K}}_{\mathrm{u}\mathrm{j}}+{\mathsf{\epsilon }}_{\mathrm{u}\mathrm{j}}$$

(7)

In Equation (7), u and j represent the provinces and year, respectively. The variables in the equation can be described as follows:

(1)

The regional gross product measures the total output of each province. The regional gross product reflects the total wealth of the province. It is the most commonly used and recognized indicator for measuring a region’s economic activity and scale. The number of employed persons in the region measures the labor input of each province. This is because the number of employed persons is a direct indicator of labor market conditions and labor supply. The number of employed persons can more accurately reflect each region’s labor input situation. This paper chooses regional total social fixed asset investment to measure capital input. Capital is the wealth that creates more wealth, and total social fixed asset investment can reflect the investment intensity and capital input levels in fixed assets across regions.

(2)

This paper selects the sum of each province’s cultivated land area and urban construction land area to represent regional land input. This indicator comprehensively considers agricultural land and urban construction land, thus providing a more complete reflection of the land factor’s input in economic activities. It is noteworthy that for Beijing, the urban construction land area for 2005 was derived from the “Comprehensive Statistical Analysis Report on Land and Resources in Beijing 2005”, which stated an increase of 3300.03 hectares (a rise of 1.03%) compared to 2004. The urban construction land area for 2010 came from the “Beijing Land Resources Protection and Development Utilization Plan during the 12th Five-Year Plan”, with urban construction land covering 1323 square kilometers. The urban construction land area for 2020 used the data from 2019 (unchanged since 2018). For Shanghai, the urban construction land area in 2005 was taken from the “Comprehensive Statistical Analysis Report on Land and Resources in Shanghai 2005”, which noted a total construction land of 240,065.92 hectares by the end of 2005. The missing urban construction land area data for 2006, 2009, 2010, and 2011 were supplemented using linear interpolation. For the Tibet Autonomous Region, the arable land area for 2020 was filled using the data from 2019.

(3)

Regional technology input is measured by the sum of each province’s internal expenditure on R & D and fixed asset depreciation. Internal expenditure on R & D represents the investment in technology research and development, while fixed asset depreciation reflects the renewal and maintenance of technological equipment. This combination can comprehensively reflect the actual situation and level of regional technology input.

Descriptive statistics for the aforementioned variables can be found in

Table 1. Descriptive statistics of variables.

Variable	Observations	Mean	Std. Dev.	Min	Median	Max
Y	558	17,622.5448	18,149.2111	189.0900	12,713.3700	110,760.9400
L	558	2425.5605	1656.0688	132.8100	2049.5000	7039.0000
T	558	4284.3161	3219.1566	229.9740	4198.9428	18,495.1985
R	558	16,180.4414	16,376.7455	21.7620	11,395.2189	93,992.3283
K	558	11,910.7886	12,382.1767	133.9600	7474.3815	59,221.8355

Data Source: The data were calculated and organized by the author using the Stata 14 software package.

.

For determining the quantitative ratio among the constituent factors of 153 sector products in China for 2020, the 2020 Chinese input–output table was utilized to construct a composition table for the 153 sector products in China in 2020 (

Table 2. Composition of products from 153 sectors in China in 2020.

Sector Product Name		Sector Product Name	Agricultural Products	Forestry Products	…	Social Security	Public Administration and Social Organizations
		Code	1	2	…	152	153
Intermediate Products	Agricultural Products	1	${\mathrm{x}}_{11}$	${\mathrm{x}}_{12}$	…	${\mathrm{x}}_{\mathrm{1,152}}$	${\mathrm{x}}_{\mathrm{1,153}}$
	Forestry Products	2	${\mathrm{x}}_{21}$	${\mathrm{x}}_{22}$	…	${\mathrm{x}}_{\mathrm{2,152}}$	${\mathrm{x}}_{\mathrm{2,153}}$
	…	…	…	…	…	…	…
	Social Security	152	${\mathrm{x}}_{\mathrm{152,1}}$	${\mathrm{x}}_{\mathrm{152,2}}$	…	${\mathrm{x}}_{\mathrm{152,152}}$	${\mathrm{x}}_{\mathrm{152,153}}$
	Public Administration and Social Organizations	153	${\mathrm{x}}_{\mathrm{153,1}}$	${\mathrm{x}}_{\mathrm{153,2}}$	…	${\mathrm{x}}_{\mathrm{153,152}}$	${\mathrm{x}}_{\mathrm{153,153}}$
	Total	I	${\displaystyle \sum _{\mathrm{n}=1}^{153}}{\mathrm{x}}_{\mathrm{n}1}$	${\displaystyle \sum _{\mathrm{n}=1}^{153}}{\mathrm{x}}_{\mathrm{n}2}$	…	${\displaystyle \sum _{\mathrm{n}=1}^{153}}{\mathrm{x}}_{\mathrm{n}152}$	${\displaystyle \sum _{\mathrm{n}=1}^{153}}{\mathrm{x}}_{\mathrm{n}153}$
Primary Factor	Labor	L	${\mathrm{x}}_{\mathrm{L}1}$	${\mathrm{x}}_{\mathrm{L}2}$	…	${\mathrm{x}}_{\mathrm{L}152}$	${\mathrm{x}}_{\mathrm{L}153}$
	Land	T	${\mathrm{x}}_{\mathrm{T}1}$	${\mathrm{x}}_{\mathrm{T}2}$	…	${\mathrm{x}}_{\mathrm{T}152}$	${\mathrm{x}}_{\mathrm{T}153}$
	Technology	R	${\mathrm{x}}_{\mathrm{R}1}$	${\mathrm{x}}_{\mathrm{R}2}$	…	${\mathrm{x}}_{\mathrm{R}152}$	${\mathrm{x}}_{\mathrm{R}153}$
	Capital	K	${\mathrm{x}}_{\mathrm{K}1}$	${\mathrm{x}}_{\mathrm{K}2}$	…	${\mathrm{x}}_{\mathrm{K}152}$	${\mathrm{x}}_{\mathrm{K}153}$
	Total	II	${\displaystyle \sum _{\mathrm{z}=\mathrm{L},\mathrm{T},\mathrm{R},\mathrm{K}}}{\mathrm{x}}_{\mathrm{z}1}$	${\displaystyle \sum _{\mathrm{z}=\mathrm{L},\mathrm{T},\mathrm{R},\mathrm{K}}}{\mathrm{x}}_{\mathrm{z}2}$	…	${\displaystyle \sum _{\mathrm{z}=\mathrm{L},\mathrm{T},\mathrm{R},\mathrm{K}}}{\mathrm{x}}_{\mathrm{z}152}$	${\displaystyle \sum _{\mathrm{z}=\mathrm{L},\mathrm{T},\mathrm{R},\mathrm{K}}}{\mathrm{x}}_{\mathrm{z}153}$
Total Composition		III	${\mathrm{X}}_1$	${\mathrm{X}}_2$	…	${\mathrm{X}}_{152}$	${\mathrm{X}}_{153}$

).

In Table 2, the intermediate products section directly uses the first quadrant of China’s 2020 input–output table. For the initial factors, labor, technology, and capital are derived from the labor compensation, fixed asset depreciation, and total fixed capital formation (new fixed capital added in the year) in China’s 2020 input–output table, respectively. Land is calculated by subtracting the sum of labor, technology, and capital from the total added value in China’s 2020 input–output table. The quantitative ratio among the constituent factors of the 153 sector products of China in 2020 is listed in

Table 3. Quantitative ratio among the constituent factors of products from 153 sectors in China in 2020.

Sector Product Name Factor	Agricultural Products	Forestry Products	…	Social Security	Public Administration and Social Organizations
Labor	${\mathrm{a}}_{\mathrm{L}1}$	${\mathrm{a}}_{\mathrm{L}2}$	…	${\mathrm{a}}_{\mathrm{L}152}$	${\mathrm{a}}_{\mathrm{L}153}$
Land	${\mathrm{a}}_{\mathrm{T}1}$	${\mathrm{a}}_{\mathrm{T}2}$	…	${\mathrm{a}}_{\mathrm{T}152}$	${\mathrm{a}}_{\mathrm{T}153}$
Technology	${\mathrm{a}}_{\mathrm{R}1}$	${\mathrm{a}}_{\mathrm{R}2}$	…	${\mathrm{a}}_{\mathrm{R}152}$	${\mathrm{a}}_{\mathrm{R}153}$
Capital	${\mathrm{a}}_{\mathrm{K}1}$	${\mathrm{a}}_{\mathrm{K}2}$	…	${\mathrm{a}}_{\mathrm{K}152}$	${\mathrm{a}}_{\mathrm{K}153}$

.

4. Empirical Results and Analysis

Combining Equations (4) and (7), the factor contribution rates in China from 2003 to 2020 at the average social production level were calculated and organized. Subsequently, the unit values of the factors were determined. This article presents the factor contribution rate (normalized results) and unit factor value of China in 2020 under the average level of social production in

Table 4. Factor contribution rate and unit value in China (2020).

Contribution Rate				Unit Value
${\mathsf{\delta }}_{\mathbf{L}}$	${\mathsf{\delta }}_{\mathbf{T}}$	${\mathsf{\delta }}_{\mathbf{R}}$	${\mathsf{\delta }}_{\mathbf{K}}$	vL	vT	vR	vK
0.232539	0.062920	0.487965	0.216575	3.132361	0.464635	0.623553	0.299019

(results rounded to 6 decimal places). It is important to note that the estimation of factor contribution rates has undergone stationarity tests and robustness checks, ensuring no multicollinearity or heteroscedasticity issues. The results of the heteroscedasticity test and robustness checks are presented in Figure 4 and

Table 5. Illustration of robustness test.

Variables	Replacement of Standard Error	GMM
	lny	lny
L.lny		0.734 ***
		(16.44)
lnl	0.215 ***	0.089 ***
	(4.03)	(3.96)
lnt	−0.125 **	−0.044 ***
	(−2.39)	(−3.24)
lnr	0.727 ***	0.104 **
	(9.90)	(2.64)
lnk	0.183 ***	0.088 ***
	(6.13)	(3.20)
Constant	0.263 *	0.501 ***
	(1.69)	(4.07)
Observations	557	526
i.id FE	YES	YES
i.t FE	YES	YES
r2_a	.	.
Number of id		31

Note: * typically indicates significance at the 10% level (p < 0.10). ** typically indicate significance at the 5% level (p < 0.05). *** typically indicate significance at the 1% level (p < 0.01).

.

From Table 4, it can be seen that in China for the year 2020, the contribution rates of labor, land, technology, and capital to the total wealth (GDP) were 23.2539%, 6.2920%, 48.7965%, and 21.6575%, respectively, with technology having the highest contribution rate. This confirms technology’s position as the primary productive force in wealth creation.

According to Figure 4, the residuals do not vary with changes in sample values, indicating the absence of heteroscedasticity.

Furthermore,

Table 6. The quantitative ratio among the constituent factors of products in 153 sectors in China, 2020.

Sector Product Name Factor	1	2	3	4	…	152	153
Labor	0.905839	0.844377	$0.850454$	0.819306	…	0.830225	0.684310
Land	0.009213	0.063178	$0.026581$	0.093001	…	0.083835	0.116547
Technology	0.068370	0.069693	$0.058026$	0.065684	…	0.076683	0.175063
Capital	0.016578	0.022753	$0.064939$	0.022010	…	0.009257	0.024080

details the quantitative ratio among the constituent factors of products in China’s 153 sectors for 2020 (results are given up to six decimal places). According to Table 6, the quantitative ratio among the constituent factors of products in different sectors varies under certain technical conditions. For example, producing one unit of agricultural products requires 0.905839 units of labor, 0.009213 units of land, 0.068370 units of technology, and 0.016578 units of capital. In contrast, producing one unit of public administration and social organization requires 0.684310 units of labor, 0.116547 units of land, 0.175063 units of technology, and 0.024080 units of capital. A comparison reveals that the amount of labor embodied in agricultural products is significantly higher than in public administration and social organization.

Combining Table 4 and Table 6, the prices of the 153 sector products in China for 2020 under the average social production level are calculated and listed in Table 5 (rounded to two decimal places). It is important to note that the physical units of sectoral products in

Table 7. Prices of products from 153 sectors in China, 2020.

Sector Product Code	p	Sector Product Code	p	Sector Product Code	p	Sector Product Code	p	Sector Product Code	p	Sector Product Code	p
1	2.89	27	2.01	53	1.50	79	1.35	105	1.63	131	1.60
2	2.72	28	2.12	54	1.35	80	1.52	106	1.62	132	1.45
3	2.73	29	2.08	55	1.48	81	1.46	107	1.48	133	1.25
4	2.66	30	1.82	56	1.47	82	1.65	108	1.74	134	1.31
5	1.80	31	1.91	57	1.49	83	1.61	109	1.72	135	2.06
6	1.59	32	1.95	58	1.62	84	1.47	110	1.98	136	1.85
7	1.27	33	2.00	59	1.50	85	1.51	111	1.71	137	1.87
8	1.30	34	1.81	60	1.37	86	1.44	112	1.71	138	1.83
9	1.35	35	1.89	61	1.38	87	1.50	113	1.92	139	1.86
10	1.55	36	1.81	62	1.32	88	1.54	114	1.43	140	1.60
11	2.28	37	1.58	63	1.38	89	1.60	115	1.59	141	1.91
12	2.51	38	1.72	64	1.32	90	1.63	116	1.53	142	2.17
13	2.40	39	1.68	65	1.39	91	1.74	117	1.59	143	2.15
14	2.28	40	1.76	66	1.51	92	1.72	118	1.62	144	2.27
15	2.14	41	1.19	67	1.46	93	1.69	119	1.20	145	2.15
16	2.32	42	1.45	68	1.40	94	1.56	120	1.73	146	2.68
17	2.25	43	1.34	69	1.45	95	1.46	121	1.78	147	2.00
18	2.33	44	1.54	70	1.49	96	1.52	122	2.11	148	2.08
19	2.19	45	1.52	71	1.45	97	1.62	123	1.92	149	2.01
20	2.07	46	1.45	72	1.55	98	1.14	124	2.12	150	2.13
21	2.05	47	1.27	73	1.44	99	1.68	125	1.43	151	1.92
22	2.21	48	1.52	74	1.46	100	1.43	126	1.65	152	2.69
23	1.78	49	1.65	75	1.46	101	1.41	127	1.74	153	2.31
24	1.92	50	1.83	76	1.50	102	1.60	128	1.69
25	2.07	51	1.42	77	1.57	103	1.52	129	1.65
26	1.02	52	1.66	78	1.64	104	1.55	130	1.26

are detailed in the physical units presented in the China 2020 Statistical Yearbook, and the names of the sectoral products corresponding to the sectoral product codes are detailed in the China 2020 input–output table.

In the above table, due to the consistent types of factors that constitute sector products, the prices of departmental products vary depending on the proportion of quantities between the factors. Furthermore, based on the “China 2021 Price Yearbook” and publicly available online data, the approximate market prices of 72 sectors of products in China in 2020 were organized. Based on China’s 2020 economic growth rate, technological progress rate, inflation rate, etc., the relative price difference of ±90% between market prices and prices calculated with resource optimization allocation as the core for 72 sector products is divided into three categories and listed in

Table 8. China 2020 product classification table for 72 sectors based on relative price differentials.

Relative Difference Range	Sector Product Code and Name
(pmarket − p)/p < −90%	57 Glass and Glass-derived Products, 63 Iron and Iron Alloy Products, 8 Ferrous Metal Mining and Selection Products, 6 Coal Mining and Washing Products, 1 Agricultural Products
−90% ≤ (pmarket − p)/p ≤ 90%	91 Communication Equipment, 93 Audio and Visual Equipment, 88 Household Appliances, 144 Education, 13 Feed Processing Products, 43 Basic Chemical Raw Materials, 7 Petroleum and Natural Gas Extraction Products, 90 Computers, 2 Forest Products, 62 Rolled Steel Products, 18 Vegetables, Fruits, Nuts, and Other Processed Agricultural and Sideline Food Products, 56 Bricks, Tiles, Stone, and Other Building Materials, 61 Steel, 114 Road Freight Transport and Transport Support Activities, 100 Electricity, Heat Production and Supply, 98 Waste Resources and Waste Material Recycling and Processing Products, 9 Non-ferrous Metal Mining and Dressing Products, 37 Paper making and Paper Products, 3 Livestock Products, 103 Residential Building Construction, 52 Rubber Products, 4 Aquatic Products, 82 Ships and Related Equipment, 146 Social Work, 143 Real Estate, 51 Chemical Fiber Products, 35 Wood Processing and Products of Wood, Bamboo, Rattan, Palm, and Straw, 41 Refined Petroleum and Nuclear Fuel Processing Products, 125 Telecommunications, 59 Refractory Material Products, 47 Synthetic Materials, 12 Grain Mill Products, 113 Urban Public Transport and Road Passenger Transport, 64 Non-ferrous Metals and Their Alloys, 17 Aquatic Product Processing, 19 Convenience Foods, 27 Cotton, Chemical Fiber Textiles, and Finishing of Printed and Dyed Fabrics, 46 Paints, Inks, Pigments, and Similar Products
(pmarket − p)/p > 90%	95 Other Electronic Equipment, 24 Beverages, 28 Wool Textiles and Dyeing and Finishing Processing, 101 Gas Production and Supply, 53 Plastic Products, 16 Slaughtering and Meat Processing Products, 38 Printing and Recording Media Replication, 49 Daily Chemical Products, 86 Wires, Cables, Optical Cables, and Electrical Equipment, 21 Condiments, Fermented Products, 102 Water Production and Supply, 15 Sugar and Sugar Products, 48 Specialty Chemical Products, Explosives, Pyrotechnics, 118 Air Freight Transport and Transport Support Activities, 66 Metal Products, 45 Pesticides, 68 Metal Processing Machinery, 149 Culture and Art, 20 Dairy Products, 65 Non-ferrous Metal Rolling Processing Products, 92 Broadcast and Television Equipment, Radar and Supporting Equipment, 14 Vegetable Oil Processing Products, 25 Refined Tea, 87 Batteries, 44 Fertilizers, 23 Alcohol and Liquor, 26 Tobacco Products, 60 Graphite and Other Non-metallic Mineral Products, 50 Pharmaceutical Products

. The formula for calculating the relative price difference is (p_market − p)/p.

Based on Table 8, departments with a relative price difference of less than −90% primarily focus on fundamental industries. These industries supply essential materials and energy for construction, manufacturing, energy provision, and food production. For instance, iron and ferroalloy products serve as foundational materials for the construction, machinery, and transportation sectors. Coal mining and washing products are among the primary energy sources in the traditional energy system. They serve as crucial materials for power generation and fuel, and chemical raw materials for industrial production. Policies and regulations, environmental changes and natural disasters, and technological transformations are the main reasons for the significant relative price differences in these products. From the perspective of policies and regulations, products such as iron and ferroalloy are influenced by government-imposed export restrictions and import tariffs. Some countries impose anti-dumping duties on ferroalloys. Governments often subsidize agriculture, establish price support policies, and offer agricultural insurance. Regarding environmental changes and natural disasters, climate change can lead to extreme weather conditions such as droughts and floods, directly affecting crop yield and quality and thereby causing significant price fluctuations. With technological advancements and the development of new materials, introducing new materials with improved functions or lower costs can replace traditional foundational materials. This substitution limits the price increase in essential products and may lead to a long-term decline in their demand and prices. For example, the rapid growth of the photovoltaic industry has reduced reliance on coal, thereby suppressing coal demand and prices. Similarly, developing carbon fiber and other advanced alloys constrains the market and price growth potential of iron and ferroalloy products.

Products with relatively stable price differences mainly belong to industries with high market maturity and intense competition, forming the mainstay of economic activities. Based on the interrelation among products, these can be roughly categorized into raw materials and essential industrial goods, energy and environmental products, construction and building materials, agricultural and food processing products, information technology and electronic products, consumer goods and services, and transportation and logistics. The reasons for stable price deviations can be attributed to several factors. First, high market maturity and long-term competition lead prices to reflect merely the production cost plus a reasonable profit margin. The cultivation of core product competitiveness shifts towards reducing production costs and enhancing output, product quality, and corporate value through technological innovation, improvement in the quality of production factors, and optimization of their combination. This shift allows companies to maintain or increase profit margins, moving beyond the limitations of using price competition to allocate resources. For instance, producers of steel and its rolled products, non-ferrous metals, and others are widespread globally, with fierce competition driving prices close to production costs. Their profit-making strategies primarily focus on cost reduction, volume increase, and quality enhancement. Second, in industries where technology is mature and widely applied, the production processes and methods tend towards standardization, leading to improved production efficiency and reduced costs. This makes production costs more straightforward to control and predict, resulting in stable prices. For example, industries like computers and communication equipment have relatively mature technologies and supply chains. The key to gaining competitive advantage in these sectors is continuous technological innovation, significantly enhancing product performance and application scope, and creating new functions or utilities to meet consumer demand for rich features, utilities, and high-quality products. Third, these products constitute the mainstay of wealth, with mature technologies and relatively stable markets, leading to stable pricing trends.

Products with a relative price difference more significant than 90% primarily focus on industries driven by high technology, dominated by international brands, and characterized by natural and administrative monopolies. In high-technology-driven industries, products such as other electronic equipment, wires, cables, optical fibers, electrical materials, broadcasting equipment, radar, and supporting devices are centered on technological innovation. High R & D investment, rapid innovation, quick product updates, strong intellectual property protection, and high demand for skilled labor characterize these industries. In industries dominated by international brands, products such as alcoholic beverages, seasonings, fermented products, sugar and sugar products, and dairy products are typically led by a few powerful global companies. Their core competitiveness stems from brand image, marketing strategies, and consumer loyalty. These industries are characterized by strong brand recognition, high consumer loyalty, global market distribution and supply chains, efficient marketing and distribution networks, and market expansion in emerging and developing countries. Natural monopoly products include gas production and supply and water production and supply. These industries typically require massive infrastructure investments and high operating costs. They are highly regulated, involve significant government intervention, cover essential public needs, and have high market entry barriers. Regarding administrative monopoly products, government policies and regulations generally shape industries such as slaughtering and meat processing, tobacco products, specialty chemicals, explosives and pyrotechnics, and air cargo transportation and ancillary activities. These industries feature high government intervention, often with only one or a few government-authorized operators and strict market entry restrictions.

Possible reasons for the relatively significant differences in the price of products in these sectors are as follows: Factors such as high R & D costs, intellectual property protection, premium market positioning, and the lack of economies of scale and scope influence high-tech products. International brand-dominated products may derive from brand value symbolizing social status or lifestyle, marketing and advertising costs being passed on, high-cost investment in global supply chains and distribution networks, and premium market positioning. Naturally, monopolistic products may be influenced by significant initial investment, high operating and maintenance costs, government-regulated pricing, higher average costs due to continuous and widespread public services, and substantial risk and safety investments. Administratively, monopolistic products may depend on high prices due to market entry restrictions, government regulation and pricing, taxes and surcharges (such as high taxes on tobacco products), the pass-through of incremental production and operating costs, the costs of fulfilling social responsibilities, and ensuring public interest. Other products, such as woolen textiles, dyeing and finishing products, and plastic products, rely on specific raw materials. An increase in the prices of these raw materials triggers a rise in the prices of downstream products. Additionally, current technology has reached its limits, leaving little room for cost reduction, thus maintaining prices at a relatively high level.

The above analysis shows that the market prices of most sector products align with or deviate within a controlled range from the price measured with resource optimization allocation as the core. Alignment and the degree of deviation are primarily determined by effective market competition. The competition for product quality is based on the expansion of product functionality and utility driven by technological progress. This validates the scientific nature of price based on resource optimization allocation and the effective market competition mechanism. It aligns with the logic of prices serving as the foundation for promoting sustainable economic development. The market prices of the remaining sector products deviate significantly from those measured with resource optimization allocation as the core due to external factors such as human intervention, government interference, and environmental and climatic conditions.

5. Conclusions

This paper proposes a price formation mechanism centered on resource optimization allocation, based on a scientific definition of the sources, composition, and boundary scope of the constituent factors of wealth creation. It empirically tests this mechanism using Leontief’s input–output theory. The main contributions and conclusions of this paper are as follows:

(1)

This paper proposes a theoretical framework for price formation centered on resource optimization allocation. By considering the constituent factors of wealth creation, it analyzes the substitution and complementarity of factors in commodity production, as well as the wealth creation and value compensation of these factors. This paper further posits that price is determined by the techno-economic relationships among a commodity’s constituent factors, thereby objectively, fairly, and impartially reflecting the value information of these factors and the quantitative ratio among the constituent factors of a commodity. By calculating the prices of 153 sector products in China in 2020 (covering the entire industrial system) and comparing them with the approximate market prices of 72 sector products for the same year, the scientific validity of the price formation based on resource optimization allocation is basically verified.

(2)

This paper proposes an effective market competition mechanism centered on quality competition. Price measured by centering on the technical and economic relationships among a commodity’s constituent factors enables the sharing of factor value information between the supply and demand sides and the sharing of factor quantitative information among enterprises. The balance and degree of deviation between the price measured by the techno-economic relationships among a commodity’s constituent factors and the market price are determined by the product quality and enterprise value, with the degree and type of deviation being limited. Market competition thus shifts towards quality competition brought about by technological innovation, factor quality improvement, and factor structure optimization. This effectively avoids a series of economic issues, such as resource misallocation, economic cycles of growth, and crises of overproduction. A comparative analysis of the prices of 153 sector products in China in 2020, centered on resource optimization allocation, and the approximate market prices of 72 sector products in the same year reveals that the balance and degree of deviation are primarily determined by effective market competition centered on quality competition driven by the expansion of product functionality and utility premised on technological advancement, aligning with effective market competition mechanism.

As the mechanism for allocating resources through effective market competition under the constraint of the price centered on resource optimization allocation operates, it will incentivize the improvement of labor skills and technological upgrades. This will further guide resources to increasingly favor the development of new technologies, products, and markets, such as artificial intelligence, quantum technology, life sciences, digital intelligence technology, and green technology. Consequently, it will foster the emergence of new industries, new models, and new growth drivers, promoting a deep optimization and upgrading of the economic structure, thereby achieving sustainable economic development.