Data and contemporary economics: role, paradox and development

Abstract

Purpose

A more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.

Design/methodology/approach

Based on the perspective of evolutionary economics, this paper re-examines economic history and existing literature to study the following: changes in the “connotation of production factors” in economics caused by the evolution of production factors; the economic paradoxes formed by data in the context of social production processes and business models, which traditional theoretical frameworks fail to solve; the disruptive innovation of classical theory of value by multiple theories of value determination and the conflicts between the data market monopoly as well as the resulting distribution of value and the real economic society. The research indicates that contemporary advancements in data have catalyzed transformative innovation within the field of economics.

Findings

The research indicates that contemporary advancements in data have catalyzed disruptive innovation in the field of economics.

Originality/value

This paper, grounded in academic research, identifies four novel issues arising from contemporary data that cannot be adequately addressed within the confines of the classical economic theoretical framework.

Citation

Jiang, H.-x. and Jiang, N.-p. (2024), "Data and contemporary economics: role, paradox and development", China Political Economy, Vol. 7 No. 2, pp. 195-216. https://doi.org/10.1108/CPE-09-2024-0014

Recently, in order to “move faster to establish institutions and mechanisms for promoting the development of the digital economy and refine the policy system for developing the digital industry and transforming traditional industries with digital technologies,” China has been actively engaged in the construction of data infrastructure, recognition of data ownership, and application of big data. A more accurate comprehension of data elements and the exploration of new laws governing contemporary data in both theoretical and practical domains constitute a significant research topic.

1. The role of contemporary data in economic society

The realization of Chinese-style modernization necessarily requires correct theories. Xi Jinping Thought significantly guides the innovation and development of economics on Socialism with Chinese Characteristics for a New Era. As Xi (2022) proposed, “[we] must uphold fundamental principles and break new ground”, and “[we] should approach every new thing with great enthusiasm and never stop broadening and deepening our understanding of the world. We must dare to say what has never been said and do what has never been done, and we must use new theory to guide new practice.” This also serves as a guideline for us to promote innovation in economics. The process of China’s modernization has demonstrated that classical economic theories fail to explain many economic phenomena adequately. Therefore, it is imperative to foster innovation in economics.

1.1 Contemporary data as a fundamental category for the development of economics

The development of any economic theory begins with the objective abstraction of real economic phenomena, commencing with the establishment of fundamental concepts and categories. Through a series of concrete methods such as deduction, reasoning, induction, and extraction — especially employing the “concrete-abstract-concrete” approach — various principles or theories and further theoretical systems or frameworks are formulated, ultimately establishing the discipline of economics. In recent years, with the emergence of the digital economy, numerous novel economic phenomena have surfaced and necessitated new economic theories for elucidation. For instance, what factors have contributed to the gradual dominance of the digital economy as a mainstream economic form in society? Why has data swiftly evolved into an unparalleled factor of production? Why have data elements supplanted certain traditional factors of production? Given that data constitutes the fundamental element of the digital economy and represents the most foundational category of economic innovation and development, studying data assumes paramount importance in innovating and developing economics.

1.2 Contemporary data have been extensively utilized in social production and daily life

In contemporary society, the value of data has become increasingly recognized. Currently, data are extensively employed in both social production and everyday life, culminating in a vast repository known as big data, which generates significant economic value. As of July 30, 2021, there were a total of 209,242 research publications addressing contemporary data and the digital economy. In recent years, there has been a notable increase in related research. He (2019) analyzed data processing theory; Wu Hequan emphasized that the new momentum supported by data drives the formation of the industrial Internet (Tang, 2018); Wang (2018) examined the mechanisms through which contemporary data, artificial intelligence, and other advanced technologies, including the Internet, are synergistically evolving to profoundly enhance the real economy; Luo (2020) and Yuan (2020) respectively elaborated on the major trend of contemporary data leading technological and structural changes in various fields of the economy, as well as driving the migration of the world’s new economy.

General Secretary Xi Jinping has consistently emphasized the imperative of considering data as a crucial factor of production, the necessity of establishing a cyber power, Digital China, and smart society, as well as the urgent need to expand and strengthen the digital economy. He pointed out, “[we should] foster the development of the digital economy, accelerate the advancement of digital industrialization, leverage information technology innovation to drive progress, consistently propel new industries, emerging business formats and innovative models, and employ novel driving forces to promote new development. Furthermore, [we should] facilitate the seamless integration between the Internet, big data, artificial intelligence, and the real economy while expediting digitization, networking, and intellectualization across manufacturing, agriculture, and service industries” (Xi, 2018). The significance of data in social production and daily life has been fully demonstrated.

1.3 Contemporary data has expedited the advancement of social development

As a pivotal resource in the digital economy, big data has accelerated the rapid development of contemporary society. According to the data from the China Academy of Information and Communications Technology (CAICT, 2020), in 2019, China’s digital economy witnessed a nominal growth rate of 15.6%, surpassing the nominal GDP growth rate of 7.85% during the same period. Furthermore, the added value of the digital economy reached 35.8 trillion yuan, constituting 36.2% of GDP and exhibiting a year-on-year increase of 1.4% (CAICT, 2020). In the past five years, big data has played an increasingly pivotal role, garnering significant attention from the academic community. As the foremost factor of production, contemporary data has catalyzed the rapid development of cutting-edge technologies represented by artificial intelligence, thus instigating profound transformations in the economic society.

1.4 The application of contemporary data gives rise to new theories

However, theories are a reflection of practice, and some existing economic theories can no longer adequately explain the real economic problems arising from big data. For instance, the classic division of labor theory elucidates the phenomenon of enhanced labor efficiency; nevertheless, in the current era of big data, the functioning of big data platforms can blur the boundaries of the division of labor and diminish its efficacy in improving efficiency. Furthermore, in the era of industrial society characterized by the separation of production and consumption, the law of supply and demand clearly delineates the opposing relationship between supply and demand. However, the emergence of prosumers on data platforms in the current era necessitates new theoretical explanations. Some Western scholars, such as Mankiw, have observed these issues. Certain French scholars expressed disappointment with the explanatory power of existing economics [1]. The French scholar McIntyre (2003) once remarked, “Most people believe that articles published in the ‘top’ economic journals in the United States are of no use to society.” However, there is a dearth of academic literature systematically investigating these issues.

The practice has given rise to new economic challenges, promoting ongoing advancements in economics. Examining and comprehending the evolution of economic history from reality to find out whether economics is undergoing disruptive innovation and development has posed a question that necessitates attention from the academic community. This is because numerous phenomena arising from data-driven economic and social development can no longer be adequately explained or summarized using classical economic principles. Some scholars, including Jia (2016) and Zhu (2019), have also raised objections to the prevailing mainstream economics. Certain media outlets, such as People’s Daily, published an article titled “Farewell, Western Economics” by Xie (2011), which ignited intense debates within academia by proposing critical perspectives on traditional Western economic theories. In the face of challenges presented by various phenomena in the era of big data to existing economic theories, failure to address them adequately will inevitably result in a crisis for economics, diminishing its explanatory power and practical guidance. It is worth noting that these new economic phenomena can be attributed to issues related to data. The development of Marxist economics, as well as modern Western economics, should be undertaken within the context of big data in the digital economy. Therefore, exploring contemporary data issues is a significant opportunity to address economic challenges and foster innovative development in economics. This paper aims to provide theoretical insight into novel economic phenomena arising from contemporary data and, based on scholars’ research, identify laws that contradict established economic theories, thereby proposing insights for fostering innovative economic development. In this regard, our discussion will focus on the following economic issues pertaining to contemporary data.

2. The role of data as a factor of production and its economic significance

2.1 Contemporary data become a new factor of production

The concept of a “factor of production” holds paramount significance in economics, specifically referring to the prerequisites for social production. In Western economics, factors of production include land, capital, labor, entrepreneurial talent, human capital, and institutional conditions. From Marx’s perspective, the production factors comprise the means of production and the labor force that establish the fundamental conditions for producing social material goods. The ownership structure of the means of production determines the nature of society. Under capitalist circumstances, both the means of production and labor represent materialized forms of capital.

From the perspective of economic history, the factors of production utilized in social production have gradually increased and expanded. During the agricultural era, the production relied predominantly on basic tools, particularly land and labor [2]. In the industrial era, production not only relied on mechanical equipment, land, and labor, but also incorporated capital and entrepreneurial talent. In the post-industrial era, institutions, human capital, social networks, technology, and even regional space are closely intertwined with production, which should also be considered as production factors [3]. In the contemporary era, cutting-edge technologies based on data, such as artificial intelligence, Internet technology, and cloud computing structures, have brought about extraordinary changes in social production. For instance, during the process of social production and sales, niche products can be turned into relatively popular ones, allowing non-mainstream manufacturers to share the mainstream market (Anderson, 2015, p. 78), thereby revolutionizing production and business models. This is why countries around the world place great importance on the application and development of contemporary data.

2.2 The connotations of contemporary data as a factor of production

In April 2020, the Central Committee of CPC and the State Council of China emphasized the necessity to “accelerate the marketization of data elements” in Opinions on Building a More Comprehensive Market-based Allocation System and Mechanism for Factors (Xinhua News Agency, 2020a). In May of the same year, Opinions on Accelerating the Improvement of the Socialist Market Economic System in the New Era was issued, reemphasizing the need to “expedite the cultivation and development of the data element market, establish a checklist management mechanism for data resources, enhance standards and measures for defining permission definition, open sharing, trading and circulation of data, as well as explore the value of social data resources. Promote the establishment of a digital government to reinforce orderly data sharing while ensuring legal protection of personal information” (Xinhua News Agency, 2020b). The academic discourse surrounding data and the digital economy has been highly contentious since then, leading to numerous theoretical achievements. However, a consensus on various aspects remains elusive. Particularly, concerning the fundamental category of “data,” there exist multiple misconceptions, such as:

• (1)

  Contemporary data are big data. Some scholars argue that contemporary data surpasses the connotation of traditional data and embodies the characteristics of big data, which serves as the fundamental basis for digital technology (He and Wang, 2021). This is because big data, being a modern information technology, carries an entirely new significance. Contemporary data refers to the datasets that are processed and managed by individuals with the aid of Internet technology and modern computer technology.

• (2)

  Contemporary data are a critical digital technology. This perspective regards contemporary data as a form of data processing technology that relies on various technical means for its formation. These technologies encompass the Internet and scalable storage systems, cloud computing platforms, distributed file systems, distributed databases, data mining, and libraries for data processing.

• (3)

  Contemporary data plays a crucial role as a fundamental digital technology and information factor of production. This perspective emphasizes the importance of data in modern production and management, highlighting its ability to enhance decision-making capabilities and optimize business management functions. Considering the pervasive nature of data across various domains, such as big data aggregation, computers, the Internet, mobile communications, cloud computing, blockchain, 3D printing, artificial intelligence, software design, and hardware RandD, it is widely acknowledged that these fields all center around data as their core focus and define it as an essential factor of production.

• (4)

  Contemporary data are an economic asset. This view emphasizes the significant role of data in promoting social production and social life, focusing on its ability to form industrial alliances among various sectors of the national economy through the Internet platform and integrate into the entire process of social reproduction, thereby generating enormous economic benefits. It also emphasizes the reality that data, with the help of mobile communications and application tools, is integrated into residents’ social lives, bringing great convenience and welfare to people, and is regarded as a capital or asset that can proliferate in the economic process.

• (5)

  Contemporary data serves as the “cell” of the new economy. This perspective posits that the so-called new economy refers to a new form of economy that deviates entirely from traditional economic models in terms of production and circulation. The so-called network economy, intelligent economy, digital economy, and other similar concepts all represent distinct manifestations of this new form. According to the Cyberspace Administration of China (CAC) (2016), “The digital economy refers to a series of economic activities that use digitized knowledge and information as the essential factors of production, take modern information networks as the key carrier, and use the effective use of information and communication technology as an important driving force for efficiency improvement and economic structural optimization.” In the context of contemporary economic activities, the digital economy fundamentally relies on digital technologies, which in turn are underpinned by data as its essential component, as digital technologies engage with data as both their object and foundation while facilitating the operation, regeneration, and expansion of data through infrastructures such as computers, cloud computing, and the Internet. Thus, it can be asserted that the vitality of this new economy—predominantly characterized by the digital economy—is intrinsically linked to data.

Of course, apart from the perspectives above, there exist other interpretations regarding the connotations of data.

2.3 Theoretical analysis of the connotations of contemporary data as a factor of production

The aforementioned perspectives on the connotations of data encompass rational elements that reflect its significance in contemporary society from various dimensions. Nevertheless, we contend that the fundamental essence of contemporary data’s connotation remains consistent with its historical context. Some scholars underscore the role and function of contemporary data, inaccurately characterizing them as “a pivotal digital technology,” “an indispensable factor in the production of digital technology and information,” “an economic asset,” or “the cell of the new economy.” The simple definition of contemporary data as “big data” not only falls into the trap of tautology but also overlooks the inherent similarities and homogeneity between data and big data. Both data and big data serve as linguistic, graphical, or symbolic representations that capture the linguistic characteristics of entities, while numbers are a specialized digital language or symbol for representing the characteristics of movement in things. The utilization of data in the past was relatively limited, characterized by its scattered and fragmented nature, resulting in less significant impacts. Conversely, contemporary data are centralized, processed, screened, and classified to generate robust datasets, known as big data. Therefore, the disparity between data and big data lies solely in their scale rather than their essence; thus, they should not be treated separately.

Given that contemporary data, big data, and data in the past are fundamentally homogeneous, any form of data represents the graphical, linguistic, or symbolic information processed by humans to reflect the movement characteristics of nature, human society, and other domains. Numbers, being a distinctive digital language or symbol, constitute the most prevalent manifestation of data in contemporary society.

What accounts for the unprecedented significance of data in contemporary society, as evidenced by assertions such as “all people and things exist as data, with data power and relations defining the entirety of social production relationships” (Lian, 2019, p. 18), along with statements like “economy will not exist without data” and “big data will revolutionize the fields of humanities and social sciences, reshaping the dynamics between business and academia” (Aiden and Michel, 2015, p. 7)? Furthermore, why has economics historically overlooked the role of data? This can be attributed to the gradual transparency of information and the scaling and structuring of data in advancing human social production. Karl Marx categorized human society into primitive society, slave society, feudal society, capitalist society, and socialist or communist society based on the nature of production relations. Conversely, scholars classify human history into the agricultural age, industrial age, and information age based on productivity (Qin, 2015, p. 44). Currently, we are in the era of big data within the information age. Generally speaking, the development of modern information technology is divided into several periods: First, the data processing period, which emphasizes enhancing data processing capabilities and digitizing information within the enterprise; second, the microcomputer period, which aims to decentralize the flow and application of information through technical means; third, the network period, characterized by information sharing across networks, enabling a significant increase in both volume and scope beyond organizational and geographical boundaries; and fourth, the current era of big data, in which the massive quantitative changes in data have led to continuous exponential growth, morphological changes, and structural transformations, making big data an independent element, capital, industry, and economic form.

2.4 The connotations of contemporary data as a factor of production reinforce and advance Marxist economic theories

The significance of data has garnered considerable attention from China’s top leaders. Xi Jinping perceives the advent of the big data era is indicative of novel characteristics and a pivotal stage in the development of informatization. He advocates for national endeavors to foster innovative development within the big data industry, fortify the establishment of a new economy with data as a fundamental factor of production, and harness big data to augment national governance, promote social security, address livelihood issues, and ensure national security. The contents mentioned above not only uphold and develop Marxist theories regarding the factor of production and related concepts in light of the significant impact of contemporary data on the economy and society but also constitute an indispensable component of 21st-century Chinese Marxist economics. To facilitate big data applications, the national government has established big national-level data centers.

From an economic perspective, data are not only an objective existence but also a “pivotal factor” in social production. This key factor in production far surpasses the role of traditional factors of production, such as land and other means of production. Simultaneously, data has evolved into a contested resource, a traded commodity, and a critical product developed by contemporary enterprises. Therefore, data are a vital resource, factor of production, and commodity in contemporary society. As a “materialized form” of contemporary capital, it represents a new form of capital that emerges as an outcome of the evolution of capital forms driven by new technologies. Data capital refers to a value capable of generating surplus value within specific social relations, with data commodities serving as its tangible manifestation. From these connotations, the contemporary data enrich the content of Marxist capital theory.

2.5 The connotations of contemporary data as a factor of production disruptively innovate Western economic theories

It is evident that classical economic theories are insufficient in elucidating the new practice caused by contemporary digitalization. Consequently, on May 27, 2021, the National Bureau of Statistics of China defined the digital economy, formed by contemporary data operations, as “a series of economic activities that utilize data resources as crucial factors of production, leverage modern information networks as significant carriers, and harness information and communication technologies effectively to drive efficiency improvement and optimize economic structure” (Qin, 2015, p. 44). It can be said that contemporary data has revolutionized all aspects of social production and social life, transforming them from tangible entities to visual representation, which has significantly improved the efficiency of social production and the quality of social life, changing the forms and modes of production and living, and providing new areas for human beings to obtain economic and social welfare. This is the essence of the practical application of contemporary data.

Due to the contradictory approach of contemporary data in comparison to traditional data in today’s social production and business models, the trend of intensified social sharing, complexity, and transparency in the future will become more prominent, highlighting the impact of “big data paradox” (Zhao and Ji, 2021). Evidently, contemporary data, through aggregation, processing, and other means without resorting to random analysis (such as sample surveys), has taken on the characteristics of 4 V (volume, velocity, variety, and value), realizing “massive, high-growth, and diverse information production with enhanced decision-making power, insight discovery capabilities, and process optimization capabilities” (Mayer-Schönberger and Cukier, 2013, p. 67) in social production and business models. This ought to give rise to the latest content that differs from traditional economics. Naturally, it is notable that the increasingly significant role played by data in contemporary society, which reveals new economic laws, does not negate the existing economic system. This is because the existing economic system reflects the economic facts of the “Industrial Era”, while the economic facts of the Information Era should be reflected by an economic system that has developed through disruptive innovation, given that from the perspective of social reality, we are in a period of rapid transformation from the Industrial Era to the Information Era [4].

3. Economic paradoxes from contemporary data

3.1 Principles disclosed by traditional western economics and the contradiction thereof with contemporary reality

Economics is a discipline that elucidates the inherent laws governing human socio-economic activities, encompassing diverse concepts, categories, principles, and conclusions. Any sound economic theory must reflect the interconnections between economic phenomena in the objective reality of society, guiding individuals to engage in economic activities proficiently and attain desired economic outcomes. By categorizing the economy into distinct eras, such as the Agricultural Era, the Industrial Era, and the Information Era, it becomes evident that during the Agricultural Era, systematic economic theories were not yet developed fully by scholars of that period, resulting in limited exploration of economic laws and theories. The advent of modern technological advancements gave rise to the emergence of industrial society and the formation of systematic economic theories. Economists have uncovered many economic laws, which are constantly applied to drive the development of economics and the operation of economic practices. Let’s take a few well-known economic laws as examples: (1) Law of Diminishing Marginal Utility, which elucidates the correlation between individuals’ inclination to consume goods and the quantity consumed, indicating that during a specific timeframe, while continuously consuming a particular good, the overall utility may augment alongside an increase in quantity; however, the marginal utility derived from each additional unit of said good diminishes. (2) Law of Diminishing Marginal Returns, which states that, under the conditions of other technical factors remaining constant, when a continuous and equal amount of a variable factor is combined with unchanged quantities of other factors, the marginal product or return initially increases, but eventually decreases after reaching a certain point of input. (3) Law of Increasing Marginal Cost, which posits that under constant labor prices and other conditions, the diminishing marginal quantity of labor results in an escalation of the marginal cost of production. As output increases, the trend of the marginal cost demonstrates an initial decline followed by an increase. (4) Law of Depletion of Production Factors, which posits that the production of any goods requires the depletion of corresponding factors of production. Once these factors are depleted or used, they no longer exist. The extent to which they are fully depleted depends solely on the duration of the production process. (5) Law of Depletion of Consumer Goods, which suggests that consumer goods necessary for human social life are continually depleted as consumption volume and time increase, resulting in a reduction of their quantity and their forms no longer remaining. Certainly, there are many other principles and laws of economics which will not be exhaustively elaborated upon herein.

Throughout the evolution of economics, challenges such as the “paradox of abundance” and the “value paradox of water and diamonds” have emerged, all of which can be effectively addressed within the framework of traditional economic theory. However, with the significant advancements brought about by the modern technological revolutionparticularly in the current information eradigitalization underpinned by big data has reached an unprecedented zenith. In terms of sheer volume, China has achieved significant milestones in digital applications and emerged as a global digital powerhouse in the field of digital services, leading globally with net exports. Over the past decade, China’s e-commerce and digital payments have grown 40 times their previous levels, accounting for a 40% share of global transaction totals. Furthermore, China has met or led international standards in digitalization across consumer-facing industries, information and communication technology, media, and finance. As early as 2017, China’s digital economy accounted for over 30% of its national GDP and reached 35% in 2020. By 2030, digitalization could revolutionize and generate between 10% and 45% of total industry revenue (McKinsey Global Institute [MGI], 2017). Furthermore, expert analysis indicates a positive correlation between the growth rate of labor productivity in various sectors in China and their overall level of digitization.

3.2 Contemporary data phenomena reveal new laws challenging traditional economics

The role of the above economic laws in the industrial society has been diminishing and even vanishing completely. People have discovered numerous new economic principles and laws that are contrary to them. Since the American economist Paul Romer proposed the New Growth Theory in the 1960s, more scholars have focused on phenomena that run counter to traditional economic principles, such as the non-depleting nature of knowledge as a specific form of data. Wang (2001, 2007), Su and Wang (2013), Yuan (2020), and others have addressed relevant issues from different perspectives, including:

• (1)

  The law of increasing marginal utility that diverges from the law of diminishing marginal utility disclosed by traditional economics. Contemporary data, facilitated by Internet utilization, generates cumulative value-added effects and value-enhancement effects. These effects continuously accumulate, enabling real-time empowerment without spatial constraints, thereby engendering a potent “butterfly effect” that leads to a substantial increase in marginal utility.

• (2)

  The law of increasing marginal returns that diverges from the traditional law of diminishing marginal returns. In the course of social production and social life in the contemporary era, especially through the operation of the Internet, regardless of other technical conditions, the output facilitated by data, unlike that driven by other factors such as the labor factor where the marginal output or return initially increases and then decreases, exhibits a persistent and strong upward trend. It is particularly notable that, similar to the aforementioned “law of increasing marginal utility,” although scholars such as Brian Arthur conducted relevant research in the Internet era and identified the law of “increasing marginal returns,” the increasing marginal returns effect in the Internet era is merely an outcome of resource sharing via the Internet platform, resulting from the tendency of marginal costs to approach the minimum and marginal returns to approach the maximum. In the current big data era, the increasing marginal effect not only depends on data platforms for resource sharing but also on data sharing, co-creation, co-existence, and integration, generating effects more rapidly and powerfully continuously.

• (3)

  The law of decreasing marginal cost that diverges from the traditional law of increasing marginal cost. In contemporary social production, data have forcefully broken through the constraints of time and space via means such as the Internet and have strongly compressed the production and circulation time. Particularly through approaches like artificial intelligence, it even results in a situation where time spent by people in selection, gaming, final decision-making, and trial-and-error all tend towards zero, thereby leading to a forceful decrease in marginal cost.

• (4)

  The law of the non-depleting nature of production factors. Contemporary data are a “crucial” and “essential” factor of social production. Unlike other factors like labor, machinery, factories, and raw materials that are consumed during the production process, data can be repeatedly utilized in the production process and even increase in quantity during the production. The continuous utilization of data breaks down professional, industry, and even national boundaries, enabling it to be “non-depleting” and shared across a broader “space.” As Drucker et al. (2009) indicates, the nation-state is not going to wither away, but it will no longer be the indispensable one.

• (5)

  The law of the non-depleting nature of consumer goods. Any consumption behavior can be categorized into productive consumption and living consumption. Productive consumption refers to the utilization of means of production or labor during the production process, while living consumption pertains to the consumption of material goods for clothing, food, shelter, and transportation. Contemporary data serves as both productive and living consumption materials. As a “key factor” in productive consumption, it possesses “non-depleting” properties that contribute significantly to production processes. Similarly, in the context of living consumption, data exhibits “non-depleting” properties and even demonstrates “regenerative” qualities. This is because the production and living consumption of data can generate new information or data, perpetuating an endless cycle. The wider and more frequently data are utilized, whether in production or consumption, the greater its utility or economic value becomes, resulting in the creation of additional data. Futurists Paul Saffo and Jeremy Rifkin argue that people’s commercial and consumer behaviors at this point have shifted towards knowledge and information represented by data, leading to the emergence of novel consumption models, such as collaborative sharing, prosumers, and biosphere lifestyles.

3.3 The significant practical driving force of contemporary data following new rules

The extensive utilization of contemporary data has not only resulted in the continuous discovery of economic laws that contradict classical economics from the past, but also in the emergence of new economic laws that challenge even these newly discovered principles, highlighting the remarkable and mysterious nature of the new economic phenomena triggered by contemporary data. Taking Moore’s Law, proposed by Intel’s founder, as an illustration, it posits that the processing power of computer chips doubles every 18 months, while their prices decrease by half [5]. Statistics from Intel show that the transistor count on a single chip has experienced significant growth, increasing from 2,300 on the 4,004 processor in 1971 to 7.5 million on the Pentium II processor in 1997. Some scholars have substantiated the validity of Moore’s Law by examining three pivotal components: microprocessors, semiconductor memory, and system software in personal computers. However, recent developments in integrated circuits in digitalization have introduced quantum-based integrated circuit technology for enhanced circuit density on silicon wafers. This exponential growth has led to heightened complexity and error rates, with line widths on chips reaching the nanometer scale. Consequently, there have been qualitative changes observed in the physical and chemical properties of materials, rendering Moore’s Law ineffective (Zhang, 2014, p. 45). Some IT professionals in China have proposed a “New Moore’s Law,” suggesting that the number of Internet hosts and online users in China doubles every six months, a trend expected to persist for several years (Qin, 2015, p. 119). To address the limitations of Moore’s Law, Robert Metcalfe, a pioneer in computer networking and the co-founder of 3Com, introduced Metcalfe’s Law in 1973, which posits that the value of a network (specifically, the value of data utilization) experienced exponential growth with the increasing number of interconnected computers, as each additional computer amplifies the value of the network. The value of data utilization (represented as V) on a network is precisely determined by the square of the number of network nodes (N). This law reveals a phenomenon that Moore’s Law fails to elucidate in an interconnected world driven by information technology, individuals and organizations are linked through network externalities, creating a virtual global economy with remarkable efficiency and growth potential. Besides, Gilder (2013) posits that within the forthcoming few decades, the bandwidth of backbone networks for data transmission will double every six months, a growth rate three times faster than the CPU growth rate predicted by Moore’s Law.

In essence, Moore’s Law describes the correlation between the economic value generated by contemporary data and the structure and technical capabilities of data production, aggregation, and recreation. However, research findings have indicated that this law continuously gives rise to paradoxes under new conditions, some of which can be disruptive. For instance, Michio. (2012) predicts that “Moore’s Law will collapse” within the next decade (p. 213). In response to this disruptive change, scholars have proposed the law of “More than Moore” (MTM), intending to shift the focus of the data industry from “more and faster” to “more and better.”

3.4 Contemporary data operations will reveal more divergent laws from classical western economics

Due to the existence of these paradoxes, experts have recognized an economic fact: the more users there are for data through the network, the greater its utility becomes, and there will be no phenomenon where more people sharing leads to less. If Metcalfe’s Law is employed to connect the multiplier effect of network externalities with information-based enterprises, a global e-commerce market that rivals the real world, full of boundless vitality and infinite imagination, can be established [6]. Evidently, these realities that overthrow the laws of traditional economics should be abstracted into new economic theories.

The extensive application of contemporary data on the Internet has accelerated technological advancement, promoted social progress, and further hastened the convergence of technological nature and technological society. Artificial intelligence is the product of algorithms and computing power driven by big data and the Internet. In this context, novel economic laws that deviate from both traditional and contemporary economics will be unveiled by individuals. For instance, Liu (2012), the founder of the Witkey theory, posits that through its manipulation of data, the Internet will evolve into an organizational structure akin to the human brain following nine laws: (1) The Law of Connection. The accelerated advancement of Internet connectivity devices will continuously extend the interface between the Internet and the human brain, leading individuals to develop a psychological reliance on this connection. (2) The Law of Mapping. With the evolution of the Internet, human brain cognitive functions are mapped, creating virtual spaces that mirror the human brain on the Internet, thus realizing an indirect link between the human brain and cyberspace. (3) The Law of Credit. Stringent verification procedures for Internet users’ identities are increasingly being implemented, contributing to gradual enhancement in the Internet credit system. (4) The Law of Simulation. The Internet will naturally evolve to simulate the structure of the human brain, creating virtual neurons and auditory, visual, and sensory systems. (5) The Law of Unification. The Internet will evolve into a uniquely unified system resembling a virtual brain structure that highly integrates commercial applications, hardware, and software infrastructure. (6) The Law of Dimensionality. Internet input and output data forms will progress from primarily one-dimensional content representation to an advanced stage dominated by three-dimensional content representation. (7) The Law of Expansion. The volume of data, hardware devices, and human brains connected to the Internet is experiencing rapid growth, with data expanding at the highest rate, followed by hardware devices, while the number of connected human brains shows the slowest increase. (8) The Law of Acceleration. Within the realm of the Internet, both hardware devices and interconnected human brains will accelerate their computing speeds, significantly enhancing human capabilities. (9) The Law of Direction. The evolution of the Internet follows a strong directional pattern rather than being chaotic or disorderly; it adheres to evolutionary principles and tends toward a unified organizational structure that closely resembles that of the human brain.

3.5 Contemporary data operations drive the development of Marxist economics

Moreover, contemporary data have given rise to the “big data paradox,” which suggests that when big data are controlled and utilized by a limited number of individuals, it can yield significant effects. However, in competitive domains, once big data are deployed, their effectiveness diminishes considerably and may even lead to disruptive consequences. This phenomenon has led experts to advocate for caution against the uncritical adoption of “dataism” and “data religion.” Given that contemporary data serves as a crucial foundational resource for technological advancement, other technological paradoxes affecting the economy and society are likely to emerge, including Moravec’s Paradox, the “new knowledge paradox,” and the “heuristic paradox” (Li, 2017). The Internet production and business models, supported by big data aggregated from contemporary data, have also given rise to the “long tail theory,” which challenges traditional economic models. This theory not only opposes the “80/20 Rule” of economic distribution but also argues that products in low demand or with low sales volume can collectively make up market share that rivals or exceeds the relatively few current bestsellers and blockbusters but only if the store or distribution channel is large enough (Anderson, 2015, p. 78). These theories, which deviate significantly from traditional economics, undoubtedly warrant a distinct position within contemporary economic discourse. In fact, Marxist economics has long been critical of the theories espoused by Western vulgar economists, and the current state of contemporary data utilization once again substantiates the validity of Marxist economic theory, showcasing its robust vitality.

In addition, with the increasingly pervasive use of data today, and the remarkable development of artificial intelligence, Internet technology, and cloud computing, among others, an unprecedented surge of economic phenomena will accelerate to emerge their appearance. Consequently, these phenomena will exhibit diversified and intricate internal connections. Henceforth, it is not surprising that a multitude of economic paradoxes have surfaced. The principles articulated by traditional Western economics were once regarded as universally applicable; however, the likelihood of their relevance across diverse economic contexts and temporal frameworks is gradually diminishing, which is not unexpected. Conversely, these contemporary circumstances offer a fresh perspective that effectively elucidates Marx’s theories of capital, production, and value, thereby providing substantial impetus for the innovative development of Marxist theoretical framework.

4. The foundation for the development of contemporary economics: the issue of determining the value of data

4.1 Historical perspectives on value and the value of data

Plato (1957) was among the earliest to focus on the issue of the value of things (pp. 74–75), while Aristotle was the first to analyze the forms of value (Marx, 1972, p. 74). Subsequently, the economic value of any entity was divided into two types: the utility value in Western economic theory and the labor value in political economy theory. Both of these values can be gauged in monetary terms and possess practical significance in economic activities, thereby manifesting their economic attributes. If we take into account that the utility value reflects the degree of human perception of the usefulness of items or commodities, and this usefulness is relatively constant, only varying in terms of the size of utility due to people’s different discovery and perception of it, then the magnitude of the utility value is entirely determined by the perception formed by people’s needs. Therefore, we do not discuss the changes in usefulness within this connotation here, nor do we explore the issue of determining the economic value of contemporary data from this perspective. Thus, we will solely explore the issue of determining the economic value of contemporary data from the perspective of the labor theory of value.

The essence of contemporary data refers to the knowledge and information symbols formed by all objective entities after undergoing graphical or non-graphical transformation. In the agricultural society, various phenomena in nature and human society conveyed diverse messages or information, which people relied on to perceive phenomena, analyze objects, and seek laws. At this point, the pattern in which people utilized information for perception in social production and life was predominantly the direct interaction of “person–object” and “person–person”. This mode demanded a considerable space and time, and the objects of perception were confined to individual cognition. Hence, it was necessary to form partial group cognition through mutual communication, teaching, and influence. Owing to the backdrop of the era and technical constraints, it was difficult for information to be transformed into data. Upon entering the industrial society, with the socialization of production and life, people began to pursue standardization and efficiency. The “personobject” and “personperson” modes of individualized perception of objective entities employed in the past were no longer applicable. Therefore, it was necessary to abstract the information conveyed by the various characteristics of objective entities into universally acceptable and readily identifiable non-graphical or graphical knowledge symbols, which were termed data. Numerals became the most concise form of information expression for data. Thus, people could abandon the traditional direct perception modes of “person–object” and “person–person” and form a more collective understanding of objective entities through indirect perception, namely “person–data”. In the industrial era, due to the rapid growth of knowledge, some scholars referred to it as the “knowledge explosion”. If it is said that the economic value of data has significantly emerged during this period, we should also note that the then economic value of data was typically concentrated on enhancing labor capital, simplifying production processes, and facilitating daily consumption. Its economic value was mainly manifested in the forms of patents, proprietary technologies, trademarks, or advanced intellectual achievements. Nevertheless, even under constraints, the development of data application in the industrial society was sufficient to drive a leap in social production and life.

4.2 The commodity perspective of contemporary data

The advent of the information age has ushered in a completely novel phase for social production and life. Just as Kevin (2014) asserted, the new economy predicated on big data will abide by 12 principles; this new economy is an economic modality based on information, and information holds substantial value; the greater the amount of information incorporated in a product, the higher its worth. The information referred to herein is actually transformed, namely, data. In October 2014, in a speech at Stanford University in the United States, Kevin Kelly further emphasized that in the future, data will be transmitted more frequently among the smart devices of each individual, and personal data will emerge as a crucial asset in the future. He contends that all businesses are intimately intertwined with data. This perspective has already indicated the trend of data commodification.

Kevin Kelly’s stance on the commodification of data conforms to objective reality. From the viewpoint of the theory of utility value, it implies that an increasing amount of data utility will be unearthed in the future. Analyzed from the perspective of the theory of labor value, as production activities escalate, data, as a commodity, will be continuously generated, thereby enhancing its value. In accordance with the theory of labor value, the original information possessed by the material world (including human society) can be regarded as a production factor, but it does not constitute a commodity and thus cannot engender genuine value. Nevertheless, since the agricultural society, with the advancement of the industrial economy, particularly the information economy, people have processed the original information to form data, and subsequently, through continuous reprocessing, have generated more data products, laying the groundwork for the establishment of a comprehensive data industry and providing the potential for the emergence of data capital. Since when data information products are produced as commodities, their value is determined by the socially necessary labor time requisite for their production. Simultaneously, because capital can yield surplus value, within this context, data capital has evolved into a specific form of capital, facilitating the transformation of data into a physical capital.

4.3 Analysis of the theory of value determination of contemporary data

The determination of the value of contemporary data are crucial for fostering disruptive and innovative development in traditional economics. In fact, the economic value and its determination of contemporary data have long been recognized by scholars. Metcalfe’s law (Gilder, 1993) argued that contemporary data and products supporting high-tech operations, such as the Internet, become valuable only when many people use them, since the consumption process of information or data are likely to occur simultaneously with its production process. The resources it contains merely stimulate more knowledge or feelings in consumers, and the more people consume it, the greater the total amount of resources it contains [7]. This is a kind of “consumption determines value” theory.

Douglas W. Hubbard, the inventor of Applied Information Economics, posits that the forthcoming digitalized world will revolutionize human production and lifestyle. Nate Silver, the author of The Signal and the Noise, contends that big data no longer emphasizes the true source of meaning but rather focuses on how meaning is generated. Lv Naiji argues that the core of big data epistemology lies in the transformation of the existence of the complex worldunstructured big data, into specific structured data tailored to different subjects’ objectives, rendering it comprehensible and organized to specific cognitive entities (Lv, 2014). Many scholars also believe that an era characterized by extensive production, sharing, and application of big data is imminent. According to McKinsey, big data will be a source of productivity“Data have swept into every industry and business function and are now an important factor of production,” and people’s mining and utilization of big data “will underpin new waves of productivity growth and consumer surplus” (MGI, 2011). Jack Ma once emphasized in a report that Alibaba will transform into a data company in the future, as “the future competition hinges on talents and the ability to generate innovative value. The extent to which your data can create value for society and monetizing it will be the true essence of success in the future.” This concept is known as a “data value creation theory” (Sun, 2014). Some scholars also predicted that a forthcoming trend of “data industry industrialization” (Qin, 2015, p. 44).

The economic value of contemporary data lies in the powerful computing capabilities of cloud computing to efficiently process massive amounts of structured and unstructured data, transforming it into economically valuable data commodities, and ultimately into data capital. The contemporary big data framework comprises three layers: data storage, data processing, and data analysis. The data storage layer addresses the challenge of storing vast and complex amounts of data, while the data processing layer focuses on ensuring timely and efficient execution of data operations. The data analysis layer generates economic value from the data, ultimately leading to the creation of data commodities. This production mechanism of data commodities has sparked disruptive insights among scholars in terms of economic theory. According to renowned economist and management expert Drucker et al. (2009, p. 87), in contemporary society, the principal resource will be knowledge, while the fundamental economic resourcemeans of productionis no longer capital, natural resources (land), or labor but rather knowledge. Stehr (1998, p. 176), a professor at the University of Alberta in Canada, argues that the key characteristic of today’s knowledge society lies in a theory concerning the value of knowledge rather than labor value. This signifies a “knowledge creates value” or “knowledge determines value” theory.

4.4 Marxist economic analysis on the value determination of contemporary data

In reality, just as Marx’s scientific labor theory of value was challenged by the emergence of “unmanned workshops” or “unmanned factories” in the 1960s, we contend that certain scholars’ assertions that the creation of value in knowledge (data derived from information or messages) solely by knowledge itself are erroneous. Firstly, the value of contemporary data is generated through the living labor of the “collective workers.” Similar to how the value of goods produced in automated “unmanned workshops” and “unmanned factories” since the 1960s is created by the living labor of the “collective workers” involved in designing, manufacturing, controlling, and maintaining these automated devices, today’s data commodities also derive their value from the living labor of the “collective workers” engaged in designing, creating, and maintaining complex forms of big data architecture. Secondly, the value of knowledge theory, which posits that knowledge possesses inherent value, succumbs to the logical fallacy of tautology as value itself cannot generate additional value. Thirdly, the increase in the economic value of data does not stem from its natural expansion but rather from the value imbued into the information or messages of objective things through living labor, which is a characteristic of data capital rather than a natural attribute of data. Fourthly, data remains a factor of production in the realm of material goods production, but it does not encompass the entirety of it. In contemporary society, data commodities can serve as both means of production and means of consumption, while also having the ability to exist independently within specific contexts and timeframes. Although they can be utilized as a means of production or consumption, they do not constitute the entire spectrum of material goods. When employed as a means of production, their value is continuously transferred to new products (commodities), akin to other means of production such as machinery, equipment, and raw materials; thus, constituting a part of their overall value. Ultimately, human society cannot solely subsist on data. Fifthly, while the economic value (labor value) of data in future societies may diminish, its use value can significantly increase. We believe that it is crucial to distinguish the economic value (labor value) of data from its use value. In accordance with Marxist economics, value, commodities, and capital are all social-historical categories, whereas use value is a natural category. In future societies, even if the labor value of data vanishes, its use value will persist and potentially be discovered and perceived in greater abundance, aligning with certain scholars’ theories of the value of knowledge [8]. In summary, the economic value of data in today’s society is still determined by socially necessary labor time and created by the living labor of contemporary collective workers. However, it is crucial to note that within the contemporary big data context, the labor theory of value also encounters disruptive and innovative advancements: (1) The living labor that determines commodity value undergoes a rapid shift from individual or direct social labor to indirect labor. (2) In the process of commodity value formation, the role of the “total workers” increasingly surpasses that of the “particular workers.” (3) The formation of commodity value involves gradually transforming living labor into embodied labor, i.e. the value of the commodity, through data. These changes have been evidenced by the disruptive traditional production model and distribution pattern.

Some scholars contend that both weak and strong artificial intelligence are inadequate for value creation, whereas superintelligent artificial intelligence has the potential to become the primary agent of value generation (Wu and Yu, 2020). This perspective is grounded in current advancements in artificial intelligence, which are bolstered by contemporary big data, computational power, and algorithms—a theory referred to as “the artificial intelligence subject determines value.” It is evident that whether one subscribes to the theories of “consumption determines value,” “knowledge determines value,” or “artificial intelligence subject determines value,” these frameworks do not entirely diverge from the theoretical construct of “utility determines value theory.” However, their connotations have undergone substantial transformations, signifying a disruptive and innovative evolution within traditional notions of utility-based valuation. According to Marxist economic principles, the concept of commodity value is merely a historical category. If we accept the premise that “the artificial intelligence subject determines value,” then this notion deviates from Marx’s original definition. In light of contemporary big data contexts, there is a notable shift in commodity valuation from labor as the principal determinant to artificial intelligence subject as a key driver, resulting in significant innovations both in form and content regarding value determination. Ultimately, determining the values associated with contemporary data has emerged as an area marked by considerable disruptive innovation in modern economics.

5. Practical issues in contemporary economics: data value distribution and market monopoly

5.1 New features of data value realization and distribution

Economics places significant emphasis on the realization and distribution of economic benefits, with various theories addressing these aspects. Notably, Say’s “trinity formula” and the subsequent “four in one” formula have become foundational classics in Western economic distribution theory, serving as a basis for later developments in this field. Marxist distribution theory analyzes the allocation of production conditions and asserts that ownership of the means of production determines the distribution of consumption goods. However, contemporary challenges related to data have given rise to new phenomena concerning value realization, capital accumulation, and commodity distribution within society, resulting in disruptive distribution of data value as special value and contributing to market monopolies.

The significance of contemporary data is increasingly acknowledged, particularly in the context of the profound effects brought about by the artificial intelligence revolution. As previously discussed, during the agricultural economy era, information was seldom processed or transformed into data, rendering it a trivial factor of production. In contrast, within both the industrial and information economy eras, data have assumed a pivotal role in enhancing labor productivity and exploring new dimensions of production and daily life. Consequently, various forms of data—both graphic and non-graphicare generated through processes such as development, reprocessing, redevelopment, and further refinement to address societal needs for production and living. This marks a transformation of mere information into data that subsequently evolves into commodities known as data capital. The realization of value for these data commodities hinges on market transactions akin to other commodities. Moreover, the appreciation in value of these data commodities or accumulation of data capital adheres to established principles governing value proliferation and capital accumulation.

However, due to the peculiarities of data resources and data commodities, such as “data as digital knowledge and information” and “without data, there is no economy” (He and Wang, 2021), alongside the fact that data resources mainly originate from the records generated by Internet activities, which exhibit characteristics such as massive, heterogeneous, diverse, distributed, rapidly generated, and dynamically changing, social production has become exceptionally efficient. Consequently, those enterprises that hold the largest and most novel data resources or commodities can acquire excessive profits in the production of material goods. The emergence of data industrialization and industrial datafication, driven by these distinctive features of contemporary data, has promoted the development of the digital economy from two perspectives. Since numbers are a significant manifestation of data, the mass production of digitized data has given rise to the data or digital industry, and even formed the third category of sectors. A considerable amount of data have been inputted into various industries, emerging as a crucial key element that expedites the circulation of industrial capital in all links and enhances the efficiency of material production, giving rise to numerous new forms of material resources that satisfy the needs of society, such as digital products and digital currencies. In the consumption sector, digital consumer goods have become an indispensable part of people’s pursuit of a better life. Clearly, the industrialization of data or digital information digitalization has provided the possibility and practical basis for the supply of a large number of data commodities; while the industry datafication and digitalization, as well as people’s increasing ability to consume data, has also provided possibilities and conditions for realizing the value of data commodities and showcasing their huge economic value.

It is worth noting that the rapid pace of data production and value appreciation make social reproduction in today’s digital economy era has led to an extraordinary expansion of social reproduction, providing a prerequisite for capital accumulation, especially the high-speed accumulation of data capital. Consequently, new challenges regarding value distribution and market monopoly are bound to arise. In general, resource allocation and value distribution through the market have become an inevitable choice for countries engaged in market economies like China and even globally. As such, the process of data element marketization is gradually deepening in China. According to experts, this entails various aspects such as the allocation, pricing, trading, competition, and market institutional arrangements within the data element market (Cyberspace Administration of China, 2016). However, all these developments must be based on clear property rights of data; determining ownership and control over data assets is crucial.

5.2 Market monopolization of data value realization and distribution

The terms “data assets” refer to “data resources owned or controlled by enterprises that can generate future economic benefits to enterprises and are recorded in a specific manner” (CAICT, 2019), which are datasets that exist in cyberspace that “possess data ownership (exploration rights, usage rights, ownership), have value, are measurable, and can be accessed” (Zhu and Ye, 2018). Specifying data ownership rights is essential for the distribution of data value, as well as for the ownership and utilization of data assets. Commonly referred to as “data rights”, these ownership rights exhibit significant differences from traditional property rights, which are typically exercised by fixed owners over tangible objects. In contrast, data rights can be dominated by different data entities at various stages of data production or formation. Moreover, data rights are inherently more complex than general property rights; they represent an amalgamation of property and personality rights, possessing both intrinsic value and shared value. Therefore, some scholars argue that “the fundamental premise of data property rights lies in that ‘data have value,’ with its core essence emphasizing that data can function as a novel form of property object” and “the core value of data personality rights is to uphold the dignity of data subjects by acknowledging their rights to enjoy freedom from deprivation, privacy from prying eyes, and protection against information from abuse” (Lian, 2019, p. 157). However, we believe that irrespective of how complex data are, once data monopolies are formed, the data holders inevitably possess the original value of the data as well as exceedingly high surplus and shared values. In such a scenario, sharing data value becomes impossible, and data personality rights can only serve as a powerful tool to protect the data owners. If this situation is neither controlled nor regulated, it will lead to a new form of polarization: on one hand, data owners will perpetually hold rapidly growing capital and wealth; on the other hand, data producers and other non-data holders, simply consumers of data, will find themselves perpetually trapped in physical and mental exhaustion and dilemmas of production and consumption, especially under conditions where they lose rights to data assets or face insurmountable digital divides, since data producers generate vast amounts of data products, resulting in significant data accumulation that complicates the production process and increases the workload; meanwhile, data consumers, facing an overwhelming volume of data, incur growing search costs, learning costs, and opportunity costs that gradually outweigh the benefits derived from easy matching of data supply and demand. This situation is even more pronounced in the context of “prosumers” who produce and consume data simultaneously. In today’s world, the era of big data has led to more prevalent phenomena of chaotic competition. The emergence of “code peon” and “involution” (over-competition) phenomena, and the initiatives of several major entrepreneurs to create data oligopolies or transform companies into “data production companies” all signal the potential for this developmental trend to become a reality [9].

Generally speaking, the production process of data commodities is fundamentally similar to the production of general commodities (including service products). However, the uniqueness of data commodity production lies in the fact that its value increases more rapidly, is more easily accumulated and aggregated, and can be transferred and generated without being constrained by temporal or spatial conditions. Despite these advantages, the realization of its value cannot be easily achieved by dispersed market entities or individuals. It requires a certain degree of market concentration (such as platforms) and the use of powerful search and aggregation functions (like Internet) to allow customers to select suitable data services at low cost and quickly. Contemporary technologies such as big data, cloud computing, and the Internet have made this a reality. Various data network platforms serve as the hubs for controlling the production and value realization of data commodities, and they are the primary market players most likely to form monopolies.

The emergence of disruptive market monopolies in contemporary data has aroused concerns among many scholars. According to Morozov (2014, p. 134), digital companies like BinCam create “smart” trash cans that monitor users’ recycling habits, with the aim of transforming social ills into positive outcomes (p. 246). He expresses apprehension regarding the ability of data market monopolies to conveniently ignore the digital ecosystem and merely perpetuate existing inequalities and power dynamics, ultimately transforming into “highly regulated proprietary systems” (p.157). Jaron (2013) fears those monopolies will lead to the “demise of democracy, mass unemployment, the erosion of the middle class, and social chaos.” Pepi (2013), on the other hand, argues that “Today, new, lighter digital frameworks quickly make redundant a large segment of the employedspecifically those who derive their value as ‘information service’ workers;” “an enchanting ideology of digital emancipation and ‘connectedness’ cloaks this threat to the eroding middle class.”

The reason behind the formation of data market monopolies lies in the fact that the production of data commodities is not constrained by time or space. Data platforms can acquire data goods produced by others either for free or even at a negative price. For instance, platform enterprises can regulate the behaviors of food delivery riders and ride-hailing drivers, continuously obtaining their innovative thinking and decision-making results without charge, thereby forming new data commodities. Similarly, creative outcomes and decision-making results contained in consumers’ opinions, reviews, likes, and even matured data commodities like creative videos, are obtained by platforms either without cost or at a negative price. Some websites or platforms integrate various resources to form links, attracting traffic and selling the usage rights of the data they possess through memberships or other means, obtaining substantial profits without the need to compensate the suppliers or producers of the data commodities. Furthermore, the gameplay and experiences created by many “meta-labor” groups, as well as their improvements in the game mechanisms, effectively form new data commodities. These commodities are both produced and consumed by the same entities, i.e. prosumers. Relevant platforms often take these innovations for granted and claim ownership without any payment, thereby continually accumulating more data capital.

5.3 Solutions to market monopolization of data value realization and distribution

The value distribution of data commodities inherently embodies equality; however, it is susceptible to fostering natural inequality in reality. The inherent equality stems from the fact that initial information or data represents the dynamics of nature and human society, resembling a natural resource accessible to anyone, anytime, and anywhere, thereby embodying a sense of public equality. Nevertheless, once processed, organized, innovated upon, and continually refined by individuals, the resulting data commodities transform into capitalized assets, appropriated by various entities across different periods, processes, and sectors within industrial and post-industrial societies for the purpose of value appreciation and capital accumulation.

Theoretically speaking, the creation of data commodities can originate from broad sources of value creators, including individual members of society, corporate employees, government officials, and various other individuals, organizations, and national or societal entities. Therefore, the value of these data commodities should also belong to them. However, the economic value of data commodities is measured by an exponential increase in their quantity—the larger the dataset, the greater the value of the data commodity collection. Owners of dispersed data are unable to capture the immense value of data commodities, thus necessitating the transfer of this value to platforms. Large platforms supported by big data, algorithms and computing power can achieve control across time and space and dominate various aspects of social production and consumption. This enables them to arbitrarily appropriate the value of data commodities emerging from social production and consumption processes, thereby realizing extraordinary capital accumulation and growth. From this mechanism, it is evident that the expansion and development of platforms are crucial conditions for the unequal distribution of economic value derived from data and the monopolization of data in the market.

This disruptive inequality in value distribution does not arise from traditional economic structures such as trusts, business conglomerates, giant multinational corporations, or strong market control; instead, it is formed through an intangible mechanism described as “voluntary by consumers and unified between producers and consumers.” Moreover, the value distribution arising from data market monopolies makes “the groups providing data receive almost no compensation or rights in return for their contributions”, thus “stifling the moral claims of 21st-century inheritors to the Paris workers’ call for ‘collective ownership’” (Pepi, 2013).

Therefore, in the era of the digital economy, the threat posed by data monopolies is far more devastating than that of any physical commodity monopoly. It is imperative to expedite efforts in three key areas to dismantle these data monopolies: Firstly, societal awareness regarding data protection and sharing must be enhanced. This entails fostering a moral ethos surrounding data sharing, promoting the voluntary relinquishment of public data monopolies, and opposing the privatization of public data. Additionally, we should resolutely oppose and halt any unjust fragmentation of public and personal data rights. Secondly, legislation aimed at safeguarding data, preventing monopolies, and ensuring equitable distribution of the economic value derived from data must be strengthened. This legislation should safeguard the legitimate rights and interests of individuals whose data are involved, promote the rational utilization of data, and guarantee a fair allocation of its economic value. Thirdly, it is crucial to develop a socialist platform economy with Chinese characteristics. While data network platforms play a vital role in driving the new economy, they also pose challenges by fostering monopolies in the data market. Given China’s contemporary national conditions, it is imperative to expedite the establishment of a platform economy structure that “takes public ownership as the mainstay and encourages the joint development of diverse forms of ownership.” This approach ensures the accurate direction and vitality of platform economic development, establishes sound principles for data value distribution, and provides institutional safeguards to mitigate the risks associated with data monopolies.

In conclusion, the multitude of novel phenomena and laws emerging from contemporary data play an unparalleled role in shaping new productive forces. Therefore, it is imperative to pay keen attention to the unconventional manifestations of contemporary data in economic theories and practices.

Notes

1. Since the beginning of the 21st century, numerous instances have emerged in which students have criticized traditional economics curriculum; for example, some students at Harvard University staged a walkout during Economics 10, while university students in France expressed discontent with the content delivered in economics courses.

2. The wooden sticks held by primitive humans were once considered as a form of capital by Ricardo. However, this connotation of capital differs significantly from what scholars refer to and has been criticized by Karl Marx.

3. Scholars articulated diverse perspectives on the connotations of the factors of production, and a consensus has gradually emerged.

4. We classify historical periods into the Agricultural Era, the Industrial Era and the Information Era from the perspective of productive forces.

5. Refer to “Moore’s Law” from Baidu Baike, available at: https://baike.baidu.com/item/%E6%91%A9%E5%B0%94%E5%AE%9A%E5%BE%8B/350634?fr=aladdin

6. The concept of Metcalfe’s law, conceived by George Gilder in 1993 and attributed to Robert Metcalfe, the pioneer of computer networks and co-founder of 3Com, in recognition of his contributions to the Internet, highlights the value of networks and the development of network technology. It asserts that the value of a network is proportional to the square of the number of nodes in the network, emphasizing that as more users join a network, both the value of the network and the value of every computer within it increase.

7. Metcalfe’s concept of exponential growth in network value, as illustrated by the exponential growth curve presented in the slides, was systematically expounded by George Gilder in Forbes magazine in 1993. He named it “Metcalfe’s Law.”

8. Scholars often confuse Marx’s theory of the two factors of commodities, namely, value and use value, leading to a negation of the labor theory of value.

9. Jack Ma announced in 2016 that Alibaba would become a giant “data production company”.