1. Introduction

The sharing economy, also known as collaborative consumption, was first proposed in 1978 by American scholars Jon Spence and Marcus Felson [1]. Later, in 2000, Ms. Robin Chase put the concept of the sharing economy into practice by founding the car-sharing company Zipcar [2]. The emergence of platforms such as Airbnb and Uber further expanded the global market share of the sharing economy. According to the *China Sharing Economy Development Report (2023)* released by the State Information Center, the total transaction volume of China's sharing economy reached approximately 3.832 trillion yuan in 2022, reflecting a year-on-year increase of about 3.9%. Ride-hailing services accounted for around 40.5% of the total taxi passenger volume, an increase of 6.4 percentage points compared to the previous year [3].

The sharing economy is an economic model that utilizes idle resources or surplus capacity. As Robin Chase put it, “Platforms can harness individual capabilities, and through organization and resource allocation, ultimately transform them into the greatest wealth of society.” [4] In traditional labor relations, employers monopolize the means of production, and labor can only be transformed into value through dependence on the employer’s ownership of those means. In contrast, the sharing economy challenges conventional notions of property rights by emphasizing the separation of ownership and usage. It advocates for the sharing of “usage rights” rather than exclusive possession—“seeking not to own talent, but to make use of it; not to possess absolutely, but to utilize fully.” [5] Precisely because of this, the sharing economy holds unique advantages over traditional economic models. As noted in the Guiding Opinions on Promoting the Development of the Sharing Economy, it enables the optimal allocation of dispersed resources, mitigates excess production capacity, and significantly enhances the efficiency of social resource utilization [6]. At the same time, for platform companies, workers only need to use the channels provided by the platform to offer services to consumers as individuals, which greatly reduces the platform’s employment costs. However, the development of the sharing economy also poses challenges to the traditional economy. Monopolistic practices and information control by sharing platforms place workers in a relatively disadvantaged position. As these platforms continue to expand, the market for traditional economic models will significantly contract. To survive and thrive, both workers and customers must “voluntarily” accept information through the sharing platforms [7].

The ride-hailing industry is one of the earliest representative sectors of the sharing economy, with three main employment models based on the sources of vehicles and drivers: the B2C (Business to Customer) model, where drivers are hired by third-party labor companies and vehicles are owned and managed by the platform; the C2C (Customer to Customer) model, where both drivers and vehicles come from third parties and the platform only facilitates information exchange; and the P2P (Person-to-Person) model, where private car owners use their own vehicles to provide services, known as the “social vehicle affiliation” model [8]. China has introduced two key regulations—the *Guiding Opinions of the General Office of the State Council on Deepening Reform and Promoting the Healthy Development of the Taxi Industry* and the *Interim Measures for the Administration of Online Ride-Hailing Services*; however, neither clearly defines the labor relationship between ride-hailing drivers and platforms, leading to numerous labor disputes; among the three employment models, the B2C model is generally recognized as establishing a labor relationship, the C2C model is usually considered not to constitute one with few disputes, while the P2P model presents a more complex relationship between platforms and drivers, resulting in a higher number of conflicts.

2. Theoretical foundations for the identification of labor relations under the sharing economy

Under the sharing economy, defining labor relations faces many challenges, especially regarding whether traditional standards of worker subordination still apply. In China, courts use the “Notice on Matters Concerning the Establishment of Labor Relations” which identifies labor relations based on subordination. This means the employer’s rules apply to the worker, the worker is managed by the employer, the worker performs paid tasks assigned by the employer, and the work is part of the employer’s business. The Notice highlights three types of subordination—personal, economic, and organizational—with personal subordination considered the key factor in determining labor relations [9]. However, the diverse and complex relationship between ride-hailing workers and platform companies in the sharing economy weakens the traditional feature of subordination, especially personal subordination. Workers in the sharing economy often have more autonomy and frequently provide their own tools, such as vehicles. Additionally, workers tend to have multiple jobs and income sources, which blurs the personal subordination in their employment. Platforms may also use outsourcing or require workers to register as individual businesses, disguising the employment relationship to avoid labor responsibilities and reduce costs [10]. In terms of economic subordination, platforms mostly use piece-rate pay instead of the fixed wages common in traditional economies, making it difficult to determine economic dependence. Regarding organizational subordination, workers’ connection to the platform is more subtle—for example, platforms often do not directly supervise workers but monitor them through customer rating systems [11]. Under the multi-sided market theory, platforms create value by connecting multiple user groups, with consumers actively participating in the platform’s management of workers.

The new features of labor relations in the sharing economy don’t mean the subordination standard is useless. Instead, platform control is more hidden, using algorithms and digital tools. Although drivers seem to work flexibly, platforms monitor them in real-time, and using their own vehicles doesn’t change this control. Platforms set prices and control driver ratings and rewards, so drivers have little bargaining power. Even if drivers work for multiple platforms, wages still pay for their labor. Platforms focus on using labor rather than owning workers. So, the basic employer-employee relationship of control remains. Piece-rate pay is not unique to the sharing economy and has long existed in traditional economies. Although platforms do not directly pay drivers’ wages, they control user data as a form of labor input, and they manage and distribute the service fees paid by consumers. From this perspective, economic subordination is actually strengthened [12]. Although ride-hailing workers are not part of traditional companies, platforms depend on their labor to make profits, showing an organizational connection. There is a conflict between workers’ apparent freedom and the platforms’ hidden control. Current labor rules need to change to balance flexibility and protection. It is unfair to deny labor relations just because they don’t fit old standards.

Second, China’s labor protections rely on a clear labor relationship, but this doesn’t fit well with the sharing economy. Many ride-hailing workers can’t prove such a relationship and can’t get legal protection. Using terms like service or outsourcing contracts to define their work causes many disputes to go unresolved [13]. Many platforms now hire workers through “cooperation agreements” instead of labor contracts, then deny the existence of a labor relationship. This shift in the sharing economy changes some workers’ status from employees to contractors or self-employed, leaving them without labor law protection. This challenges the traditional “dual system” and raises the need to rethink whether labor laws should offer some protection even when a clear labor relationship doesn’t exist.

3. Current situation and issues in the identification of labor relations in the ride-hailing industry

The author discusses the judicial practice of identifying labor relations in the ride-hailing industry by analyzing three typical cases: "Chifeng Yun Su Express Service Co. vs. Lu" (Case 1), "Luo Yanxia, Liu Chun, Zhang Aiying vs. Didi Chuxing Technology Co." (Case 2), and "The Third Batch of Typical Labor Dispute Cases Case 1" (Case 3) [14].

3.1. Judicial disagreements and platform’s technical avoidance

3.1.1. Legal ambiguity created by technical means

Traditional labor process theory explains control through three mechanisms: technical, organizational, and normative control. Technical control uses tools and work processes (like factory assembly lines), organizational control manages work time and tasks (such as attendance rules and job assignments), and normative control enforces discipline through rules and performance reviews (like rewards and promotions). Employers use these three controls to fully manage the labor process [15]. In contrast, labor control in the digital age shows three key changes: control shifts from physical tools to data and code, control intensity changes from strict commands to flexible guidance, and control effects move from immediate to delayed feedback.

Platforms use algorithms to control workers by setting rules like minimum orders and ratings, replacing traditional management. For example, workers must meet order targets or face penalties, and rejecting too many orders lowers their priority. These hidden controls are stronger than old methods, but courts haven’t recognized them as signs of employment [16]. Platforms keep data secret, making it hard for workers to prove their case. In Case 2, Didi blocked order logs with encryption, so workers couldn’t show they were controlled. In Case 1, Yun Su Express kept attendance records private, calling workers “partners.”

Platforms’ evasive tech tactics have caused clear disagreements in courts over applying the “subordination” standard. Specifically, disputes arise between using “behavior control” and “outcome control” criteria. When judges rely on “behavior control,” they tend to focus on visible facts—like whether the platform sets work processes or hours—rather than weighing the overall relationship. This approach is subjective, giving judges too much discretion and leading to inconsistent rulings in similar cases.

3.1.2. The platform’s dual evasion strategy

Judicial cases show platforms use technology to control workers but deny labor relations in law. This “de-laborization” causes confusion and makes it hard for workers to protect their rights. Legally, platforms hide labor relations in “cooperation agreements” or “partnerships.” For example, in Case 1, a company used a contract to hide commission and deposit rules. In Case 3, another company claimed partnership but controlled workers with daily order minimums and GPS tracking. Technically, platforms say algorithms just share information, but actually manage workers with rules and rewards. In Case 3, the platform set fees, monitored work, and punished or rewarded workers based on performance. This control is hidden by algorithms. Platforms separate form from reality, controlling labor through technology while avoiding legal responsibility by claiming “technical neutrality.” normal (web) The platform’s avoidance strategies not only cause confusion in legal judgments but also make it hard for workers to prove their cases. Platforms control the data, making it difficult for workers to get key evidence. Although the law requires employers to provide evidence or face consequences, platforms use technology to block access to data. For example, Didi blocks order logs, and YunSuPaotui controls attendance records, making it hard for workers to prove control over their work. This weakens legal protections. The following case comparisons illustrate this issue.

The comparison shows that data control decides if workers can prove their case. In Case Two, workers couldn’t get key data and lost social security rights. In Guiding Case 179, the court forced the platform to share data and confirmed the employment relationship. normal (web) To deal with platforms’ evasion tactics, courts need new rules. Instead of checking if platforms give direct orders, they should see if algorithms control the work. Personal control means looking at how drivers depend on platform rules, even if they use their own vehicles. For organizational control, focus on whether the driver’s work is part of the platform’s business. For economic control, check if drivers rely mainly on the platform for income. China can learn from Europe, where workers earning over 50% from one company are seen as partly controlled. Also, platforms controlling key data is important in deciding if there is an employment relationship. In 2024, a court in Henan said this data control helps confirm labor ties between drivers and platforms [17]. Second, regarding drivers’ burden of proof, since shifting the burden of proof helps workers defend their rights and is fairer for both sides, China should promote Guiding Case No.179’s approach and develop clearer rules for applying this shift.

3.2. Evading labor responsibility: contractual arrangements and risk shifting in the platform economy

3.2.1. Mismatch between contract form and employment reality

China’s laws don’t clearly define “labor relationship.” The current standards focus on personal, economic, and organizational control. But in the platform economy, these don’t fit well. Courts use local rules instead. For example, Guangdong allows agreements that share risks and profits to exclude labor relationships. Shanghai checks if both sides agree when deciding if a labor relationship exists. normal (web) Local rules still rely on the old 2005 standards and don’t solve new problems. Platforms use their power to call work “cooperation” or “partnership” agreements instead of labor contracts to avoid responsibilities. A 2024 survey shows only 31% of new economy workers have formal contracts; 69% have loose agreements [18]. This apparent agreement hides real inequality: workers with little bargaining power are forced to accept "de-labor relationship" terms, making labor protections ineffective. normal (web) The differing rulings in the three typical cases further expose flaws in judicial review standards. The specific differences are shown in the table below:

In Case One and Case Two, some judges focused too much on the contract wording and ignored how much control the platform really had over workers’ schedules, pricing, and penalties. In Case Three, judges looked deeper at facts like strict order rules, income mostly from the platform, and heavy fines, and confirmed an actual employment relationship. This shows the need for two steps in review: first check the contract words, then look closely at the real working conditions to find the true relationship.

Unlike many Chinese courts that focus on contracts, California’s AB5 law recognizes platform workers as employees unless the platform proves: (1) workers are not controlled; (2) the work is outside the company’s main business; and (3) workers usually do similar work elsewhere. This “deep review” looks past contracts to real control and is a good example for China. normal (web) In summary, relying only on the formality of "mutual agreement" allows platforms to exploit the system. Only by focusing on real work facts—like mandatory work rules, income dependence, and effective penalties—can labor law truly protect vulnerable workers.

3.2.2. Economic incentives behind risk-shifting mechanisms

Platform companies shift risks to workers by using contracts and algorithms, avoiding employer responsibilities and cutting costs.

For example, in Case 1, a delivery company charged a deposit but denied medical costs after a worker’s accident, saying they weren’t an employer. This deposit controls workers and shifts injury risks to them. In Case 2, a ride-hailing platform makes drivers buy insurance only while working. If drivers stop due to injury or vehicle trouble, insurance ends, and they pay repair and medical costs themselves.

Platforms avoid labor responsibility by using different rules for workers.

For stable workers like Liu (Case 3), the platform controls hours and tasks but calls it “cooperation” to deny labor relations.

For flexible workers like Lu (Case 1), workers choose orders freely, but the platform uses fees and deposits to keep control.

Both are called “independent contractors” in contracts.

This shifts risks to workers: Liu is controlled but has no benefits; Lu can’t get compensation after accidents.

Platforms cut labor costs by shifting risks to workers. For example, with 100,000 drivers, social security alone would cost 360 million yuan a year. But by calling drivers “independent contractors” in contracts, platforms avoid paying these costs and make workers pay for accidents and insurance. In Case 3, Liu works over 8 hours a day but gets a fixed fee without social security. The platform calls this “cooperation,” cutting costs to almost zero. This means workers lose protections and bear all risks, while platforms keep control and profits.

3.3. Dual mechanism of institutional evasion

3.3.1. Tech-driven exclusion

Platforms use legal tactics to classify workers as "partners" to avoid social security payments. For example, in Case 1, YunSu Paotui labeled worker Lu as a "partner" in their agreement, while in Case 3, an IT company defined worker Liu as a "cooperator" in a vehicle management contract. This "de-laborization" strategy traps workers: platforms deny employment ties to skip social security obligations, while flexible-work insurance rules require business licenses, excluding unregistered workers.

Meanwhile, platforms use algorithms to adjust workloads (e.g., Liu must complete ≥4 tasks/day, with bonuses for extra tasks and penalties for shortages), avoiding fixed labor ratios and further reducing social security access. According to the Ministry of Human Resources, less than 12% of ride-hailing drivers have social security coverage, far below the 83% average for urban workers [19].

Platforms distort social security responsibility allocation via algorithms. An IT firm forced workers to buy commercial insurance through self-deduction, replacing social security with voluntary plans. Didi used a "work-insure, stop-lapse" model, risking coverage gaps. Algorithms cut earnings: 20% management fees and excluded social security costs made self-insurance unaffordable. This tech-economic control lags social security adaptation to platform labor.

3.3.2. The closed loop of legal strengthening

At the level of legal relations, platforms invoke the Contract Law to deny labor relations, depriving workers of the qualification as negotiating subjects. In Cases 1 and 2, courts recognized workers as "independent contractors" based on the text of "cooperation agreements," preventing them from asserting collective rights through trade unions (Article 3 of the Trade Union Law restricts union membership to those "primarily relying on wage income for livelihood"). Although Case 3 confirmed labor relations, the platform unilaterally set service fee standards and deduction rules, leaving workers with no room for negotiation on key issues such as algorithmic commission adjustments (e.g., the data black box of the driver's 200,700 yuan income in Case 2). This suppression mechanism combining law and technology has made individual rights protection extremely difficult for workers.

Additionally, platforms use algorithms to break down work into isolated tasks, preventing workers from uniting around shared demands. For example, ride-hailing apps split drivers' roles into separate steps like accepting orders, receiving ratings, and getting assigned trips. Similarly, some companies divide work into tracked segments using GPS and performance-based rewards. This tech-driven fragmentation—like separating food delivery into order-taking and drop-off—makes collective action difficult. Some service agreements even ban worker alliances, blocking organized efforts. As a result, the legal denial of labor rights and the technical division of work reinforce each other, creating a closed system of exclusion.

4. Literature review

4.1. Reforming labor classification: scholarly debates on gig work subordination in China

4.1.1. ​​Domestic scholars' perspectives​

The gig economy challenges traditional labor classification standards and the employee/independent contractor divide.

The gig economy has exposed the limitations of traditional labor classification standards based on subordination (personal, organizational, and economic dependence), with scholars noting these criteria are increasingly inadequate for platform work. China's labor framework lacks clear employment definitions, relying instead on outdated subordination principles that fail to accommodate gig workers who provide their own tools and set flexible schedules, as platforms emphasize labor utilization over ownership. Experts like Qin Guorong argue this model erodes traditional employment concepts, while Xie Zengyi acknowledges its adaptability but stresses the need for modernization, particularly as rigid "checklist" approaches struggle to address complex platform-worker relationships where drivers and delivery riders often fall outside legal protections, highlighting the urgent need for labor classification reforms tailored to the gig economy's unique characteristics.

Scholars propose two solutions to this challenge. The first approach suggests updating traditional labor standards to fit the gig economy. Experts argue that while gig work appears independent, platforms still control workers through rules, algorithms and income dependency - creating a new form of modern subordination that deserves labor protections.

The second approach proposes creating a new intermediate legal category between traditional employment and independent contracting, moving from a binary to a tripartite classification system. While China currently uses the binary model, its inability to accommodate diverse modern work arrangements has led scholars to advocate for reform. Ban Xiaohui notes that while gig drivers enjoy flexibility in accepting orders and scheduling, they remain bound by platform-set pricing and operational rules, making their status ambiguous under existing frameworks—too autonomous for traditional employment yet too dependent for pure contracting. This gray zone has prompted proposals like Yu Ying's typology distinguishing "typical" and "atypical" gig work with corresponding legal classifications, and Zhou Wenting's call to legally recognize a "third category" of workers with hybrid characteristics, ensuring protections for those falling between standard employees and the self-employed.

4.1.2. ​​Critical analysis of domestic scholarship​

Given China's market characteristics, revising traditional subordination criteria proves more viable than creating new labor categories. The gig economy retains core employment attributes—platforms merely replace visible control with algorithmic management (e.g., real-time monitoring, performance scoring, and income determination), which functionally constitutes modernized subordination. This aligns with the UK Uber ruling that classified drivers as employees due to the company's control over trip terms and disciplinary systems. Practically, China's "inclusive and prudent" regulatory approach favors gradual evolution: judicial interpretations or guiding court cases—like recognizing algorithmic supervision and punitive rating systems as subordination indicators—could dynamically update labor standards without destabilizing the fast-growing platform sector. The existing Labor Contract Law's subordination foundation allows such calibrated adjustments, balancing worker protections with innovation flexibility at lower institutional cost than legislative overhaul.

Given that gig workers exhibit "weak subordination but strong economic dependence"—a hybrid status fitting neither traditional employment nor pure self-employment—creating an "intermediate labor category" appears theoretically sound. However, this approach faces implementation hurdles: (1) Defining measurable thresholds for "weak subordination"remains technically challenging; (2) Overhauling the binary-based labor protection systemwould require costly reforms, such as contribution mechanisms scaled to workers’ actual subordination levels.

4.2. Global variations in labor classification: evidence-based pathways for localizing reforms in China

4.2.1. ​​International legislative and judicial practices​

Globally, countries diverge significantly in labor classification systems, primarily following either a ​​binary model​​ (employment vs. independent contracting) or a ​​tripartite model​​ (adding an intermediate category). The binary approach, adopted by China, Japan, and the U.S., faced a critical test with California's 2019 AB5 Bill—later expanded nationally in 2024—which presumes gig workers are employees unless companies prove: (A) workers operate free from control, (B) work falls outside the company's core business, and (C) workers engage independently in trade. This shifted the burden of proof to platforms like Uber, which initially resisted until a 2020 California Superior Court ruling compelled them to reclassify drivers as employees entitled to full benefits (health insurance, paid leave, etc.). The AB5 precedent demonstrates how binary systems struggle to adapt without legislative intervention, as they force an artificial choice between two rigid categories despite the hybrid reality of platform work [20].

Countries take different approaches to classifying gig workers. Some like the US use a simple employee-or-contractor system (as seen in California's AB5 law), while others like Germany recognize a middle category for semi-dependent workers who earn most income from one platform. These models show the global struggle to fit modern gig work into traditional labor frameworks [21].

Similarly, Japan's interpretation of "labor subordination" combines both ​​personal subordination​​ (workers following employers' direct control over work schedules, locations, and methods) and ​​economic subordination​​ (workers' dependent status in socioeconomic terms and predetermined contract terms). This dual approach reflects how Japanese labor law assesses worker-employer relationships by examining both behavioral control and economic vulnerability [22]. Many countries use ​​quantitative thresholds​​ to assess economic subordination. For instance, Canada's "dependent contractor" classification requires that ​​80% of a worker's income​​ come from a single client to establish economic dependence—a clear, measurable standard for determining worker protections under labor laws [23]. Spain defines a special worker category for those who get 75% of their income from one client, making them eligible for labor protections.

The ​​control test​​—predominant in Anglo-American jurisprudence—assesses employment status based on the employer's degree of supervision (e.g., enforcing work rules and disciplining non-compliance). Its limitations spurred development of the ​​multifactor test​​, exemplified by California's 1989 Borello standard, which evaluates: (1) termination-at-will rights; (2) worker’s independent business operation; (3) local industry norms for supervision; (4) specialized skill requirements; (5) provision of tools/workspace; (6) job duration; (7) payment method (time-based vs. lump-sum); (8) alignment with employer’s core business; and (9) parties’ mutual intent. This holistic approach acknowledges that modern work relationships defy binary categorization through singular metrics [24].

4.2.2. ​​Lessons from international practices for China

International experience suggests China could benefit from adopting the ​​U.S. approach of shifting the burden of proof to employers​​—similar to China's existing doctrine of inverted burden of proof in labor disputes. This model effectively reduces workers' evidentiary challenges, particularly in protecting gig economy laborers. For instance, California's AB5 Bill requires employers to ​​prove a worker qualifies as an independent contractor​​ rather than assuming employee status by default. Such a design better safeguards vulnerable workers who might otherwise be excluded from labor protections due to difficulties in providing evidence, while maintaining flexibility for genuinely autonomous contractors. The approach aligns with China's legal tradition while addressing platform work's unique evidentiary asymmetries.

The international experience with labor classification—comparing the binary (employee/contractor) and tripartite (adding a middle category) approaches—provides valuable lessons for China. The tripartite model, as seen in Germany's "employee-like persons" and Spain's "economically dependent self-employed," offers a potential solution to protect platform workers while maintaining labor market flexibility. However, these intermediate categories also present challenges, such as complex eligibility criteria that may increase enforcement costs or create loopholes for companies to exploit. On the other hand, strict binary systems (like those in the U.S. and Japan) often fail to adequately protect vulnerable gig workers, leaving them exposed to income instability and lack of basic benefits. For China, the key lies in developing a balanced approach: establishing clear, quantifiable standards (e.g., income thresholds or working hour requirements) to define an intermediate worker category, while implementing safeguards against corporate misuse and minimizing administrative burdens. This could involve pilot programs in high-impact sectors like ride-hailing, using platform data to streamline verification, and setting penalties for misclassification—ensuring protections reach those who need them without overextending traditional labor frameworks.

China's labor relationship recognition system requires urgent reform to address the challenges posed by platform economy employment. International practices suggest three key improvements: (1) adopting a flexible multi-factor assessment model (like the U.S. Borello test) that evaluates work autonomy, tool provision and payment methods; (2) introducing quantitative thresholds for economic dependence (e.g., Canada's 80% income rule or Spain's 75% standard) to complement the current overemphasis on personal subordination; and (3) following Germany's approach of equalizing economic and personal subordination in legal evaluations. This reform package should include establishing weighted evaluation criteria, setting clear economic dependence benchmarks (e.g., >60% platform income share), mandating platform algorithm transparency, and enhancing digital oversight capabilities - creating a balanced system that protects workers while accommodating new employment models [25]. These changes would effectively resolve current regulatory gaps where rigid standards fail to protect platform workers despite their actual economic dependence.

5. Conclusion​

The core dilemma in determining labor relationships within the sharing economy stems from a structural conflict between traditional legal frameworks and digital labor models. Platforms like ride-hailing companies exploit algorithmic control, standardized contracts, and risk-transfer mechanisms to create a paradoxical "strong technical control–weak legal attachment" system, trapping workers in "dependent labor without protections." Current judicial practices relying on "contract terms + visible supervision" as subordination criteria fail to penetrate hidden algorithmic management (e.g., dispatch systems, data-driven ratings), causing inconsistent rulings. Theoretically, the rigid employee/contractor binary cannot accommodate hybrid workers exhibiting "weak subordination but strong economic dependence"—a gap international solutions address through quantitative dependency thresholds (e.g., Germany’s 50% income rule) or inverted burden of proof (e.g., California’s AB5).