Food-delivery giants in China and Russia exert market power not just via prices but through data, algorithms and ecosystem lock‑in; regulators should combine competition law, algorithmic transparency and social protections for couriers, with special measures for remote regions such as Yakutia.

Main Finding

Digital food-delivery platforms in China and Russia exercise market power not only through price and commission structures but via control over data, algorithms, access rules, ratings and courier work practices. These non-price mechanisms — network effects, scale/scope economies, data control, algorithmic management and ecosystem lock‑in — materially increase platform influence, especially in remote and northern regions (e.g., Sakha Republic/Yakutia). Effective regulation therefore requires a combined approach addressing competition, algorithmic transparency/explainability, access to platform data/APIs and social protection for platform workers.

Key Points

• Traditional indicators (market share, price, commission) understate platform influence because platforms shape markets through:
  • Data control (transactional, behavioural, and logistical data)
  • Algorithmic management (task allocation, dynamic ranking and pricing, surge rules)
  • Access rules and gatekeeping (onboarding, visibility of restaurants/shops)
  • Ratings and reputation systems that shape demand
  • Control of couriers’ labor practices and schedules
• Common mechanisms across China and Russia:
  • Strong network effects (users, merchants, couriers)
  • Economies of scale and scope (logistics, instant retail integrations)
  • Ecosystem lock‑in via integrated services (payments, e‑commerce, social)
• Comparative specifics:
  • China: larger user base, high instant‑retail density, more advanced antitrust and algorithmic regulation, growth of social guarantees in new employment forms, consolidated ecosystems (Meituan, Ele.me/Taobao, JD).
  • Russia: rapid eGrocery/O2O growth, rising ecosystem roles of major digital players, an emerging legal framework for platforms; regional dependence is accentuated in sparsely populated northern areas.
• Regional (Sakha/Yakutia) findings:
  • Remoteness, low population density, seasonal navigation and high logistics/last‑mile costs increase dependence on intermediaries.
  • Concentration of demand in regional centers (Yakutsk) means platforms can obtain de facto market power even where national market shares look modest.
• Policy relevance:
  • Market power mitigation requires integrated regulatory responses: competition law upgrades, mandated algorithmic transparency/explainability, data access/portability, and social protections for couriers.
  • Remote regions may need targeted measures (subsidies, non‑discrimination delivery obligations, public alternatives) to prevent abusive platform dependence.

Data & Methods

• Comparative case analysis of major Chinese delivery platforms: Meituan, Ele.me/Taobao Instant Commerce, JD Waimai.
• Statistical data analysis drawing on Russian and Chinese market statistics (user adoption, delivery volumes, eGrocery growth).
• Review and analysis of academic literature on platform economics, algorithmic management, and platform labor.
• Analysis of regulatory/legal frameworks governing online trade, competition, algorithms and platform employment in both countries.
• Regional empirical block using the Sakha Republic (Yakutia) as a case study, incorporating:
  • Territorial scale and population density metrics
  • Demand concentration (urban vs rural)
  • Seasonal navigation and northern supply constraints
  • Last‑mile cost structure and labor supply shortages
• Synthesis to identify common mechanisms and context‑specific pathways to market power and dependence.

Implications for AI Economics

• Measurement and identification:
  • Expand competition metrics beyond market share/price to include measures of data control, algorithmic prominence (ranking exposure), API access, and control over labor allocation.
  • Develop empirical methods to quantify algorithmic market power (e.g., A/B audits, natural experiments, platform field experiments, access to logs).
• Algorithmic governance:
  • Algorithmic transparency and explainability are central to mitigating market power: require disclosures of ranking/assignment criteria, audit access to model outputs and training-data provenance for competition and labor regulators.
  • Consider mandated model‑cards, decision‑flow documentation and third‑party algorithmic audits focused on anti‑competitive treatment (self‑preferencing, discriminatory visibility).
• Data access and interoperability:
  • Data portability and regulated API access can reduce ecosystem lock‑in and lower entry barriers for competitors and research; designs should balance privacy with competition needs (tiered/controlled researcher access).
  • Interoperability standards (e.g., order-routing, courier dispatch) can reduce single‑provider dependence, particularly in sparsely populated regions where natural monopolies emerge.
• Labor and market design:
  • Algorithmic management creates asymmetries in bargaining power — regulators should pair algorithmic oversight with worker protections: minimum pay guarantees, transparent performance metrics, redress channels and collective bargaining facilitation.
  • Platform design choices (e.g., multi‑tasking, batching, location‑aware pricing) materially affect courier earnings and service availability—these should be included in impact assessments.
• Policy experiments and regional targeting:
  • Remote and northern regions require tailored interventions: public delivery obligations, subsidies for last‑mile, support for cooperative or municipal alternatives to avoid extraction by dominant platforms.
  • Cross‑country comparative policy experiments (China’s algorithmic rules vs Russia’s emerging framework) offer opportunities to study regulatory impacts on competition, worker outcomes and consumer welfare.
• Research directions:
  • Causal studies on how algorithmic rules affect market structure, prices, entry/exit of merchants, worker incomes and service quality.
  • Methods to audit platform algorithms at scale under limited data access (black‑box probing, crowd audits).
  • Modeling of multi‑sided platform ecosystems incorporating data externalities and regionally heterogeneous costs to inform regulation design.

Summary takeaway: Platform market power in food delivery is as much an algorithmic and data phenomenon as a pricing one. AI economics must therefore integrate algorithmic transparency, data access, labor implications and regionally targeted interventions into competition and regulatory frameworks.