China pursues centralized, state‑led data governance to mobilize large, sovereign data projects; the United States relies on decentralized, rights‑based institutions emphasizing transparency and accountability. Rather than strict opposites, openness and security are treated as layered, adaptable governance processes with distinct implications for data access, innovation and competition.

Main Finding

China and the United States manage the openness–security trade-off in scientific data governance through distinct institutional logics: China uses a centralized, developmentalist, techno‑sovereignty approach that privileges coordinated state direction and control; the United States favors decentralized, rights‑based coordination emphasizing procedural transparency and public accountability. Openness and security are described as co‑evolving, layered institutional processes rather than strict binaries.

Key Points

• Comparative framework: analysis organized across four dimensions — coordination objectives, institutional actors, governance mechanisms, and stakeholder legitimacy.
• China:
  • Centralized governance, state‑led coordination.
  • Developmentalist framing: data governance tied to national development goals and techno‑sovereignty.
  • Legitimacy built through state authority and performance imperatives.
• United States:
  • Decentralized governance, plural institutional actors (agencies, courts, civil society).
  • Rights‑based framing prioritizing transparency, accountability, and procedural safeguards.
  • Legitimacy rooted in procedural legitimacy and public accountability.
• Theoretical contribution: extends modular coordination theory by treating openness–security trade-offs as layered, adaptive institutional processes embedded in political regimes and “legitimacy economies.”
• Conceptual claim: openness and security are not mutually exclusive; they co‑evolve and can be balanced modularly across institutional layers.
• Limitations: study is based on national‑level formal policy texts (18 documents per country); it does not measure enforcement, implementation outcomes, or public reactions.

Data & Methods

• Data: 36 national‑level policy documents (18 from China; 18 from the United States) focused on scientific data governance.
• Method: qualitative content analysis, coding documents against the four analytical dimensions (coordination objectives, institutional actors, governance mechanisms, stakeholder legitimacy).
• Analytical output: a comparative typology distinguishing centralized/developmentalist/techno‑sovereignty coordination (China) from decentralized/rights‑based/procedurally transparent coordination (U.S.).
• Scope and limitations: textual, policy‑level analysis only — no direct observation of enforcement, compliance behavior, or downstream economic outcomes.

Implications for AI Economics

• Data availability and model building:
  • Centralized, sovereignty‑oriented regimes (e.g., China) may enable large, state‑facilitated data aggregation projects that lower data costs for favored actors but restrict cross‑border flows and outsider access.
  • Decentralized, rights‑based regimes (e.g., U.S.) may preserve individual and institutional controls that can increase transactional frictions but support market entry via clearer procedural safeguards.
• Innovation incentives and competition:
  • State‑led coordination can rapidly mobilize resources and scale national champions, altering competitive dynamics and potentially creating winner‑take‑most outcomes.
  • Decentralized governance can foster a more pluralistic ecosystem but may produce fragmentation and underinvestment in public‑goods data infrastructure.
• International fragmentation and trade:
  • Divergent governance regimes increase the risk of data localization, interoperability frictions, and regulatory fragmentation — raising costs for multinational AI development and limiting global model generalizability.
• Regulatory design and firm strategy:
  • Firms must design data strategies that anticipate layered, adaptive rules rather than one‑off compliance regimes; modular policy instruments (varying by data type or actor) can change marginal returns to data collection and sharing.
• Legitimacy, trust, and uptake:
  • Legitimacy economies matter: public trust and stakeholder legitimacy influence willingness to share data and participate in collaborative research, with direct economic consequences for data‑intensive innovation.
• Modeling and empirical research recommendations:
  • Economic models of AI markets should incorporate institutional regime types (centralized vs decentralized), enforcement uncertainty, and legitimacy effects as parameters affecting data access costs, R&D productivity, and market concentration.
  • Empirical work should extend beyond policy texts to measure enforcement, firm behavior, cross‑border data flows, and innovation outcomes to quantify the economic impacts implied by the study.
• Policy implications for economics-informed design:
  • Policymakers seeking to balance openness and security should consider layered, adaptive instruments that can be tuned by sector or actor; economic analysis can help identify where centralized coordination yields scale economies versus where decentralized rights‑based approaches preserve competition and trust.

Caveat: Because the study analyzes formal policy documents without observing enforcement or public response, economic implications are conditional — empirical validation of how these governance frameworks affect data flows, innovation, and market structure is required.