Speaker
Description
National Statistical Offices (NSOs) are pivotal in producing data essential for analyzing sociodemographic and economic trends. Most national statistical laws enshrine the confidentiality of collected data to safeguard citizens’ privacy. Yet, these laws frequently lack specific, actionable measures to enforce this principle. Consequently, supplementary normative frameworks are critical to establish clear, practical guidelines that enable NSOs to uphold statistical confidentiality effectively.
Mexico offers a compelling example through its 2021 Policy for the Management of Confidentiality in Statistical and Geographical Information. This study examines Mexico’s approach to crafting this regulation, detailing its key conceptual foundations, structural design considerations, and the collaborative process of stakeholder consensus-building. Furthermore, it explores the critical factors that supported the policy’s adoption, implementation, and ongoing evaluation, ensuring its practical applicability.
This presentation aims to distill valuable insights for other NSOs yet to develop comparable frameworks. A well-defined normative document not only strengthens statistical confidentiality practices but also enhances public trust and confidence in official statistics. Mexico’s experience underscores the importance of context-specific regulations that strike a balance between data utility and privacy protection, offering a model for advancing global standards in statistical governance.
