Abstract The leaf economic spectrum is a widely studied axis of plant trait variability that defines a trade‐off between leaf longevity and productivity. While this has been investigated at the global scale, where it is robust, and at local scales, where deviations from it are common, it has received less attention at the intermediate scale of plant functional types ( PFT s). We investigated whether global leaf economic relationships are also present within the scale of plant functional types ( PFT s) commonly used by Earth System models, and the extent to which this global‐ PFT hierarchy can be used to constrain trait estimates. We developed a hierarchical multivariate Bayesian model that assumes separate means and covariance structures within and across PFT s and fit this model to seven leaf traits from the TRY database related to leaf longevity, morphology, biochemistry, and photosynthetic metabolism. Although patterns of trait covariation were generally consistent with the leaf economic spectrum, we found three approximate tiers to this consistency. Relationships among morphological and biochemical traits (specific leaf area [ SLA ], N, P) were the most robust within and across PFT s, suggesting that covariation in these traits is driven by universal leaf construction trade‐offs and stoichiometry. Relationships among metabolic traits (dark respiration [ R d ], maximum RuBisCo carboxylation rate [ V c,max ], maximum electron transport rate [ J max ]) were slightly less consistent, reflecting in part their much sparser sampling (especially for high‐latitude PFT s), but also pointing to more flexible plasticity in plant metabolistm. Finally, relationships involving leaf lifespan were the least consistent, indicating that leaf economic relationships related to leaf lifespan are dominated by across‐ PFT differences and that within‐ PFT variation in leaf lifespan is more complex and idiosyncratic. Across all traits, this covariance was an important source of information, as evidenced by the improved imputation accuracy and reduced predictive uncertainty in multivariate models compared to univariate models. Ultimately, our study reaffirms the value of studying not just individual traits but the multivariate trait space and the utility of hierarchical modeling for studying the scale dependence of trait relationships.