Finding meaningful ways to determine the dependency between two random variables 𝜉 and 𝜁 is a timeless statistical endeavor with vast practical relevance. In recent years, several concepts that aim to extend classical means (such as the Pearson correlation or rank-based coe cients like Spearman's 𝜌) to more general spaces have been introduced and popularized, a well-known example being the distance correlation. In this article, we propose and study an alternative framework for measuring statistical dependency, the transport dependency 𝜏 ≥ 0, which relies on the notion of optimal transport and is applicable in general Polish spaces. It can be estimated consistently via the corresponding empirical measure, is versatile and adaptable to various scenarios by proper choices of the cost function, and intrinsically respects metric properties of the ground spaces. Notably, statistical independence is characterized by 𝜏 = 0, while large values of 𝜏 indicate highly regular relations between 𝜉 and 𝜁 . Indeed, for suitable base costs, 𝜏 is maximized if and only if 𝜁 can be expressed as 1-Lipschitz function of 𝜉 or vice versa. Based on sharp upper bounds, we exploit this characterization and de ne three distinct dependency coe cients (a-c) with values in [0, 1], each of which emphasizes di erent functional relations. ese transport correlations a ain the value 1 if and only if 𝜁 = 𝜑 (𝜉), where 𝜑 is a) a Lipschitz function, b) a measurable function, c) a multiple of an isometry. e properties of coe cient c) make it comparable to the distance correlation, while coe cient b) is a limit case of a) that was recently studied independently by Wiesel (2021). Numerical results suggest that the transport dependency is a robust quantity that e ciently discerns structure from noise in simple se ings, o en out-performing other commonly applied coe cients of dependency.