iops: a Python package for the Input-Output Product Space methodology

Industrial diversification towards increasingly com-plex products is understood to be key to economic development. The Product Space (PS) is a tool that supports the analysis of countries' productive structures and the identification of promising routes for future development. The PS and associ-ated complexity indices highlight the relationship between the capabilities of countries and the successful export of complex products. The Input-Output Product Space (IO-PS) applies the PS and its associated metrics to a value chain. The results from the tool can inform industry-specific analyses by identifying avenues of development and quantifying their potential impact. Despite the utility of these approaches, the open-source packages that support PS- related analyses and their accessibility have left much to be desired. This has likely hampered the even greater uptake of these approaches. This article provides an overview of the most comprehensive implementations of the PS and IO-PS in Python, MATLAB and R to date. It identifies a number of incongruities between these packages and gaps in their application. Based on the shortcomings identified, a novel open-source Python package for the IO-PS that consolidates the disparate implementations is presented. It applies the IO-PS analysis approach to a chosen value chain and includes extended functionality compared to the original IO-PS in MATLAB and PS in Python and R. It incorporates trade data summation, analysis without a value chain and normalization toggling. The article makes a contribution by providing the first open-source Python IO-PS package and consolidating the existing implementations by explaining - and addressing - their differences.