Industrial strategy design is only as good as the empirics and analysis that underpin it. Yet, economic analysis methodologies tend to ‘look where the light is’ rather than directing the light toward the concrete, granular data required to build meaningful, actionable policy insights. There are pragmatic reasons for this: Data providers, such as governments and multilateral organizations, generally provide low-resolution, aggregated data structured in standardized datasets that cater to a maximally generic audience (for example, at its highest resolution, trade data is reported in 6-digit HS codes that can contain hundreds (if not thousands) of technologically distinct products under a single code). Consequently, datasets are almost never fit for specific analytical purposes and analysts are forced to make compromises due to the substantial effort (millions of human labor hours) that would be required to create bespoke datasets from scratch. These limitations hinder the accuracy and precision of academic research and often become fatal bottlenecks in the context of industrial policy design and implementation, which require more fine grained data to identify current, latent, potential, and strategic productive capabilities. Our methodology papers discuss how these limitations constrain the utility of common frameworks and tools for industrial policy and trade analysis: Estevez, Chang, and Schollmeyer (2025) and Schollmeyer et al. (2025).

Our visualization dashboard uses the solar PV value chain as an illustrative example to offer a glimpse into our methodologies for value chain decomposition, technology analysis, trade code mapping, company mapping, competitiveness proxies, and trade balances along the value chain.

The 12,800 components and types are fully navigable (see the gif below). By clicking on any of the components or types, you can access detailed information about, e.g., their function, mechanism, as well as their 6-digit trade code. Moreover, you can navigate upstream to explore the inputs required to produce a specific type. While still experimental and incomplete, this mapping is the first of its kind and we aim to make these detailed breakdowns available for as many value chains as possible in the near future for the benefit of policymakers and researchers. Try it yourself.