In this project, we quantify, expand upon, and implement the Comparative Institutional Analysis (CIA) Framework, initially proposed by Assistant Professor Leandro S. Pongeluppe at the Wharton School. The CIA Framework is a method of predicting investment risk and macroeconomic indicators within any given country through relative strength of economic and political institutions. Our research compiles a unified dataset of various proxies for institutional strength and economic performance, using it to develop a web-based data visualization platform to analyze relationships within the CIA Framework.

This project draws from three main sources of data: Data Bank (published by the World Bank), the Index of Economic Freedom, and the Political Constraint (POLCON) index, which provide various ways of tracking economic and political institutions over time, as well as their impacts. The sources are merged using the pandas Python library and NLU-enabled (sentence transformers) semantic searching. Users are able to analyze the data using various Cartesian visualizations on an interactive website, exploring the relationship between different indicators. They are able to incorporate date range and geographic region into their analyses as well as a third axes in the form of population size or GDP per capita.

Usage of the tool and analysis of the underlying data confirms the CIA Framework hypothesis: large "Delta CIA" values, the sum of deltas of beneficial changes in political institutions and economic regimens, are predictors for subsequent economic outperformance.