PPI Python Tutorials

This page provides access to the Python source code as well as to a package that can be installed via PyPI. The tutorials below explain how to use the Python version of the PPI model, as an alternative to the app.

These tutorials will take you from pre-processing raw data on development indicators and government spending to performing more sophisticated counterfactual analysis such as detecting bottlenecks.

You can view the tutorials via this page, and access the source code through GitHub. In the official repository, you will find that much of the content makes reference to the forthcoming book: Complexity Economics and Sustainable Development, which will come out later in 2023. For the time being, you can use the latest PPI paper as the main scientific reference.

Tutorial 1A

Data preparation – interdependency networks

This tutorial explains how you can prepare a dataset of indicators for use within the model

Tutorial 1B

Data preparation – development indicators

How to prepare the data for the network input of PPI.

Tutorial 1C

Data preparation – expenditure

This tutorial will show you how to prepare a dataset containing expenditure programmes linked to development indicators.

Tutorial 2

Model calibration

This tutorial explains how to calibrate the free parameters of PPI's model.

Tutorial 3

Simple prospective simulation

This tutorial demonstrates how to perform simple prospective analysis by simulating the indicators forward 10 years

Tutorial 4

Structural bottlenecks

This tutorial expands on the idea of expenditure sensitivity in order to quantify structural bottlenecks.