Authoritarian by Proxy: using Neo4j and Python to calculate second order democracy indices

Authoritarian by Proxy: using Neo4j and Python to calculate second order democracy indices

Authoritarian by Proxy: using Neo4j and Python to calculate second order democracy indices from trade data.

The following is the first part of a little hobby project, I have been having fun with during my son’s naptime and in the evenings while I was/am on paternity leave. I started it for me to become better acquainted with Neo4j and the Cypher query language syntax, but I have chosen to do a sort of back-and-forth with Python to demonstrate how easy and powerful it is to use these together. Seeing as there are probably others out there with similar interests, I thought it worthwhile to try and present it in a relatively succinct form.

This will not be a “basic introduction to Neo4j” or the Cypher query language with a buildup from the bottom. We will, however, see examples of how to build a graph in Neo4j from Python, how to run Cypher queries from Python (and Neo4j) with comments on the structure of the queries and how to update existing nodes with calculated values from the graph.

In addition to learning something about Neo4j/Cypher, there will be some interesting findings along the way (at least they were to me), for example that authoritarian regimes are a more heterogenous bunch when it comes to the democraticness of their counterparties than are full democracies.

The full notebook (wip) which contains a lot more code than I will show below is available **[here**](https://github.com/Kenneth-HM-Nielsen/Neo4j).

At the end I explain how the ideas presented here are transferable to ESG scores and credit risk evaluations of companies.

data-science neo4j python graph-analytics trade-data

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