Pitch deck for my projects found here: https://drive.google.com/file/d/1yjCCO-2-ClpKiLTNyijbf0XdHbMqJfCq/view?usp=sharing
This project is a research initiative to develop a set of principles and guidelines that future projects can use when reporting results - imagine it as GAAP for impact markets.
Just like standards of disclosure have improved trust and reach for financial markets, I intend to help make future impact markets more successful by making it easier for future recipients to inform their investors on the impact of their projects. The tools developed here should also help future investors make decisions about which projects to fund.
The tools developed in this project will be trialled for reporting results from the "Forecast Dissemination Mini-Markets", which are deliberately designed to be similar in order to aid comparison between them.
I’m a professional forecaster working for Amazon (I also won the ACX 2022 forecasting contest). I’ve previously done work in survey research and causal inference, with a focus on evaluating the effectiveness of educational interventions.
I used to work in litigation, where I conducted research projects for expert testimony in labor and accounting-related lawsuits. This gave me familiarity with accounting principles and a great deal of experience reading financial reports.
My recent professional focus has been on automating the use of forecasts in business decisionmaking, and this project is a part of my personal interest in “aligning” institutions and market outcomes with human values.
Funding received for this project will go towards supporting the development of standards for reporting that future impact markets might adopt. I will publish the results of this work by September, on Manifold if possible, and otherwise on my own website. The amount of time spent will vary based on funding, but I will commit at least one hour of dedicated work to this project for each $50 of funding received. At project completion in September, I will also publish a breakdown of my research process, with the time spent on each component.