This is a periodic newsletter of the interesting things we’ve seen and what we are thinking about in open source policy analysis.

Astronomers capture picture of black hole using open-source software commonly used in policy analysis. The first ever image of a black hole was captured with the Event Horizon Telescope (EHT), an earth-sized virtual telescope created by linking eight physical telescopes from around the world. The software to connect the eight telescopes and capture the image relies on a number of open-source projects that are often used in policy analysis by OSPC-incubated projects and others, including popular data science packages like Numpy, Pandas, Matplotlib, and Jupyter. Not only does the EHT rely on open-source software, but the international team of EHT researchers published their code on GitHub. Link and link

Tax-Brain added to Policy Simulation Library (PSL) catalog. The OSPC-incubated Tax-Brain has met PSL’s criteria for transparency and is now cataloged with PSL. Tax-Brain is a Python package that wraps multiple economic models, including Tax-Calculator and Behavioral-Responses, into one easy-to-use interface. Link

A look at the new SALT deduction cap. Ernie Tedeschi uses the OSPC-incubated Tax-Calculator to analyze the marginal effect of the Tax Cut and Jobs Act’s (TCJA) cap on the deduction for state and local taxes (SALT). He finds that the provision pinched about 8 percent of all 2018 filers but that many of those filers still enjoyed a tax cut from the TCJA. (Ben Casselman from The New York Times replied on Twitter, “this is the chart I’ve been waiting for.”) Link

Cost-of-Capital-Calculator (CCC) at the April PSL meeting. OSPC will host the April PSL meeting at AEI headquarters in Washington, DC, on April 29. After updates from the open-source community, Jason DeBacker (University of South Carolina) will present and demo CCC, an OSPC-incubated model that evaluates the effects of US federal taxes on businesses’ investment incentives. Link

A novel method of data stewardship. A data trust is a method of data sharing that gives an independent “trust” the responsibility of holding data and deciding how it is shared. A recent Open Data Institute report makes the case that data trusts are an effective way of balancing interests when sharing data by simultaneously increasing data accessibility and protecting people and organizations from harm. Link

Edited by Matt Jensen
American Enterprise Institute