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

November Policy Simulation Library Meetup recap. The DC Policy Simulation Library (PSL) Meetup series kicked off on November 27, with Cody Kallen (University of Wisconsin) and Weifeng Zhong (AEI) presenting their open source models to a room of professional policy analysts, modelers, students, and others. The DC PSL Meetup is hosted by OSPC at AEI’s DC headquarters. Kallen demoed the Paid Family Leave-Cost Model (PFL-CM), which was recently used prominently by a multi-institution research project including scholars from the Brookings Institution, American Action Forum, and AEI. Zhong introduced the Policy Change Index (PCI) of China, which uses machine learning to “read” China’s state-run newspaper, “People’s Daily,” and predicts changes in the Chinese government’s priorities and policies. If you missed the Meetup, check out the video from Kallen and Zhong’s presentations. Link

Open source policy analysis in Berkeley. The Berkeley Initiative for Transparency in the Social Sciences (BITSS), a community that works toward strengthening the quality and integrity of research used for policymaking, will hold its annual meeting on December 10, including a panel discussion on open policy analysis, specifically. The panel will be moderated by Fernando Hoces de la Guardia (BITSS) and composed of Hilary Hoynes (University of California, Berkeley), Josh Rosenberg (GiveWell), Kevin Perese (Congressional Budget Office), and OSPC’s Matt Jensen (AEI). Link

Models for global development. In 2015, the United Nations (UN) established sustainable development goals, covering a range of social and economic development issues. As a useful byproduct of this effort, the UN Department of Economic and Social Affairs and the UN Development Program began developing a suite of open source modeling tools to be adapted by individual countries to simulate policy decision-making. For example, the Fuel Tax and Development model simulates the potential trade-offs and synergies involved in the implementation of a fuel tax. Specifically, the model helps government decide how to spend newly generated revenue from a fuel tax by simulating the economic impact of different spending scenarios. Link

Open sourcing monetary policy analysis. Dynamic stochastic general equilibrium (DSGE) models are used by central banks around the world to inform monetary policy. On a basic level, DSGE models help academics and policymakers understand how today’s monetary policy choices will affect the future economy. In 2015, the Federal Reserve Bank of New York (FRBNY) implemented its DSGE model in Julia, a free and open source programming language, and released it as open source software. Now that the model is on GitHub, anyone can download the code and even propose changes to the existing model. The most recent model update was only a few days ago, and the project has many contributors. Link

New Tax-Calculator highlight. “Taxes, transfers, progressivity, and redistribution: Part 3” by Sita Slavov (George Mason University; AEI) and Alan Viard (AEI) uses Tax-Calculator – an open source project in the Policy Simulation Library and incubated by OSPC – to assess the distributional effects of various tax policies. Specifically, Slavov and Viard use Tax-Calculator to investigate how a select tax policy may be progressive among low earners but regressive among high earners. By reshaping how policymakers and analysts think about “progressivity” and “regressivity,” the authors hope to inform the public discussion on debt reduction. Link

Edited by Matt Jensen
American Enterprise Institute