Partial-equilibrium behavioral-responses module that works with Tax-Calculator
Behavioral-Responses, which is part of the Policy Simulation Library (PSL) collection of USA tax models, estimates partial-equilibrium behavioral responses to changes in the US federal individual income and payroll tax system as simulated by Tax-Calculator. It provides two ways of doing this: (1) the
response function, which contains higher-level logic that supports the Tax-Brain "Partial Equilibrium Simulation" capability and requires specification of only the elasticities, and (2) the
quantity_response function, which contains lower-level logic that requires specification of the quantity whose response is to be estimated, requires specification of the marginal tax rates and elasticities to be used in the response calculation, and allows the response estimation to be conducted by subgroup with different elasticities for each subgroup.
- Matt Jensen
Evaluates the effect of US federal taxes on businesses' investment incentives
Cost-of-Capital-Calculator is a model that can be used to evaluate the effect of US federal taxes on the investment incentives of corporate and non-corporate businesses. Specifically, Cost-of-Capital-Calculator uses data on the business assets and financial policy, as well as microdata on individual tax filers, to compute marginal effective tax rates on new investments. In modeling the effects of changes to the individual income tax code, Cost-of-Capital-Calculator works with Tax-Calculator, another open source model of US federal tax policy. Cost-of-Capital-Calculator is written in Python, an interpreted language that can execute on Windows, Mac, or Linux.
- Jason DeBacker
Overlapping-Generations Model for Evaluating Fiscal Policy in the United States
OG-USA is an overlapping-generations (OG) model of the economy of the United States (USA) that allows for dynamic general equilibrium analysis of federal tax policy. The model output focuses changes in macroeconomic aggregates (GDP, investment, consumption), wages, interest rates, and the stream of tax revenues over time. Documentation of the model theory--its output, and solution method--is available here and is regularly updated. Documentation for the Python API for OG-USA is available here.
A machine learning project that analyzes state-run media to predict policy changes.
China's industrialization process has long been a product of government direction, be it coercive central planning or ambitious industrial policy. For the first time in the literature, we develop a quantitative indicator of China's policy priorities over a long period of time, which we call the Policy Change Index for China (PCI-China). The PCI-China is a leading indicator that runs from 1951 to the most recent quarter and can be updated in the future. In other words, the PCI-China not only helps us understand the past of China's industrialization but also allows us to make short-term predictions about its future directions.
The design of the PCI-China has two building blocks: (1) it takes as input data the full text of the People's Daily --- the official newspaper of the Communist Party of China --- since it was founded in 1946; (2) it employs a set of machine learning techniques to "read" the articles and detect changes in the way the newspaper prioritizes policy issues.
The source of the PCI-China's predictive power rests on the fact that the People's Daily is at the nerve center of China's propaganda system and that propaganda changes often precede policy changes. Before the great transformation from the central planning under Mao to the economic reform program after Mao, for example, considerable efforts were made by the Chinese government to promote the idea of reform, move public opinion, and mobilize resources toward the new agenda. Therefore, by detecting (real-time) changes in propaganda, the PCI-China is, effectively, predicting (future) changes in policy.
For details about the methodology and findings of this project, please see the following research paper:
- Chan, Julian TszKin and Weifeng Zhong. 2019. "Reading China: Predicting Policy Change with Machine Learning." AEI Economics Working Paper No. 2018-11 (latest version available here).
- Julian TszKin Chan
- Weifeng Zhong
Library for parameter processing and validation with a focus on computational modeling projects
Integrator package for multiple open source tax models
Tax-Brain makes it easy for users to simulate the US tax system by providing a single interface for multiple tax models. Currently, Tax-Brain interfaces with Tax-Calculator and Behavior-Response. Additional models will be added in the near future to expand Tax-Brain's capabilities to include modeling business taxation and running dynamic general equilibrium simulations.
To learn more about how Tax-Brain works, see this document.
- Anderson Frailey
Tax-Calculator is an open-source microsimulation model for static analysis of USA federal income and payroll taxes.
Calculates federal tax liabilities from individual data under different policy proposals
Tax-Cruncher calculates federal tax liabilities from individual data under different policy proposals.
Tax-Cruncher accepts inputs similar to NBER's TAXSIM Version 27, converts those inputs to a format usable by Tax-Calculator, an open-source microsimulation model of federal individual income and payroll tax law, and uses Tax-Calculator capabilities to analyze the user-specified inputs under various tax policy proposals.
- Peter Metz